Recombinant Arabidopsis thaliana RING-H2 finger protein ATL73 (ATL73)

What is the structural composition of ATL73 and how does it relate to other ATL family proteins?

ATL73 belongs to the prolific ATL family of RING-H2 finger domain E3 ubiquitin ligases in Arabidopsis thaliana. The structural hallmarks of ATL proteins include a characteristic RING-H2 domain with a precise arrangement of eight zinc ligands, a region rich in hydrophobic amino acids that likely functions as a transmembrane domain, and a conserved GLD (named for three conserved amino acids) region with unknown function . While most ATL family members contain a single hydrophobic region, some lineages possess two or three such regions. The RING-H2 domain is crucial for binding to E2 ubiquitin-conjugating enzymes, typically from the Ubc4/Ubc5 subfamily .

What distinguishes ATL73 from other members of the ATL family in terms of evolutionary conservation?

The ATL family proteins are present throughout seed plants but show distinct evolutionary patterns. Like other ATL proteins, ATL73 contains conserved domains that are seed plant-specific, with no homology to genes in lower plants, fungi, or animals . Phylogenetic analysis through databases such as PLAZA 4.0 can help determine the evolutionary relationships between ATL73 and other family members, providing insights into functional conservation and divergence across species . Comparative genomics approaches reveal that while the core RING-H2 domain structure remains highly conserved, the regulatory regions have diversified substantially, suggesting functional specialization within the ATL family.

How can researchers confirm the subcellular localization of ATL73?

To determine the subcellular localization of ATL73, researchers should:

• Construct an ATL73-GFP fusion protein expressed under a strong promoter such as the Cauliflower Mosaic Virus (CaMV) 35S promoter

• Generate stably transformed Arabidopsis plants expressing this fusion

• Examine the fluorescence pattern using confocal microscopy

• Compare experimental results with predictions from subcellular localization databases such as SUBA3

This approach has been successfully used for other ATL family members, such as ATR7, which was confirmed to localize to the nucleus despite its hydrophobic regions . The results should be validated across different tissues and developmental stages to account for potential context-dependent localization patterns.

What expression patterns characterize ATL73 in response to stress conditions?

Many ATL family genes show early and transient responses to stress stimuli, particularly pathogen-associated molecular patterns (PAMPs). For example, ATL2 transcripts accumulate rapidly after treatment with elicitors, even in the presence of cycloheximide, indicating that its induction is independent of de novo protein synthesis . To characterize ATL73 expression:

• Perform time-course RT-qPCR analysis following exposure to various stressors

• Examine expression in the presence of cycloheximide to determine if transcriptional upregulation requires new protein synthesis

• Analyze the 3'UTR for regulatory elements like the DST element, which can contribute to rapid transcript degradation and is found in other ATL family members

• Use promoter-reporter constructs to visualize tissue-specific expression patterns in response to stress conditions

Understanding these expression dynamics provides crucial insights into the potential role of ATL73 in stress response pathways.

What are the optimal methods for producing recombinant ATL73 protein for in vitro ubiquitination assays?

Producing functional recombinant ATL73 for in vitro studies requires careful consideration of expression systems and purification strategies:

• Expression System Selection:

  • Prokaryotic systems (E. coli): Use for the isolated RING-H2 domain when full-length protein expression is challenging

  • Eukaryotic systems (insect cells or yeast): Preferable for full-length protein to ensure proper folding and post-translational modifications

• Purification Strategy:

  • Include affinity tags (His, GST, or MBP) that don't interfere with RING-H2 domain function

  • Use gentle elution conditions to maintain structural integrity

  • Verify protein folding through circular dichroism spectroscopy

• Activity Validation:

  • Perform in vitro ubiquitination assays using members of the Ubc4/Ubc5 subfamily of E2 conjugases

  • Include appropriate controls to verify specific activity

  • Analyze ubiquitination products by western blotting and mass spectrometry

The structural integrity of the RING-H2 domain is critical for E2-E3 recognition and ubiquitin ligase activity, as demonstrated by NMR spectroscopy studies of the rice ATL protein EL5 .

