Recombinant Arabidopsis thaliana UPF0496 protein At3g19330 (At3g19330)

What is the structural characterization of recombinant At3g19330 protein?

Recombinant Arabidopsis thaliana UPF0496 protein At3g19330 is typically available as a full-length protein (1-382 amino acids) with a His-tag for research purposes . The protein belongs to the UPF0496 family, which remains functionally uncharacterized but is structurally defined. When expressing this protein for research, E. coli is commonly used as the expression system to achieve sufficient yields for structural and functional analyses . The His-tag modification facilitates purification through immobilized metal affinity chromatography (IMAC), allowing for isolation of the protein with high purity. Researchers should verify structural integrity through circular dichroism or limited proteolysis after purification to ensure the recombinant protein maintains native-like folding properties.

What expression systems are optimal for producing functional At3g19330 protein?

The most widely documented expression system for At3g19330 is E. coli, which has proven effective for producing the full-length protein with a His-tag . For optimal expression, researchers should consider using BL21(DE3) or Rosetta strains to address potential codon bias issues when expressing plant proteins in bacterial systems. Expression optimization typically involves testing multiple induction conditions including IPTG concentration (0.1-1.0 mM), temperature (16-37°C), and induction duration (4-24 hours). For proteins that prove difficult to express in soluble form, researchers may benefit from using fusion tags beyond His-tag, such as MBP (maltose-binding protein) or SUMO tags, which can enhance solubility. Alternative expression systems such as insect cells (Sf9 or High Five) or plant-based systems may be considered if post-translational modifications are suspected to be important for At3g19330 function.

How can I design experiments to investigate At3g19330's potential role in plant immune responses?

When investigating At3g19330's potential role in plant immune responses, researchers should design experiments that examine the protein's behavior during pathogen challenge or exposure to pathogen-associated molecular patterns (PAMPs). Based on research with other Arabidopsis proteins, consider treating wild-type and At3g19330 knockout plants with pathogen-derived molecules such as lipopolysaccharides (LPS) from bacteria like Xanthomonas campestris . Monitor immune response markers including calcium influx, reactive oxygen species production, and defense gene expression at various time points (0-24 hours) post-treatment. Comparative proteomics approaches can identify changes in the plasma membrane-associated protein fraction following treatment, which may indicate whether At3g19330 participates in defense signaling complexes. Co-immunoprecipitation experiments with known immune components such as BAK1 (BRASSINOSTEROID INSENSITIVE 1-associated receptor kinase 1) would help determine if At3g19330 participates in established immune complexes .

What purification strategies yield the highest purity of recombinant At3g19330?

For achieving high-purity preparations of recombinant At3g19330, a multi-step purification strategy is recommended based on the protein's properties. The initial capture step should utilize Immobilized Metal Affinity Chromatography (IMAC) with Ni-NTA or TALON resin, exploiting the His-tag present on the recombinant protein . Following IMAC, researchers should conduct size exclusion chromatography (SEC) to remove aggregates and achieve higher purity, using columns such as Superdex 200 or Sephacryl S-200. Ion exchange chromatography may serve as an intermediate step if the theoretical isoelectric point of At3g19330 differs significantly from contaminant proteins. Typical yields from optimized purification protocols range from 5-15 mg of purified protein per liter of bacterial culture, though this varies based on expression conditions. Researchers should verify final purity by SDS-PAGE (>95% purity desired) and consider Western blot confirmation of identity using anti-His antibodies or, if available, antibodies specific to At3g19330.

How can affinity chromatography methods be optimized for studying At3g19330 protein interactions?

Optimizing affinity chromatography for At3g19330 interaction studies requires careful consideration of bait immobilization and binding conditions. Based on successful affinity capture approaches with other Arabidopsis proteins, researchers should immobilize purified At3g19330 on NHS-activated or epoxy-activated resins rather than through the His-tag to keep potential interaction surfaces accessible . Control matrices without immobilized protein should always be run in parallel to identify non-specific binding partners. For identifying plant membrane proteins that interact with At3g19330, small-scale sucrose-density gradient centrifugation can be employed to enrich plasma membrane fractions from Arabidopsis leaf tissue, which requires less starting material than aqueous two-phase partitioning . Varying buffer conditions (pH range 6.0-8.0, salt concentrations 50-300 mM) during binding and washing steps helps identify stable versus transient interactions. Captured proteins should be analyzed by mass spectrometry with appropriate score thresholds (such as Byonic scores) to ensure confidence in identification .

