Recombinant PPIC demonstrates protective effects against oxidative stress:
ROS Reduction: At 0.001–1 µg/ml, it decreases hydrogen peroxide (H₂O₂)-induced reactive oxygen species (ROS) in HepG2 cells .
Enzyme Activation: Enhances catalase (CAT), glutathione peroxidase (GPx), and thioredoxin reductase (TRR) activities, critical for ROS scavenging .
Gene Expression: Upregulates CAT, GPx, and TRR mRNA levels, suggesting modulation of the Keap1/Nrf2/ARE pathway .
iNKT Cell Development: Murine studies show Ppic deficiency reduces invariant Natural Killer T (iNKT) cell numbers in the thymus and periphery, implicating PPIC in immune cell differentiation .
Calcineurin/NFAT Pathway: PPIC interacts with cyclophilin C-associated protein (CyCAP) to activate microglia and macrophages via this pathway .
Cancer Biomarker: PPIC is overexpressed in epithelial ovarian cancer (EOC) and may serve as a circulating tumor cell marker .
Neuroprotection: PPIC-CyCAP complexes mitigate ischemic brain damage by enhancing survival mechanisms .
Cardioprotection: Cyclophilins, including PPIC, are activated during ischemia-reperfusion injury, suggesting roles in tissue repair .
| PPIC Concentration (µg/ml) | CAT Activity | GPx Activity | TRR Activity | SOD Activity |
|---|---|---|---|---|
| 0.001 | ↑ 45% | ↑ 38% | ↑ 28% | ↔ |
| 0.01 | ↑ 60% | ↑ 52% | ↑ 45% | ↑ 30% |
| 1 | ↔ | ↔ | ↔ | ↑ 53% |
| Data from HepG2 cells treated with H₂O₂ and recombinant PPIC . |
Recombinant PPIC binds multiple partners, including:
Cyclosporine A (CsA): Forms immunosuppressive complexes to inhibit calcineurin .
NFATc1: Links PPIC to transcriptional regulation in immune cells .
While recombinant PPIC shows promise in antioxidant and immunomodulatory studies, challenges include:
Concentration-Dependent Effects: High doses (e.g., 1 µg/ml) may saturate pathways, reducing efficacy .
Mechanistic Gaps: The exact role of PPIC in cancer progression remains unclear .
Further studies are needed to explore its in vivo therapeutic potential and interactions with redox-sensitive pathways like PRDX-TRR .
KEGG: ece:Z5286
STRING: 155864.Z5286
Peptidyl-prolyl cis-trans isomerase C (ppiC) belongs to a family of enzymes that catalyze the cis/trans isomerization of peptidyl-prolyl peptide bonds in proteins and oligopeptides. This isomerization represents a critical rate-limiting step in protein folding processes. The primary function of ppiC is to accelerate protein folding by catalyzing the interconversion between cis and trans conformations of the peptide bond preceding proline residues. This catalytic activity is essential for generating properly folded, functionally active proteins in both prokaryotic and eukaryotic systems .
The isomerization reaction catalyzed by ppiC involves a 180° rotation around the peptidyl-prolyl bond, which significantly impacts the three-dimensional structure of proteins. Without PPIases like ppiC, some denatured proteins would refold extremely slowly (minutes to hours), whereas the presence of these enzymes can dramatically accelerate the process to milliseconds or seconds .
The PPIase superfamily consists of three main families: cyclophilins (Cyps), FK506-binding proteins (FKBPs), and parvulins. Each family has distinct structural features, substrate specificities, and inhibitor sensitivities:
Cyclophilins: These are receptors for the immunosuppressive drug cyclosporin A (CsA). They have a broad substrate specificity and are abundant in cells, constituting approximately 0.4% of cellular dry mass .
FK506-binding proteins: These bind to immunosuppressive drugs FK506 and rapamycin. They often contain additional functional domains that mediate various cellular processes .
Parvulins: The family to which ppiC typically belongs. These enzymes show distinct substrate preferences compared to the other families and are not sensitive to CsA or FK506 .
ppiC has specific structural features and catalytic properties that distinguish it from other PPIases, including substrate specificity toward particular proline-containing sequence motifs. Understanding these differences is crucial for developing specific research applications or therapeutic targeting strategies.
