Recombinant Lactobacillus johnsonii Orotidine 5'-phosphate decarboxylase (pyrF)

What is the function of Orotidine 5'-phosphate decarboxylase (pyrF) in Lactobacillus johnsonii and why is it significant?

Orotidine 5'-phosphate decarboxylase (OMPdecase) catalyzes the final step in de novo pyrimidine biosynthesis, converting orotidine-5'-monophosphate (OMP) to uridine-5'-monophosphate (UMP) . This reaction is essential for the production of pyrimidine nucleotides required for DNA and RNA synthesis in L. johnsonii.

The enzyme is particularly significant because:

• It represents one of the most catalytically efficient enzymes known, accelerating the decarboxylation reaction by approximately 17 orders of magnitude compared to the uncatalyzed reaction

• It achieves this remarkable catalytic efficiency without requiring metal cofactors or prosthetic groups, relying solely on strategically positioned amino acid residues in the active site

• It serves as an essential gene in many organisms, making it valuable as a selection marker in genetic engineering

In L. johnsonii specifically, the pyrF gene is part of the core genome that supports the basic metabolic functions of this probiotic bacterium, which has been studied extensively for its health-promoting properties in the gastrointestinal tract .

What expression systems are most effective for producing recombinant L. johnsonii pyrF?

Based on studies with similar bacterial OMPdecase enzymes, several expression systems have proven effective:

E. coli Expression Systems:

• The pT7-7/E. coli BL21(DE3) system has been successfully used for Pseudomonas aeruginosa pyrF expression and purification

• For L. johnsonii proteins, E. coli is commonly used as evidenced by recombinant production of other L. johnsonii enzymes

Yeast Expression Systems:

• Saccharomyces cerevisiae systems are particularly valuable when studying eukaryotic OMPdecase variants or when post-translational modifications are required

Methodological considerations:

• Vector selection: Vectors containing strong inducible promoters (T7, tac) are recommended

• Induction conditions: Typical IPTG concentrations range from 0.1-1.0 mM at mid-log phase

• Temperature optimization: Lower temperatures (16-25°C) during induction often improve solubility

• Codon optimization: May be necessary due to codon bias differences between L. johnsonii and the expression host

When designing expression experiments, researchers should be aware that pyrF can serve as a selection marker in some expression systems, as demonstrated by the complementation of E. coli and P. aeruginosa pyrF mutants with the P. aeruginosa pyrF gene .

What purification strategies yield the highest purity and activity for recombinant L. johnsonii pyrF?

Based on purification protocols for homologous OMPdecase enzymes, the following multi-step strategy is recommended:

Step 1: Initial Clarification

• Cell lysis via sonication or mechanical disruption in buffer containing 50 mM Tris-HCl (pH 7.5-8.0), 100-300 mM NaCl, and 1-5 mM DTT or 2-mercaptoethanol

• Centrifugation at ~15,000 × g for 30 minutes to remove cell debris

Step 2: Chromatography Sequence

• Anion Exchange Chromatography

  • Using Q-Sepharose or DEAE matrices with a pH between 7.8-8.5

  • Elution with NaCl gradient (0-500 mM)

  • This approach has been successful for P. aeruginosa OMPdecase purification

• Affinity Chromatography (if tagged construct)

  • His-tagged variants can be purified using Ni-NTA

  • Elution with imidazole gradient (20-250 mM)

• Size Exclusion Chromatography

  • Final polishing step using Superdex 75 or Superdex 200 columns

  • Helps isolate the dimeric form and remove aggregates

Activity Assessment:

• Monitor enzyme activity throughout purification using spectrophotometric assays that follow the conversion of OMP to UMP

• Calculate specific activity (units/mg) at each step to track purification efficiency

• Verify purity using SDS-PAGE with expected molecular weight of ~24-26 kDa for monomeric form

The purification protocol should be optimized based on the specific properties of L. johnsonii pyrF, including its isoelectric point, which may differ from the P. aeruginosa enzyme (pI = 6.65) .

What are the basic kinetic parameters of L. johnsonii pyrF and how do they compare to other bacterial homologs?

While specific kinetic parameters for L. johnsonii pyrF are not directly reported in the provided research, comparative analysis with other bacterial OMPdecase enzymes provides a reference framework:

What structural mechanisms contribute to the extraordinary catalytic efficiency of OMPdecase, and how might these apply to L. johnsonii pyrF?

