Recombinant Oncorhynchus mykiss Estrogen receptor beta (esr2), partial

What are the Oncorhynchus mykiss estrogen receptor beta subtypes?

Rainbow trout possess two distinct estrogen receptor beta subtypes, commonly referred to as erb1 and erb2. These are part of the four total estrogen receptor isoforms found in this species (era1, era2, erb1, erb2) . The ER beta subtypes function as nuclear transcription factors that, upon binding estrogens or estrogen-like compounds, regulate gene expression related to reproductive development, metabolic processes, and various physiological responses. The two beta subtypes exhibit differential tissue distribution and distinct expression patterns during development, suggesting specialized roles in estrogen signaling pathways .

What distinguishes ER beta from ER alpha subtypes in rainbow trout?

Rainbow trout ER beta subtypes differ from their alpha counterparts in several important ways. Expression analysis during ovarian development reveals that era1 closely associates with vitellogenin expression during maturation, while era2 increases in mature ovarian stages . In contrast, erb1 shows a negative correlation with serum estradiol levels, while erb2 remains relatively unchanged during ovarian development . Structurally, the ligand binding domains of beta subtypes have unique conformations that result in different binding affinities for various compounds. For example, raloxifene demonstrates high affinity for alpha subtypes but significantly lower affinity for beta forms . These differences translate to distinct physiological roles, with ER beta subtypes potentially serving as modulatory or antagonistic factors in certain estrogen-responsive pathways.

How does recombinant ER beta differ from native receptors?

Recombinant rainbow trout ER beta may exhibit differences from native receptors depending on the expression system and purification methods employed. These differences include potential variations in post-translational modifications, protein folding, and association with chaperone proteins. While recombinant receptors maintain core binding functionality, binding affinities can sometimes be lower than those observed in native contexts. Molecular dynamics (MD) simulations have proven valuable for estimating binding affinities when purified proteins are unavailable, demonstrating that computational approaches can complement experimental methods with recombinant receptors . When properly expressed and purified, recombinant ER beta subtypes reliably reproduce the binding preferences observed in natural systems, making them valuable tools for research on endocrine disruption and reproductive biology.

What are the optimal assays for studying recombinant O. mykiss ER beta?

Multiple validated assays are available for studying recombinant rainbow trout ER beta, each with specific advantages depending on research objectives:

• Binding Assays:

  • Rainbow trout hepatic cytosolic estrogen receptor binding assay (cyto rtERαβ): Measures direct binding but includes metabolic capabilities that may transform test compounds

  • Rainbow trout hepatic nuclear estrogen receptor binding assay (nuc rtERαβ): Provides binding data with limited metabolic interference

  • Rainbow trout recombinant estrogen receptor binding assay (rec rtERα): Offers highly specific binding information without metabolic transformation

• Functional Assays:

  • Rainbow trout liver slice Vtg mRNA expression assay: Measures physiological responses to receptor activation, including metabolism and downstream effects

  • Yeast-based reporter assays: As described in a streamlined β-galactosidase assay, allows for large-scale screening (more than 600 samples/day) with minimal manipulation

• Computational Methods:

  • Molecular dynamics simulations: Estimate binding affinities when purified proteins are unavailable, allowing comparison of environmental estrogens to natural ligands

Selection of the appropriate assay depends on whether direct binding information or downstream functional responses are the primary research focus.

What controls should be included when testing ligand binding to rainbow trout ER beta?

Properly designed controls are essential for valid interpretation of rainbow trout ER beta binding studies:

Table 1: Essential Controls for ER Beta Binding Assays

Including these controls enables validation of assay performance and provides appropriate comparisons for test compounds. The differential binding of raloxifene to alpha versus beta subtypes serves as a valuable control for isoform specificity .

How can I optimize expression of recombinant O. mykiss ER beta?

