Recombinant Arabidopsis thaliana UPF0496 protein At5g66670 (At5g66670)

What is UPF0496 protein At5g66670 and where is it located in the Arabidopsis genome?

At5g66670 encodes a member of the UPF0496 protein family located on chromosome 5 of Arabidopsis thaliana. According to genomic data, it belongs to a family of uncharacterized proteins (UPF) with the specific designation 0496 . The protein is expressed from the At5g66670 locus, which can be accessed through various genomic databases including TAIR and Araport . The full-length protein consists of 408 amino acids based on recombinant protein expression data . For researchers beginning work with this protein, it is essential to utilize the accurate genomic coordinates from the most recent Arabidopsis genome release (TAIR10) to design primers and experimental approaches .

What resources and materials are available for studying At5g66670?

Several resources are available for studying At5g66670:

For genetic studies, the SALK_078286 line contains a T-DNA insertion in the At5g66670 gene and is available from ABRC . This line is currently in the T2 or T3 generation and is segregating for the insertion, requiring PCR-based genotyping to confirm the presence of the insertion. As noted in the ABRC database, the kanamycin resistance gene may be silenced, so PCR or hybridization-based segregation analysis is essential to confirm the presence of the insertion . For protein-level studies, recombinant At5g66670 protein can be produced using in vitro E. coli expression systems .

What expression systems are optimal for producing recombinant At5g66670?

The most documented expression system for At5g66670 is the in vitro E. coli expression system . This approach has been validated to yield functional protein for research applications. The methodology is as follows:

• Clone the full-length coding sequence of At5g66670 (1-408 amino acids) into an E. coli expression vector with an appropriate tag (typically His-tag)

• Transform the construct into an expression strain of E. coli (BL21 or derivatives)

• Induce protein expression under optimized conditions (temperature, IPTG concentration, duration)

• Harvest cells and proceed with purification

To optimize expression, consider the following parameters:

• Expression temperature (typically 16-30°C)

• Induction conditions (0.1-1.0 mM IPTG)

• Expression duration (4-24 hours)

• Media composition (standard LB or enriched media)

For researchers experiencing difficulty with protein solubility, alternative approaches include:

• Fusion with solubility-enhancing tags (MBP, GST, SUMO)

• Cold-shock expression protocols

• Co-expression with molecular chaperones

What strategies can enhance the solubility and stability of recombinant At5g66670?

As an uncharacterized protein, At5g66670 may present challenges regarding solubility and stability. Researchers should consider implementing these methodological approaches:

• Buffer optimization: Screen various buffer conditions systematically:

  • pH range (6.0-8.5)

  • Salt concentration (100-500 mM NaCl)

  • Addition of stabilizing agents (5-10% glycerol, 1-5 mM DTT)

  • Detergents for membrane-associated forms (0.01-0.1% non-ionic detergents)

• Protein engineering: Strategically modify the construct design:

  • Truncate flexible or disordered regions identified through prediction tools

  • Introduce stabilizing mutations based on structural homology models

  • Create fusion constructs with solubility-enhancing proteins

• Storage conditions: Establish optimal conditions for protein preservation:

  • Test flash freezing versus gradual freezing

  • Determine optimal protein concentration (typically 1-5 mg/ml)

  • Evaluate additives (trehalose, sucrose, arginine) for enhanced stability

How can genome editing technologies be applied to study At5g66670 function?

CRISPR-Cas9 technology has revolutionized functional genomics in Arabidopsis and can be effectively applied to study At5g66670 function. Based on genome editing protocols for Arabidopsis:

• gRNA design: Select target sites within the At5g66670 coding sequence using design tools that minimize off-target effects. The Arabidopsis genome has been bioinformatically analyzed to identify optimal gRNA target sequences, with over 1.4 million unique gRNA target sequences cataloged across exons, covering >99% of nuclear protein-encoding genes .

• Vector construction and delivery: The optimized plant codon Cas9 (pcoCas9) and gRNA can be co-expressed in Arabidopsis using appropriate vectors. The gRNA should be transcribed from the Arabidopsis U6 polymerase III promoter, while pcoCas9 can be expressed under the hybrid constitutive 35SPPDK promoter .

