Di-methyl-Histone H3(K9) Monoclonal Antibody

What is the biological significance of H3K9 dimethylation?

H3K9 dimethylation serves as a repressive epigenetic mark associated with transcriptional silencing and heterochromatin formation. High-resolution mapping of histone modification patterns has revealed that H3K9me2 is specifically enriched in the transcriptional start sites of silenced genes, while H3K9me1 is present in promoter regions of active genes . This differential distribution indicates that the methylation state of H3K9 functions as an important mark denoting the transcriptional status of specific genes. The modification plays essential roles in embryonic development and has been implicated in carcinogenesis processes . H3K9me2 modification is particularly important because it serves as a docking site for effector proteins containing methyl-lysine binding modules such as chromodomains, PHD fingers, tudor domains, and WD-40 domains, which further mediate downstream chromatin remodeling and gene silencing events .

How does H3K9me2 differ functionally from mono- and trimethylated states?

While all three methylation states occur at the same lysine residue, they serve distinct functions within chromatin. H3K9me1 is predominantly found in the promoter regions of active genes and can be associated with transcriptional activation . In contrast, H3K9me2 and H3K9me3 are enriched at transcriptional start sites of silenced genes and heterochromatic regions, functioning as repressive marks . These different methylation states are established by specific histone methyltransferases - SuvH39H1 or G9a can catalyze the addition of methyl groups to create mono-, di-, or trimethylated H3K9 . The transition between methylation states is tightly regulated by the balanced activities of methyltransferases and demethylases like JMJD1A, LSD1, or JMJD2C, allowing for dynamic control of gene expression patterns .

What applications can Di-methyl-Histone H3(K9) antibodies be used for in epigenetic research?

Di-methyl-Histone H3(K9) antibodies are versatile tools with multiple applications in epigenetic research:

For optimal ChIP results, researchers should use approximately 20 μl of antibody with 10 μg of chromatin (equivalent to approximately 4 × 10^6 cells) per immunoprecipitation . These antibodies have been validated using SimpleChIP® Enzymatic Chromatin IP Kits, ensuring reliable performance for mapping H3K9me2 distribution across the genome .

How should samples be prepared for optimal detection of H3K9me2?

Proper sample preparation is critical for accurate detection of H3K9me2. For cell culture experiments, cells should be harvested at 70-80% confluence to ensure consistent epigenetic states. When extracting histones, it is essential to include protease inhibitors, phosphatase inhibitors, and deacetylase inhibitors in all buffers to prevent enzymatic modification of the samples during processing. For Western blotting applications, acid extraction methods using 0.2N HCl or specialized histone extraction kits yield the purest histone preparations . When performing ChIP experiments, crosslinking conditions must be optimized (typically 1% formaldehyde for 10 minutes at room temperature) to preserve protein-DNA interactions without introducing artifacts . For immunofluorescence, cells should be fixed with 4% paraformaldehyde followed by permeabilization with 0.1-0.5% Triton X-100 to enable antibody access to nuclear antigens while preserving nuclear architecture .

What controls should be included when working with H3K9me2 antibodies?

Rigorous experimental design requires appropriate controls to validate H3K9me2 antibody specificity and performance:

• Peptide competition assays: Pre-incubating the antibody with increasing concentrations of H3K9me2 peptide should progressively diminish signal intensity, confirming specificity.

• Cross-reactivity controls: Testing against peptides containing H3K9me1 and H3K9me3 modifications to ensure the antibody specifically recognizes the dimethylated state.

• Positive controls: Including samples known to be enriched for H3K9me2 (e.g., heterochromatic regions, silenced genes) to confirm detection capability.

• Negative controls: Using samples with reduced H3K9me2 levels, such as cells treated with G9a inhibitors or G9a knockout cells .

• Input controls: For ChIP experiments, analyzing a portion of pre-immunoprecipitated chromatin to normalize for DNA abundance differences.

• IgG controls: Using non-specific IgG from the same species as the H3K9me2 antibody to determine background signal levels in immunoprecipitation experiments.

Including these controls ensures reliable interpretation of experimental results and facilitates troubleshooting when unexpected outcomes occur .

What is the recommended protocol for ChIP-seq using H3K9me2 antibodies?

