Recombinant Arabidopsis thaliana Putative RING-H2 finger protein ATL21B (ATL21B)

What expression systems are commonly used for producing Recombinant ATL21B?

The most commonly documented expression system for Recombinant Arabidopsis thaliana Putative RING-H2 finger protein ATL21B is Escherichia coli. When expressing this protein for research purposes, the following methodological considerations should be taken into account:

• Expression Vector: Typically containing an N-terminal His-tag for purification

• Host Strain: E. coli expression strains optimized for recombinant protein production

• Expression Conditions: Induction parameters must be optimized for yield and solubility

• Purification Method: Affinity chromatography using Ni-NTA or similar matrices

The recombinant protein is commonly produced with specific tags (such as His-tag) to facilitate purification and downstream applications . When selecting an expression system, researchers should consider the protein's natural post-translational modifications and functional requirements.

What are the optimal storage and handling conditions for Recombinant ATL21B?

For maintaining the stability and activity of Recombinant Arabidopsis thaliana Putative RING-H2 finger protein ATL21B, the following storage and handling protocols are recommended:

These conditions are essential for maintaining protein stability and preventing degradation or loss of activity . When planning long-term storage for experimental continuity, researchers should consider creating multiple small aliquots to avoid repeated freeze-thaw cycles.

How can Recombinant ATL21B be used in protein-protein interaction studies?

For investigating protein-protein interactions involving Recombinant ATL21B, researchers can employ several methodological approaches:

• Co-Immunoprecipitation (Co-IP) coupled with Mass Spectrometry:

  • Express tagged ATL21B (e.g., GFP-tagged) in Arabidopsis thaliana

  • Extract proteins following appropriate stimulus (e.g., pathogen inoculation)

  • Perform immunoprecipitation using tag-specific antibodies or affinity matrices

  • Analyze precipitated protein complexes using LC-MS/MS

  • Validate identified interactions using reciprocal Co-IP or other methods

• Network Analysis Approach:

  • Compare experimentally identified interactions with predicted interactions from databases such as STRING

  • Filter high-confidence interactions based on multiple evidence channels

  • Analyze subcellular co-localization patterns of interacting proteins

  • Assess functional relationships through Gene Ontology analysis

The experimental design should include appropriate controls, such as non-tagged or GFP-only controls, to identify and exclude non-specific interactions . This comprehensive methodology has been successfully applied to related proteins (AtNHR2A and AtNHR2B) and can be adapted for ATL21B interaction studies.

What analytical techniques are most effective for studying ATL21B's E3 ubiquitin ligase activity?

To characterize the predicted E3 ubiquitin ligase activity of Recombinant ATL21B, researchers should consider the following analytical approaches:

• In Vitro Ubiquitination Assays:

  • Reconstitute ubiquitination reaction with purified E1, E2, ATL21B (E3), ubiquitin, and potential substrates

  • Detect ubiquitination through western blotting or mass spectrometry

  • Include appropriate controls (reactions lacking individual components)

  • Quantify ubiquitination efficiency under varying conditions

• Substrate Identification:

  • Perform proteomic analysis on cells expressing wild-type vs. catalytically inactive ATL21B

  • Isolate ubiquitinated proteins using tandem ubiquitin binding entities (TUBEs)

  • Compare ubiquitination patterns to identify specific substrates

• Structure-Function Analysis:

  • Generate point mutations in the RING-H2 domain

  • Assess the impact on protein-protein interactions and ubiquitination activity

  • Correlate structural features with enzymatic function

When designing these experiments, researchers should consider the potential stimulus-dependent activation of ATL21B, as has been observed with related proteins in response to pathogen challenge .

How can high-throughput approaches be utilized to study ATL21B's role in plant immunity networks?

For investigating ATL21B's potential role in plant immunity networks, the following high-throughput methodologies can be implemented:

• Integrated Network Analysis:

  • Perform Co-IP/MS studies of ATL21B-GFP following pathogen inoculation

  • Identify interacting proteins through LC-MS/MS analysis

  • Validate interactions using orthogonal methods (Y2H, BiFC)

  • Compare with existing protein interaction databases such as STRING

  • Construct functional networks based on subcellular localization and protein function

• Transcriptomic Response Analysis:

  • Compare gene expression profiles between wild-type and ATL21B knockout/overexpression lines

  • Analyze differential expression following pathogen challenge

  • Identify co-regulated gene clusters that may represent functional modules

• Phenomic Screening:

  • Assess resistance/susceptibility phenotypes against diverse pathogens

  • Quantify physiological responses using automated phenotyping platforms

  • Correlate phenotypic data with molecular interaction networks

Based on approaches used with related proteins, researchers should consider collecting samples at specific time points post-inoculation (e.g., 6 hours post inoculation) when ATL21B expression or activity may be maximally induced .

What are the current challenges in resolving ATL21B's subcellular localization and how can they be addressed?

