Recombinant Fragaria ananassa Putative UDP-rhamnose:rhamnosyltransferase 1 (GT4)

How does FaRT1/GT4 compare with other glycosyltransferases identified in Fragaria ananassa?

Fragaria ananassa contains multiple glycosyltransferases that differ in function and substrate specificity. The strawberry genome encodes several characterized glycosyltransferases including FaGT1, FaGT2, FaGT3, and FaRT1 . These enzymes can be distinguished based on their evolutionary relationships, substrate preferences, and expression patterns:

Unlike FaGT1-3 that preferentially use UDP-glucose as sugar donors, FaRT1 is predicted to specifically utilize UDP-rhamnose . This distinction is important for researchers designing experiments to characterize enzymatic activities, as sugar donor specificity significantly impacts experimental design for in vitro activity assays.

What are the expression patterns of FaRT1/GT4 during strawberry fruit development?

The expression pattern of FaRT1/GT4 in Fragaria ananassa tissues has not been as extensively characterized as other glycosyltransferases like FaGT2. For comparison, FaGT2 transcripts accumulate to high levels during strawberry fruit ripening and to lower levels in flowers, with expression positively correlating with the in planta concentration of various metabolites .

Research methodologies to determine FaRT1 expression patterns typically include:

• Quantitative real-time PCR (qRT-PCR) analysis across developmental stages

• RNA-seq based transcriptomic profiling

• Promoter-reporter gene fusion studies in transgenic plants

While specific data for FaRT1 expression is limited in the provided search results, researchers studying this enzyme would typically analyze expression patterns in relation to metabolite accumulation during fruit development. This temporal correlation provides important insights into the potential in vivo substrates and biological functions of the enzyme.

What methodologies are recommended for studying FaRT1/GT4 regulation in response to environmental stresses?

To investigate FaRT1/GT4 regulation under environmental stresses, researchers should employ a multi-faceted approach:

• Transcriptional analysis:

  • Perform RNA-seq or qRT-PCR on strawberry tissues under various stresses (UV-B radiation, temperature extremes, drought, pathogen exposure)

  • Compare expression patterns to those seen in other glycosyltransferases that respond to stress (e.g., UV-B induced upregulation)

• Promoter analysis:

  • Isolate and characterize the FaRT1 promoter region

  • Identify stress-responsive elements through bioinformatic analysis

  • Validate using promoter-reporter constructs in transient or stable transformation systems

• Metabolite correlation:

  • Quantify potential substrate and product levels using LC-MS under stress conditions

  • Correlate metabolite changes with FaRT1 expression changes

• Protein stability and post-translational modifications:

  • Assess protein abundance using western blotting

  • Investigate phosphorylation or other post-translational modifications affecting enzyme activity

The experimental design should include appropriate controls and time-course analyses to capture both rapid and prolonged responses to environmental stresses.

What are the optimal conditions for assaying recombinant FaRT1/GT4 activity in vitro?

Establishing optimal assay conditions for recombinant FaRT1/GT4 requires systematic testing of multiple parameters:

Recommended protocol for in vitro activity assays:

• Protein preparation:

  • Express recombinant FaRT1/GT4 with appropriate tags (His, GST, etc.)

  • Purify using affinity chromatography followed by size exclusion chromatography

  • Store in Tris-based buffer with 50% glycerol at -20°C for short-term use or -80°C for long-term storage

  • Avoid repeated freeze-thaw cycles

• Basic reaction conditions:

  • Buffer: Typically Tris-HCl (pH 7.5-8.0) or HEPES (pH 7.0-7.5)

  • Divalent cations: Test Mg²⁺, Mn²⁺, Ca²⁺ (1-10 mM)

  • Temperature: 25-30°C (optimal for most plant GTs)

  • Reaction time: 15-60 min (establish linearity)

• Substrate considerations:

  • Sugar donor: UDP-rhamnose (primary) and other UDP-sugars for comparison

  • Acceptor molecules: Test various potential acceptors including flavonoids, phenolic compounds

  • Concentration ranges: 10 μM to 1 mM for both donor and acceptor

• Analysis methods:

  • HPLC or LC-MS for product detection

  • TLC for preliminary screening

  • Radiochemical assays using ¹⁴C or ³H labeled UDP-sugars for high sensitivity

Researchers should note that unlike other strawberry glycosyltransferases that use UDP-glucose, FaRT1 is predicted to use UDP-rhamnose as its primary sugar donor , which may be more challenging to obtain commercially and might require enzymatic synthesis prior to assays.

