ERF104 Antibody

What is ERF104 and why is it important in plant molecular research?

ERF104 is an ethylene-responsive transcription factor that belongs to the ERF (Ethylene Response Factor) family in plants. This transcription factor is particularly significant in plant research as it functions at the intersection of multiple stress response pathways. ERF104 has been identified as a key regulator in plant defense mechanisms, particularly in response to pathogen infection, where it works within the ethylene/jasmonic acid signaling pathways to mediate defense responses . The protein is encoded by the AT5G61600 gene in Arabidopsis thaliana and participates in transcriptional regulation by binding to GCC box elements in the promoters of target genes . Understanding ERF104 function is crucial for elucidating plant stress response networks, particularly in how plants integrate multiple hormonal signals to respond to environmental challenges.

What are the primary applications of ERF104 antibodies in plant research?

ERF104 antibodies serve multiple critical functions in plant molecular biology research. They are primarily used for protein detection in Western blot analyses to study ERF104 protein levels, modifications, and accumulation patterns in response to various stimuli. Immunoprecipitation (IP) assays using these antibodies help identify interaction partners of ERF104, elucidating its role in transcriptional complexes. For studying the tissue-specific localization of ERF104, immunohistochemistry (IHC) and immunofluorescence (IF) applications are valuable. Perhaps most significantly, ERF104 antibodies are essential tools in chromatin immunoprecipitation (ChIP) assays to identify direct genomic targets of this transcription factor . These applications collectively enable researchers to understand the molecular mechanisms through which ERF104 regulates gene expression during stress responses and developmental processes in plants.

How does ERF104 function in plant stress response pathways?

ERF104 functions as a critical component in plant stress response networks through several interconnected mechanisms. The transcription factor directly binds to GCC box elements in the promoters of stress-responsive genes, as demonstrated by electrophoretic mobility shift assays (EMSA) and chromatin immunoprecipitation (ChIP) analyses . Significantly, ERF104 works redundantly with other ERF family members, particularly ORA59 and ERF1B, to regulate the expression of key stress response genes like NRT1.8 . The protein's activity is primarily regulated at the posttranslational level, particularly by MAP kinase cascades in response to pathogen stimuli . The function of ERF104 represents a convergence point where ethylene and jasmonic acid signaling pathways intersect, as demonstrated by the synergistic effects of these hormones on ERF104-mediated gene expression . Through these mechanisms, ERF104 orchestrates complex transcriptional responses that help plants adapt to and survive various environmental stresses.

How should researchers design experiments to validate ERF104 antibody specificity?

Validating antibody specificity is crucial for obtaining reliable experimental results when studying ERF104. A comprehensive validation approach should include multiple complementary techniques. First, researchers should perform Western blot analysis comparing wild-type plants with erf104 knockout mutants to verify the absence of the specific band in the mutant . Antibody specificity can be further confirmed through preabsorption tests, where the antibody is incubated with purified recombinant ERF104 protein before immunodetection—a specific antibody will show reduced or eliminated signal after preabsorption. Expression of tagged ERF104 protein in plant cells followed by parallel detection with both anti-tag and anti-ERF104 antibodies provides additional validation. For definitive confirmation, mass spectrometry analysis of immunoprecipitated proteins should be performed to verify that the antibody is indeed capturing ERF104. Finally, cross-reactivity with other closely related ERF family members (particularly ERF1B and ORA59) should be assessed through Western blot analysis of recombinant proteins .

What controls should be included when using ERF104 antibody in Western blot analyses?

Robust experimental design for Western blot analyses using ERF104 antibody requires several critical controls. Positive controls should include samples from plants overexpressing ERF104, while negative controls should utilize erf104 knockout or knockdown mutants to demonstrate specificity . Loading controls with antibodies against constitutively expressed proteins (such as actin or tubulin) are essential to normalize protein amounts across samples. When studying ERF104 phosphorylation, researchers should include phosphatase-treated samples to confirm that any observed mobility shifts are indeed due to phosphorylation events. Competition assays where the primary antibody is pre-incubated with the immunizing peptide or recombinant ERF104 protein should demonstrate signal reduction if the antibody is specific. For phospho-specific ERF104 antibodies, additional controls using phospho-mimetic (S/T to D/E) and phospho-dead (S/T to A) ERF104 mutants should be included to validate phosphorylation site specificity . These comprehensive controls ensure the reliability and reproducibility of experimental results when studying this critical transcription factor.