The extraordinary catalytic efficiency of OMPdecase (accelerating reactions by ~10¹⁷-fold) arises from several structural mechanisms that likely apply to L. johnsonii pyrF:

Enthalpic Barrier Reduction:

• OMPdecase primarily achieves catalysis by reducing the activation enthalpy (ΔH‡) by approximately 28 kcal/mol, with minimal contribution from entropy changes

• Despite this significant reduction, the enzyme still requires approximately 15 kcal/mol of activation energy after enzyme-substrate complex formation

Key Structural Elements:

• Active Site Architecture:

  • A tetrad of charged residues creates an electrostatic environment favoring decarboxylation

  • A phosphate-binding residue (like R203 in Mt-OMPDC) anchors the substrate

  • Hydrophobic residues in the barrel cavity contribute to substrate positioning

• Loop Conformational Changes:

  • Crystal structure comparisons between apo and inhibitor-bound forms reveal conformational changes (RMSD of 0.5 Å) that include loop closure

  • This shields the active site from solvent exposure, creating an optimized microenvironment

• Transition State Stabilization:

  • The enzyme likely stabilizes a vinyl carbanion-carbene intermediate

  • Studies of Mt-OMPDC showed that a K72 residue plays a crucial role, with K72A mutation reducing activity by five orders of magnitude

Thermal Energy Transfer Networks:
Recent research using temperature-dependent hydrogen-deuterium exchange mass spectrometry (TDHDX) has identified specific protein networks that transfer thermal energy from solvent to enzyme active sites . In Mt-OMPDC, specific residues like L123 appear to be critical, as L123A mutation increased the activation enthalpy barrier by 2.2 kcal/mol .

Table 2: Kinetic Parameters of Wild-type and L123 Mutants of Mt-OMPDC

These insights suggest that studies of L. johnsonii pyrF should focus on identifying analogous residues and networks that contribute to its catalytic efficiency.

How can temperature-dependent hydrogen-deuterium exchange mass spectrometry (TDHDX) be applied to study the thermal energy networks in L. johnsonii pyrF?

TDHDX represents a powerful approach for uncovering region-specific changes in the enthalpic barrier for local protein flexibility, providing insights into thermal energy transfer networks critical for enzymatic catalysis. Based on recent studies with Mt-OMPDC , the following methodology can be applied to L. johnsonii pyrF:

Experimental Protocol:

• Protein Preparation:

  • Express and purify L. johnsonii pyrF to >95% homogeneity

  • Prepare both wild-type and strategic mutants (e.g., mutations of hydrophobic residues analogous to L123 in Mt-OMPDC)

  • Verify activity and structural integrity via circular dichroism

• Single-Temperature HDX Analysis:

  • Incubate protein samples (apo-form) directly with deuterated buffer

  • For ligand-bound studies, pre-incubate with substrate analogs or inhibitors (e.g., 6-azaUMP) for 30 minutes before D₂O exposure

  • Conduct HDX experiments across multiple timepoints (10 seconds to 4 hours)

  • Quench the reaction, perform proteolytic digestion, and analyze via LC-MS

  • Achieve >90% protein sequence coverage using non-overlapping peptides

• TDHDX Implementation:

  • Perform HDX at multiple temperatures (typically 10-40°C range)

  • Calculate temperature-dependent protection factors for each peptide

  • Determine region-specific activation enthalpies by Arrhenius analysis

  • Compare enthalpic barriers between wild-type and mutant variants

• Data Analysis and Visualization:

  • Generate differential deuterium uptake maps to visualize changes in local flexibility

  • Map the results onto homology models of L. johnsonii pyrF

  • Identify regions with altered dynamics upon mutation or ligand binding

Applications to L. johnsonii pyrF:

• Identify residues and structural regions involved in thermal energy transfer networks

• Correlate regional flexibility with catalytic parameters

• Map the communication pathways between solvent-exposed regions and the active site

• Discover potential allosteric sites that influence catalytic efficiency

This approach can reveal how thermal energy from solvent collisions is directed into the active site of L. johnsonii pyrF to enable efficient thermal activation of the reaction, providing unprecedented mechanistic insights into this highly efficient enzyme.

What methods are most effective for resolving contradictions in kinetic data for L. johnsonii pyrF?