• Mutation detection: Following transformation, mutations can be detected by PCR amplification of the target region followed by sequencing. In Arabidopsis, single nucleotide deletions, insertions, or substitutions are most frequently observed, with mutation rates ranging from 1.1 to 5.6% .

• Validation: For thorough validation, both protoplast transient expression and stable transformation approaches should be employed. In protoplasts, mutation frequencies can be assessed rapidly before proceeding to whole-plant transformation .

For homology-directed repair (HDR), researchers should note that the efficiency in Arabidopsis is intrinsically low (14.2% in Nicotiana benthamiana vs. much lower in Arabidopsis) . Exploration of cell-cycle regulators like CYCD3 to enhance HDR has shown limited success .

What phenotypic analyses should be prioritized when investigating At5g66670 mutants?

When characterizing At5g66670 mutant lines, implement a systematic phenotypic analysis program:

• Growth and development assessment:

  • Measure germination rates, seedling establishment

  • Document developmental milestones (rosette size, flowering time)

  • Quantify vegetative and reproductive growth parameters

• Stress response evaluation:

  • Test responses to abiotic stressors (drought, salt, temperature extremes)

  • Assess susceptibility to biotic stressors (pathogens)

  • Analyze ROS accumulation and antioxidant enzyme activities

• Cellular and subcellular analyses:

  • Determine protein localization using fluorescent protein fusions

  • Examine cell morphology and organelle structure

  • Analyze cell wall composition if relevant

• Molecular phenotyping:

  • Conduct transcriptome analysis to identify affected pathways

  • Perform metabolome profiling to detect metabolic alterations

  • Analyze protein interaction networks using co-immunoprecipitation or yeast two-hybrid approaches

What approaches can identify protein interacting partners of At5g66670?

To elucidate the functional context of At5g66670, identify protein interacting partners through these complementary approaches:

• Affinity purification-mass spectrometry (AP-MS):

  • Express tagged At5g66670 in Arabidopsis (GFP, HA, or FLAG tags)

  • Perform immunoprecipitation under native conditions

  • Identify co-purified proteins by mass spectrometry

  • Validate interactions with reciprocal pulldowns

• Yeast two-hybrid screening:

  • Use full-length At5g66670 and domain-specific constructs as baits

  • Screen against Arabidopsis cDNA libraries

  • Validate interactions through directed Y2H assays

  • Confirm in planta using BiFC or FRET approaches

• Proximity labeling techniques:

  • Fuse At5g66670 with BioID or TurboID

  • Express fusion proteins in Arabidopsis

  • Identify biotinylated proteins in proximity to At5g66670

  • This approach is particularly valuable for capturing transient interactions

• In silico prediction:

  • Use structural homology models to predict interaction surfaces

  • Apply co-expression analysis to identify functionally related genes

  • Implement machine learning algorithms to predict potential interactors

What structural prediction tools are most effective for analyzing At5g66670?

For structural characterization of At5g66670, employ a multi-tiered prediction approach:

• Primary sequence analysis:

  • Identify conserved domains and motifs using InterPro, Pfam

  • Predict secondary structure elements using PSIPRED, JPred

  • Analyze disorder regions with PONDR, IUPred

  • Predict post-translational modification sites with NetPhos, UbPred

• Tertiary structure prediction:

  • Generate homology models using SWISS-MODEL, Phyre2

  • Apply ab initio modeling with Rosetta, I-TASSER

  • Utilize newer AI-based approaches like AlphaFold2, RoseTTAFold

  • Validate predictions through multiple algorithms and quality assessment tools

• Functional site prediction:

  • Identify potential ligand binding pockets using CASTp, fpocket

  • Predict functional residues with ConSurf, Evolutionary Trace

  • Analyze electrostatic surface properties using APBS

  • Map conservation data onto structural models

How can researchers experimentally validate structural predictions of At5g66670?

To validate computational predictions experimentally:

• Limited proteolysis:

  • Treat purified At5g66670 with various proteases at limited concentrations

  • Identify protected regions by mass spectrometry

  • Compare results with predicted domain boundaries and structured regions

• Circular dichroism (CD) spectroscopy:

  • Analyze secondary structure content (α-helices, β-sheets)

  • Monitor thermal stability and conformational changes

  • Validate secondary structure predictions

• Hydrogen-deuterium exchange mass spectrometry (HDX-MS):

  • Map solvent-accessible regions of the protein

  • Identify stable core regions versus flexible domains

  • Compare experimental data with computational predictions

• Site-directed mutagenesis:

  • Target predicted functional residues

  • Assess impact on protein stability, solubility, and function

  • Use results to refine structural models iteratively

How can phosphoproteomics be applied to study At5g66670 regulation?