Optimizing expression of recombinant rainbow trout ER beta requires consideration of several key factors:

• Expression System Selection:

  • Yeast systems have been successfully used for expressing functional estrogen receptors, allowing for high-throughput screening applications

  • Bacterial systems (E. coli) provide high yield but may lack post-translational modifications

  • Insect cell systems offer eukaryotic processing capabilities while maintaining high expression levels

• Vector Design:

  • Include appropriate affinity tags (His, GST) for purification

  • Use strong, inducible promoters for controlled expression

  • Consider codon optimization for the host system

• Culture Conditions:

  • Optimize induction timing based on cell density

  • Adjust temperature and duration of expression

  • Supplement with appropriate cofactors as needed

• Cell Wall Processing:

  • For yeast-based systems, efficient cell wall processing is critical

  • Lyticase (zymolase) digestion followed by hypoosmotic shock lysis has proven effective

  • This approach enables complete processing in 96-well plates for high-throughput applications

• Quality Control:

  • Verify expression by Western blotting

  • Confirm functionality with known high-affinity ligands

  • Assess purity by SDS-PAGE

These optimization strategies significantly enhance recombinant ER beta yield and functionality, facilitating downstream applications in binding and functional studies.

How do you interpret binding affinity data for rainbow trout ER beta?

Interpreting binding affinity data for rainbow trout ER beta requires understanding several key metrics and considerations:

• Relative Binding Affinity (RBA):

  • Calculated as a percentage relative to estradiol-17β (set at 100%)

  • Lower percentages indicate weaker binding

  • Example values: alkylcyclohexanols typically show very low RBA values (0.000029-0.00009%)

  • Biological significance generally requires RBA values above 0.0001%

• Binding Curves:

  • Complete displacement curves provide most reliable data

  • Partial curves (e.g., "50% binding curve" observed with some compounds) indicate limited affinity

  • Assess curve steepness as indicator of binding mechanism

• Structure-Activity Relationships:

  • Compare compounds with similar structural features

  • Example: para-substituted alkylphenols typically show approximately 10-fold higher potency than alkylcyclohexanols with identical carbon chain lengths

  • Analyze effects of functional groups, chain length, and positioning

• Correlation with Biological Effects:

  • Compare binding data with functional assays (e.g., vitellogenin induction)

  • The intensity of estrogenic response generally correlates with ER binding affinity in target organs

  • Discrepancies between binding and functional data may indicate alternative mechanisms

• Cross-Isoform Comparison:

  • Compare binding profiles across all four rainbow trout ER subtypes

  • Selective binding may indicate isoform-specific physiological roles

When RBA values differ significantly between assay types, consider metabolic transformation of the test compound, as observed with certain alkylcyclohexanones and alkylcyclohexanols .

What factors influence ER beta expression during rainbow trout development?

Rainbow trout ER beta expression is regulated by multiple factors during development, particularly during ovarian maturation:

• Hormonal Regulation:

  • Correlation analysis reveals a negative relationship between serum estradiol-17β (E2) levels and ovarian erb1 expression

  • This suggests potential negative feedback mechanisms controlling erb1 expression

• Developmental Stage:

  • erb2 expression remains relatively unchanged during first ovarian development

  • This contrasts with era1, which closely associates with vitellogenin expression during maturation

• Environmental Stressors:

  • High rearing density conditions significantly alter ER expression patterns

  • Histological analysis shows retarded ovarian development in higher densities with fewer vitellogenin accumulation

  • Trout in high densities show decreased serum E2 and era expression with increasing trends of erb expression

  • A notable increase in ovarian erb2 expression occurs when density approaches 50 kg/m³

• Tissue-Specific Regulation:

  • Expression patterns differ between liver and ovarian tissue

  • Hepatic erb2 shows negative correlation with serum E2 levels

These findings suggest that ER beta subtypes may play important roles in stress response and adaptation to suboptimal environmental conditions, potentially serving as negative regulators in certain estrogen-responsive pathways.

How are recombinant ER beta assays used in environmental toxicology?