How can researchers identify and validate the substrate proteins targeted by ATL73?

Identifying the targets of E3 ubiquitin ligases like ATL73 requires multiple complementary approaches:

• Initial Substrate Identification:

  • FLAG tag affinity purification coupled with mass spectrometry analysis

  • Yeast two-hybrid screening against Arabidopsis cDNA libraries

  • Proximity-dependent biotin identification (BioID) or TurboID approaches

• Interaction Validation:

  • Co-immunoprecipitation assays in plant tissues

  • GST pull-down assays with recombinant proteins

  • Bimolecular fluorescence complementation (BiFC) in planta

• Ubiquitination Confirmation:

  • In vitro ubiquitination assays with purified components

  • Cell-free degradation assays to assess substrate stability

  • In vivo analysis of substrate levels in wild-type versus atl73 mutant plants

For example, ATL31 was shown to target 14-3-3 proteins for ubiquitination and degradation through a similar methodological progression: initial identification through FLAG tag affinity purification, confirmation via yeast two-hybrid and co-immunoprecipitation, and demonstration of ubiquitination activity in vitro .

What approaches can be used to investigate the interplay between ATL73 and other post-translational modification pathways?

Investigating cross-talk between ubiquitination and other post-translational modifications requires sophisticated experimental designs:

• Phosphorylation-Dependent Ubiquitination:

  • Use phosphatase treatments or phosphomimetic mutations to examine how phosphorylation status affects substrate recognition

  • Employ the PTM viewer in resources like PeptideAtlas to identify existing phosphorylation sites on potential substrates

  • Perform kinase inhibitor studies to identify regulatory kinases

• Conditional Protein Interaction Studies:

  • Use split-ubiquitin yeast two-hybrid assays to detect membrane-associated interactions

  • Implement FRET-based sensors to monitor interactions in living cells under different conditions

  • Apply chemical crosslinking followed by mass spectrometry (XL-MS) to capture transient interactions

• Integrated Multi-Omics Approach:

  • Combine proteomics, phosphoproteomics, and ubiquitinomics datasets

  • Generate correlation networks to identify patterns of coordinated modifications

  • Validate key regulatory nodes through targeted mutagenesis

This multi-layered approach can reveal how ATL73 functions within complex regulatory networks involving multiple post-translational modifications.

How can researchers assess the phenotypic consequences of ATL73 mutation or overexpression?

Comprehensive phenotypic characterization requires systematic analysis across development and stress conditions:

• Genetic Material Preparation:

  • Generate knockout mutants using T-DNA insertion lines from repositories or CRISPR-Cas9 gene editing

  • Create overexpression lines using the CaMV 35S promoter or tissue-specific promoters

  • Develop complementation lines expressing ATL73 in knockout backgrounds

  • Design point mutations that disrupt specific functions (e.g., E3 ligase activity)

• Phenotypic Analysis Pipeline:

  Analysis Level	Methods	Expected Outcomes
  Molecular	RT-qPCR, Western blotting	Transcript and protein levels
  Cellular	Microscopy, ROS detection	Subcellular phenotypes, stress responses
  Developmental	Growth measurements, developmental timing	Vegetative and reproductive development
  Physiological	Stress tolerance assays	Response to biotic/abiotic stressors
  Metabolic	Targeted metabolite analysis	Changes in key metabolic pathways

• Stress Response Characterization:

  • Assess tolerance to oxidative stress using treatments such as paraquat or aminotriazole

  • Evaluate responses to pathogen challenge

  • Examine carbon/nitrogen balance responses, which are regulated by some ATL family members

For example, the atr7 mutant exhibited pronounced tolerance to oxidative stress, with normal growth and fertility under non-stress conditions , illustrating how specific stress conditions may be required to reveal ATL-related phenotypes.

What technologies can be employed to study the temporal dynamics of ATL73-mediated protein degradation?