What are the optimal storage and handling conditions for ERF104 antibodies?

Optimal storage and handling of ERF104 antibodies are critical for maintaining their functionality and specificity. ERF104 antibodies are typically supplied in lyophilized form and should be stored at recommended temperatures (usually -20°C or -80°C) upon receipt . When reconstituting lyophilized antibodies, researchers should use sterile buffers and avoid repeated freeze-thaw cycles, which can lead to antibody degradation and reduced performance . For long-term storage, aliquoting the reconstituted antibody into smaller volumes is recommended to minimize freeze-thaw cycles. Working dilutions should be prepared fresh or stored at 4°C for short periods (typically less than one week). Preservatives such as sodium azide (0.02-0.05%) can be added to prevent microbial contamination in working dilutions, but researchers must ensure this doesn't interfere with downstream applications. Prior to use, antibody solutions should be centrifuged briefly to collect any precipitates that may have formed during storage. For optimal results, researchers should strictly follow the manufacturer's recommendations for storage temperature, buffer composition, and handling procedures specific to their ERF104 antibody preparation .

How can ERF104 antibodies be used to study protein-protein interactions in plant stress response pathways?

ERF104 antibodies enable sophisticated analyses of protein-protein interactions critical to plant stress response networks. Co-immunoprecipitation (Co-IP) experiments using ERF104 antibodies can identify native interaction partners in plant tissues under various stress conditions. When coupled with mass spectrometry, this approach provides an unbiased view of the ERF104 interactome. Proximity ligation assays (PLA) using ERF104 antibodies in combination with antibodies against suspected interaction partners can visualize and quantify in situ protein interactions with subcellular resolution. For studying dynamic interactions, researchers can employ bimolecular fluorescence complementation (BiFC) in conjunction with immunofluorescence using ERF104 antibodies to confirm protein localization. Chromatin immunoprecipitation sequencing (ChIP-seq) combined with sequential ChIP (re-ChIP) using ERF104 antibodies along with antibodies against other transcription factors can identify genomic regions where multiple factors co-localize . These advanced techniques reveal how ERF104 functions within larger protein complexes to orchestrate transcriptional responses during stress, providing insight into the molecular mechanisms underlying plant stress adaptation.

What methodologies are recommended for studying ERF104 phosphorylation dynamics using phospho-specific antibodies?

Studying ERF104 phosphorylation dynamics requires a multi-faceted approach centered around phospho-specific antibodies. Researchers should first identify potential phosphorylation sites through computational prediction tools and validate these sites using mass spectrometry of immunoprecipitated ERF104 protein. Once phosphorylation sites are confirmed, phospho-specific antibodies can be generated against these modified residues . Time-course experiments using these antibodies can track phosphorylation dynamics following treatment with stress hormones like ethylene (or its precursor ACC) and jasmonic acid, revealing the kinetics of ERF104 activation . When combined with pharmacological inhibitors of specific kinases (particularly MAPKs), these experiments can identify the kinases responsible for ERF104 phosphorylation . Phos-tag SDS-PAGE provides enhanced separation of phosphorylated ERF104 isoforms compared to conventional Western blotting. For in vivo visualization, immunofluorescence with phospho-specific antibodies can track the subcellular localization of phosphorylated ERF104 during stress responses. These approaches collectively reveal how phosphorylation regulates ERF104 stability, DNA-binding capacity, and transcriptional activity in response to environmental stimuli.

How does ERF104 phosphorylation by MAP kinases affect its activity and stability?