Recombinant rainbow trout ER beta assays serve as valuable tools in environmental toxicology for several key applications:

• Screening Environmental Contaminants:

  • High-throughput formats allow screening of over 600 samples per day

  • Enable evaluation of direct estrogenic potency of chemicals

  • Provide accurate determination of EC50 values with minimal manipulations

• Structure-Activity Relationship Studies:

  • Systematic evaluation of chemical classes

  • Example: Assessment of alkylcyclohexanones and alkylcyclohexanols with varying chain lengths, positioning, and branching

  • Findings reveal structure-dependent effects: para-substituted compounds show different activities compared to non-para substituted analogs

• Comparative Potency Analysis:

  • Determination of relative estrogenic potency across compound classes

  • Example finding: "para substituted alkylphenols with the same size carbon chain...were roughly an order of magnitude more potent" than corresponding alkylcyclohexanols and alkylcyclohexanones

• Metabolic Consideration Studies:

  • Investigation of parent compounds versus metabolites

  • Finding: Both alkylcyclohexanones and alkylcyclohexanols can bind directly to ERs with similar potencies

  • Comparison of assays with different metabolic capabilities helps determine if parent compounds or metabolites drive observed effects

• Selective Estrogen Receptor Modulator (SERM) Identification:

  • Identification of compounds with differential activity across ER subtypes

  • Example: Raloxifene shows high affinity for alpha subtypes but not beta

These applications contribute significantly to environmental risk assessment and understanding of endocrine disruption mechanisms in aquatic ecosystems.

What are the current challenges in rainbow trout ER beta research?

Despite significant advances, several challenges persist in rainbow trout ER beta research:

• Isoform-Specific Analysis:

  • Limited availability of isoform-specific antibodies for erb1 versus erb2

  • Challenges in distinguishing functional roles of individual beta subtypes

  • As noted in result , the "functional roles of isoforms 2, 4, and 5 remain unclear" (referring to human ER beta isoforms, highlighting a common challenge in ER research)

• Metabolic Considerations:

  • Biotransformation complicates interpretation of results in certain assays

  • Finding: "The biotransformation of the ketones in the cyto rtERαβ assay and both the alcohols and ketones in the slice assay made it impossible to definitively designate a response to the dose chemical in these assays"

• Assay Discrepancies:

  • Conflicting results between binding and functional assays

  • Example: Compounds that show no binding but induce gene expression, suggesting alternative mechanisms

• Limited Protein Availability:

  • Difficulties in obtaining purified receptor proteins for detailed structural studies

  • Reliance on computational methods when purified proteins are unavailable

• Environmental Relevance:

  • Bridging laboratory findings to environmental concentrations and exposure scenarios

  • Understanding mixture effects of multiple weak estrogens commonly found in aquatic environments

• Physiological Significance:

  • Determining the functional consequences of differential isoform expression

  • Understanding the biological significance of very weak binders in environmental contexts

Addressing these challenges requires integration of multiple methodological approaches and careful experimental design.

How do endocrine disrupting compounds differentially affect rainbow trout ER subtypes?

Endocrine disrupting compounds (EDCs) exhibit distinct interaction patterns with rainbow trout ER subtypes, providing insights into their mechanisms of action and potential ecological impacts:

Table 2: Differential Effects of EDCs on Rainbow Trout ER Subtypes

Research highlights several important patterns:

• Structural determinants of activity:

  • Para-substituted compounds typically show higher activity than non-para substituted analogs

  • Chain length and branching significantly influence binding affinity

  • Functional groups (ketones vs. alcohols) have minor effects on binding strength

• Subtype selectivity:

  • Some compounds (like raloxifene) show strong subtype preference

  • This selective binding may explain tissue-specific effects observed in vivo

• Correlation with in vivo effects:

  • Binding affinities generally correlate with biological responses

  • As noted: "The intensity of this estrogenic (biological) response has been equated with the strength of ER binding affinities in target organs"

• Predictability of effects:

  • Molecular dynamics simulations successfully predict binding preferences

  • Results show "significant differences between these environmental estrogens that are in concert with their known estrogenic response in biological assays"

These differential interaction patterns help explain how environmental contaminants can disrupt normal endocrine function in rainbow trout and other aquatic species.

How can I address inconsistent results between different ER beta assay types?