What mass spectrometry approaches are most effective for characterizing At3g19330 and its interacting partners?

For comprehensive characterization of At3g19330 and its interacting partners, a multi-faceted mass spectrometry approach is recommended. Bottom-up proteomics using tryptic digestion followed by LC-MS/MS analysis on high-resolution instruments (such as Orbitrap or Q-TOF) provides sequence coverage for identification and post-translational modification mapping. To achieve maximum sequence coverage, complementary proteases (such as chymotrypsin or GluC) should supplement standard trypsin digestion. For interactome analysis following affinity purification, SWATH-MS (Sequential Window Acquisition of all Theoretical Mass Spectra) offers advantages over traditional data-dependent acquisition by providing more comprehensive and quantitative data on protein-protein interactions. Cross-linking mass spectrometry (XL-MS) using reagents like DSS or BS3 prior to digestion can provide valuable structural information about the interaction interfaces between At3g19330 and its binding partners. For all MS analyses, appropriate controls and statistical thresholds are essential, with multiple biological replicates (minimum n=3) recommended for confident identification of true interacting partners versus background contaminants .

How do different LPS chemotypes affect At3g19330's potential role in plant immune signaling?

To investigate how different lipopolysaccharide (LPS) chemotypes might influence At3g19330's role in plant immune signaling, researchers should design comparative experiments using structurally characterized LPS variants. Drawing from methodologies used with other Arabidopsis plasma membrane proteins, consider using LPS from Xanthomonas campestris pv. campestris (Xcc) wild-type 8004 (smooth-type LPS with O-chain) and mutant 8530 (truncated core without O-chain) to pre-treat Arabidopsis plants expressing tagged At3g19330 . Proteomic analysis of the plasma membrane fraction should be conducted at multiple time points (0, 1, 3, 6, 12, and 24 hours) following treatment to capture the dynamic response. Differential changes in protein abundance, post-translational modifications, or interaction partners between treatments would suggest LPS chemotype-specific effects. Researchers should also monitor whether At3g19330 co-localizes with known LPS-responsive proteins such as BAK1 or BPI/LBP family proteins (At1g04970) using co-immunoprecipitation or fluorescence microscopy . The distinct structural features between wild-type and mutant LPS likely influence downstream signaling events and may reveal whether At3g19330 functions in specific or general LPS perception pathways.

What experimental designs can resolve contradictory findings about At3g19330's subcellular localization?

When addressing contradictory findings regarding At3g19330's subcellular localization, researchers should implement a multi-technique approach that combines biochemical fractionation with microscopy-based localization. Begin with careful subcellular fractionation using differential centrifugation to separate major cellular compartments (plasma membrane, cytosol, nuclear, and organellar fractions), followed by Western blot analysis using antibodies against At3g19330 and marker proteins for each compartment. Complement biochemical approaches with fluorescent protein fusions, creating both N- and C-terminal GFP/YFP fusions of At3g19330 expressed under native promoters in Arabidopsis, as tag position may affect localization. Live-cell imaging should be performed under various conditions including standard growth, abiotic stress, and pathogen challenge to determine if localization is dynamic or condition-dependent. Super-resolution microscopy techniques such as STORM or PALM offer superior resolution for precise localization, particularly if At3g19330 localizes to membrane microdomains. For definitive resolution of contradictions, implement a complementary approach using immunogold electron microscopy with specific antibodies against At3g19330, which provides nanometer-scale resolution of protein localization .

How can researchers distinguish between direct and indirect protein interactions with At3g19330?