Phosphorylation of ERF104 by MAP kinases represents a critical regulatory mechanism that modulates multiple aspects of this transcription factor's function. MAPK-mediated phosphorylation significantly affects ERF104 protein stability by protecting it from proteasomal degradation, leading to accumulation of the protein in response to stress stimuli . This phosphorylation also influences ERF104's subcellular localization, promoting its nuclear retention and thereby enhancing its access to target gene promoters. Additionally, phosphorylation modulates ERF104's DNA-binding affinity to GCC box elements, as demonstrated through EMSA experiments with phosphorylated versus non-phosphorylated protein . The transcriptional activation capacity of ERF104 is enhanced following phosphorylation, as shown by increased expression of target genes like NRT1.8 in response to stress hormones and abiotic stressors . Interestingly, phosphorylation can also affect ERF104's protein-protein interactions, potentially altering its associations with cofactors, other transcription factors, or chromatin modifiers. Through these multifaceted effects, MAPK-mediated phosphorylation serves as a crucial molecular switch that activates ERF104 function in stress response pathways.

What strategies can overcome cross-reactivity issues with closely related ERF family members?

Cross-reactivity with related ERF family members presents a significant challenge when working with ERF104 antibodies due to the high sequence similarity within this transcription factor family. To address this issue, researchers should first select antibodies raised against unique regions of ERF104 that show minimal sequence conservation with other ERFs, particularly the closely related ERF1B and ORA59 . Antibody specificity should be rigorously validated using recombinant proteins of multiple ERF family members in Western blot analyses to assess potential cross-reactivity. For immunoprecipitation experiments, high-stringency washing conditions can reduce non-specific binding, though optimization is necessary to avoid disrupting specific interactions. Genetic approaches using erf104 knockout lines alongside lines with knockouts of related ERFs provide definitive controls to distinguish specific from cross-reactive signals . Pre-absorption of antibodies with recombinant proteins of closely related ERFs can reduce cross-reactivity while maintaining specific binding to ERF104. For comprehensive studies, complementary techniques such as mass spectrometry following immunoprecipitation should be employed to confirm the identity of detected proteins. These strategies collectively enhance the specificity of ERF104 detection in complex plant samples.

How can researchers optimize chromatin immunoprecipitation (ChIP) protocols for ERF104 studies?

Optimizing chromatin immunoprecipitation (ChIP) protocols for ERF104 requires systematic refinement of several critical parameters. Crosslinking conditions should be carefully optimized, typically starting with 1% formaldehyde for 10-15 minutes, as excessive crosslinking can mask epitopes while insufficient crosslinking results in poor DNA recovery. Sonication parameters must be adjusted to consistently yield DNA fragments between 200-500 bp for optimal resolution . The antibody-to-chromatin ratio requires empirical determination, generally starting with 2-5 μg of antibody per ChIP reaction and adjusting based on results. Pre-clearing chromatin with protein A/G beads reduces non-specific binding, while extended incubation times (overnight at 4°C) maximize specific antibody-antigen interactions. Washing stringency dramatically affects signal-to-noise ratios; typically, increasing salt concentration in wash buffers reduces background but may decrease signal if too stringent. For plant tissues with high levels of phenolic compounds and polysaccharides, additional purification steps using PVPP or specific plant ChIP kits improve results. When analyzing ERF104 binding to specific promoters like NRT1.8, carefully designed primers targeting GCC-box elements are essential . Finally, including appropriate controls—such as ChIP with pre-immune serum and analysis of non-target genomic regions—ensures the specificity and reliability of identified ERF104 binding sites.

What are the best approaches for detecting low-abundance ERF104 protein in plant tissues?

Detecting low-abundance ERF104 protein in plant tissues requires specialized techniques to enhance sensitivity while maintaining specificity. Immunoprecipitation followed by Western blotting (IP-Western) can concentrate ERF104 from large sample volumes, dramatically improving detection compared to direct Western blotting. Enhanced chemiluminescence (ECL) substrates with extended exposure times or digital imaging systems with signal accumulation features can further increase detection sensitivity. For tissues where ERF104 expression is particularly low, researchers should consider treating plants with ethylene and jasmonic acid simultaneously, as this combination synergistically induces ERF104 expression . Tyramide signal amplification (TSA) techniques can significantly enhance detection in immunohistochemistry applications without increasing background signal. When analyzing phosphorylated ERF104, Phos-tag gels improve separation and detection of modified forms that may be present at very low levels . Mass spectrometry with selected reaction monitoring (SRM) or parallel reaction monitoring (PRM) provides highly sensitive, targeted detection of specific ERF104 peptides. Additionally, proximity ligation assays (PLA) can detect low-abundance proteins through signal amplification when ERF104 interacts with known partner proteins. These specialized approaches enable reliable detection of ERF104 even in tissues or conditions where its expression is minimal.