Several methodological approaches are available to measure ppiC enzymatic activity:
Spectrophotometric Assay:
The most common assay uses chromogenic peptide substrates containing a proline residue (e.g., succinyl-Ala-Phe-Pro-Phe-4-nitroanilide). The reaction sequence involves:
Allowing the substrate to reach cis/trans equilibrium
Adding ppiC to catalyze isomerization
Adding chymotrypsin, which specifically cleaves after the proline residue only in the trans conformation
Measuring the released 4-nitroanilide by monitoring absorbance at 390 nm
The specificity constant can be calculated from this assay, with active PPIases typically showing values in the range of 10^7 M^-1s^-1. For example, NifM from A. vinelandii showed a specificity constant of 1.09 × 10^7 M^-1s^-1 .
Rheological Measurements:
For assessing the impact of ppiC on extracellular matrix (ECM) properties, rheological measurements can be used to evaluate:
Cell Surface PPIase Activity Assay:
A specialized assay for detecting PPIase activity on living cell surfaces can be correlated with ECM development and cellular physiological states .
The optimal expression system for recombinant ppiC production depends on research objectives and downstream applications. Based on established protocols for similar PPIases, the following systems have proven effective:
Most commonly used for PPIases due to high yield and simplicity
Typical vectors include pET series (especially pET21d+) with T7 promoter and lac operator for inducible expression
Expression can be induced using IPTG
PCR amplification of the ppiC gene
Cloning into expression vector downstream of T7 promoter
Transformation into expression host (typically BL21(DE3) or similar)
Culture growth to optimal density followed by IPTG induction
Cell harvesting and lysis
Provide eukaryotic post-translational modifications
Suitable when proper folding is challenging in bacterial systems
S. cerevisiae or P. pastoris are commonly used hosts
For applications requiring mammalian-specific modifications
HEK293 or CHO cells are preferred for complex proteins
Yield optimization factors include growth temperature (often reduced to 16-25°C during induction), induction time, and media composition. Codon optimization of the ppiC sequence for the host organism can significantly improve expression levels.
A multi-step purification protocol typically yields the highest purity and activity for recombinant ppiC:
Primary purification step for His-tagged ppiC
Ni-NTA or Co-based resins are commonly used
Imidazole gradient elution improves purity
Typical yield: 10-20 mg protein per liter of bacterial culture
Secondary purification step for separating monomeric ppiC from aggregates
Also provides information on the oligomeric state of ppiC (typically monomeric in its native form)
Optional additional step for removing remaining impurities
Selection of cation or anion exchange depends on ppiC's isoelectric point
SDS-PAGE and Western blotting with anti-His or specific anti-ppiC antibodies
Activity assays using chromogenic substrates
Circular dichroism to confirm proper secondary structure
Fluorescence spectroscopy to verify tertiary structure integrity
The purification protocol should include protease inhibitors throughout the process, and care should be taken to maintain an appropriate buffer system to preserve activity. Typically, a phosphate or Tris buffer at pH 7.0-8.0 with 100-150 mM NaCl provides a suitable environment for ppiC stability.
Verification of proper folding and structural integrity of recombinant ppiC involves several complementary biophysical techniques:
Far-UV CD (190-260 nm) reveals secondary structure content
Properly folded ppiC typically shows characteristic negative minima at 208 nm and 222 nm, indicating alpha-helical content
Intrinsic tryptophan fluorescence indicates tertiary structure integrity
Emission maximum shifts reflect the local environment of aromatic residues
Properly folded protein shows defined fluorescence spectrum that changes upon denaturation
Confirms homogeneity and oligomeric state
Recombinant ppiC typically exists as a monomer in its native state
The most functional verification of proper folding
Standard protease-coupled assay using chromogenic peptides
Active site integrity can be confirmed by inhibitor binding studies
Measures protein stability and can detect if ligands or substrates bind to the protein
Uses fluorescent dyes that bind to hydrophobic regions exposed during unfolding
A properly folded, active recombinant ppiC should demonstrate both the expected structural characteristics by spectroscopic methods and catalytic activity comparable to the native enzyme.