To investigate potential phosphorylation-dependent regulation of At5g66670:

• Global phosphoproteomic profiling:

  • Identify phosphorylation sites on endogenous At5g66670 under different conditions

  • Implement enrichment strategies (TiO₂, IMAC) to capture phosphopeptides

  • Use quantitative MS approaches (TMT, SILAC) to measure changes in phosphorylation status

  • Compare results across developmental stages and stress conditions

• Site-specific phosphorylation analysis:

  • Generate phospho-site specific antibodies for key regulatory sites

  • Create phosphomimetic (S/T→D/E) and phospho-null (S/T→A) mutants

  • Assess impact on protein localization, stability, and interaction partners

  • Identify kinases responsible using inhibitor studies and in vitro kinase assays

• Functional characterization of phosphorylation:

  • Express phospho-variants in at5g66670 knockout background

  • Assess phenotypic rescue capabilities of each variant

  • Determine if phosphorylation affects protein turnover using cycloheximide chase assays

  • Investigate changes in protein interactions using AP-MS with phospho-variants

What systems biology approaches can position At5g66670 within cellular networks?

For network-level understanding of At5g66670 function:

• Integrative multi-omics:

  • Combine transcriptomics, proteomics, and metabolomics data from at5g66670 mutants

  • Identify perturbed pathways and metabolic shifts

  • Apply computational approaches to infer causal relationships

  • Validate key network nodes through targeted experiments

• Co-expression network analysis:

  • Identify genes that show coordinated expression patterns with At5g66670

  • Construct condition-specific co-expression networks

  • Apply clustering algorithms to identify functional modules

  • Map At5g66670 to known biological processes based on network position

• Protein-protein interaction network expansion:

  • Use At5g66670 interactors as seeds for network expansion

  • Apply network analysis metrics (centrality, betweenness) to identify key nodes

  • Implement network visualization tools for hypothesis generation

  • Validate network predictions through targeted protein-protein interaction studies

How can CRISPR base editing be applied for precise manipulation of At5g66670?

CRISPR base editing represents an advancement over traditional CRISPR-Cas9 for introducing precise modifications without double-strand breaks:

• Cytosine base editor (CBE) applications:

  • Convert C→T to introduce nonsense mutations or change amino acid identity

  • Target conserved cysteines or catalytic residues for functional studies

  • Implement dCBE for targeted demethylation to study epigenetic regulation

• Adenine base editor (ABE) applications:

  • Convert A→G to modify key residues within functional domains

  • Target specific sites to alter protein-protein interaction surfaces

  • Introduce synonymous mutations to study codon usage effects on expression

• Implementation strategy:

  • Design gRNAs that position target nucleotides in the optimal editing window

  • Express base editors using appropriate promoters for desired tissues

  • Screen transformants using targeted sequencing approaches

  • Validate editing outcomes at protein level using mass spectrometry

• Methodological considerations:

  • Evaluate potential off-target effects using whole-genome sequencing

  • Compare editing efficiency across various base editor variants

  • Combine with inducible or tissue-specific expression systems for spatial and temporal control

How can cryo-electron microscopy contribute to understanding At5g66670 structure and function?

Cryo-EM offers powerful approaches for structural characterization of challenging proteins:

• Single-particle analysis workflow:

  • Optimize protein purification to achieve high homogeneity

  • Screen grid preparation conditions for optimal particle distribution

  • Collect high-resolution data on modern direct electron detectors

  • Process data using reference-free classification approaches

  • Build and refine atomic models based on density maps

• Advantages for At5g66670 research:

  • No crystallization requirement, reducing purification constraints

  • Ability to visualize different conformational states

  • Potential to capture protein-protein complexes

  • Lower protein quantity requirements compared to crystallography

• Complementary approaches:

  • Combine with hydrogen-deuterium exchange mass spectrometry for dynamics information

  • Integrate with crosslinking mass spectrometry for interaction interface mapping

  • Validate models using molecular dynamics simulations

  • Correlate structural insights with functional assays