How should researchers interpret conflicting results between ERF104 transcript and protein levels?

Discrepancies between ERF104 transcript and protein levels are common and reflect the complex post-transcriptional and post-translational regulation of this transcription factor. When interpreting such conflicting data, researchers should consider several key regulatory mechanisms. Post-transcriptional processes such as mRNA stability, alternative splicing, and microRNA-mediated degradation can significantly affect the correlation between transcript abundance and protein production. At the protein level, ERF104 is subject to extensive post-translational modifications, particularly phosphorylation by MAPKs, which can dramatically alter protein stability and half-life without affecting transcript levels . Notably, research has demonstrated that ERF104 regulation occurs preferentially at the posttranslational level in response to ethylene (ACC), which explains why transcript levels might remain relatively unchanged while protein abundance increases significantly . Proteasome-mediated degradation rates can vary under different environmental conditions, further decoupling transcript and protein abundance. Time-course experiments measuring both transcript and protein levels are essential to capture these dynamic relationships, as peak transcript levels often precede peak protein accumulation. Comprehensive interpretation requires integration of transcript, protein, and functional data to understand the biological significance of observed changes in ERF104 regulation.

What statistical approaches are recommended for analyzing ChIP-seq data for ERF104 binding sites?

Analyzing ChIP-seq data for ERF104 binding sites requires robust statistical methodologies to identify genuine binding events while minimizing false positives. Peak calling algorithms such as MACS2 (Model-based Analysis of ChIP-seq) should be employed with appropriate parameters for transcription factor binding, typically using a q-value threshold of 0.01 or 0.05. Differential binding analysis between experimental conditions is best performed with specialized packages like DiffBind or MAnorm, applying appropriate normalization methods to account for sequencing depth variations. Motif enrichment analysis using MEME-ChIP or similar tools should focus on identifying GCC-box elements and potential novel binding motifs in ERF104 peak regions . For integration with gene expression data, researchers should analyze the statistical significance of correlations between ERF104 binding and expression changes using approaches such as Gene Set Enrichment Analysis (GSEA). False discovery rate (FDR) correction is essential when performing multiple tests across thousands of potential binding sites or genes. Visualization tools like deepTools can generate statistical heatmaps showing binding intensity patterns across different genomic features. For complex experimental designs comparing multiple conditions or genotypes, linear mixed models may be necessary to account for both fixed and random effects. These statistical approaches ensure rigorous identification and characterization of biologically meaningful ERF104 binding events across the genome.

How can researchers differentiate the specific contributions of ERF104 from other closely related ERF transcription factors in stress responses?

Differentiating the specific contributions of ERF104 from related ERF transcription factors in plant stress responses requires sophisticated experimental approaches that exploit their overlapping yet distinct functions. Genetic approaches using single, double, and triple mutants (such as ora59-1 erf104 and ora59-1 erf104 erf1b) enable researchers to assess the individual and combinatorial effects of these transcription factors on stress response gene expression . RNA-seq analysis of these mutant lines under various stress conditions can reveal genes uniquely regulated by ERF104 versus those with redundant regulation. ChIP-seq comparing the genomic binding profiles of ERF104, ERF1B, and ORA59 can identify unique and shared target genes, particularly when combined with motif analysis to detect subtle differences in binding preferences . Temporal expression analysis is particularly valuable, as these transcription factors may act sequentially rather than simultaneously during stress responses. Promoter-specific effects can be assessed through reporter gene assays using promoter fragments with mutated GCC-box elements, revealing differential requirements for specific ERFs. Phosphorylation site mapping and functional analysis can identify regulatory mechanisms specific to ERF104, as post-translational modifications often differ between otherwise similar transcription factors . Together, these approaches enable researchers to map the distinct yet interconnected roles of ERF104 and its related family members in plant stress response networks.