Understanding the kinetics of ATL73-mediated substrate degradation requires specialized approaches:

• Real-Time Monitoring Systems:

  • Fluorescent timer proteins fused to potential substrates

  • Luciferase-based reporters with substrate fusion proteins

  • Doxycycline-inducible expression systems coupled with cycloheximide chase assays

• Single-Cell Resolution Methods:

  • Live-cell imaging with fluorescently tagged substrates

  • FRAP (Fluorescence Recovery After Photobleaching) to measure turnover rates

  • Optogenetic tools to trigger ATL73 activity at specific timepoints

• Proteomic Time-Course Analysis:

  • Tandem Mass Tag (TMT) labeling for multiplexed quantitative proteomics

  • Parallel Reaction Monitoring (PRM) for targeted quantification of specific substrates

  • Pulse-SILAC approaches to distinguish new protein synthesis from degradation

These approaches can reveal how ATL73 activity responds to environmental cues and how substrate degradation kinetics contribute to stress adaptation mechanisms.

How should researchers design crossing experiments to study the genetic interaction of ATL73 with other stress response components?

Genetic interaction studies require careful experimental design:

• Crossing Strategy:

  • Generate double mutants between atl73 and other genes of interest

  • Create F2 mapping populations with appropriate genetic backgrounds

  • Consider the recombination landscape in Arabidopsis F2 populations, which typically involve only one or two crossovers per chromosome pair

• Population Size Considerations:

  • Account for potential segregation distortion, which occurs in over half of Arabidopsis mapping populations

  • Use large sample sizes (>100 F2 individuals) to detect subtle genetic interactions

  • Be aware that recombination frequencies vary between populations but consistently increase adjacent to centromeres

• Phenotypic Analysis:

  • Develop quantitative phenotyping methods to detect additive, synergistic, or epistatic interactions

  • Consider conditional phenotypes that may only appear under specific stress conditions

  • Implement automated phenotyping platforms for high-throughput analysis

Understanding the segregation patterns and recombination landscape is crucial for designing mapping experiments with sufficient statistical power to detect genetic interactions involving ATL73.

What transcriptomic approaches provide the most insight into ATL73-regulated pathways?

Modern transcriptomics offers powerful tools for understanding ATL73 function:

• RNA-seq Experimental Design:

  • Compare wild-type, atl73 mutant, and ATL73-overexpressing lines

  • Include time-course sampling after stress treatment

  • Consider tissue-specific or cell-type-specific RNA isolation

• Advanced Analysis Methods:

  • Differential transcript usage analysis to identify regulated isoforms

  • Co-expression network analysis to identify functionally related genes

  • Integration with ChIP-seq data to identify direct vs. indirect regulation

• Transcript Isoform Resolution:

  • Utilize resources like the Arabidopsis Reference Transcript Dataset 3 (AtRTD3) for accurate transcript quantification

  • Investigate differential transcription start and polyadenylation site usage under stress conditions

  • Consider long-read sequencing technologies for unambiguous isoform identification

These approaches can reveal how ATL73 influences global gene expression patterns and specific stress response pathways.

How can researchers effectively utilize proteomics databases to gain insights into ATL73 expression and modification?

Proteomics databases offer valuable resources for ATL research:

• Arabidopsis PeptideAtlas Utilization:

  • Access MS/MS spectra matched to the reference genome

  • Examine protein coverage and expression across different tissues/conditions

  • Investigate post-translational modifications through the PTM viewer

  • The resource contains 17,858 uniquely identified proteins at the highest confidence level

• Data Mining Strategies:

  • Search for ATL73-specific peptides and their detection in different experiments

  • Examine co-occurring proteins in the same samples

  • Investigate modification patterns across stress conditions

• Integration with Other Resources:

  • Connect proteomics data with transcriptomics from resources like AtRTD3

  • Link to genomic visualization tools such as JBrowse

  • Cross-reference with functional annotations from TAIR and UniProtKB

Effective utilization of these resources can provide insights into ATL73 expression, modifications, and interactions without performing additional experiments.

What bioinformatic tools are most effective for predicting potential substrates of ATL73?