Recombinant ppiC can serve as a valuable tool for investigating ECM dynamics through several experimental approaches:
Rheological Analysis of ECM Biomaterials:
Researchers can directly assess the impact of ppiC on the mechanical properties of various ECM components:
Fibrin gelation: Addition of recombinant ppiC (1-10 μM) to fibrinogen before thrombin-induced polymerization can significantly enhance the storage modulus (stiffness) of the resulting hydrogel
Collagen gelation: Both pH-induced and temperature-induced gelation of collagen can be modulated by ppiC, affecting matrix organization
Self-healing properties: ppiC influences the recovery rate and extent after mechanical disruption of ECM networks
Viscosity measurements of cell-protein mixtures: ppiC can alter the interaction between cells and ECM proteins, detectable through changes in the viscometric properties of the suspension
Adhesion dynamics: Controlling ppiC activity (through addition of recombinant protein or specific inhibitors) during cell-ECM attachment can reveal kinetic aspects of this interaction that are prolyl isomerization-dependent
ECM Remodeling Studies:
Recombinant ppiC can be used to understand how prolyl isomerization contributes to:
Folding and assembly of ECM proteins
Structural dynamics of dense polymer networks
Temporal control of ECM maturation
This approach offers unique insights by isolating prolyl isomerization from other dynamic events in the complex ECM environment, providing a molecular-level understanding of matrix regulation.
Several methods are available to characterize interactions between ppiC and its binding partners:
Uses antibodies against ppiC or the suspected binding partner
Can identify novel protein-protein interactions in cellular contexts
Western blotting confirms the identity of co-precipitated proteins
Provides real-time binding kinetics (kon and koff rates)
Quantifies binding affinity (KD values)
Experimental setup:
Immobilize purified recombinant ppiC on a sensor chip
Flow potential binding partners over the surface
Measure association and dissociation phases
Analyze data to determine binding constants
Measures thermodynamic parameters of binding
Provides KD, stoichiometry, enthalpy (ΔH), and entropy (ΔS)
No labeling required, performed in solution
Förster Resonance Energy Transfer (FRET)
Fluorescence polarization for studying smaller ligands
Microscale Thermophoresis (MST) for determining binding affinities
Using tagged recombinant ppiC (His-tag, GST, etc.)
Coupled with mass spectrometry for unbiased identification of binding partners
For discovering novel protein interactions
Can be followed by validation using the methods above
These methods can reveal how ppiC interacts with substrates, inhibitors, or other cellular components, providing insights into its biological roles and regulatory mechanisms.
Recombinant ppiC can be used to study protein folding dynamics in several experimental paradigms:
Basic Protocol:
Denature model proteins containing proline residues (e.g., ribonuclease T1, staphylococcal nuclease)
Initiate refolding by dilution into native buffer conditions
Monitor refolding kinetics with and without recombinant ppiC
Detect folding using spectroscopic methods (fluorescence, CD, NMR)
Quantitative Analysis:
Measure folding rate acceleration
Determine concentration-dependent effects
Calculate catalytic efficiency (kcat/KM)
Label newly synthesized proteins
Add recombinant ppiC to cell extracts
Monitor acquisition of native structure over time
Compare folding rates and native protein yields
Using optical tweezers or atomic force microscopy
Directly observe the effect of ppiC on individual protein folding events
Measure force-extension curves with and without ppiC
Recombinant ppiC typically accelerates the refolding of proline-containing proteins, with the most pronounced effects observed for proteins with critical prolines in cis conformation in their native state. The degree of acceleration depends on:
The number and position of proline residues
The intrinsic isomerization rates of specific prolyl bonds
The structural context surrounding the proline residues
Concentration and activity of the recombinant ppiC
These studies help elucidate the role of prolyl isomerization as a rate-limiting step in protein folding and how ppiC can overcome this kinetic barrier.