Determining the precise subcellular localization of ATL21B presents several methodological challenges that can be addressed through the following approaches:

• Complementary Imaging Techniques:

  • Confocal microscopy of fluorescently tagged ATL21B (consider both N- and C-terminal tags)

  • Super-resolution microscopy for enhanced spatial resolution

  • Electron microscopy with immunogold labeling for ultrastructural localization

  • Correlative light and electron microscopy (CLEM) to bridge resolution gaps

• Subcellular Fractionation and Biochemical Validation:

  • Isolate cellular compartments through differential centrifugation

  • Verify protein presence through western blotting with compartment-specific markers

  • Complement imaging data with biochemical evidence

• Dynamic Localization Studies:

  • Monitor localization changes in response to stimuli (e.g., pathogen exposure)

  • Implement time-lapse imaging with appropriate temporal resolution

  • Correlate localization changes with functional outcomes

Research on related proteins suggests potential associations with various cellular compartments, including chloroplasts . When designing localization experiments, researchers should consider that membrane-associated RING-H2 proteins may show complex distribution patterns across multiple cellular compartments.

What statistical approaches are most appropriate for analyzing ATL21B interactome data?

For robust analysis of ATL21B interactome data, researchers should implement the following statistical and computational strategies:

• Filtering and Validation Criteria:

  • Set stringent peptide identification thresholds (≥2 peptide hits per protein)

  • Maintain false discovery rate (FDR) below 1% based on decoy database searches

  • Implement control samples (e.g., GFP-only) to exclude non-specific interactions

  • Apply consistent criteria across experimental replicates

• Network Construction and Analysis:

  • Calculate interaction confidence scores based on peptide spectral matches

  • Compare experimental data with predicted interactions from databases

  • Implement network topology analysis to identify hub proteins

  • Perform functional enrichment analysis of interacting partners

• Visualization and Interpretation:

  • Use platforms such as Cytoscape for network visualization

  • Incorporate subcellular localization and functional category data

  • Identify statistically enriched biological processes or cellular components

These approaches have been successfully applied to related proteins, where approximately 40% of experimentally identified interactions were also predicted in computational networks, providing validation of the experimental approach .

How can researchers differentiate between direct and indirect interactions with ATL21B in complex protein networks?

Distinguishing direct from indirect interactions in ATL21B protein networks requires a multi-layered methodological approach:

• Complementary Interaction Methods:

  • Co-IP/MS: Identifies both direct and indirect interactions within complexes

  • Yeast Two-Hybrid (Y2H): Detects direct binary interactions

  • Bimolecular Fluorescence Complementation (BiFC): Validates proximity in cellular context

  • Surface Plasmon Resonance (SPR) or Isothermal Titration Calorimetry (ITC): Quantifies direct binding parameters

• Domain-Based Interaction Mapping:

  • Generate truncated protein variants containing specific domains

  • Map interaction interfaces through systematic deletion analysis

  • Confirm critical residues through site-directed mutagenesis

• Computational Prediction and Validation:

  • Apply structural modeling to predict interaction interfaces

  • Calculate binding energy and stability for putative interactions

  • Validate predictions through experimental approaches

When interpreting interaction data, researchers should consider that E3 ubiquitin ligases like ATL21B may form both stable and transient interactions with different partners, including E2 conjugating enzymes, substrates, and regulatory proteins.

How does ATL21B functionality compare with other members of the Arabidopsis ATL family?

The Arabidopsis ATL (Arabidopsis Tóxicos en Levadura) family comprises numerous RING-H2-type E3 ubiquitin ligases with diverse functions. When comparing ATL21B with other family members, researchers should consider:

• Structural Comparison:

  • Analyze conserved domains and motifs across ATL family members

  • Assess sequence conservation particularly within the RING-H2 domain

  • Identify unique structural features of ATL21B

• Expression Pattern Analysis:

  • Compare tissue-specific and stimulus-induced expression profiles

  • Analyze promoter elements controlling transcriptional regulation

  • Determine co-expression patterns with potential functional partners

• Functional Redundancy Assessment:

  • Generate combinatorial knockout/knockdown lines with related ATL genes

  • Perform complementation assays with different ATL family members

  • Identify unique vs. overlapping phenotypes

Research on related proteins such as AtNHR2A and AtNHR2B has revealed shared interacting proteins and potential functional overlap . Similar approaches can be applied to understand ATL21B's relationship with other ATL family members.

What experimental approaches can elucidate ATL21B's role in plant responses to biotic and abiotic stresses?

To investigate ATL21B's potential roles in plant stress responses, researchers should consider implementing the following experimental strategies:

• Genetic Manipulation and Phenotypic Analysis:

  • Generate knockout/knockdown and overexpression lines

  • Assess resistance/susceptibility to diverse pathogens (bacterial, fungal, viral)

  • Evaluate responses to abiotic stressors (drought, salinity, temperature)

  • Quantify physiological and biochemical parameters under stress conditions

• Transcriptional Response Analysis:

  • Perform RNA-Seq on mutant vs. wild-type plants under various stress conditions

  • Identify differentially regulated genes and pathways

  • Correlate expression changes with phenotypic outcomes

• Protein-Level Regulation:

  • Monitor ATL21B protein abundance and modification under stress conditions

  • Identify potential substrates that are ubiquitinated in an ATL21B-dependent manner

  • Characterize the impact of ubiquitination on substrate function and stability

Based on research with related proteins, monitoring plant responses at specific time points following pathogen inoculation (e.g., 6 hours post-inoculation) may capture critical regulatory events .