Distinguishing between direct and indirect protein interactions with At3g19330 requires implementing a hierarchical experimental approach that progressively filters interaction candidates. Initial large-scale affinity chromatography or co-immunoprecipitation experiments will identify potential interacting proteins from complex mixtures like plant cell lysates or membrane fractions . These initial candidates should then be subjected to binary interaction assays, such as yeast two-hybrid (Y2H) or split-luciferase complementation, which can suggest direct interactions but may still yield false positives. For definitive characterization of direct interactions, in vitro binding assays using purified recombinant proteins are essential. Surface plasmon resonance (SPR) or isothermal titration calorimetry (ITC) can provide quantitative binding parameters (KD, kon, koff) for direct interactions. For complex formation analysis, analytical ultracentrifugation or size exclusion chromatography coupled with multi-angle light scattering (SEC-MALS) can determine stoichiometry and complex size. The gold standard for confirming direct interactions is structural characterization through X-ray crystallography, cryo-electron microscopy, or NMR of co-purified complexes, which provides atomic-level details of interaction interfaces.

What phenotypic assays are most informative for At3g19330 knockout or overexpression lines?

When characterizing At3g19330 knockout or overexpression lines, researchers should implement a comprehensive phenotyping strategy that examines multiple aspects of plant development and stress responses. Primary phenotypic analysis should include detailed growth measurements (rosette diameter, plant height, flowering time) under standard conditions to identify any fundamental developmental roles. More informative insights may come from stress response assays, particularly focusing on biotic stress challenges given the potential immune-related functions of plasma membrane-associated proteins in Arabidopsis . Pathogen challenge assays using bacterial (Pseudomonas syringae, Xanthomonas campestris), fungal (Botrytis cinerea), and oomycete (Hyaloperonospora arabidopsidis) pathogens can reveal resistance or susceptibility phenotypes. PAMP-triggered immunity assays measuring ROS burst, callose deposition, and MAPK activation in response to flg22, elf18, or LPS provide molecular phenotypes even in the absence of visible morphological differences . Transcriptome analysis using RNA-seq comparing wild-type and mutant lines under both control and pathogen-challenged conditions can identify dysregulated defense pathways when At3g19330 function is altered.

How can researchers determine if At3g19330 functions within specialized membrane domains?

To investigate whether At3g19330 functions within specialized membrane domains such as lipid rafts or microdomains, researchers should employ both biochemical and imaging approaches. Biochemically, detergent-resistant membrane (DRM) isolation using cold Triton X-100 extraction followed by sucrose gradient ultracentrifugation can separate raft-associated proteins from bulk membrane proteins. Western blot analysis of gradient fractions can determine whether At3g19330 co-fractionates with established raft markers like flotillin . For higher resolution of membrane domain organization, use methyl-β-cyclodextrin to disrupt cholesterol-rich domains and observe whether At3g19330 distribution is altered. Complementary imaging approaches include co-localization studies using confocal microscopy with known microdomain markers labeled with different fluorophores. Super-resolution techniques such as STORM, PALM, or STED microscopy provide nanoscale resolution of protein clustering within membrane domains that is not possible with conventional microscopy. Fluorescence recovery after photobleaching (FRAP) experiments can determine if At3g19330 exhibits restricted lateral mobility characteristic of microdomain-associated proteins. If At3g19330 interacts with LPS or other immune elicitors, researchers should examine whether this interaction occurs preferentially within specific membrane domains, as has been observed for other plant immune components .

What bioinformatic approaches can predict potential functions of At3g19330 based on structural homology?

For predicting potential functions of At3g19330 based on structural homology, researchers should implement a multi-layer bioinformatic approach that extends beyond sequence similarity searches. While standard BLAST searches against protein databases provide initial insights, more sophisticated approaches are necessary for UPF0496 family proteins that lack well-characterized functions. Structure prediction using AlphaFold2 or RoseTTAFold can generate high-confidence 3D models of At3g19330, which can then be compared against structural databases using DALI or VAST to identify proteins with similar folds despite low sequence identity. Molecular docking simulations can predict potential ligand binding sites or protein-protein interaction surfaces based on the predicted structure. Researchers should analyze conserved domains and motifs using InterProScan, PFAM, and PROSITE to identify functional elements that may be shared with proteins of known function. Genomic context analysis examining synteny and gene neighborhood across plant species can provide evolutionary insights into functional associations. Co-expression network analysis using publicly available transcriptomic datasets can place At3g19330 in functional modules by identifying genes with similar expression patterns across different conditions, particularly focusing on stress and immune response datasets . Integrating these computational predictions with experimental approaches will provide the most comprehensive functional characterization.