For successful ChIP-seq analysis of H3K9me2 distribution, the following methodological approach is recommended:

• Crosslinking: Fix cells with 1% formaldehyde for 10 minutes at room temperature, then quench with 125mM glycine.

• Chromatin preparation: Lyse cells and sonicate chromatin to achieve fragments of 200-500bp. Verify fragment size by agarose gel electrophoresis.

• Immunoprecipitation: Incubate 10μg of sonicated chromatin with 20μl of H3K9me2 antibody overnight at 4°C. Include IgG control and input samples .

• Bead capture: Add protein A/G magnetic beads and incubate for 2-4 hours at 4°C.

• Washing: Perform sequential washes with increasing stringency buffers to reduce background.

• Elution and reversal: Elute chromatin and reverse crosslinks by incubation at 65°C overnight.

• DNA purification: Treat with RNase A and Proteinase K, then purify DNA using column-based methods.

• Library preparation: Prepare sequencing libraries following platform-specific protocols, ensuring proper adapter ligation and size selection.

• Sequencing: Perform paired-end sequencing with sufficient depth (20-30 million reads) to ensure comprehensive coverage.

• Data analysis: Use specialized bioinformatics pipelines to identify H3K9me2-enriched regions and correlate with gene expression data .

This protocol can be adapted based on specific research requirements and cell types being investigated.

How do environmental factors influence H3K9 dimethylation patterns?

Environmental factors can significantly alter H3K9me2 patterns, with hypoxia being a well-documented example. Research has demonstrated that hypoxic stress increases global H3K9me2 levels in multiple mammalian cell lines . This increase correlates temporally with elevated histone methyltransferase G9a protein expression and enhanced enzymatic activity. Experimental evidence from G9a-/- mouse embryonic stem cells shows significantly reduced hypoxia-induced H3K9me2, confirming G9a's critical role in this process .

Additionally, hypoxic mimetics such as deferoxamine and dimethyloxalylglycine have been found to increase both H3K9me2 levels and G9a protein expression/activity . The mechanism appears to involve both increased methyltransferase activity and inhibition of demethylation processes. At the gene-specific level, hypoxia increases H3K9me2 in the promoter regions of genes like Mlh1 and Dhfr, with these increases temporally correlating with transcriptional repression . These findings highlight how environmental stressors can reshape the epigenetic landscape, potentially contributing to pathological conditions including cancer progression.

What is the relationship between H3K9 methyltransferases and demethylases in maintaining H3K9me2 homeostasis?

H3K9me2 levels are maintained through a dynamic equilibrium between the activities of methyltransferases (writers) and demethylases (erasers). The primary methyltransferases responsible for establishing H3K9me2 include G9a and SuvH39H1 . These enzymes catalyze the transfer of methyl groups from S-adenosylmethionine (SAM) to lysine 9 on histone H3.

Counterbalancing these writers are H3K9-specific demethylases including JMJD1A (KDM3A), LSD1, and JMJD2C . These erasers remove methyl groups through distinct catalytic mechanisms. Research on JMJD1A has revealed particularly interesting insights about demethylase function. This enzyme operates as a homodimer with two active sites that work cooperatively to efficiently convert dimethylated H3K9 to the unmethylated state . Experiments demonstrate that increasing JMJD1A concentration facilitates efficient production of unmethylated product while decreasing release of monomethylated intermediates .

The balanced activities of these opposing enzymes allow for responsive and reversible regulation of chromatin states. Disruption of this balance, as seen in hypoxic conditions or disease states, can lead to aberrant gene silencing or activation with significant biological consequences .

How do changes in H3K9me2 patterns contribute to disease pathogenesis?

Alterations in H3K9me2 distribution contribute to various pathological conditions through dysregulation of gene expression programs:

• Cancer: Hypoxia-induced increases in H3K9me2 have been directly linked to the repression of genes like Mlh1 and Dhfr, which may contribute to solid tumor progression . Mlh1 silencing impairs DNA mismatch repair, potentially increasing mutation rates and genomic instability. Abnormal H3K9me2 patterns can lead to inappropriate silencing of tumor suppressor genes or activation of oncogenic programs.