Site-directed mutagenesis provides powerful insights into the catalytic mechanism of ppiC by allowing systematic modification of key residues. The following methodological approach has proven effective:
Key Residues for Mutagenesis:
Based on structural studies of PPIases, several conserved residues are typically targeted:
Catalytic site residues that directly participate in isomerization
Substrate-binding pocket residues that determine specificity
Residues involved in maintaining structural integrity
Design of Mutations:
Conservative substitutions (maintaining charge/polarity)
Non-conservative substitutions (altering properties)
Creation of catalytically inactive variants as controls
Generation of Mutants:
PCR-based site-directed mutagenesis
Verification by DNA sequencing
Expression and purification using identical conditions as wild-type
Functional Characterization:
Enzyme kinetics (kcat, KM, kcat/KM) using standard PPIase assays
Substrate specificity profiles using peptide libraries
Structural integrity confirmation by CD and fluorescence
Structure-Function Correlation:
Molecular dynamics simulations to predict effects of mutations
X-ray crystallography or NMR of mutant proteins
Correlation of structural changes with altered catalytic parameters
| Mutation | Region | Activity (% of WT) | KM (μM) | kcat (s⁻¹) | kcat/KM (M⁻¹s⁻¹) |
|---|---|---|---|---|---|
| WT | - | 100 | 86.5 | 940 | 1.09 × 10⁷ |
| R55A | Catalytic | <10 | ND | ND | ND |
| F60Y | Binding pocket | 65 | 120.3 | 710 | 5.9 × 10⁶ |
| H94Q | Secondary shell | 85 | 92.1 | 830 | 9.0 × 10⁶ |
ND = Not determined due to very low activity
Note: Values in this table are representative examples based on similar PPIases
Through this approach, researchers can identify residues essential for catalysis versus those involved in substrate binding or structural stability, ultimately elucidating the catalytic mechanism of ppiC at the molecular level.
PPIases including ppiC play crucial roles in cellular stress responses, particularly in protein quality control and adaptation to environmental challenges. Several experimental approaches can elucidate these functions:
Subject cells (with normal or altered ppiC levels) to various stressors:
Heat shock (37-42°C)
Oxidative stress (H₂O₂, paraquat)
ER stress (tunicamycin, thapsigargin)
Nutrient deprivation
Monitor survival, growth rates, and recovery kinetics
Measure stress marker expression through RT-qPCR or western blotting
Use reporter proteins prone to misfolding (e.g., luciferase, GFP variants)
Analyze aggregation patterns with and without functional ppiC
Apply techniques such as:
Fluorescence microscopy for aggregate visualization
Filter trap assays for quantification
FRET-based folding sensors
Comparative proteomics of wild-type vs. ppiC-deficient cells under stress
Pulse-chase experiments to track protein turnover rates
Identification of stress-responsive ppiC substrates through crosslinking and mass spectrometry
RNA-seq to identify genes differentially expressed in ppiC-deficient cells
ChIP-seq to detect changes in transcription factor binding
Analysis of stress-responsive promoter activities using reporter constructs
Track ppiC localization during stress using fluorescent protein fusions
Co-localization studies with stress granules, processing bodies, or chaperone complexes
Live-cell imaging to monitor real-time responses
Research has shown that PPIases like ppiC may contribute to stress responses through:
Preventing aggregation of misfolded proteins
Facilitating assembly/disassembly of stress-responsive complexes
Modulating activity of transcription factors through conformational changes
Participating in DNA repair processes and cell cycle regulation under stress conditions
These experimental approaches provide complementary insights into the multifaceted roles of ppiC in cellular adaptation to stress.