Resolving contradictions in kinetic data for L. johnsonii pyrF requires a multi-faceted approach that combines diverse experimental techniques and rigorous data analysis:

1. Pre-Steady State Kinetics:

• Employ stopped-flow techniques to resolve rapid enzyme-substrate interactions

• Measure fluorescence quenching during OMP decarboxylation (as observed with yeast OMPdecase showing ~20% quenching)

• Perform single-turnover experiments under conditions where [enzyme] > [substrate]

• Use global fitting of time-course data to discriminate between competing kinetic models

2. Oligomeric State Assessment:

• Determine the active oligomeric state using analytical ultracentrifugation

• Apply size exclusion chromatography coupled with multi-angle light scattering (SEC-MALS)

• Investigate the effects of protein concentration, buffer conditions, and salt concentration on oligomeric equilibria

• Consider that yeast OMPdecase functions as a dimer with a dissociation constant of 0.25 μM, while other homologs may have different oligomeric states

3. pH-Dependent Kinetics:

• Conduct comprehensive pH-activity profiles (pH 5-10) using overlapping buffers

• Determine pK_a values of catalytically important residues

• Compare with known pH dependencies, such as P. aeruginosa OMPdecase which maintains activity between pH 7.8-10.2

• Analyze pH effects on both k_cat and K_m parameters separately

4. Isotope Effects:

• Measure primary kinetic isotope effects using isotopically labeled substrates

• Employ solvent isotope effects to probe proton transfer steps

• Combine with computational modeling to interpret experimental results

5. Reconciliation Framework:

• Develop mathematical models that integrate diverse data sets

• Use Bayesian statistical approaches to quantify uncertainty in parameter estimates

• Identify experimental conditions that might lead to artifactual results (protein aggregation, substrate limitations, inhibition by products)

• Consider substrate depletion and product inhibition in data analysis

Example Reconciliation Case:
When faced with contradictory K_m values from different studies, researchers could:

• Standardize expression and purification protocols

• Verify enzyme purity and structural integrity

• Employ multiple assay methods (spectrophotometric, radiometric, coupled enzyme)

• Control for buffer components, ionic strength, and temperature

• Analyze data using multiple kinetic models (including cooperative binding if relevant)

This systematic approach helps distinguish true mechanistic complexity from experimental artifacts and facilitates the resolution of seemingly contradictory kinetic data.

How do site-directed mutations in the active site of L. johnsonii pyrF affect catalytic efficiency and substrate specificity?

Site-directed mutagenesis offers powerful insights into structure-function relationships in L. johnsonii pyrF. Based on studies of homologous OMPdecase enzymes, particularly the Mt-OMPDC system , the following methodological approach is recommended:

Strategic Mutation Design:

• Catalytic Residue Mutations:

  • Target conserved active site residues analogous to D70, K72, and D75 in Mt-OMPDC

  • Create conservative substitutions (e.g., K→R, D→E) and more disruptive changes (K→A, D→N)

  • Examine mutations that modify electrostatic interactions with the transition state

• Substrate Binding Pocket Mutations:

  • Target hydrophobic residues that form the binding pocket

  • Create variants with altered side chain volumes (e.g., L→A, L→V, L→I) as demonstrated with L123 mutations in Mt-OMPDC

  • Examine the impact on substrate binding (K_m) versus catalytic turnover (k_cat)

• Loop Region Mutations:

  • Target residues involved in conformational changes upon substrate binding

  • Engineer disulfide bonds to restrict loop movement

  • Create glycine insertions to increase loop flexibility

Comprehensive Kinetic Analysis:

For each mutant, determine the following parameters and compare to wild-type:

• k_cat and K_m values for OMP

• Activation energy parameters (ΔH‡, ΔS‡, E_a)

• pH-activity profiles to identify shifts in ionization states

• Temperature dependence of catalytic parameters

Table 3: Expected Effects of Key Mutations Based on Homologous Enzymes

Advanced Characterization:

• Substrate Specificity Testing:

  • Examine activity with OMP analogs (variations in the pyrimidine ring or phosphate group)

  • Determine if mutations alter substrate preferences

• Structural Verification:

  • Use circular dichroism to confirm that mutations don't disrupt global folding

  • Employ HDX-MS to assess changes in local dynamics

  • Where possible, obtain crystal structures of key mutants

• Computational Support:

  • Perform molecular dynamics simulations to predict effects of mutations

  • Calculate electrostatic potential maps to visualize changes in charge distribution

These approaches can provide detailed insights into how specific residues contribute to the extraordinary catalytic efficiency of L. johnsonii pyrF and may reveal unexpected aspects of its catalytic mechanism.