Computational prediction of E3 ligase substrates involves multiple approaches:

• Sequence-Based Predictions:

  • Search for conserved degron motifs in Arabidopsis proteome

  • Apply machine learning algorithms trained on known E3-substrate pairs

  • Analyze protein disorder and accessibility of potential ubiquitination sites

• Structure-Based Methods:

  • Use homology modeling to predict ATL73 structure based on related proteins

  • Perform molecular docking with potential substrate candidates

  • Apply molecular dynamics simulations to assess interaction stability

• Network-Based Approaches:

  • Integrate protein-protein interaction data

  • Apply guilt-by-association methods based on known substrates of related ATLs

  • Use genetic interaction networks to identify functional relationships

• Experimental Validation Pipeline:

  Prediction Confidence	Recommended Validation Approach
  High	Direct biochemical testing (in vitro ubiquitination)
  Medium	Co-immunoprecipitation or pull-down assays
  Low	Yeast two-hybrid screening

This multi-tiered approach can prioritize potential substrates for experimental validation, making the substrate discovery process more efficient.

How can researchers investigate the role of ATL73 in coordinating responses to multiple simultaneous stresses?

Plants in natural environments often face concurrent stresses, requiring integrated research approaches:

• Multi-Stress Experimental Systems:

  • Design factorial experiments combining different stressors (e.g., drought + pathogen)

  • Develop controlled environmental systems that can impose multiple stresses simultaneously

  • Implement field-based phenotyping to capture complex stress interactions

• Systems Biology Framework:

  • Perform multi-omics profiling (transcriptomics, proteomics, metabolomics) under combined stress conditions

  • Develop computational models of stress response networks incorporating ATL73

  • Apply network perturbation analysis to identify critical nodes in multi-stress responses

• Comparative Analysis Across ATL Family:

  • Investigate whether different ATL proteins specialize in specific stress responses

  • Examine the evolutionary divergence of stress response functions within the ATL family

  • Study potential redundancy and cooperation between ATL73 and other family members

This integrated approach can reveal how ATL73 contributes to stress response prioritization and coordination when plants face multiple challenges simultaneously.

What emerging technologies could advance our understanding of temporal and spatial regulation of ATL73 activity?

Cutting-edge technologies offer new opportunities for ATL73 research:

• Single-Cell Technologies:

  • Single-cell RNA-seq to uncover cell-type-specific responses

  • Single-cell proteomics to detect protein-level changes

  • Spatial transcriptomics to map expression patterns with tissue context

• Protein Engineering Approaches:

  • Engineered ubiquitin variants to trap E3-substrate complexes

  • Proximity-dependent labeling with improved sensitivity and temporal control

  • Synthetic degron systems to study substrate specificity

• Advanced Imaging Technologies:

  • Super-resolution microscopy to visualize ubiquitination events in situ

  • Light-sheet microscopy for whole-organ imaging with cellular resolution

  • Correlative light and electron microscopy to connect molecular events with ultrastructural changes

These emerging technologies can provide unprecedented insights into how ATL73 activity is regulated at the cellular and subcellular levels during plant stress responses.

How should researchers integrate findings about ATL73 into broader models of plant stress resilience?

Effective integration of ATL73 research requires contextualizing findings within larger biological frameworks:

• Multi-Scale Integration:

  • Connect molecular mechanisms to cellular responses and whole-plant phenotypes

  • Consider how ATL73-mediated protein degradation contributes to cellular homeostasis

  • Examine evolutionary aspects to understand conservation of ATL73 function across species

• Translational Approaches:

  • Apply knowledge from Arabidopsis to crop species with ATL73 orthologs

  • Develop potential genetic markers for stress resilience breeding programs

  • Consider synthetic biology approaches to engineer optimized stress response networks

• Collaborative Framework:

  • Establish interdisciplinary collaborations combining molecular biology, systems biology, and field-based research

  • Develop shared resources and standardized methodologies for ATL family research

  • Create integrated databases that connect phenotypic, genetic, and molecular data