ERF109 Antibody

What is ERF109 and what cellular processes does it regulate?

ERF109 is a transcription factor that acts as a crosstalk node between jasmonic acid signaling and auxin biosynthesis in plants. It directly regulates auxin biosynthesis genes such as YUC2 and ASA1 during lateral root formation in Arabidopsis. Recent research has shown that ERF109 also regulates auxin transport by modulating the expression of key transport-related genes including PIN2, PIN4, and PID . This dual regulatory role in both auxin biosynthesis and transport establishes ERF109 as a critical regulator of auxin maxima formation required for lateral root initiation .

What binding sites does ERF109 recognize in its target genes?

ERF109, as a member of the ERF (Ethylene Response Factor) family, directly binds to GCC-box cis-elements found in the promoters or transcribed regions of its target genes. Specifically, research has identified that ERF109 can bind to GCC-boxes in PIN2, PIN4, and PID genes . The exact binding has been confirmed through both yeast-one-hybrid (Y1H) assays and chromatin immunoprecipitation (ChIP) assays, demonstrating that this interaction occurs both in vitro and in vivo . Different GCC-boxes exhibit varying binding affinities for ERF109, with specific fragments (such as fragment A from PIN2 and PIN4) showing particularly high affinity .

What are the optimal techniques for using ERF109 antibodies in chromatin immunoprecipitation (ChIP) assays?

When performing ChIP assays with ERF109 antibodies, researchers should consider the following methodological approaches:

• Tagged protein approach: Using HA-tagged ERF109 transgenic plants with anti-HA antibodies has proven effective in ChIP assays, as demonstrated in recent studies . This approach can circumvent potential issues with direct ERF109 antibody specificity.

• Tissue selection: Isolate chromatin from 10-day-old seedlings for optimal results, as this developmental stage shows significant ERF109 activity in root development processes .

• PCR validation: Following immunoprecipitation, both standard PCR and quantitative PCR (qPCR) should be used to confirm the enrichment of DNA fragments containing GCC-box elements from target genes. This dual validation provides reliable confirmation of binding events .

• Controls: Include negative controls (non-GCC-box containing regions) and input chromatin controls to accurately assess enrichment levels.

How should researchers optimize Western blotting protocols for ERF109 detection?

For optimal Western blot detection of ERF109:

• Sample preparation: Extract total protein from plant tissues using a buffer containing protease inhibitors to prevent degradation of ERF109 protein.

• Separation conditions: Use 10-12% SDS-PAGE gels for optimal separation of ERF109 protein, which has a molecular weight of approximately 25 kDa.

• Transfer parameters: Transfer to PVDF membranes at 100V for 60-90 minutes in cold transfer buffer to ensure efficient protein transfer.

• Blocking optimization: Block membranes with 5% non-fat dry milk in TBST for 1 hour at room temperature to minimize non-specific binding.

• Antibody incubation: For primary ERF109 antibody incubation, use a 1:1000 to 1:2000 dilution and incubate overnight at 4°C for best results. For tagged ERF109 constructs, anti-tag antibodies (such as anti-HA) can be used at manufacturer-recommended concentrations.

How do different experimental conditions affect ERF109 binding affinity to target genes?

The binding affinity of ERF109 to its target genes can be influenced by several experimental factors:

• GCC-box sequence context: Different GCC-box elements show varying affinities for ERF109 binding. For instance, yeast-one-hybrid assays have demonstrated that specific fragments (such as fragment A from PIN2 and PIN4) display higher affinity for ERF109 than others .

• Chromatin state and accessibility: The chromatin state around GCC-box elements can significantly impact ERF109 binding. Open chromatin regions generally facilitate better access for transcription factors like ERF109.

• Post-translational modifications: ERF109 activity may be regulated by post-translational modifications that affect its binding affinity to target genes. Researchers should consider the phosphorylation state of ERF109 when analyzing binding efficiency.

• Competing factors: Other transcription factors that recognize similar cis-elements might compete with ERF109 for binding sites, affecting observed binding affinity in different cellular contexts.

What are the comparative advantages of using ChIP-seq versus ChIP-qPCR for studying ERF109 binding sites?

ChIP-seq provides a comprehensive genome-wide view of ERF109 binding sites and can uncover previously unknown targets beyond the established PIN2, PIN4, and PID genes . This approach is particularly valuable for researchers seeking to expand the network of genes regulated by ERF109.

In contrast, ChIP-qPCR is more suitable for targeted validation of specific binding sites and requires less antibody. The ChIP-qPCR approach has been successfully employed to confirm ERF109 binding to specific GCC-box elements in PIN2, PIN4, and PID genes , and represents a cost-effective strategy for focused investigations.

How can researchers distinguish between direct and indirect effects of ERF109 on gene expression?

Distinguishing direct from indirect effects of ERF109 on gene expression requires a multi-faceted approach:

• Integrative binding and expression analysis: Combine ChIP data (showing direct binding) with expression profiling (RNA-seq or qRT-PCR) of wild-type, erf109 mutant, and 35S:ERF109 overexpression lines to identify genes that are both bound by ERF109 and differentially expressed .

• Time-course experiments: Implement inducible ERF109 expression systems and measure rapid changes in target gene expression, as direct targets typically show faster response times than indirect targets.

• Motif analysis: Genes directly regulated by ERF109 should contain GCC-box elements in their regulatory regions. Motif analysis can help predict direct targets, which can then be validated experimentally .

• Genetic complementation: For genes showing altered expression in erf109 mutants, test whether a GCC-box mutated version of the gene's promoter fails to respond to ERF109, confirming direct regulation.

What are common challenges in ERF109 antibody specificity and how can they be addressed?