Investigating the role of ppiC in microtubule dynamics requires specialized approaches that connect enzymatic activity with cytoskeletal function:
Purified Tubulin Polymerization Assays:
Measure polymerization kinetics of purified tubulin with/without recombinant ppiC
Monitor assembly by light scattering or fluorescence
Quantify parameters: nucleation rate, elongation rate, steady-state polymer mass
Experimental setup:
Incubate purified tubulin (10-20 μM) with GTP
Add varying concentrations of recombinant ppiC (0.1-10 μM)
Monitor turbidity at 350 nm over time
Calculate polymerization rates from the slopes of assembly curves
Microscopy-Based Analysis:
Total Internal Reflection Fluorescence (TIRF) microscopy of fluorescently-labeled microtubules
Measure:
Growth/shrinkage rates (μm/min)
Catastrophe frequency (transitions from growth to shrinkage)
Rescue frequency (transitions from shrinkage to growth)
Pause duration
Live Cell Imaging:
Express fluorescent tubulin markers (e.g., GFP-α-tubulin)
Manipulate ppiC levels through:
Overexpression of wild-type or mutant ppiC
siRNA/shRNA-mediated knockdown
CRISPR/Cas9 gene editing
Track individual microtubule dynamics using spinning disk confocal microscopy
Quantify parameters using tracking software (e.g., plusTipTracker)
Fixed Cell Analysis:
Immunofluorescence staining of tubulin and associated proteins
Evaluate microtubule organization, density, and post-translational modifications
Analyze co-localization of ppiC with microtubules or regulatory factors
Co-sedimentation Assays:
Mix preformed microtubules with recombinant ppiC
Centrifuge to pellet microtubules and associated proteins
Analyze supernatant and pellet fractions by SDS-PAGE
Determine binding affinity from saturation curves
Identification of Tubulin or MAP Substrates:
Screen for proline-containing regions in tubulins and MAPs
Perform in vitro isomerization assays with synthetic peptides
Use mass spectrometry to detect cis/trans isomer ratios
This comprehensive approach can reveal whether ppiC influences microtubule dynamics directly through isomerization of tubulin subunits or indirectly via microtubule-associated proteins (MAPs) .
Researchers often encounter several challenges when producing active recombinant ppiC. Here are the most common issues and their solutions:
| Challenge | Solution Approach | Implementation Details |
|---|---|---|
| Codon bias | Codon optimization | Adapt codons to match host preference; commercially synthesize optimized gene |
| Toxicity to host | Tightly regulated expression | Use tunable promoters; lower induction temperatures (16-25°C); reduce inducer concentration |
| Protein degradation | Protease inhibition | Add protease inhibitor cocktail; use protease-deficient host strains |
| Poor solubility | Solubility enhancement | Fuse with solubility tags (MBP, SUMO, TrxA); optimize buffer conditions |
| Challenge | Solution Approach | Implementation Details |
|---|---|---|
| Inclusion body formation | Refolding protocols | Solubilize inclusions in urea/guanidine; perform gradual dialysis with redox buffers |
| Misfolded soluble protein | Chaperone co-expression | Co-express with GroEL/GroES or trigger factor; add chemical chaperones to media |
| Disulfide bond issues | Oxidative environment | Express in E. coli Origami or SHuffle strains; add glutathione to refolding buffer |
| Challenge | Solution Approach | Implementation Details |
|---|---|---|
| Protein instability | Buffer optimization | Screen different pH values (6.5-8.0); add stabilizing agents (glycerol 10-20%, low concentrations of detergents) |
| Metal-induced inactivation | Chelator addition | Include EDTA (0.1-1 mM) in buffers; avoid metal-containing resins if problematic |
| Oxidative damage | Reducing agents | Add DTT or β-mercaptoethanol (1-5 mM); handle under nitrogen atmosphere |
| Activity loss during storage | Cryoprotection | Add glycerol (25-50%); flash-freeze in liquid nitrogen; avoid freeze-thaw cycles |
Implementing robust quality control at each stage can prevent downstream issues:
Verify gene sequence before expression
Use analytical SEC to confirm monodispersity
Perform activity assays immediately after purification and after storage
Validate proper folding using spectroscopic methods as described in section 2.3
By systematically addressing these challenges, researchers can significantly improve the yield, purity, and activity of recombinant ppiC preparations, ensuring reliable results in downstream applications.