How can researchers determine the substrate specificity of FaRT1/GT4?

Determining substrate specificity requires a systematic approach:

• Broad substrate screening:

  • Test a diverse panel of potential acceptor molecules including flavonoids, phenylpropanoids, terpenoids, and other secondary metabolites

  • Compare activity with related glycosyltransferases (FaGT1-3) that have known substrate preferences

• Structural analysis of enzyme-substrate interactions:

  • Perform homology modeling based on crystal structures of related glycosyltransferases

  • Identify potential substrate binding pockets

  • Conduct molecular docking simulations

• Site-directed mutagenesis:

  • Target amino acids predicted to be important for substrate recognition

  • Generate mutants and test activity changes

  • Similar to how Gln375 and Gln391 were identified as important residues for glucosyl transfer activity in other UGTs

• Kinetic analysis:

  • Determine kinetic parameters (Km, Vmax, kcat) for different substrates

  • Calculate catalytic efficiency (kcat/Km) to quantitatively compare substrate preferences

• In vivo validation:

  • Express FaRT1 in heterologous systems like yeast, tobacco, or Arabidopsis

  • Analyze metabolite changes using untargeted metabolomics

  • Perform transient expression assays in model systems like Nicotiana benthamiana

The results should be presented as a comprehensive substrate preference profile with kinetic constants for each substrate tested.

What CRISPR/Cas9 strategies are most effective for functional analysis of FaRT1/GT4 in the octoploid strawberry genome?

The octoploid nature of Fragaria × ananassa (2n = 8x = 56) with a genome size of approximately 780-850 Mb presents unique challenges for gene editing approaches . Effective CRISPR/Cas9 strategies for FaRT1/GT4 functional analysis should consider:

• Homoeolog targeting strategy:

  • Determine whether to target all homoeologous copies (likely 4 copies across subgenomes) or specific alleles

  • Design sgRNAs targeting conserved regions across homoeologs for complete knockout

  • For selective targeting, design sgRNAs targeting unique regions of specific homoeologs

• Transformation methodology:

  • Agrobacterium-mediated transformation of leaf explants

  • Direct protoplast transformation with RNP complexes for DNA-free editing

  • Select appropriate strawberry cultivars with higher transformation efficiency

• Mutation screening approach:

  • High-throughput sequencing to identify mutations in all homoeologous copies

  • CAPS (Cleaved Amplified Polymorphic Sequences) assays for rapid screening

  • Digital droplet PCR for quantitative assessment of editing efficiency across homoeologs

• Validation in diploid model:

  • Consider parallel experiments in diploid Fragaria vesca for comparison

  • Be aware that results may differ due to differences in heterozygosity and genetic redundancy

• Phenotypic analysis:

  • Metabolomic profiling to identify changes in glycosylated compounds

  • Analysis across developmental stages where FaRT1 is normally expressed

  • Stress response assessment if FaRT1 is implicated in stress responses

Researchers should consider the differences between editing FaRT1 in the octoploid Fragaria × ananassa versus the diploid Fragaria vesca model system, as the diploid tends to be more homozygous at a given locus .

How can heterologous expression systems be optimized for functional characterization of FaRT1/GT4?

Optimizing heterologous expression of FaRT1/GT4 requires consideration of several factors:

• Expression system selection:

  System	Advantages	Limitations	Recommended for
  E. coli	Rapid, inexpensive, high yields	Lacks post-translational modifications, inclusion body formation	Initial screening, mutagenesis studies
  Yeast (S. cerevisiae/P. pastoris)	Eukaryotic processing, secretion possible	Moderate yields, different glycosylation	In vitro biochemical characterization
  Insect cells (Sf9/Sf21)	Near-native folding, PTMs	Expensive, technically demanding	Structural studies, complex enzymes
  Plant systems (N. benthamiana)	Native PTMs, co-expression of pathway enzymes	Lower yields, longer timeframe	In vivo functional validation

• Construct optimization:

  • Codon optimization for the selected expression host

  • Addition of appropriate tags (His, FLAG, GST) for purification and detection

  • Inclusion of trafficking signals if targeting specific subcellular compartments

  • Testing different promoters for optimal expression levels

• Co-expression considerations:

  • Co-express enzymes producing UDP-rhamnose if studying in non-plant systems

  • Consider co-expression with chaperones to improve folding

  • For pathway reconstruction, co-express enzymes producing potential acceptor substrates

• Validation approaches:

  • Western blotting to confirm expression

  • Enzymatic assays to verify activity

  • Metabolite analysis to detect products formed in vivo

  • Transient expression in tobacco for in planta validation

• Troubleshooting strategies:

  • If facing solubility issues, try lower expression temperatures, fusion partners, or solubility tags

  • For activity issues, ensure sufficient UDP-rhamnose availability

  • Consider native vs. tagged protein for activity comparisons

Researchers have successfully used transient expression in Nicotiana benthamiana to test the function of related glycosyltransferases , suggesting this approach may be valuable for FaRT1 characterization.

How does FaRT1/GT4 relate evolutionarily to other plant glycosyltransferases?

Evolutionary analysis of FaRT1/GT4 within the broader context of plant glycosyltransferases provides insights into its functional specialization:

• Phylogenetic relationships:

  • FaRT1 displays highest homology to a petunia (Petunia hybrida) rhamnosyltransferase

  • It belongs to a distinct clade from other characterized strawberry glycosyltransferases (FaGT1-3)

  • It likely evolved from an ancestral glycosyltransferase with subsequent specialization for rhamnosylation

• Conservation across species:

  • Sequence analysis reveals conserved motifs characteristic of family 1 glycosyltransferases

  • The PSPG (Plant Secondary Product Glycosyltransferase) box is highly conserved among UDP-sugar-dependent GTs

  • Substrate recognition regions show greater divergence, reflecting functional specialization

• Selection pressure analysis:

  • Comparison of synonymous vs. non-synonymous substitution rates can reveal areas under positive or purifying selection

  • Conserved catalytic residues typically show strong purifying selection

  • Substrate binding regions may display signatures of positive selection related to host-specific adaptation

• Gene duplication patterns:

  • Similar to other UGT families, FaRT1 likely arose through gene duplication events

  • The expansion of the UGT gene family in plants is primarily driven by tandem and proximal duplication events

  • This expansion pattern facilitates functional diversification and specialization

• Structural evolution:

  • Despite sequence divergence, glycosyltransferases typically maintain a conserved GT-B fold

  • Sugar donor specificity is often determined by specific residues in the C-terminal domain

  • The last residue in the PSPG box (often arginine) can play a decisive role in sugar donor specificity

The evolutionary trajectory of FaRT1 reflects the broader pattern of UGT diversification in plants, where gene duplication followed by neofunctionalization has generated enzymes with diverse substrate and sugar donor preferences.

What methodological approaches are recommended for comparative analysis of rhamnosyltransferase functions across plant species?

A comprehensive comparative analysis of rhamnosyltransferases requires integrating multiple methodological approaches:

• Sequence-based comparative analysis:

  • Perform multiple sequence alignment of rhamnosyltransferases from diverse plant species

  • Use BLAST, HMMER, and phylogenetic tools to identify orthologs and paralogs

  • Construct maximum likelihood or Bayesian phylogenetic trees to resolve evolutionary relationships

  • Implement selection analysis tools (PAML, HyPhy) to identify sites under selection

• Structural comparison:

  • Generate homology models based on crystal structures of related glycosyltransferases

  • Perform comparative analysis of substrate binding pockets and catalytic sites

  • Use molecular dynamics simulations to assess structural flexibility and substrate interactions

  • Identify structural determinants of sugar donor specificity

• Functional characterization:

  • Express recombinant enzymes from different species under identical conditions

  • Compare enzyme kinetics with standardized substrate panels

  • Perform domain swapping or site-directed mutagenesis to identify regions responsible for functional differences

  • Use transient expression in model systems like Nicotiana benthamiana for in planta validation

• Expression and regulation comparison:

  • Analyze transcriptomic data across species to compare expression patterns

  • Identify conserved regulatory elements in promoter regions

  • Compare stress responsiveness and developmental regulation

  • Use systems biology approaches to identify conserved co-expression networks

• Metabolic context analysis:

  • Compare the metabolic profiles of plant species with different rhamnosyltransferase activities

  • Identify metabolic pathways where rhamnosyltransferases play key roles

  • Analyze the ecological and physiological significance of rhamnose-containing metabolites

This integrated approach allows researchers to understand both conservation and divergence in rhamnosyltransferase function across plant species, providing insights into their role in plant secondary metabolism and potential biotechnological applications.