What role might L. johnsonii pyrF play in the bacterium's probiotic activities, and how can it be investigated?

While pyrF's primary function is in pyrimidine biosynthesis, recent research on L. johnsonii suggests potential connections between basic metabolism and probiotic functionality that warrant investigation:

Potential Connections to Probiotic Activities:

• Metabolic Adaptation in the GI Tract:

  • Efficient nucleotide biosynthesis may contribute to L. johnsonii's ability to colonize specific niches in the gastrointestinal tract

  • Pyrimidine metabolism could influence growth rates under the nutrient-limited conditions of the gut

• Interaction with Host Immune System:

  • Metabolites from nucleotide biosynthesis pathways might modulate host immune responses

  • L. johnsonii has been shown to reduce inflammatory metabolites in maternal plasma and breastmilk

• Competition with Pathogens:

  • Efficient pyrimidine biosynthesis could provide competitive advantages against pathogens like Helicobacter pylori, which L. johnsonii has been shown to inhibit

  • L. johnsonii has demonstrated antagonistic effects against Salmonella enteric serovar Infantis

Methodological Approaches for Investigation:

• Genetic Manipulation Strategies:

  • Create conditional pyrF mutants with tunable expression

  • Develop CRISPR-Cas9 systems for precise genomic modifications in L. johnsonii

  • Compare wild-type to pyrF-attenuated strains in various probiotic assays

• Host-Microbe Interaction Studies:

  • Examine colonization efficiency of pyrF-modified strains in animal models

  • Measure immunomodulatory effects using in vitro co-culture with immune cells

  • Assess competitive exclusion against pathogens in mixed culture systems

• Metabolic Profiling:

  • Conduct comparative metabolomic analysis of wild-type and pyrF-modified strains

  • Track pyrimidine metabolites in culture supernatants and host tissues

  • Identify potential bioactive metabolites derived from pyrimidine metabolism

• Transcriptomic Analysis:

  • Compare gene expression profiles between wild-type and pyrF-modified strains

  • Identify regulatory networks connecting pyrimidine metabolism to stress responses

  • Examine host transcriptional responses to L. johnsonii with altered pyrF function

Experimental Model Design:

A comprehensive investigation could include:

• In vitro growth studies comparing wild-type and pyrF-modified strains under various conditions

• Cell culture models examining interactions with intestinal epithelial cells and immune cells

• Mouse models to assess colonization efficiency and health impacts

• Combined 'omics approaches (transcriptomics, proteomics, metabolomics) to develop a systems biology view of pyrF's role

Such studies could reveal unexpected roles of basic metabolic enzymes like pyrF in the complex probiotic activities of L. johnsonii, potentially opening new avenues for strain improvement and therapeutic applications.

How can isotope labeling and NMR spectroscopy be combined to elucidate the catalytic mechanism of L. johnsonii pyrF?

Isotope labeling combined with NMR spectroscopy offers powerful insights into enzyme mechanisms by tracking specific atoms through the reaction pathway. For L. johnsonii pyrF, the following methodology could elucidate its catalytic mechanism:

Isotope Labeling Strategies:

• Substrate Labeling:

  • Synthesize ¹³C-labeled OMP with enrichment at specific carbons:

    • ¹³C at C6 (decarboxylation site)

    • ¹³C at C5 (adjacent to reaction center)

    • ¹³C at C2 (distant from reaction center, as control)

  • Prepare ¹⁵N-labeled OMP to track nitrogen atom involvement

  • Synthesize ¹⁸O-labeled substrate at phosphate or ribose positions

• Enzyme Labeling:

  • Express L. johnsonii pyrF in minimal media with ¹⁵N sources for uniform labeling

  • Use selective amino acid labeling to focus on catalytic residues (e.g., ¹⁵N-lysine)

  • Incorporate ¹³C-labeled amino acids at key positions based on homology models

NMR Experimental Design:

• Time-Resolved NMR:

  • Employ rapid-mixing devices coupled to NMR instruments

  • Record spectra at millisecond intervals to capture transient intermediates

  • Use temperature control to slow the reaction when necessary

• Multi-Nuclear NMR Techniques:

  • ¹³C-NMR to track carbon movement during decarboxylation

  • ¹⁵N-NMR to monitor protonation states of nitrogen atoms

  • ³¹P-NMR to assess phosphate group involvement

  • ¹H-NMR to follow proton transfers

• Advanced Correlation Experiments:

  • HSQC (Heteronuclear Single Quantum Coherence) for ¹H-¹³C and ¹H-¹⁵N correlations

  • NOESY (Nuclear Overhauser Effect Spectroscopy) to determine spatial proximities

  • HMBC (Heteronuclear Multiple Bond Correlation) to detect long-range couplings

Mechanistic Information Obtained:

• Carbanion Intermediate Verification:

  • Direct observation of the proposed carbanion-carbene intermediate

  • Measurement of the lifetime of transient species

  • Correlation with computational predictions

• Proton Transfer Pathways:

  • Identification of the proton donor to C6 position (likely a lysine residue)

  • Determination of protonation sequence and rate-limiting steps

  • Assessment of solvent participation in proton transfers

• Conformational Changes:

  • Detection of enzyme structural changes upon substrate binding

  • Correlation of dynamic loop movements with catalytic events

  • Identification of residues experiencing environmental changes during catalysis

Integration with Other Methods:

• Combine NMR data with X-ray crystallography to correlate solution dynamics with static structures

• Support findings with computational approaches like QM/MM calculations

• Validate mechanistic hypotheses through site-directed mutagenesis of implicated residues

This comprehensive approach would provide unprecedented atomic-level insights into the extraordinary catalytic efficiency of L. johnsonii pyrF, potentially revealing novel mechanistic features that contribute to its remarkable rate enhancement of 10¹⁷-fold over the uncatalyzed reaction .

How can computational methods be integrated with experimental data to advance understanding of L. johnsonii pyrF catalysis?

Integrating computational methods with experimental data creates a powerful framework for understanding enzymatic catalysis at multiple scales. For L. johnsonii pyrF, this approach can reveal atomic-level details of the catalytic mechanism while connecting to macroscopic observables:

Multi-scale Computational Approach:

• Homology Modeling and Structural Refinement:

  • Generate L. johnsonii pyrF models based on crystal structures of homologous enzymes

  • Refine models using molecular dynamics simulations in explicit solvent

  • Validate structural predictions against experimental data (e.g., HDX-MS patterns, SAXS profiles)

• Quantum Mechanical Calculations:

  • Employ QM cluster models of the active site (50-200 atoms)

  • Calculate reaction energy profiles using DFT methods

  • Evaluate electronic effects of key residues on transition state stabilization

  • Methods: B3LYP, M06-2X, or ωB97X-D functionals with 6-31+G(d,p) basis sets

• Hybrid QM/MM Simulations:

  • Treat the active site quantum mechanically while representing the rest of the protein with molecular mechanics

  • Calculate free energy profiles along reaction coordinates

  • Identify contributions of specific residues to transition state stabilization

  • Computationally test hypotheses derived from mutagenesis studies

• Classical Molecular Dynamics:

  • Perform microsecond-scale simulations to capture conformational dynamics

  • Identify networks of residues that facilitate thermal energy transfer

  • Analyze water networks and proton transfer pathways

  • Implement enhanced sampling methods (metadynamics, umbrella sampling) to access rare events

Integrating Computational and Experimental Data:

• Validating Mechanistic Hypotheses:

  • Compare calculated kinetic isotope effects with experimental measurements

  • Test predictions about mutational effects on catalytic parameters

  • Correlate calculated transition state structures with inhibitor binding affinities

• Explaining Structure-Function Relationships:

  • Use computational alanine scanning to predict effects of mutations

  • Compare with experimental data from site-directed mutagenesis

  • Example: Validate computational predictions about residues analogous to L123 in Mt-OMPDC, where mutation to alanine increased the activation enthalpy

• Machine Learning Integration:

  • Develop ML models trained on experimental and computational data sets

  • Predict properties of enzyme variants without extensive experimental testing

  • Identify non-intuitive correlations between structural features and catalytic parameters

Practical Implementation Workflow:

• Initial Phase:

  • Generate homology models of L. johnsonii pyrF

  • Dock substrate and transition state analogs

  • Identify key residues for experimental investigation

• Iterative Refinement:

  • Perform mutagenesis of identified residues

  • Measure kinetic parameters and compare to computational predictions

  • Refine computational models based on experimental results

• Advanced Investigation:

  • Implement QM/MM simulations for refined mechanistic insights

  • Design transition state analogs as potential inhibitors

  • Explore allosteric networks through long-timescale MD simulations

This integrated computational-experimental approach can provide a comprehensive understanding of L. johnsonii pyrF catalysis, potentially revealing novel insights into the extraordinary catalytic efficiency of this enzyme and suggesting strategies for engineering variants with enhanced properties.

What are the current challenges in crystallizing L. johnsonii pyrF for structural studies, and how can they be overcome?

Protein crystallization remains a significant challenge in structural biology, and L. johnsonii pyrF likely presents specific obstacles. Based on experiences with homologous decarboxylases, the following challenges and solutions are relevant:

Common Crystallization Challenges:

• Protein Stability and Homogeneity:

  • OMPdecase enzymes often exhibit concentration-dependent oligomerization, as seen with yeast OMPdecase

  • Dynamic loop regions critical for catalysis may adopt multiple conformations

  • Post-translational modifications or proteolytic degradation can create heterogeneity

• Buffer and pH Considerations:

  • OMPdecase activity and stability are pH-dependent, with P. aeruginosa OMPdecase showing a broad pH optimum (7.8-10.2)

  • The oligomeric state may be influenced by pH, as observed with ornithine decarboxylase from Lactobacillus 30a, which dissociates from dodecamers to dimers at high pH

• Ligand-Induced Conformational Changes:

  • Substrate or inhibitor binding can induce significant conformational changes, as demonstrated by the RMSD of 0.5 Å observed between apo and 6-azaUMP-bound Mt-OMPDC

Strategic Solutions for L. johnsonii pyrF Crystallization:

• Protein Engineering Approaches:

  • Surface Entropy Reduction (SER): Mutate surface-exposed lysine and glutamate clusters to alanines

  • Loop Truncation or Stabilization: Modify flexible loops while preserving catalytic function

  • Fusion Partner Strategy: Express pyrF with a crystallization chaperone (e.g., T4 lysozyme, MBP)

  • Disulfide Engineering: Introduce disulfides to stabilize specific conformations

• Crystallization Condition Optimization:

  • High-throughput Screening: Test thousands of conditions using nanoliter-scale robotics

  • Additive Screening: Include small molecules that may stabilize crystal contacts

  • Ligand Co-crystallization: Include substrate analogs, product, or inhibitors like 6-azaUMP

  • Controlled Dehydration: Manipulate crystal solvent content to improve diffraction

  • Salt Screening: Test the effect of NaCl concentration, which is known to stabilize the dimeric form of yeast OMPdecase

• Alternative Crystallization Methods:

  • Lipidic Cubic Phase (LCP): Though typically used for membrane proteins, sometimes effective for soluble proteins

  • Microseeding: Introduce crystal nuclei to promote controlled crystal growth

  • Counter-diffusion Crystallization: Create a gradient of precipitant concentration

  • Crystallization Under Oil: Slow vapor diffusion rates for more ordered crystal growth

• Complementary Structural Approaches:

  • Cryo-electron Microscopy: May be suitable if crystallization proves intractable

  • Small-Angle X-ray Scattering (SAXS): Obtain low-resolution structural information in solution

  • Nuclear Magnetic Resonance (NMR): For structural studies of specific domains or interactions

Practical Implementation Strategy:

• Initial Assessment:

  • Evaluate protein stability using thermal shift assays across various buffers and pH values

  • Determine oligomeric state under different conditions using SEC-MALS

  • Assess conformational homogeneity via limited proteolysis

• Systematic Optimization:

  • Start with commercial sparse matrix screens at multiple protein concentrations

  • Focus on conditions where microcrystals or phase separation occurs

  • Optimize promising conditions by varying precipitant concentration, pH, and additives

• Iterative Improvement:

  • Use initial diffraction data to guide further optimization

  • Consider crystal transfer to stabilizing solutions to improve diffraction quality

  • Employ post-crystallization treatments (dehydration, annealing) if necessary