How should researchers address data inconsistencies when studying At3g19330 interactions with other proteins?

When confronting data inconsistencies in At3g19330 protein interaction studies, researchers should implement a systematic troubleshooting and validation approach. First, critically evaluate experimental conditions across conflicting datasets, looking for variations in protein extraction methods, buffer compositions, detergents used for membrane protein solubilization, and affinity tag positions that might affect interaction surfaces. Quantitative comparison of interaction strengths across different techniques (co-IP, Y2H, BiFC) can help establish confidence levels for each reported interaction, recognizing that each method has inherent biases and limitations. For validation of key interactions, implement at least three independent techniques, ideally combining in vitro (SPR, pull-down) and in vivo (co-IP, FRET) approaches . Statistical rigor is essential; each experiment should include appropriate technical and biological replicates (minimum n=3) with clearly defined scoring criteria for positive interactions. When reporting interactions, researchers should explicitly state the experimental conditions, quantitative measures of interaction strength, and any limitations of the approaches used. Consider using a standardized confidence scoring system similar to those used in interactome databases to communicate the reliability of each reported interaction. Publication of negative results along with positive findings provides a more complete picture of protein interaction specificity.

What statistical approaches are appropriate for analyzing differential protein expression in At3g19330 studies?

For analyzing differential protein expression in At3g19330 studies, particularly from proteomics experiments, researchers should employ robust statistical methods that account for the complexities of proteomics data. When comparing protein abundance changes between wild-type and At3g19330 knockout/overexpression lines, or between different treatment conditions, start with data normalization to address technical variations using methods such as total ion current (TIC) normalization or normalization to invariant housekeeping proteins. For label-free quantification data, implement linear models with empirical Bayes statistics (such as those in the limma R package) which are well-suited for handling the high dimensionality of proteomics datasets . Multiple testing correction using Benjamini-Hochberg procedure should be applied to control false discovery rates, with typical thresholds set at FDR < 0.05 or 0.01. Define significance thresholds beyond statistical p-values, including minimum fold-change requirements (typically |log2FC| > 1) and detection in multiple biological replicates. For complex experimental designs with multiple factors (genotype, treatment, time), consider ANOVA models or mixed-effects models that can identify interactions between factors. Visualize data using volcano plots, heatmaps with hierarchical clustering, and principal component analysis to identify patterns and outliers. Functional enrichment analysis using gene ontology (GO) or pathway databases can provide biological context to differential expression results, potentially revealing coordinated changes in specific cellular processes.

How can researchers distinguish specific At3g19330-dependent effects from general stress responses?

To differentiate specific At3g19330-dependent effects from general stress responses, researchers must design experiments with appropriate controls and comparative analyses. The experimental design should include both At3g19330 knockout/knockdown lines and control lines with mutations in unrelated genes to identify responses unique to At3g19330 disruption versus generic stress phenotypes. Time-course experiments capturing early (minutes to hours) and late (hours to days) responses following stress application can separate immediate At3g19330-dependent signaling events from downstream general stress adaptation . Comparative transcriptomics or proteomics across multiple genotypes and conditions can identify gene sets or proteins uniquely regulated in an At3g19330-dependent manner, filtered against databases of common stress-responsive genes. Network analysis approaches such as Weighted Gene Co-expression Network Analysis (WGCNA) can identify modules of co-regulated genes specifically associated with At3g19330 function rather than general stress. Pharmacological approaches using specific inhibitors of known stress signaling pathways (MAPK cascades, calcium signaling, hormone pathways) can help delineate whether At3g19330 functions upstream, within, or downstream of these canonical pathways. Careful statistical analysis using interaction terms in ANOVA or regression models can formally test whether responses to stress differ significantly between wild-type and At3g19330 mutant plants beyond additive effects.

How does At3g19330 potentially interact with known immune signaling components?