Inconsistencies between different assay types when studying rainbow trout ER beta are common and can be systematically addressed:

• Identifying Sources of Discrepancy:

  • Metabolic transformation: "The biotransformation of the ketones in the cyto rtERαβ assay and both the alcohols and ketones in the slice assay made it impossible to definitively designate a response to the dose chemical"

  • Alternative mechanisms: Some compounds may induce gene expression through non-ER pathways

  • Example: "(+/-)-CP gave conflicting results in that it did not competitively bind to either the cyto rtERαβ or rec rtERα in the binding assays, but slices exposed to CP had increased gene expression"

• Systematic Approach to Resolution:

  • Progressive assay deployment strategy:

    1. Start with binding assays with limited metabolic capabilities (rec rtERα or nuc rtERαβ)

    2. Compare with cytosolic assays that include metabolism (cyto rtERαβ)

    3. Confirm with functional assays (Vtg mRNA expression)

  • This approach helps distinguish parent compound activity from metabolite effects

• Analytical Chemistry Integration:

  • "Analytical chemistry was employed in both binding and gene expression assays to determine concentration of parent chemical and metabolites"

  • Monitoring chemical transformation throughout the assay period

  • Correlating observed effects with actual compound concentrations

• Assay Selection Based on Research Questions:

  • For direct binding assessment: Use receptors with limited metabolic capabilities

  • For environmental relevance: Include assays that incorporate metabolism

  • For mechanistic understanding: Compare results across multiple assay types

What factors affect recombinant ER beta stability and functionality?

Maintaining stability and functionality of recombinant rainbow trout ER beta preparations requires attention to several critical factors:

• Expression System Considerations:

  • Yeast-based systems offer advantages for certain applications

  • A streamlined approach using "digestion of the yeast cell wall by lyticase (zymolase), a beta-glucanase isolated from Arthrobacter luteus, followed by a hypoosmotic shock lysis" enables efficient processing

  • This protocol "significantly advances recombinant yeast manipulation"

• Buffer Composition:

  • Include stabilizing agents such as glycerol

  • Consider adding low concentrations of reducing agents to prevent oxidation

  • Optimize ionic strength and pH for rainbow trout proteins

• Storage Conditions:

  • Minimize freeze-thaw cycles by preparing small aliquots

  • Store at -80°C for long-term preservation

  • Consider addition of stabilizing ligands during storage

• Quality Control Measures:

  • Regularly verify binding capacity using reference compounds

  • Monitor protein concentration and purity

  • Assess homogeneity through size exclusion chromatography

• Handling Protocols:

  • Maintain consistent temperature during assays

  • Minimize exposure to air and oxidizing conditions

  • Ensure consistent mixing and incubation times

Implementing these practices significantly improves the reproducibility and reliability of experiments utilizing recombinant rainbow trout ER beta.

How can I optimize binding assay sensitivity for weak estrogens?

Detecting and accurately characterizing weak estrogens binding to rainbow trout ER beta requires specialized approaches:

• Assay Sensitivity Enhancements:

  • Increase receptor concentration within the linear response range

  • Optimize signal-to-noise ratio through buffer modifications

  • Use high specific activity labeled ligands

  • Extend incubation times to reach equilibrium for weak binders

• Concentration Range Considerations:

  • Employ wide concentration ranges spanning multiple orders of magnitude

  • Example: Testing "from 0.0001 to 10 μM" for compounds like flumethrin

  • Include sufficient points to generate complete binding curves

• Data Analysis Refinements:

  • Use appropriate mathematical models for weak binding

  • Apply statistical methods that account for higher variability at low binding percentages

  • Consider non-standard binding models for unusual compounds

• Control Selection:

  • Include weak positive controls with known low RBA values

  • Example weak binders: alkylcyclohexanols with RBA values of 0.000029-0.00009%

  • These serve as reference points for validating assay sensitivity

• Complementary Approaches:

  • Combine binding data with sensitive gene expression assays

  • Utilize specialized techniques like fluorescence polarization or surface plasmon resonance

  • Consider molecular dynamics simulations for theoretical binding assessment

These optimization strategies enable reliable detection and characterization of environmentally relevant weak estrogens that might otherwise be overlooked in standard assay formats.