How can proteomics approaches enhance ERF104 antibody-based research?

Integrating proteomics with ERF104 antibody-based research creates powerful synergies for understanding this transcription factor's function in comprehensive molecular detail. Immunoprecipitation coupled with mass spectrometry (IP-MS) enables identification of ERF104 interacting partners and protein complexes under various stress conditions. Post-translational modification (PTM) analysis using phosphoproteomics can map all modification sites on ERF104, not just those recognized by existing phospho-specific antibodies, revealing the complete regulatory landscape . Targeted proteomics approaches like selected reaction monitoring (SRM) or parallel reaction monitoring (PRM) provide absolute quantification of ERF104 protein levels with high sensitivity. SILAC (Stable Isotope Labeling with Amino acids in Cell culture) or TMT (Tandem Mass Tag) labeling enables precise comparison of ERF104 abundance across multiple experimental conditions simultaneously. Protein turnover studies combining pulse-chase experiments with mass spectrometry can determine ERF104 half-life under different conditions. Structural proteomics techniques such as hydrogen-deuterium exchange mass spectrometry (HDX-MS) can reveal how phosphorylation affects ERF104 conformation and binding properties . Cross-linking mass spectrometry (XL-MS) provides detailed information about interaction interfaces between ERF104 and its binding partners. These complementary proteomics approaches significantly enhance the molecular insights gained from traditional antibody-based detection of ERF104 in plant stress responses.

What CRISPR-based approaches can complement ERF104 antibody studies?

CRISPR-based technologies provide powerful complements to ERF104 antibody studies by enabling precise genetic manipulations that enhance functional characterization. CRISPR/Cas9-mediated knockout of ERF104 generates definitive negative controls for antibody validation while revealing phenotypic consequences of ERF104 loss . For subtler manipulations, CRISPR base editing can introduce specific point mutations at phosphorylation sites without disrupting the entire gene, allowing functional analysis of particular regulatory modifications . CRISPR activation (CRISPRa) and CRISPR interference (CRISPRi) enable modulation of endogenous ERF104 expression levels without overexpression artifacts, providing valuable models for antibody-based detection of dose-dependent effects. CRISPR-mediated knock-in of epitope tags (such as FLAG or HA) at the endogenous ERF104 locus facilitates reliable detection when native antibodies have limitations, while preserving natural expression patterns and regulatory mechanisms. Prime editing allows introduction of precise modifications to regulatory elements controlling ERF104 expression, enabling studies of cis-regulatory mechanisms. CRISPR screens targeting potential upstream regulators of ERF104 can identify novel components of ERF104 regulatory pathways for subsequent validation with antibody-based approaches. These complementary genetic technologies enhance the specificity, versatility, and interpretability of ERF104 antibody studies in plant molecular research.

How can computational modeling help interpret ERF104 binding and activity data?

Computational modeling provides crucial frameworks for interpreting complex experimental data on ERF104 binding and activity. Sequence-based binding site prediction models incorporating position weight matrices (PWMs) derived from ChIP-seq data can identify potential ERF104 binding sites genome-wide, guiding targeted experimental validation . Network modeling using algorithms such as weighted gene co-expression network analysis (WGCNA) can place ERF104 in the context of broader transcriptional networks, revealing key hubs and connections. Protein structure prediction using AlphaFold or similar tools can generate detailed models of ERF104 structure, particularly how phosphorylation might induce conformational changes affecting DNA binding or protein interactions . Systems biology approaches integrating transcriptomic, proteomic, and metabolomic data can simulate how ERF104 perturbations propagate through cellular networks. Machine learning classifiers trained on experimental data can predict which genes are likely to be direct versus indirect targets of ERF104 regulation. Dynamic modeling using ordinary differential equations (ODEs) can capture the temporal aspects of ERF104 activity, including feedback and feedforward loops. Comparative genomics approaches across plant species can identify evolutionarily conserved ERF104 targets, suggesting particularly important functional roles. These computational approaches transform isolated experimental observations into coherent models of ERF104 function in plant stress response networks, generating testable hypotheses for further experimental investigation.