Common challenges with ERF109 antibody specificity include:

• Cross-reactivity with related ERF proteins: The ERF family contains multiple members with similar structural domains, potentially leading to cross-reactivity. Researchers should:

  • Validate antibody specificity using erf109 knockout mutants as negative controls

  • Consider using epitope-tagged ERF109 (HA-ERF109) and corresponding tag-specific antibodies as demonstrated in published research

  • Perform pre-absorption tests with recombinant ERF proteins to assess cross-reactivity

• Background signal in plant tissues: Plant tissues often contain compounds that can interfere with antibody specificity. To mitigate this:

  • Optimize extraction buffers to reduce interference from plant compounds

  • Implement more stringent washing steps in immunoprecipitation protocols

  • Use appropriate blocking agents specifically optimized for plant samples

• Fixation-related epitope masking: Formaldehyde fixation used in ChIP can sometimes mask ERF109 epitopes. Researchers can:

  • Test different fixation times (8-15 minutes) to find optimal conditions

  • Explore alternative fixation methods compatible with ERF109 antibody recognition

How can researchers interpret contradictory results between ChIP and expression data for ERF109 targets?

When facing contradictions between ChIP binding data and gene expression results:

What are the optimal experimental designs for studying ERF109 function in different plant tissues?

When designing experiments to study ERF109 function across different plant tissues:

• Tissue-specific expression systems: Given that ERF109 and its targets (like PIN2, PIN4) have different tissue-specific expression patterns , consider using tissue-specific promoters to drive ERF109 expression rather than constitutive promoters like 35S, which may create non-physiological effects.

• Developmental time course: Sample collection should follow a detailed developmental time course, as ERF109 function may vary during different stages of plant development. For root development studies, analyzing samples at 5, 7, 10, and 14 days after germination provides comprehensive coverage .

• Cellular resolution approaches: Combine tissue-level approaches with cellular-resolution techniques:

  • Use fluorescent reporter lines (GFP-tagged transporters) to visualize protein localization as demonstrated in ERF109 studies

  • Implement FACS (Fluorescence-Activated Cell Sorting) to isolate specific cell types for ERF109 binding and expression analysis

  • Consider single-cell RNA-seq to capture cell-type-specific responses to ERF109 modulation

• Hormone treatments: Include appropriate hormone treatments (jasmonic acid, auxin) in experimental designs, as ERF109 functions at the crossroads of hormone signaling pathways .

How should genetic backgrounds be selected for rigorous validation of ERF109 antibody specificity?

For rigorous validation of ERF109 antibody specificity:

• Essential control lines:

  • Complete erf109 knockout mutants (verified by RT-PCR and quantitative RT-PCR)

  • ERF109 overexpression lines (35S:ERF109)

  • Wild-type (Col-0 for Arabidopsis) as baseline control

• Additional recommended controls:

  • Inducible ERF109 expression lines to observe dynamic changes

  • ERF109 point mutants affecting DNA binding but not protein stability

  • Closely related ERF family members overexpression lines to test cross-reactivity

• Combined genetic approaches: When studying ERF109 interactions with target genes, use genetic combinations like:

  • erf109 pin2 double mutants

  • erf109 pin4 double mutants

  • erf109 pid double mutants
    These genetic combinations help validate functional relationships between ERF109 and its putative targets .

What statistical approaches are most appropriate for analyzing ChIP-qPCR data for ERF109 binding?

For robust statistical analysis of ERF109 ChIP-qPCR data:

• Normalization methods:

  • Percent input method: Calculate enrichment as percentage of input chromatin

  • Internal control normalization: Use non-target regions (lacking GCC-boxes) as internal controls

  • IgG control comparison: Compare ERF109 antibody pulldown to IgG control pulldown

• Statistical tests:

  • For comparing enrichment between different GCC-box regions: One-way ANOVA followed by Tukey's post-hoc test

  • For comparing wild-type versus mutant enrichment: Student's t-test or Mann-Whitney U test (if non-normally distributed)

  • For multi-factor experiments: Two-way ANOVA to assess interaction effects

• Replication requirements:

  • Minimum of three biological replicates

  • At least two technical replicates per biological replicate

  • Consistent threshold cycles (Ct) between technical replicates (variation < 0.5 Ct)

• Visualization approaches:

  • Bar graphs showing fold enrichment with error bars representing standard error

  • Include both statistical significance indicators and effect size measures

How can researchers integrate ChIP data with transcriptome analyses to build comprehensive models of ERF109 regulatory networks?

To integrate ChIP and transcriptome data for building ERF109 regulatory networks:

• Data integration workflow:

  • Identify direct ERF109 targets through ChIP experiments (genes with GCC-box binding)

  • Compare differentially expressed genes in erf109 mutant versus wild-type and/or overexpression lines

  • Identify overlap between bound genes and differentially expressed genes as high-confidence direct targets

  • Categorize remaining differentially expressed genes (without binding evidence) as potential indirect targets

• Network analysis tools:

  • Gene Ontology (GO) enrichment analysis to identify biological processes regulated by ERF109

  • Co-expression network analysis to identify genes with similar expression patterns to known ERF109 targets

  • Motif enrichment analysis to identify additional potential binding sites

• Validation approaches:

  • Transient expression assays using promoter-reporter constructs

  • EMSA (Electrophoretic Mobility Shift Assay) to confirm binding to specific motifs

  • Targeted mutagenesis of binding sites followed by expression analysis

• Model refinement:

  • Incorporate temporal dynamics by analyzing time-course data

  • Include data on post-translational modifications of ERF109

  • Integrate information about co-factors and competing transcription factors