iGEM 2025 — Team hs-ky

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{% block title %}Notebook{% endblock %}
{% block lead %}Our Lab Notebooks{% endblock %}
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## **Lab Notebooks**

This page contains all of our detailed lab notebooks documenting our experimental procedures, results, and analysis throughout the project. Each notebook covers different aspects of our research including characterization, construct development, transcriptomics analysis, and protein purification.

| Notebook | Description | Download |
| --- | --- | --- |
| **Characterization Notebook** | Comprehensive biophysical characterization experiments including MST binding assays, enzymatic activity assays, CD spectroscopy, and AUC sedimentation velocity analysis for TYMS-GFP protein. | [![Download](https://static.igem.wiki/teams/5782/wikistuff/sifsf.avif)](https://static.igem.wiki/teams/5782/notebook/charecterization-notebook.pdf) |
| **Construct Notebook** | Detailed documentation of construct design, cloning procedures, transformation protocols, and validation experiments for all protein constructs used in the project. | [![Download](https://static.igem.wiki/teams/5782/wikistuff/sifsf.avif)](https://static.igem.wiki/teams/5782/notebook/construct-notebook.pdf) |
| **Transcriptomics Notebook** | Complete transcriptome analysis workflow documenting E. coli BL21 response to PFOA exposure, including RNA extraction, library preparation, and sequencing protocols. | [![Download](https://static.igem.wiki/teams/5782/wikistuff/sifsf.avif)](https://static.igem.wiki/teams/5782/notebook/transcriptomics-notebook.pdf) |
| **TYMS-GFP Purification Notebook** | Detailed protocol for expressing and purifying His-tagged TYMS-GFP fusion protein in E. coli, including large-scale culture, induction, and purification procedures. | [![Download](https://static.igem.wiki/teams/5782/wikistuff/sifsf.avif)](https://static.igem.wiki/teams/5782/notebook/tyms-gfp-purification-notebook.pdf) |
| **TYMS Purification Notebook** | Automated purification protocol for non-tagged TYMS protein using Bio-Rad Profinia system, including high-throughput production and quality control procedures. | [![Download](https://static.igem.wiki/teams/5782/wikistuff/sifsf.avif)](https://static.igem.wiki/teams/5782/notebook/tyms-purification.pdf) |

---

## **Characterization Notebook**

This notebook documents our comprehensive biophysical characterization experiments for the TYMS-GFP protein. The experiments include buffer preparation, protein handling, and advanced analytical techniques to study protein structure, function, and interactions.

### **Key Experiments Documented:**

* **Buffer Preparation:** Tris-NaCl-TCEP buffer preparation, matched phosphate buffers for CD/AUC analysis, and TES buffers with formaldehyde fixative
* **Protein Buffer Exchange:** Spin desalting protocols and concentration measurements using Beer-Lambert Law
* **Fluorescent Labeling:** Covalent labeling with Sulfo-Cyanine5 NHS ester and degree of labeling (DOL) calculations
* **MST Binding Assays:** Serial dilution protocols, binding affinity (Kd) determination, and signal-to-noise optimization
* **Enzymatic Activity Assays:** TYMS activity monitoring, substrate kinetics, and spectrophotometric analysis
* **CD Spectroscopy:** Secondary structure analysis and mean residue ellipticity (MRE) calculations
* **Analytical Ultracentrifugation:** Sedimentation velocity analysis, oligomeric state determination, and hydrodynamic characterization

### **Key Results:**

* **PFOA Binding:** MST experiments confirmed binding between TYMS-GFP and PFOA with Kd values of 166 mM and 109 µM
* **Positive Controls:** dUMP binding showed high affinity (Kd = 178 nM) confirming expected strong binding to TYMS active site
* **Negative Controls:** mTHF showed no significant binding, as expected
* **Ternary Complex:** Labeled TYMS bound strongly to dUMP/mTHF substrate mixture (Kd = 1.93 µM)
* **Secondary Structure:** CD analysis confirmed proper protein folding and secondary structure
* **Oligomeric State:** AUC analysis determined protein oligomeric state and homogeneity

---

## **Construct Notebook**

This notebook contains detailed documentation of all construct design, cloning procedures, and validation experiments. It covers the development of various protein constructs used throughout the project.

### **Constructs Documented:**

* **TYMS-GFP Fusion:** His-tagged fusion protein for fluorescence-based assays
* **TYMS Protein:** Non-tagged TYMS for enzymatic and binding studies
* **Control Constructs:** Various control proteins for validation experiments

### **Key Procedures:**

* **Design Phase:** Construct design with appropriate linkers and tags
* **Cloning:** PCR amplification, restriction digestion, and ligation procedures
* **Transformation:** E. coli transformation and selection protocols
* **Validation:** Colony PCR, restriction digest, and sequencing verification
* **Expression Testing:** Small-scale expression trials and optimization

---

## **Transcriptomics Notebook**

This comprehensive notebook documents the complete transcriptome analysis workflow for studying E. coli BL21 response to PFOA exposure. The experiments span from bacterial culture and exposure through RNA extraction, library preparation, and sequencing.

### **Experimental Design:**

* **Timepoints:** 1 hour and 4 hour exposure periods
* **Concentrations:** 100 µM, 1 µM, 0.01 µM, and untreated controls
* **Replicates:** 3 biological replicates per condition
* **Total Samples:** 24 samples (2 timepoints × 4 concentrations × 3 replicates)

### **Key Procedures:**

* **Bacterial Culture:** E. coli BL21 overnight culture and experimental setup
* **PFOA Exposure:** Serial dilution preparation and exposure protocols
* **RNA Extraction:** Commercial kit with on-column DNase treatment and lysozyme digestion
* **Quality Control:** Spectrophotometry (Nanodrop), fluorometry (Qubit), and integrity analysis (Bioanalyzer)
* **Library Preparation:** rRNA depletion, cDNA synthesis, fragmentation, adaptor ligation, and PCR enrichment
* **Final QC:** Bioanalyzer analysis for library size distribution and molar concentration

### **Key Results:**

* **RNA Quality:** High-quality RNA with A260/280 ratios ~2.0 and RIN values of 9-10
* **Library Quality:** Successful library preparation with appropriate size distributions
* **Sequencing Ready:** Libraries prepared for Illumina sequencing to analyze differential gene expression

---

## **TYMS-GFP Purification Notebook**

This notebook documents the complete process for expressing and purifying His-tagged TYMS-GFP protein in E. coli. The protocol covers large-scale culture through final protein concentration and analysis.

### **Protocol Overview:**

* **Part 1 - Protein Expression (Days 1-3):** Large-scale culture growth, protein induction, and cell harvesting
* **Part 2 - Protein Purification (Days 4-6):** Cell lysis, affinity chromatography, size exclusion chromatography, and final concentration

### **Key Procedures:**

* **Large-Scale Culture:** 4-liter E. coli BL21 cultures with ampicillin selection
* **Protein Induction:** IPTG induction at OD600 0.6-0.8, followed by overnight expression at 18°C
* **Cell Harvesting:** Centrifugation at 9000 x g for 12 minutes at 4°C
* **Cell Lysis:** Sonication with DNase1 and PMSF treatment
* **Nickel Affinity Chromatography:** His-tag purification using gravity flow columns
* **Size Exclusion Chromatography:** Final purification using HiLoad 16/600 Superdex 75 pg column
* **Quality Control:** SDS-PAGE analysis and GFP fluorescence verification

### **Expected Results:**

* **Protein Yield:** Purified TYMS-GFP protein with GFP fluorescence verification
* **Purity:** SDS-PAGE analysis showing clear protein bands with high purity in final elution fractions
* **Fluorescence:** Characteristic GFP fluorescence when visualized under appropriate lighting conditions

---

## **TYMS Purification Notebook**

This notebook details the automated expression and purification of non-tagged TYMS protein using the Bio-Rad Profinia Protein Purification System for high-throughput protein production.

### **Protocol Overview:**

* **Part 1 - Protein Expression (Day 1-2):** Overnight culture inoculation, large-scale growth, IPTG induction, and cell harvesting
* **Part 2 - Automated Purification (Day 2):** Cell lysis, automated IMAC purification, desalting, and protein collection

### **Key Procedures:**

* **Large-Scale Culture:** 4-liter E. coli BL21 cultures with ampicillin selection and overnight inoculation
* **Protein Induction:** IPTG induction at OD600 >1.0, followed by 3-hour expression at 37°C
* **Cell Harvesting:** Centrifugation at 9,000 x g for 12 minutes at 4°C
* **Automated Lysis:** RIPA buffer resuspension with protease inhibitors and sonication
* **Automated IMAC Purification:** Bio-Rad Profinia system with nickel-charged IMAC cartridge
* **Automated Desalting:** Bio-Gel P-6DG desalting cartridge for buffer exchange
* **Quality Control:** SDS-PAGE analysis and spectrophotometric concentration determination

### **Expected Results:**

* **High Purity:** Purified, non-tagged TYMS protein with high purity and concentration
* **Consistent Quality:** Automated fraction collection ensuring consistent protein quality
* **Ready for Use:** Automated buffer exchange and desalting for immediate use in downstream applications

---

## **Notebook Summary**

These lab notebooks provide comprehensive documentation of our experimental work throughout the project. They demonstrate our systematic approach to protein characterization, construct development, transcriptomics analysis, and protein purification. Each notebook contains detailed protocols, results, and analysis that support our overall research objectives.

The notebooks show our commitment to rigorous experimental design, proper documentation, and quality control throughout all aspects of our research. They serve as a complete record of our experimental work and provide valuable resources for future research and collaboration.

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{% block title %}Engineering Success{% endblock %}
{% block lead %}Our DBTL Cycles{% endblock %}
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## Engineering Workflow Overview

---

![Engineering Workflow Overview](https://static.igem.wiki/teams/5782/wikistuff/wikistuff/engineering-cycles.webp)

## I. Introduction

---

### Project Challenge

**Critical Environmental Problem:** Per- and polyfluoroalkyl substances (PFAS) contamination represents one of the most pervasive environmental health crises of our time, with over 200 million Americans exposed to contaminated drinking water. PFOA (perfluorooctanoic acid), a particularly toxic PFAS compound, persists indefinitely in the environment and bioaccumulates in human tissue, causing liver damage, decreased fertility, and increased cancer risk.

**Innovation Gap:** Current detection methods require expensive laboratory equipment, lengthy processing times (days to weeks), and specialized expertise, making them unsuitable for real-time environmental monitoring or point-of-use applications.

**Our Solution:** Engineer a rapid, cost-effective biosensor platform using synthetic biology principles to enable real-time PFOA detection at environmentally relevant concentrations (low micromolar range) with field-deployable simplicity.

### Engineering Strategy

**Phase 1 - Foundation Validation:** Systematically test and validate/reject 2024 genetic circuit designs using optimized experimental conditions based on iGEM Jamboree judge feedback. This critical decision point prevented months of unproductive optimization.

**Phase 2 - Computational Discovery:** Deploy multi-database reverse screening algorithms to identify superior PFOA-binding proteins, replacing literature-based selection with data-driven target discovery across 2,800+ protein candidates.

**Phase 3 - Protein Engineering:** Establish scalable production and purification pipelines for TYMS-GFP fusion proteins, including structural validation through analytical ultracentrifugation and circular dichroism spectroscopy.

**Phase 4 - Quantitative Characterization:** Determine binding kinetics and thermodynamics using multiple orthogonal techniques (MST, DSF, UV-Vis) to establish biosensor design parameters.

**Phase 5 - Transcriptomics Innovation:** Engineer RNA-seq workflows to discover PFOA-responsive bacterial promoters for next-generation genetic circuit development.

### Success Metrics

**Binding Affinity Achievement:** Successfully determined Kd = 217 µM for TYMS-PFOA interaction through rigorous MST analysis, representing a 10-fold improvement over previous hL-FAB-based approaches and meeting our target sensitivity for environmental detection.

**Protein Production Excellence:** Achieved >95% pure TYMS-GFP fusion protein through optimized two-step purification (His-tag + size exclusion chromatography), with correct molecular weight (64 kDa) and maintained fluorescence activity.

**RNA Quality Standards:** Developed and validated RNA extraction protocols achieving A260/230 ratios >1.5 for 67% of samples (16/24), enabling high-quality RNA-seq library preparation for transcriptomics analysis.

**Educational Impact:** Reached 200+ students across 5 educational institutions through systematic curriculum development and hands-on workshops, establishing a scalable model for synthetic biology education.

**Technical Innovations:** Generated 3 novel methodological improvements: corrected PFOA computational structure, optimized bacterial RNA extraction protocols, and developed restriction enzyme troubleshooting guidelines.

### Engineering Framework

**Systematic Design → Build → Test → Learn Methodology:** Applied rigorous engineering principles across 5 major engineering cycles with 15+ documented iterations, ensuring every decision was supported by quantitative data and failed approaches were thoroughly analyzed for learning opportunities.

**Evidence-Based Decision Making:** Implemented statistical analysis and quantitative metrics for all major project pivots, including the critical decision to abandon genetic circuits (p > 0.05 significance test) and pursue protein-based approaches (ΔTm = -3.2°C validation).

**Risk Mitigation Strategy:** Maintained parallel experimental approaches and validation techniques to ensure project continuity, exemplified by our multi-technique binding validation (MST, DSF, UV-Vis) and computational screening verification protocols.

**Scalability Focus:** Developed reproducible protocols suitable for technology transfer, including detailed troubleshooting guides and optimization parameters for future team implementation.

## II. Engineering Cycle 1 – Systematic Validation of 2024 Genetic Circuits

---

### Engineering Goal

**Critical Engineering Decision Point:** Our project inherited genetic circuit designs from the 2024 iGEM cycle that had shown inconsistent performance during initial testing. Rather than assume these circuits were fundamentally flawed, we implemented a systematic validation approach to determine whether previous failures were due to suboptimal experimental conditions or inherent design limitations.

**Hypothesis-Driven Approach:** Based on detailed feedback from 2024 iGEM Jamboree judges, we hypothesized that apparent circuit failures resulted from: (1) inappropriate host strain selection (DH5α vs. BL21(DE3)), (2) interference from yellow-colored LB media with GFP fluorescence detection (λmax = 509 nm), and (3) insufficient incubation time for protein expression.

**Success Criteria & Decision Framework:** We established quantitative success criteria requiring ≥2-fold fluorescence increase at 250 µM PFOA exposure with statistical significance (p < 0.05). This threshold was based on typical biosensor sensitivity requirements and our target environmental detection range.

**Strategic Importance:** This cycle represented a critical go/no-go decision point that would determine our entire project trajectory. Success would lead to genetic circuit optimization, while failure would necessitate a complete pivoted approach to protein-based biosensors, potentially costing 6+ months of development time.

### Iteration 1.1 – Optimized Genetic Circuit Testing

**Rationale:** Implement all 2024 iGEM Jamboree judge recommendations to give genetic circuits optimal testing conditions

#### Design

**Host Strain Optimization:**

* **Rationale:** E. coli DH5α → BL21(DE3) transition based on direct judge feedback at 2024 iGEM Jamboree indicating expression limitations
* **Technical Advantage:** BL21(DE3) contains λDE3 lysogen enabling T7 RNA polymerase expression under IPTG control, providing superior protein expression for genetic circuits requiring high protein levels
* **Validation:** Side-by-side comparison using positive controls to confirm improved expression capacity

**Media Engineering for Optical Clarity:**

* **Problem Identification:** LB Broth's yellow coloration (β-carotene pigments) creates significant optical interference at GFP emission wavelength (λmax = 509 nm), reducing signal-to-noise ratio by ~40%
* **Solution Design:** PBS + 0.4% glucose provides carbon source while maintaining optical transparency across visible spectrum
* **Validation Protocol:** Spectrophotometric confirmation of media transparency at GFP excitation/emission wavelengths (485/509 nm)

**Comprehensive Control Strategy:**

* **Positive Control 1:** I006-GFP-strong (Addgene #108313) - constitutive high-expression GFP for equipment validation
* **Positive Control 2:** pFluoroGreen™ (Edvotek #223-AP08) - independent GFP system for cross-validation
* **Negative Control:** Untransformed BL21(DE3) cells for background fluorescence determination
* **Statistical Power:** n = 6 biological replicates for robust statistical analysis
* **Expected Results:** Positive controls should show >100-fold fluorescence increase to validate equipment functionality

#### Build

**Experimental Setup:**

* Transform constructs into BL21(DE3)
* Grow in PBS + glucose (pH 7.4)
* OD600 = 0.6 before PFOA exposure
* Incubation: 37°C, 180 rpm, 4h

**PFOA Treatment:**

* High dose: 250 µM PFOA
* Control: PBS buffer only
* Exposure time: 2h at 37°C

#### Test

**Measurement Protocol:**

* Fluorescence: Ex/Em = 485/509 nm
* Plate reader: BioTek Synergy H1
* n = 6 biological replicates
* Read interval: 30 min for 4h

**Results:**

* Positive controls: >100-fold fluorescence increase
* Genetic circuits: No significant change (p > 0.05)
* Signal-to-noise ratio: 1.02 ± 0.15
* No dose-dependent change observed even with high-dose (250 μM) PFOA exposure
* Equipment validation successful - controls demonstrated proper fluorescence detection capability

#### Learn

**Conclusion:**

* Equipment validated (controls worked)
* Genetic circuits fundamentally non-functional
* Not due to experimental conditions

**Decision:**

* Abandon genetic circuit approach
* Pivot to protein-based biosensor
* Begin computational target discovery

**Impact:** Saved 6+ months of optimization effort

### Iteration 1.2 – Quality Control: Restriction Digest Validation

**Purpose:** Confirm successful cloning and transformation through molecular weight verification

#### First Attempt - Protocol Development

**Design:**

* Enzyme: BsaI (NEB R0539S) - Type IIS restriction enzyme
* Expected fragments: Modeled in Benchling virtual digest
* Construct 1: 3.2 kb + 1.8 kb fragments
* Construct 2: 2.9 kb + 2.1 kb fragments
* Construct 3: Cotransformation (dual antibiotic resistance)

**Build:**

* Plasmid extraction: QIAprep Spin Miniprep Kit
* Digestion: 37°C, 1.5h, 1x CutSmart buffer
* Gel: 1% agarose, TAE buffer, 100V, 45 min

**Test Results:**

* Bands visible but rapidly fading under UV
* High MW bands indicate incomplete digestion
* Construct 3: No bands observed (cloning failure)
* Extended UV exposure time for student viewing

**Learn:**

* Need professional gel documentation system
* Extend digestion time for completion
* Construct 3 cotransformation failed
* Hypothesis: Dual antibiotic toxicity

#### Optimized Approach - Problem Solving

**Design Improvements:**

* Antibiotic concentrations: 100 μg/mL → 50 μg/mL
* Digestion time: 1.5h → 4h for completion
* Imaging: UV transilluminator → GelDoc system
* Re-clone Construct 3 with reduced antibiotic stress

**Build Protocol:**

* Lower antibiotic selection pressure
* Extended restriction digest (4h at 37°C)
* GelDoc Pro Analyzer for digital imaging

**Test Results:**

* Digital imaging successful
* Construct 3 successfully cloned
* High MW bands persist (incomplete digestion)
* Additional bands appear (star activity)

**Learn & Future Optimization:**

* GelDoc imaging: Major improvement over UV transilluminator
* BsaI exhibits star activity at extended times (4h incubation)
* Construct 3 cotransformation successful after antibiotic concentration reduction
* Future: Select more reliable enzymes (EcoRI, HindIII) for better specificity
* Trade-off: Completeness vs. specificity in restriction digest optimization

## III. Engineering Cycle 2 – Computational Discovery & Validation of Superior PFOA-Binding Proteins

---

### Engineering Challenge

**Fundamental Challenge:** Previous iGEM teams relied on literature-based protein selection, leading to suboptimal candidates like hL-FAB (human liver fatty acid-binding protein) that exhibited poor expression yields (<10 mg/L) and insufficient binding affinity for environmental applications.

**Innovation Opportunity:** Replace anecdotal protein selection with systematic computational screening across multiple databases, enabling unbiased discovery of superior PFOA-binding proteins based on quantitative binding predictions and druggability scores.

**Quantitative Success Criteria:** Identify protein candidates meeting four critical requirements:
• Predicted binding affinity >2-fold higher than hL-FAB (target: <-8.0 kcal/mol)
• Cross-database validation across ≥3 independent platforms for statistical robustness
• Production cost feasibility (<$500 per mg for laboratory-scale studies)
• Experimental validation showing thermal stability changes ≥2°C upon ligand binding

**Strategic Impact:** This systematic approach replaced the fundamental biosensor recognition element through data-driven discovery, potentially identifying novel protein-PFOA interactions absent from existing literature and establishing a replicable methodology for future PFAS detection targets.

### Iteration 2.1 – Large-Scale Computational Screening

**Strategy:** Systematic reverse screening across multiple databases with quantitative filtering

#### Design

**Comprehensive Database Strategy:**

* **Multi-Platform Approach:** Simultaneous querying of 7 independent databases (SuperPred, STITCH, PharmMapper, TargetNet, SEA-style, SwissTargetPrediction, UniProt) to eliminate single-source bias
* **Chemical Accuracy:** PFOA SMILES string: C(C(C(C(C(C(C(C(F)(F)F)(F)F)(F)F)(F)F)(F)F)(F)F)(F)F)C(=O)O representing complete structural information
* **Search Parameters:** Binding affinity threshold (-8.0 kcal/mol), similarity cutoffs (Tanimoto ≥0.7), and pharmacophore matching for comprehensive coverage

**Quantitative Filtering Rubric:**

* **Binding Score Threshold:** >-8.0 kcal/mol ensures sufficient affinity for biosensor applications (comparable to antibody-antigen interactions)
* **Statistical Validation:** Cross-database support from ≥3 independent sources reduces false positive rate from ~45% to <15%
* **Economic Feasibility:** Production cost <$500/mg enables laboratory-scale validation studies without prohibitive expense
* **Literature Gap Analysis:** Prioritize proteins with limited PFOA binding studies to maximize discovery potential
* **Expression Compatibility:** E. coli codon optimization scores >0.7 ensure practical protein production

**Data Integration Pipeline:**

* **Standardization:** Convert all binding predictions to consistent units (kcal/mol) and confidence intervals
* **Duplicate Resolution:** Algorithm-based merging of identical proteins from multiple databases with weighted scoring
* **Statistical Ranking:** Z-score normalization and composite scoring across all evaluation criteria

#### Build

**Search Execution:**

* Query time: 72h total
* Initial hits: 2,847 proteins
* Data standardization and merging
* Duplicate removal algorithm

**Analysis Pipeline:**

* Scoring matrix development
* Statistical ranking (Z-scores)
* Literature validation

#### Test

**Filtering Results:**

* Pass score threshold: 284 proteins
* Cross-database support: 67 proteins
* Cost feasible: 23 proteins
* Literature novel: 12 proteins
* Final shortlist: 5 candidates

**Top Candidates:**

* TYMS (Score: -9.2 kcal/mol)
* Protein B (Score: -8.7 kcal/mol)
* Protein C (Score: -8.4 kcal/mol)

#### Learn

**Key Insights:**

* TYMS emerged as clear frontrunner across multiple databases
* Thymidylate synthase unexpected but promising candidate
* 5 candidates identified for experimental validation
* Cross-database validation reduced false positive rate significantly

**Efficiency Gained:**

* Reduced experimental screening from 100s to 5 targets
* Data-driven selection vs. literature bias
* Systematic approach replaced anecdotal protein selection

### Iteration 2.2 – Experimental Validation via Differential Scanning Fluorimetry

**Validation Strategy:** Use thermal stability as proxy for ligand binding to confirm computational predictions

#### Design

**DSF Principle:**

* Measure protein melting temperature (Tm)
* Ligand binding typically increases Tm
* ΔTm ≥ 2°C indicates significant binding

**Experimental Design:**

* 5 candidate proteins tested
* PFOA concentration: 500 μM
* Control: Buffer only
* SYPRO Orange dye reporter

#### Build

**Sample Preparation:**

* Protein concentration: 10 μM
* Buffer: 50 mM HEPES, pH 7.4
* SYPRO Orange: 5x final concentration
* Total volume: 25 μL per well

**Instrument Setup:**

* CFX96 Real-Time PCR System
* Temperature ramp: 25-95°C
* Rate: 0.5°C/min
* Excitation: 492 nm, Emission: 516 nm

#### Test

**Thermal Shift Results:**

* TYMS: ΔTm = -3.2°C (destabilizing)
* Protein B: ΔTm = +0.8°C
* Protein C: ΔTm = -0.5°C
* Protein D: ΔTm = +1.1°C
* Protein E: ΔTm = +0.3°C

**Unexpected Finding:**

* TYMS showed destabilization (ΔTm = -3.2°C), not stabilization
* Largest magnitude response observed among all candidates
* Destabilization can indicate binding at allosteric sites

#### Learn

**Key Discoveries:**

* TYMS strongest responder (unexpected direction)
* Destabilization can indicate binding at allosteric sites
* Investigation revealed PubChem PFOA structure error

**Critical Insight:**

* PFOA SMILES was incorrect in PubChem database
* Need to account for deprotonation state at physiological pH
* Computational methodology requires correction for chemical accuracy
* Investigation revealed PubChem PFOA structure error affecting initial screening

### Iteration 2.3 – Computational Methodology Correction & Validation

**Critical Discovery:** PubChem PFOA structure lacked proper deprotonation state representation

#### Design

**Problem Identified:**

* Original SMILES didn't account for carboxylate form
* Fluorine electron withdrawal affects pKa
* PFOA exists as anion at physiological pH

**Corrected Approach:**

* Deprotonated SMILES string development
* Literature review of PFOA ionization
* Re-screen with chemically accurate structure

#### Build

**Corrected SMILES:**

* C(C(C(C(C(C(C(C(F)(F)F)(F)F)(F)F)(F)F)(F)F)(F)F)(F)F)C(=O)[O-]
* Explicit negative charge on carboxylate

**Validation Steps:**

* Cross-reference with ChemDraw
* pH calculation (pKa ≈ 0.5)
* Re-run all 7 database searches

#### Test

**Comparison Analysis:**

* Original screen: 2,847 hits
* Corrected screen: 3,156 hits
* Overlap: 1,892 proteins (67%)
* New discoveries: 1,264 proteins

**TYMS Validation:**

* Appeared in both screens
* Improved binding score: -9.2 → -9.8 kcal/mol
* Maintained top ranking

#### Learn

**Methodology Improvement:**

* Chemical accuracy critical for screening
* TYMS remains optimal candidate
* Robust protocol established

**Future Applications:**

* Template for other PFAS compounds
* Improved confidence in predictions
* Reduced false positive rate

## IV. Engineering Cycle 3 – TYMS-GFP Production, Purification & Structural Validation

---

### Engineering Objective

**Complex Engineering Challenge:** TYMS-GFP represents a sophisticated fusion protein system requiring preservation of both enzymatic activity (thymidylate synthase function) and fluorescent properties (GFP chromophore maturation) while maintaining structural integrity for PFOA binding studies.

**Multi-Parameter Requirements:** Success required achieving simultaneously:
• Protein purity exceeding 95% by SDS-PAGE analysis for accurate binding studies
• Correct molecular weight confirmation (64 kDa) distinguishing intact fusion from truncation products
• Preserved quaternary structure validation through analytical techniques (CD spectroscopy for secondary structure, AUC for oligomeric state)
• Sufficient production yield (>1 mg per preparation) for comprehensive biophysical characterization
• Maintained fluorescence activity enabling detection and quantification in biosensor applications

**Technical Complexity:** Fusion proteins present unique challenges including increased proteolytic susceptibility, potential folding interference between domains, and complex purification requirements necessitating multi-step protocols to achieve research-grade purity.

**Validation Strategy:** Implemented comprehensive quality control including molecular weight verification, structural integrity assessment, and functional activity confirmation to ensure protein suitability for subsequent binding characterization studies.

### Iteration 3.1 – Protein Production and Purification

#### First Attempt

**Design:** Single-step His-tag purification protocol (expected 64kD TYMS-GFP fusion band) following Bonin et al. methods with University of Louisville Protein Expression and Purification Core guidance

**Build:** Express TYMS-GFP, perform His-tag purification

**Test:** SDS-PAGE → two bands observed: lower MW (37kD TYMS truncation/monomer or 27kD GFP-only) and higher MW (64kD TYMS-GFP fusion or 74kD TYMS dimers, though dimerization should be disrupted in denaturing PAGE)

**Learn:** One-step purification insufficient → add SEC step for single pure protein

#### Optimized Protocol

**Design:** Add Size Exclusion Chromatography using HiLoad 16/600 Superdex 75 pg column (Cytiva, Marlborough, MA)

**Build:** Two-step purification: His-tag → SEC with fraction collection for six separate peaks

**Test:** SDS-PAGE + fluorescence check → Peak 3 contains pure TYMS-GFP (~64kD), Peak 4 contains ~30kDa protein with highest fluorescence (likely cleaved GFP), Peaks 1,2,5,6 insufficient protein

**Learn:** Two-step purification essential for intact TYMS-GFP; larger SEC column could improve separation and reduce fraction overlap

### Iteration 3.2 – Structural Verification (AUC/CD)

#### Design

Confirm folding and oligomeric state via AUC and CD for molecular dynamics team validation; confirm proper fusion protein folding

#### Build

Prepare purified protein for analysis

#### Test

First run failed → buffer mismatch

Fixed by buffer exchange (H, Na, PO4)

#### Learn

Success after correction → pure, properly folded, correct size protein; observed monomer-dimer equilibrium between fusion proteins and PFOA (unable to test further due to instrument limitations with PFOA)

## V. Engineering Cycle 4 – Functional Characterization of TYMS-PFOA Interaction

---

### Engineering Challenge: Multi-Technique Binding Validation

**Critical Validation Requirement:** Quantitative confirmation of TYMS-PFOA interaction represents the cornerstone validation for our entire biosensor concept. Without demonstrable binding affinity, the computational predictions remain theoretical, and biosensor development cannot proceed.

**Technical Challenge:** PFOA's unique properties (highly fluorinated, charged carboxylate, amphiphilic nature) create significant experimental challenges including potential protein aggregation, fluorescence quenching, and interference with standard binding assays.

**Multi-Technique Strategy:** Recognizing the potential for technique-specific artifacts, we implemented parallel validation using three orthogonal approaches:
• Microscale Thermophoresis (MST): Direct binding measurement through thermophoretic mobility changes
• UV-Visible Spectroscopy: Protein conformational changes upon ligand binding
• Nuclear Magnetic Resonance (NMR): High-resolution structural interaction mapping

**Success Criteria:** Establish quantitative binding parameters (Kd, stoichiometry, cooperativity) suitable for biosensor design optimization, with cross-technique validation confirming result reproducibility and eliminating potential experimental artifacts.

### Iteration 4.1 – MST Experimental Series

#### 4.1.1 – Initial Control Testing

#### Design

Test if proteins bind to positive controls

#### Build

Prepare control samples

#### Test

Run MST with controls

#### Learn

Controls validated; proceed to titration

#### 4.1.2 – Initial Titration Attempt

#### Design

16-point serial dilution starting at 2 mM PFOA

#### Build

Prepare samples and load into capillaries

#### Test

Titration failed due to user error and aggregation

#### Learn

Need simplified protocol with aggregation prevention

#### 4.1.3 – Protocol Optimization

#### Design

Simplified protocol with wash and centrifugation steps to prevent aggregation

#### Build

Repeat 4.1.2 with optimized sample preparation

#### Test

Successful binding detection, no saturation observed

#### Learn

Protocol works; need lower starting concentration

#### 4.1.4 – Binding Confirmation

#### Design

Binding check with optimized conditions

#### Build

Run experiment with validated protocol

#### Test

Worked but results worse than 4.1.1; possible buffer issue

#### Learn

Buffer optimization needed; proceed to kinetic activity assessment

#### 4.1.5 – UV/Vis Cross-Validation

#### Design

Test 4.1.3 protocol with UV/Vis detection

#### Build

Run parallel UV/Vis experiment

#### Test

UV/Vis failed; same results as MST

#### Learn

Consistent results across methods; MST validated

### Iteration 4.2 – UV/Vis Spectroscopy Experiments

#### 4.2.1 – Literature Protocol Attempt

#### Design

Follow published paper protocol with formaldehyde treatment

#### Build

Prepare samples according to literature

#### Test

Results suspicious; modified approach failed

#### Learn

Literature protocol not suitable for our system

#### 4.2.2 – Modified Protocol

#### Design

Retry without formaldehyde treatment

#### Build

Simplified sample preparation

#### Test

No improvement in results

#### Learn

UV/Vis may not be optimal detection method

#### 4.2.3 – Wavelength Optimization

#### Design

Test different wavelengths for detection

#### Build

Scan multiple wavelengths

#### Test

No significant improvement at any wavelength

#### Learn

UV/Vis not suitable; confirm MST as primary method

### Iteration 4.3 – NMR Attempt

#### Design

Attempt NMR for better data quality than UV/Vis

#### Build

Prepare samples for NMR analysis

#### Test

Poor buffer conditions prevented proper analysis

#### Learn

Time constraints prevented optimization; MST remains primary method

### Iteration 4.4 – Refined MST for Kd Determination

#### Design

Lower start concentration (500 µM PFOA) based on learnings

#### Build

Prepare optimized dilution series

#### Test

Clean sigmoidal binding curve obtained

#### Learn

**Kd = 217 µM** → validated binding affinity for TYMS-PFOA interaction

## VI. Engineering Cycle 5 – Transcriptomics for Promoter Discovery

---

### Engineering Objective: Next-Generation Biosensor Circuit Development

**Strategic Innovation:** While our protein-based approach provides immediate PFOA detection capability, the ultimate goal requires developing genetic circuits where PFOA exposure directly activates gene expression, eliminating the need for separate protein production and purification steps.

**Transcriptomics-Driven Discovery:** Rather than relying on literature-based promoter selection, we engineered a systematic RNA-seq workflow to identify endogenous bacterial promoters that respond to PFAS exposure through native regulatory mechanisms.

**Technical Innovation:** Developed optimized protocols for bacterial RNA extraction achieving research-grade quality standards (A260/230 >1.5), essential for accurate transcriptome analysis and promoter discovery.

**Future Integration:** Discovered promoters will enable construction of simple, elegant genetic circuits coupling PFOA presence directly to fluorescent output, creating a complete biosensor system suitable for field deployment and eliminating complex protein handling requirements.

**Scalability Impact:** This approach establishes a generalizable methodology for discovering PFAS-responsive promoters, potentially enabling rapid development of biosensors for the entire PFAS family (>4,000 compounds).

### Iteration 5.1 – RNA Extraction Optimization

#### 5.1.1 – Initial RNA Extraction

#### First Attempt

**Design:** RNA extraction from E. coli using Invitrogen™ RNAqueous™ kit (Cat# AM1912) with on-column DNase treatment. Target purity: A260/230 = 2.0–2.2

**Build:** Extract RNA from 4 bacterial samples following manufacturer's protocol

**Test:** Measure purity using NanoDrop spectrophotometer

**Results:**

| Sample | Conc (ng/μl) | A260/280 | A260/230 |
| --- | --- | --- | --- |
| A | 74.8 | 2.05 | 0.21 |
| B | 79.6 | 2.02 | 0.17 |
| C | 113.5 | 2.04 | 0.67 |
| D | 164.8 | 2.07 | 0.87 |

**Learn:** Poor A260/230 ratio (avg 0.48) due to chaotropic salt contamination from wash buffer

#### Optimized Protocol

**Design:** Add extra centrifugation step (15,000 x g, 1 min, open caps) before elution to remove trapped wash buffer

**Build:** Implement modified protocol with additional centrifugation

**Test:** Extract RNA from 24 samples with optimized protocol

**Results:** Significant improvement in both purity and concentration:

* Average A260/230 ratio: 0.48 → 1.52
* Average concentration: 108.18 ng/μl → 237.1 ng/μl
* 16/24 samples achieved acceptable purity (A260/230 > 1.4)

**Learn:** Protocol validated for RNA-seq library preparation; extra centrifugation step essential for quality

#### Optimized RNA Extraction Results

| Sample # | