UGT71C1 Antibody

What is UGT71C1 and what is its primary function in plants?

UGT71C1 (encoded by At2g29750 in Arabidopsis thaliana) is a member of the UDP-glycosyltransferase 71 family, which belongs to group E, one of the largest UGT groups in plants. This enzyme primarily functions as a glycosyltransferase that catalyzes the transfer of glucose to various phenylpropanoid compounds.

Specifically, UGT71C1 demonstrates high glucosyltransferase activity towards a range of substrates including:

• Phenylpropanoids such as caffeic acid, o-coumaric acid, and p-coumaric acid

• Flavonoids including quercetin and luteolin

• Lignans like pinoresinol and lariciresinol

The enzyme shows regioselectivity in its glucosylation patterns, preferentially targeting specific hydroxyl groups on its substrates. For example, with caffeic acid, it forms caffeoyl-3-O-glucoside, showing preference for the 3-position hydroxyl group .

What detection methods are available for studying UGT71C1 expression?

Several methodological approaches can be employed to detect and study UGT71C1:

Antibody-Based Detection:

• Western blotting using polyclonal antibodies against UGT71C1

• Immunohistochemistry on paraffin-embedded sections (IHC-P)

• Direct ELISA

Molecular Biology Techniques:

• Northern blot analysis for mRNA expression levels

• qRT-PCR for quantitative gene expression analysis

• T-DNA insertion lines (e.g., SALK_111765) for studying loss-of-function phenotypes

Activity Assays:

• In vitro enzyme activity assays using recombinant UGT71C1 protein and various substrates

• UPLC-MS/MS analysis for quantification of glucosylated products

When selecting a detection method, consider the specific research question, available equipment, and whether you're studying protein expression, enzyme activity, or metabolite profiles.

How does the substrate specificity of UGT71C1 compare to other UGT71 family members?

UGT71C1 demonstrates a relatively broad substrate acceptance profile compared to some other UGT71 family members, though with clear preferences:

The substrate recognition pattern of UGT71 family members appears to be influenced by the presence of specific hydroxyl groups. For instance, UGT71C1 can glucosylate isoferulic acid (with a hydroxyl group on the 3 position) but not ferulic acid, demonstrating its regioselectivity .

Research indicates that while UGT71 members often show high specificity for donor substrates (UDP-glucose), they typically exhibit broader tolerance for acceptor substrates in vitro .

What are the optimal experimental design considerations for studying UGT71C1 enzyme kinetics?

When designing experiments to study UGT71C1 enzyme kinetics, several critical factors should be considered:

Protein Expression and Purification:

• Express recombinant UGT71C1 in E. coli or other expression systems

• Optimize purification protocols to ensure high enzyme purity and activity

• Consider adding affinity tags (His-tag) for easier purification, while validating that tags don't interfere with activity

Enzymatic Assay Conditions:

• Buffer composition: 10 mM sodium phosphate buffer (pH 7.4) has been successfully used

• Temperature: 37°C is typically optimal for in vitro reactions

• Incubation time: 30 minutes is sufficient for initial rate measurements

• Substrate concentrations: Test a range (10-500 μM) to establish Km values

• UDP-glucose concentration: Maintain excess (5 mM) to prevent it from being rate-limiting

Analytical Methods:

• UPLC-MS/MS with an ACQUITY UPLC HSS C18 1.8 μm column

• Negative mode detection with water/acetonitrile/acetic acid mobile phase

• Multiple reaction monitoring (MRM) parameters should be optimized for each substrate-product pair

Controls:

• Include vector-only expressed protein as negative control

• Use heat-denatured enzyme as additional negative control

• Test alternative sugar donors (e.g., UDP-glucuronic acid) to confirm specificity

For more complex in vivo studies, consider using ugt71c1 knockout mutants alongside wild-type plants to confirm phenotypes observed in vitro.

How can researchers effectively troubleshoot inconsistent results when using UGT71C1 antibodies?

When facing inconsistent results with UGT71C1 antibodies, a systematic troubleshooting approach is essential:

Antibody Validation Issues:

• Confirm antibody specificity using positive controls (recombinant UGT71C1) and negative controls (knockout tissue)

• Test cross-reactivity with other UGT family members, particularly UGT71C3 which has high sequence similarity

• Validate epitope accessibility in your experimental conditions

Sample Preparation Considerations:

• Optimize protein extraction buffers to ensure UGT71C1 remains stable and properly folded

• Test different fixation protocols for IHC-P applications

• Consider native vs. denaturing conditions based on epitope requirements

Technical Optimization:

• Perform titration experiments to determine optimal antibody concentration

• Adjust blocking conditions to reduce background

• If using fluorescent secondary antibodies, check for autofluorescence from plant tissues

Result Interpretation:

• Compare results between different detection methods (Western blot, IHC, ELISA)

• Correlate protein detection with enzymatic activity assays

• Validate with orthogonal approaches like mRNA quantification

A common source of inconsistency stems from the high homology between UGT family members. For example, commercial antibodies may cross-react with multiple UGT71 isoforms unless specifically validated for selectivity.

What methodological approaches can resolve contradictory findings about UGT71C1 substrate specificity?

When confronted with contradictory findings regarding UGT71C1 substrate specificity, several methodological approaches can help resolve discrepancies:

Comprehensive Substrate Profiling:

• Test a diverse panel of potential substrates under identical conditions

• Include structural analogs with systematic modifications to identify key recognition features

• Use competitive assays to determine relative preferences when multiple substrates are present

Detailed Product Analysis:

• Employ multiple analytical techniques (UPLC-MS/MS, NMR) to identify precise regioselectivity

• Confirm product identity using authentic standards or structural elucidation

• Quantify multiple reaction products to detect potential alternative glucosylation sites

Enzyme Structure-Function Analysis:

• Perform site-directed mutagenesis of putative substrate binding residues

• Create chimeric proteins with other UGT71 family members to identify specificity-determining regions

• Utilize molecular modeling based on crystallographic data from related UGTs

Biological Context Evaluation:

• Compare in vitro activity with in vivo phenotypes using knockout/overexpression lines

• Analyze metabolite profiles in wild-type vs. ugt71c1 plants to identify endogenous substrates

• Consider subcellular localization and co-localization with potential substrates

As demonstrated in research with quercetin, UGT71C1 can glucosylate multiple hydroxyl groups (3′-O-glucoside, 7-O-glucoside, and 7,3′-di-O-glucoside) on a single substrate, which may explain seemingly contradictory findings if different analytical methods detect only subsets of products .

What are the optimal experimental conditions for studying UGT71C1 in transgenic plant systems?

When designing experiments using transgenic systems to study UGT71C1, careful consideration of multiple parameters is essential:

Promoter Selection:

• Constitutive promoters (CaMV35S) provide high expression levels suitable for overexpression studies

• Native UGT71C1 promoter maintains physiological expression patterns

• Inducible promoters allow temporal control of expression for developmental studies

Transformation and Selection:

• Create multiple independent transgenic lines (minimum 9 recommended)

• Select homozygous lines through multiple generations to ensure stable expression

• Confirm transgene insertion using PCR and expression levels via Northern blot or qRT-PCR

Phenotypic Analysis:

• Compare metabolite profiles between transgenic and wild-type plants using UPLC-MS/MS

• Analyze both shoots and roots separately as UGT71C1 activity may differ between tissues

• Challenge plants with abiotic/biotic stresses to reveal conditional phenotypes

Enzymatic Characterization:

• Extract and measure UGT71C1 enzyme activity from transgenic tissues

• Correlate expression levels with enzyme activity and metabolite profiles

• Compare in vitro activity of plant-derived enzyme with recombinant versions

Previous research with UGT71C1 transgenic lines demonstrated that constitutive expression driven by the CaMV35S promoter resulted in enhanced UGT71C1 expression, higher enzyme activity, and increased levels of caffeoyl-3-O-glucoside . When analyzing metabolite changes, it's advisable to examine multiple developmental stages and growth conditions to fully capture the functional impact of UGT71C1 manipulation.

How can UGT71C1 research be integrated into broader studies on plant stress responses?

UGT71C1 research can be strategically integrated into plant stress response studies through several approaches:

Stress-Induced Expression Analysis:

• Monitor UGT71C1 expression under various stresses (drought, cold, pathogen exposure)

• Compare expression patterns with other stress-responsive genes

• Use UGT71C1 promoter-reporter constructs to visualize tissue-specific stress responses

Metabolite Profiling:

• Track changes in UGT71C1-glucosylated products during stress progression

• Correlate metabolite changes with physiological stress indicators

• Compare wild-type and ugt71c1 mutant metabolite profiles under stress conditions

Functional Studies:

• Evaluate stress tolerance of UGT71C1 overexpression and knockout lines

• Assess whether UGT71C1-mediated glucosylation enhances or reduces bioactivity of stress-related compounds

• Investigate potential detoxification roles of UGT71C1 under xenobiotic stress

Cross-Talk with Stress Signaling:

• Examine interactions between UGT71C1 activity and stress hormone pathways (ABA, SA, JA)

• Investigate whether UGT71C1 modulates signaling molecule bioavailability through glucosylation

• Study potential feedback regulation between stress signaling and UGT71C1 expression

Research with related UGT71 family members has already established connections to stress responses. For example, UGT71C3 knockout plants showed increased resistance to Pseudomonas syringae infection with elevated MeSA and SA levels, suggesting that UGT71C1 may similarly modulate defense responses through substrate glucosylation .

What experimental designs would best assess the potential redundancy between UGT71C1 and other UGT family members?

Investigating functional redundancy between UGT71C1 and other UGT family members requires sophisticated experimental designs:

Multiple Knockout Approaches:

• Generate single knockouts of UGT71C1 and related UGTs (UGT71C3, UGT71B6)

• Create double and triple knockout combinations using CRISPR/Cas9 or crossing single mutants

• Compare phenotypes across knockout combinations to identify synergistic effects suggesting redundancy

Expression Compensation Analysis:

• Quantify expression levels of remaining UGT family members in ugt71c1 mutants

• Look for upregulation of related UGTs that might compensate for UGT71C1 loss

• Use RNA-seq to identify global transcriptional adjustments in response to UGT71C1 absence

Substrate Competition Studies:

• Perform in vitro enzymology with purified UGT71C1 and related UGTs using the same substrate panel

• Determine overlapping substrate preferences and catalytic efficiencies

• Conduct mixed enzyme reactions to detect competition for preferred substrates

Complementation Experiments:

• Express different UGT family members in ugt71c1 background

• Test which UGTs can rescue phenotypes of ugt71c1 mutants

• Create chimeric proteins to identify domains responsible for functional complementation

Research has shown that the UGT71 family shares some substrate preferences but with differing specificities. For example, while multiple UGT71 members can glucosylate ABA (UGT71B6, UGT71B7, UGT71B8, UGT71C5), UGT71C1 preferentially glucosylates phenylpropanoids and flavonoids, suggesting partial functional specialization within this family .

How do methodological variations impact the interpretation of UGT71C1 function across different research studies?

Methodological variations can significantly impact the interpretation of UGT71C1 function, requiring careful consideration when comparing across studies:

Enzyme Source Variations:

• Recombinant protein expression systems (E. coli, yeast, insect cells) may affect protein folding and activity

• Plant-derived vs. recombinant UGT71C1 may show different post-translational modifications

• Affinity tags (His, GST, MBP) can influence enzyme kinetics and substrate accessibility

Assay Condition Differences:

• Buffer composition (pH, ionic strength) significantly affects glucosyltransferase activity

• Temperature conditions vary between studies (25°C to 37°C)

• Substrate concentrations and reaction times impact product formation and detectable activity

Analytical Method Sensitivity:

• UPLC-MS/MS detection limits may cause studies to miss minor glucosylated products

• Different MS/MS parameters can result in selective detection of certain product types

• Chromatographic separation conditions affect the resolution of isomeric glucosides

Data Interpretation Frameworks:

• In vitro activity may not translate directly to in vivo function

• Phenotypic analyses of knockout lines may be influenced by growth conditions

• Background genotype differences can modulate apparent UGT71C1 phenotypes

To address these variations, standardized protocols should be developed and comprehensive reporting of methodological details should be encouraged. When comparing studies, it's essential to consider these methodological differences—for example, UGT71C1 reactions performed at 37°C for 30 minutes in sodium phosphate buffer (pH 7.4) may yield different results than those conducted under alternative conditions.

What validation steps are essential before using a new UGT71C1 antibody in research?

Before employing a new UGT71C1 antibody in research, thorough validation is critical to ensure reliable results:

Specificity Testing:

• Test against recombinant UGT71C1 protein as positive control

• Evaluate cross-reactivity with other UGT family members, particularly UGT71C3

• Verify absence of signal in ugt71c1 knockout tissue extracts

• Perform peptide competition assays to confirm epitope specificity

Application-Specific Validation:

• For Western blot: Confirm single band at expected molecular weight (~55 kDa)

• For IHC-P: Validate staining pattern matches known expression sites

• For ELISA: Establish standard curves with purified protein

• For IP: Verify pull-down efficiency and specificity

Technical Parameter Optimization:

• Determine optimal antibody concentration through titration experiments

• Test multiple blocking agents to minimize background

• Optimize incubation times and temperatures

• Evaluate different detection systems (chemiluminescence, fluorescence)

Reproducibility Assessment:

• Test across multiple biological replicates

• Validate with different antibody lots if available

• Compare with alternative antibodies targeting different epitopes

• Correlate antibody detection with orthogonal measurements (mRNA levels, activity)

When using commercial antibodies like the rabbit IgG polyclonal antibody for UGT1A1 detection (which may cross-react with UGT71C1), it's essential to perform these validation steps to ensure specificity in your experimental system .

How can researchers optimize protein extraction methods for maximum UGT71C1 recovery from plant tissues?

Optimizing protein extraction for UGT71C1 requires careful consideration of enzyme characteristics and plant tissue properties:

Buffer Composition:

• Start with standard extraction buffer: 50 mM Tris-HCl (pH 7.5), 150 mM NaCl, 10% glycerol, 1 mM EDTA

• Include protease inhibitors (PMSF, leupeptin, pepstatin A) to prevent degradation

• Add 0.1-1% mild detergents (Triton X-100 or NP-40) to solubilize membrane-associated enzyme

• Include 5-10 mM β-mercaptoethanol or DTT to maintain reduced state

Physical Disruption Methods:

• For leaves: Use mortar and pestle grinding with liquid nitrogen

• For roots: Consider bead-beating or ultrasonic homogenization

• Optimize tissue-to-buffer ratio (typically 1:3-1:5 w/v)

• Keep samples cold throughout processing to preserve enzyme activity

Fractionation Approaches:

• Perform differential centrifugation to separate cellular compartments

• Use ammonium sulfate precipitation for initial enrichment

• Consider aqueous two-phase partitioning to preserve native activity

• For activity assays, avoid harsh detergents that may denature the enzyme

Post-Extraction Processing:

• Clarify extracts by centrifugation (≥ 20,000 × g for 20 minutes)

• Filter through 0.45 μm membranes to remove particulates

• Desalt using gel filtration if buffer components interfere with downstream applications

• Concentrate protein using ultrafiltration when needed

In previous studies, UGT71C1 activity was successfully preserved in plant extracts using sodium phosphate buffer (pH 7.4) . For quantitative recovery, optimization may be required for each plant species and tissue type.

What controls are essential for accurately interpreting UGT71C1 antibody staining patterns in immunohistochemistry?

When performing immunohistochemistry with UGT71C1 antibodies, comprehensive controls are essential for accurate interpretation:

Positive Controls:

• Include tissues known to express UGT71C1 (e.g., Arabidopsis stems contain lignans requiring UGT71C1)

• Use transgenic plants overexpressing UGT71C1 as strong positive reference

• Consider recombinant UGT71C1 protein spotted onto sections as staining standard

Negative Controls:

• Analyze ugt71c1 knockout plant tissues to confirm antibody specificity

• Perform parallel staining with pre-immune serum or isotype control IgG

• Include secondary antibody-only control to assess non-specific binding

• Use tissues known not to express UGT71C1 as biological negative controls

Methodological Controls:

• Include peptide competition control (pre-absorbing antibody with immunizing peptide)

• Perform serial dilution of primary antibody to optimize signal-to-noise ratio

• Test multiple fixation protocols to ensure epitope preservation

• Include autofluorescence control when using fluorescent detection systems

Cross-Validation Approaches:

• Compare antibody staining patterns with UGT71C1 promoter::reporter expression

• Correlate with in situ hybridization for UGT71C1 mRNA

• Verify colocalization with known subcellular markers when using high-resolution imaging

• Validate key findings with at least two independent antibodies when possible

For plant tissues, special attention should be paid to cell wall autofluorescence and potential cross-reactivity with endogenous peroxidases when using HRP-based detection systems.

How should researchers interpret contradictory results between UGT71C1 protein levels and enzyme activity measurements?

When encountering contradictory results between UGT71C1 protein levels and enzyme activity, apply these analytical approaches:

Possible Mechanisms for Discrepancies:

• Post-translational modifications affecting enzyme activity

• Presence of endogenous inhibitors in extracts

• Substrate availability limitations in activity assays

• Antibody detection of inactive enzyme forms

• Differences in protein stability vs. catalytic activity

Systematic Investigation Strategy:

• Perform Western blot analysis under both native and denaturing conditions

• Analyze enzyme activity across different subcellular fractions

• Test for inhibitors by mixing extracts with recombinant protein

• Examine activity with multiple substrate concentrations

Complementary Approaches:

• Measure substrate and product levels directly in tissues

• Analyze mRNA expression levels as additional reference point

• Use activity-based protein profiling to detect active enzyme fraction

• Perform in situ activity assays to localize active enzyme

Statistical Analysis:

• Calculate correlation coefficients between protein levels and activity

• Perform multivariate analysis to identify factors affecting the relationship

• Use time-course studies to detect temporal disconnects between expression and activity

Research on UGT71C1 overexpression has shown that increased enzyme levels should correlate with higher glucoside production . When this correlation fails, consider technical issues like protein denaturation during extraction or biological mechanisms like substrate limitation or feedback inhibition.

What statistical approaches are most appropriate for analyzing UGT71C1 enzyme kinetic data?

Appropriate statistical analysis of UGT71C1 enzyme kinetics requires careful consideration of experimental design and data characteristics:

Kinetic Parameter Estimation:

• Use non-linear regression to fit data to Michaelis-Menten, Hill, or other appropriate enzyme kinetic models

• Apply weighted regression when measurement errors vary across substrate concentrations

• Calculate confidence intervals for Km and Vmax to assess parameter reliability

• Use global fitting for multiple datasets to improve parameter estimation

Model Comparison and Selection:

• Apply Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC) to select best-fitting kinetic model

• Perform F-tests to compare nested models (e.g., Michaelis-Menten vs. substrate inhibition)

• Use residual analysis to detect systematic deviations from model assumptions

• Consider enzyme mechanism-based models for complex kinetic patterns

Experimental Variation Analysis:

• Perform analysis of variance (ANOVA) to evaluate factors affecting enzyme activity

• Use mixed-effects models for experiments with multiple random factors

• Apply bootstrap resampling to estimate parameter uncertainty

• Calculate coefficients of variation to assess assay reproducibility

Comparative Kinetics Analysis:

• Use statistical tests (t-test, ANOVA) to compare kinetic parameters between UGT71C1 variants or conditions

• Apply enzyme kinetics-specific statistical tools like direct linear plots for outlier detection

• Calculate specificity constants (kcat/Km) and their confidence intervals for substrate preference comparisons

• Use multivariate methods to analyze structure-activity relationships across multiple substrates

When analyzing UGT71C1 kinetics with multiple substrates, as seen in studies with phenylpropanoids and lignans, substrate-specific regression models should be developed to accurately capture the distinct kinetic profiles .

What emerging technologies could advance our understanding of UGT71C1 structure-function relationships?

Several cutting-edge technologies hold promise for deepening our understanding of UGT71C1 structure-function relationships:

Structural Biology Approaches:

• Cryo-electron microscopy for high-resolution structure determination without crystallization

• AlphaFold2 and other AI-based protein structure prediction tools for modeling UGT71C1-substrate interactions

• Hydrogen-deuterium exchange mass spectrometry to map dynamic protein regions during substrate binding

• Time-resolved X-ray crystallography to capture conformational changes during catalysis

Functional Genomics Tools:

• CRISPR/Cas9-mediated saturation mutagenesis to systematically probe functional residues

• Deep mutational scanning coupled with activity assays for comprehensive structure-function mapping

• CRISPR base editing for precise modification of catalytic residues without disrupting protein structure

• DNA-encoded chemical libraries to identify novel substrates and inhibitors

Advanced Imaging Techniques:

• Single-molecule FRET to observe conformational dynamics during substrate binding and catalysis

• Super-resolution microscopy for subcellular localization and co-localization with substrates

• Label-free imaging methods to track UGT71C1 activity in living cells

• Correlative light and electron microscopy to link function with ultrastructural context

Computational Methods:

• Molecular dynamics simulations to model substrate binding and catalytic mechanisms

• Quantum mechanics/molecular mechanics (QM/MM) calculations to study the reaction mechanism

• Machine learning approaches to predict substrate specificity from protein sequence

• Virtual screening for identifying novel substrates or inhibitors based on structural models

Research has shown that UGT71C1 shares the conserved secondary and tertiary structures common to plant UGTs, featuring two Rossmann folds with a conserved C-terminal sugar donor binding domain and variable N-terminal region for sugar acceptor binding . New technologies could help elucidate how these structural features determine the unique substrate preferences observed for UGT71C1.

How might systems biology approaches enhance our understanding of UGT71C1's role in plant metabolic networks?

Systems biology approaches offer powerful frameworks for integrating UGT71C1 function into broader plant metabolic networks:

Multi-Omics Integration:

• Combine transcriptomics, proteomics, and metabolomics data from wild-type and ugt71c1 plants

• Identify metabolic modules affected by UGT71C1 activity using pathway enrichment analysis

• Track co-expression networks to identify genes functionally related to UGT71C1

• Perform time-resolved multi-omics to capture dynamic network responses

Metabolic Modeling:

• Incorporate UGT71C1 reactions into genome-scale metabolic models

• Perform flux balance analysis to predict metabolic consequences of UGT71C1 perturbation

• Create kinetic models of UGT71C1-involved pathways to simulate dynamic responses

• Use ensemble modeling to account for parameter uncertainty in metabolic predictions

Network Analysis:

• Construct substrate-product networks centered on UGT71C1 activity

• Identify metabolic choke points or branch points regulated by UGT71C1

• Analyze network robustness to UGT71C1 perturbation through in silico knockouts

• Map cross-talk between UGT71C1-affected pathways and other metabolic processes

Integrative Phenotyping:

• Connect molecular-level changes to whole-plant physiological responses

• Use high-throughput phenotyping to capture subtle effects of UGT71C1 modification

• Implement machine learning to identify patterns linking metabolite changes to phenotypic outcomes

• Develop predictive models for plant performance based on UGT71C1 status and environmental conditions

What experimental designs could best elucidate UGT71C1's role in plant-environment interactions?

To effectively investigate UGT71C1's role in plant-environment interactions, consider these experimental design approaches:

Controlled Environment Studies:

• Expose wild-type and ugt71c1 plants to precisely defined stress gradients (drought, temperature, light intensity)

• Implement factorial designs to test interactions between multiple environmental factors

• Use time-course sampling to capture dynamic responses to changing conditions

• Include recovery phases to assess resilience mechanisms potentially involving UGT71C1

Field-Based Approaches:

• Conduct common garden experiments with UGT71C1 variants across diverse environments

• Implement rainout shelters, temperature gradients, or pest exclusion to isolate specific factors

• Monitor seasonal changes in UGT71C1 expression, activity, and metabolic products

• Correlate natural environmental fluctuations with UGT71C1-dependent metabolite levels

Biotic Interaction Studies:

• Challenge plants with pathogens, herbivores, or beneficial microbes

• Compare UGT71C1 substrate and product profiles before and after biotic interactions

• Conduct microbiome analysis to identify effects of UGT71C1 activity on plant-associated communities

• Test cross-kingdom signaling potential of UGT71C1-modified metabolites

Multi-Scale Integration:

• Link molecular-level responses (enzyme activity, metabolite profiles) to cellular phenotypes

• Connect tissue-specific responses to whole-plant physiological measurements

• Scale up to population-level studies investigating natural variation in UGT71C1 function

• Develop ecosystem-level experiments to assess broader ecological consequences