In investigating At3g19330's potential interactions with established immune signaling components, researchers should focus on its relationship with key pattern recognition receptors and co-receptors. Based on studies of other Arabidopsis proteins involved in LPS perception, At3g19330 may functionally associate with BAK1 (BRASSINOSTEROID INSENSITIVE 1-associated receptor kinase 1; At4g33430), which has been identified as an LPS-interacting protein in previous affinity chromatography studies . To test this hypothesis, co-immunoprecipitation experiments using tagged versions of At3g19330 and BAK1 should be performed under both basal and elicitor-treated conditions. Bimolecular fluorescence complementation (BiFC) assays can visualize potential interactions in planta, while in vitro pull-down assays with purified recombinant proteins can determine if any observed interactions are direct. Additional candidates for interaction studies include LBP/BPI family proteins (such as At1g04970) which resemble mammalian LPS-binding proteins, and proteins involved in PAMP-triggered immunity signaling cascades . Proximity labeling approaches using BioID or APEX2 fused to At3g19330 can capture transient or weak interactions that might be missed by traditional co-immunoprecipitation. Functional validation of identified interactions through genetic approaches (double mutant analysis) and biochemical studies (phosphorylation assays if kinases are involved) will establish the biological relevance of any physical interactions detected.

What role might At3g19330 play in membrane trafficking and signaling complexes?

Investigating At3g19330's potential role in membrane trafficking and signaling complexes should begin with examining its associations with membrane transport machinery. The reported interactions between LPS-responsive Arabidopsis proteins and Ras-related proteins (RABE1c, RABA1f, RABG3a) suggest potential involvement in vesicular trafficking pathways . To explore this connection for At3g19330, researchers should perform co-localization studies with fluorescently-tagged RAB GTPases using confocal microscopy under both normal and stress conditions. Live-cell imaging with dual-labeled proteins can track potential co-transport of At3g19330 with vesicle markers during immune responses. Pharmacological inhibitors of endocytosis (Tyrphostin A23, Wortmannin) or exocytosis (Brefeldin A) can help determine if At3g19330 localization or function depends on specific trafficking pathways. For signaling complex analysis, Blue Native PAGE combined with Western blotting can identify native protein complexes containing At3g19330 without disrupting weak interactions. Stimulation with PAMPs like LPS may trigger dynamic assembly or disassembly of such complexes, which can be captured through time-course experiments . Quantitative proteomic analysis of immunoprecipitated At3g19330 complexes before and after stimulation can reveal stimulus-dependent interaction partners. If At3g19330 functions within specialized membrane domains as suggested for other immune components, density gradient fractionation of detergent-resistant membranes can determine if it co-fractionates with lipid raft markers or known signalosome components.

How can protein-protein interaction data be integrated with transcriptomic changes to understand At3g19330 function?

To create a comprehensive functional model of At3g19330, researchers should integrate protein-protein interaction data with transcriptomic changes through multi-omics data integration approaches. Begin by generating complementary datasets including At3g19330 interactome data (from immunoprecipitation-mass spectrometry), transcriptome profiles (from RNA-seq of wild-type vs. At3g19330 mutant plants), and if possible, phosphoproteomics data to capture signaling events . Network integration tools such as Cytoscape with appropriate plugins (NetworkAnalyzer, BiNGO) can visualize and analyze the relationships between interacting proteins and differentially expressed genes. Look for enrichment of differentially expressed genes among the interaction partners of At3g19330 or within second-degree nodes in the interaction network, which might indicate direct transcriptional regulation. Consider time-resolved experiments capturing both early interactome changes (minutes to hours after stimulation) and subsequent transcriptional responses (hours to days) to establish causality between protein interactions and gene expression changes. Machine learning approaches such as random forest or support vector machines can identify patterns in the integrated data that predict functional categories. Bayesian network modeling can infer directional relationships between At3g19330, its interaction partners, and transcriptional outputs. Experimental validation of key predictions from integrated analyses, such as testing whether disrupting specific protein interactions affects the transcriptional response, provides crucial feedback to refine computational models. This integrative approach can reveal whether At3g19330 functions primarily in signal perception, transduction, or regulation of transcriptional reprogramming during plant immune responses .