How can substrate-specificity engineering of FaRT1/GT4 be achieved for novel glycoside synthesis?

Engineering FaRT1/GT4 for altered substrate specificity involves sophisticated protein engineering approaches:

• Structure-guided rational design:

  • Generate a high-quality homology model based on related glycosyltransferases

  • Identify residues forming the substrate binding pocket

  • Design substitutions predicted to accommodate novel substrates

  • Focus on specific amino acid residues known to be important for glycosyl transfer activity, similar to how Gln375 and Gln391 were identified in other UGTs

• Semi-rational approaches:

  • Perform site-saturation mutagenesis at key residues in the substrate binding pocket

  • Create small libraries of variants focusing on multiple residues simultaneously

  • Screen variants for activity with target substrates using high-throughput assays

  • Implement iterative cycles of mutagenesis and screening

• Directed evolution:

  • Generate larger libraries using error-prone PCR or DNA shuffling

  • Develop an effective high-throughput screening method for desired activity

  • Consider selection strategies that link enzyme activity to cell survival

  • Combine beneficial mutations identified in separate rounds

• Domain swapping and chimeras:

  • Identify domains responsible for substrate recognition in related enzymes

  • Create chimeric enzymes combining domains from different glycosyltransferases

  • Fine-tune junction points to maintain proper protein folding

  • Test activity with various substrates to identify successful chimeras

• Computational approaches:

  • Use molecular dynamics simulations to understand substrate binding dynamics

  • Implement computational enzyme design tools to predict beneficial mutations

  • Validate computational predictions with experimental testing

  • Apply machine learning models trained on glycosyltransferase sequence-function relationships

Successful engineering examples could be documented in a table format showing the mutations introduced, the changes in substrate specificity, and the kinetic parameters for both native and novel substrates.

What are the most effective strategies for investigating the role of FaRT1/GT4 in strawberry flavor and aroma compound biosynthesis?

Investigating FaRT1/GT4's role in flavor and aroma compound biosynthesis requires a comprehensive approach:

• Genetic manipulation strategies:

  • CRISPR/Cas9 knockout of FaRT1 in cultivated strawberry

  • RNAi-mediated silencing for partial downregulation

  • Overexpression studies using constitutive and fruit-specific promoters

  • Complementation studies in knockout backgrounds

• Metabolomic analysis:

  • Untargeted LC-MS/MS metabolomics to identify glycosylated compounds affected by FaRT1 manipulation

  • Targeted GC-MS analysis of volatile compounds (free and glycosidically bound)

  • Stable isotope labeling to track metabolic flux

  • Comparative analysis across developmental stages and in response to environmental conditions

• Enzymatic characterization:

  • In vitro activity assays with potential flavor precursors as substrates

  • Determination of kinetic parameters for relevant substrates

  • Analysis of product structures using NMR and MS/MS fragmentation patterns

  • Competition assays to determine substrate preferences

• Sensory analysis integration:

  • Correlate changes in metabolite profiles with sensory attributes

  • Perform trained panel evaluations of fruits with altered FaRT1 expression

  • Conduct consumer preference studies to assess impact on flavor perception

  • Identify key compounds contributing to sensory differences

• Systems biology approach:

  • Co-expression network analysis to identify genes coordinately regulated with FaRT1

  • Integration of transcriptomic, proteomic, and metabolomic data

  • Pathway modeling to understand metabolic flux changes

  • Comparative analysis across strawberry cultivars with different flavor profiles

This multi-faceted approach would provide comprehensive insights into how FaRT1 contributes to the complex network of flavor and aroma compound biosynthesis in strawberry fruits.