When confronted with contradictory results across different experimental systems, researchers should implement a systematic troubleshooting approach:
System-Specific Variables Analysis:
Document all experimental conditions precisely
Create a comparative table of variables across systems:
Protein concentrations
Buffer compositions
Temperature and pH
Presence of cofactors or inhibitors
Cell types or strains used
Assay detection methods
Protein Quality Assessment:
Verify ppiC activity in each system using standardized assays
Confirm structural integrity through biophysical methods
Assess batch-to-batch variation with reference standards
Check for post-translational modifications across systems
Methodological Validation:
Implement positive and negative controls in each experiment
Use orthogonal methods to confirm key findings
Perform spike-in experiments to detect potential inhibitors
Cross-validate with different detection technologies
Reconciliation Strategies:
| Contradiction Type | Investigation Approach | Resolution Strategy |
|---|---|---|
| Activity differences | Enzyme kinetics under varied conditions | Identify system-specific factors affecting catalysis |
| Binding partner discrepancies | Cross-validation with multiple interaction assays | Map condition-dependent interaction networks |
| Functional impact variations | Dose-response studies across systems | Determine threshold concentrations for effects |
| Subcellular localization conflicts | Live-cell imaging with controlled expression levels | Identify dynamic localization patterns |
Statistical and Quantitative Analysis:
Apply rigorous statistical methods appropriate for each dataset
Consider Bayesian approaches to integrate conflicting data
Perform meta-analysis when multiple studies are available
Develop mathematical models to explain system-dependent behaviors
Biological Context Interpretation:
Consider that contradictions may reflect genuine biological complexity
Investigate tissue-specific or condition-dependent regulation
Examine potential redundancy with other PPIases
Explore context-dependent protein-protein interactions
By systematically analyzing contradictions, researchers can often transform apparent discrepancies into deeper insights about the context-dependent functions of ppiC, leading to more nuanced understanding of its biological roles.
Current methodologies for studying ppiC activity present several limitations that affect data interpretation and application. Understanding these constraints and emerging solutions is critical for advancing the field:
Spectroscopic Assay Limitations:
| Limitation | Impact | Emerging Solutions |
|---|---|---|
| Artificial peptide substrates | May not reflect native substrate specificity | Development of protein-based substrates with fluorescent reporters at conformationally sensitive positions |
| Indirect measurement via coupled enzymes | Potential for false positives/negatives due to interaction with coupling enzyme | Direct measurement using NMR or mass spectrometry to detect cis/trans isomers without coupling enzymes |
| Limited throughput | Restricts comprehensive substrate profiling | Adaptation to microplate formats; development of continuous fluorescence-based assays |
| Poor performance in complex environments | Difficult to measure activity in cellular contexts | Cell-penetrating fluorogenic substrates; genetically encoded sensors for intracellular activity |
Structural and Mechanistic Understanding:
| Limitation | Impact | Emerging Solutions |
|---|---|---|
| Limited structural data on enzyme-substrate complexes | Incomplete understanding of catalytic mechanism | Time-resolved crystallography; cryo-EM studies of conformational states |
| Difficulty capturing transition states | Missing critical mechanistic details | Computational approaches (QM/MM); transition state analogs as probes |
| Challenges in distinguishing catalysis from binding | Confounding interpretation of mutational studies | Single-molecule studies to directly observe catalytic events |
Cellular and Physiological Context:
| Limitation | Impact | Emerging Solutions |
|---|---|---|
| Redundancy among PPIase families | Compensation masks phenotypes in knockout studies | Development of highly specific inhibitors; CRISPR-based acute depletion strategies |
| Limited temporal resolution | Missing dynamic aspects of ppiC function | Optogenetic control of ppiC activity; degron-based rapid protein depletion |
| Difficulty identifying physiological substrates | Incomplete understanding of biological roles | Proximity labeling approaches (BioID, APEX); covalent trapping of enzyme-substrate complexes |
| Challenges in measuring ECM-related activities | Missing important extracellular functions | Development of ECM-specific reporters; advanced rheological techniques with molecular readouts |
Integration with Systems Biology:
| Limitation | Impact | Emerging Solutions |
|---|---|---|
| Isolation from broader cellular networks | Missing regulatory context | Multi-omics approaches to connect ppiC function with proteome, transcriptome, and metabolome |
| Limited predictive models | Difficulty extrapolating from in vitro to in vivo | Machine learning approaches to integrate diverse datasets; network models of PPIase function |
Single-molecule FRET-based assays: Direct visualization of individual isomerization events in real-time
CRISPR-based screening: Systematic identification of genetic interactions and functional networks
Advanced imaging technologies: Super-resolution microscopy combined with specific probes for ppiC activity
In-cell NMR spectroscopy: Direct measurement of protein conformational changes in cellular environments
AI-driven experimental design: Optimization of experimental conditions based on machine learning predictions
These emerging approaches promise to overcome current limitations and provide deeper insights into the multifaceted roles of ppiC in biological systems, ultimately enabling more precise targeting for therapeutic applications .