• Neurodevelopmental disorders: Proper H3K9 methylation is essential for neural development and neuroplasticity. Disruptions in enzymes regulating H3K9me2 have been associated with intellectual disability and autism spectrum disorders.

• Cardiovascular disease: Studies have linked aberrant H3K9 methylation to vascular dysfunction and atherosclerosis development through altered gene expression in endothelial and smooth muscle cells.

• Inflammatory conditions: H3K9me2 modifications regulate inflammatory gene expression, with dysregulation potentially contributing to chronic inflammatory diseases.

The therapeutic potential of targeting H3K9me2-modifying enzymes is being actively explored, with several small molecule inhibitors of histone methyltransferases and demethylases in preclinical and clinical development .

How can researchers distinguish between antibody specificity for H3K9me2 versus cross-reactivity with other histone modifications?

Ensuring antibody specificity is essential for accurate interpretation of H3K9me2 studies. Researchers should implement multiple verification approaches:

• Peptide array analysis: Testing antibody binding against a panel of modified histone peptides containing various methylation states (H3K9me1, H3K9me2, H3K9me3) and modifications at other lysine residues (H3K4me, H3K27me, etc.) to determine cross-reactivity profiles.

• Dot blot validation: Spotting different concentrations of modified histone peptides on membranes to quantitatively assess antibody preference for H3K9me2 over other modifications.

• Western blot with knockout controls: Comparing immunoblot signals between wild-type cells and cells lacking specific methyltransferases (e.g., G9a-/- cells) to confirm signal specificity .

• Mass spectrometry correlation: Validating ChIP-seq peaks against mass spectrometry analysis of histone modifications at the same genomic regions.

• Competitive binding assays: Demonstrating that pre-incubation with H3K9me2 peptides, but not peptides containing other modifications, abolishes antibody signals.

The antibody's immunogen information is also critical - antibodies raised against peptides including dimethyl-lysine 9 of histone H3 have shown reliable specificity for the H3K9me2 mark .

What are the implications of using polyclonal versus monoclonal antibodies for H3K9me2 detection?

The choice between polyclonal and monoclonal antibodies significantly impacts experimental outcomes:

Polyclonal antibodies against H3K9me2 (like those described in search results and ) recognize multiple epitopes around the dimethylated lysine 9, potentially providing higher sensitivity but with increased risk of cross-reactivity. Monoclonal antibodies (like the mouse monoclonal antibody in result ) recognize a single epitope, offering greater consistency between experiments but potentially lower signal intensity .

How does antibody functionality vary across different species when studying H3K9me2?

H3K9me2 is a highly conserved histone modification across eukaryotes, but antibody performance can vary significantly between species. According to the search results, several commercially available H3K9me2 antibodies demonstrate cross-reactivity across multiple species including Human (H), Mouse (M), Rat (R), Monkey (Mk), and Drosophila melanogaster (Dm) .

• Validation requirements: Even with claimed cross-reactivity, each new species application should be independently validated using controls specific to that organism.

• Optimization needs: Antibody concentrations and experimental conditions often require species-specific optimization, particularly for techniques like ChIP where chromatin accessibility may differ between organisms.

• Context differences: The genomic distribution and biological functions of H3K9me2 may vary between species despite the conservation of the modification itself. For example, heterochromatin organization differs between mammals and Drosophila.

• Background considerations: Non-specific background binding patterns may differ between species, necessitating careful control selection and experimental design.

When studying non-model organisms, researchers should conduct preliminary validation studies using multiple antibodies and complementary approaches to confirm specificity before proceeding with comprehensive analyses .

What strategies can address weak or inconsistent H3K9me2 signals in ChIP experiments?

When facing weak or inconsistent H3K9me2 signals in ChIP experiments, researchers should consider these methodological approaches:

• Crosslinking optimization: Test different formaldehyde concentrations (0.5-2%) and incubation times (5-20 minutes) to improve protein-DNA crosslinking without over-fixation.

• Chromatin fragmentation assessment: Verify sonication efficiency by checking fragment sizes (optimal range: 200-500bp). Over-sonication can destroy epitopes while under-sonication reduces antibody accessibility.

• Antibody titration: Perform a titration series (10-40μl per 10μg chromatin) to determine the optimal antibody concentration for your specific cell type .

• Micrococcal nuclease alternative: Consider enzymatic digestion with MNase instead of sonication for more consistent fragmentation of heterochromatic regions where H3K9me2 is enriched.

• Increased cell number: H3K9me2 can be enriched in specific genomic regions; increasing starting material may improve signal-to-noise ratios.

• Blocking optimization: Include blocking agents (BSA, non-fat milk) to reduce non-specific binding and background signal.

• Sequential ChIP: For regions with multiple modifications, sequential ChIP can help distinguish specific modification combinations.

• Alternative buffer systems: Different salt concentrations and detergents in wash buffers can significantly impact antibody performance and specificity.

For especially challenging targets, combining multiple antibodies (monoclonal and polyclonal) against H3K9me2 in the same experiment can sometimes improve signal detection while maintaining specificity .

How can researchers integrate H3K9me2 ChIP-seq data with other epigenomic datasets for comprehensive analysis?

Integrative analysis of H3K9me2 with other epigenomic datasets provides deeper insights into chromatin regulation:

• Multi-omics data integration strategies:

  • Combine H3K9me2 ChIP-seq with RNA-seq to correlate repressive marks with transcriptional output

  • Integrate with ATAC-seq or DNase-seq to analyze chromatin accessibility at H3K9me2-marked regions

  • Overlap with DNA methylation data to identify regions with multiple silencing mechanisms

  • Compare with histone acetyltransferase (HAT) and histone deacetylase (HDAC) binding sites to understand antagonistic modifications

• Computational approaches:

  • Implement genome segmentation algorithms (e.g., ChromHMM) to identify chromatin states based on combinations of modifications

  • Use correlation analyses across multiple marks to identify coordinated epigenetic changes

  • Apply machine learning approaches to predict functional outcomes based on epigenetic patterns

  • Employ network analysis to identify regulatory hubs coordinating epigenetic modifications

• Visualization tools:

  • Utilize genome browsers with multiple track display capabilities

  • Generate heatmaps clustering regions by similar modification patterns

  • Create metaplots showing average signals across genomic features

• Functional validation:

  • Design CRISPR-based epigenome editing experiments targeting discovered regulatory regions

  • Validate predictions through targeted modulation of H3K9 methyltransferases or demethylases

This integrated approach can reveal how H3K9me2 cooperates with other epigenetic marks to establish and maintain specific transcriptional states and chromatin domains.

What are the latest advances in understanding the enzymatic mechanisms of H3K9me2 demethylases?

Recent research has provided significant insights into the mechanisms of H3K9me2 demethylases:

• JMJD1A homodimer substrate channeling model: Studies have revealed that JMJD1A functions as a homodimer with two active sites that work cooperatively during demethylation. Experimental evidence demonstrates that:

  • Increasing JMJD1A concentration facilitates efficient production of unmethylated product from dimethyl-H3K9

  • Higher enzyme concentrations decrease the release of monomethylated intermediates

  • Substituting one of the two active sites with an inactive mutant significantly reduces demethylation rate without changing affinity for the intermediate

  These findings support a substrate channeling model where the dimethylated substrate is processed sequentially through both active sites without releasing the monomethylated intermediate, ensuring efficient conversion to the unmethylated state .

• Regulation by hypoxic conditions: Research has demonstrated that hypoxia inhibits H3K9 demethylation processes while simultaneously activating methyltransferases like G9a, creating a dual mechanism for increasing H3K9me2 levels. This provides insight into how environmental conditions can modulate enzymatic activity to reshape the epigenetic landscape .

• Structural insights: Crystallographic and cryo-EM studies have elucidated the structural basis for substrate recognition and catalysis by H3K9 demethylases, revealing how these enzymes achieve specificity for particular methylation states.

• Cofactor dependencies: Investigations have clarified the roles of cofactors like iron and alpha-ketoglutarate in the catalytic mechanism of Jumonji domain-containing demethylases, explaining how cellular metabolic state can influence epigenetic regulation.

These mechanistic insights are essential for understanding the dynamic regulation of H3K9me2 and developing potential therapeutic interventions targeting these enzymes .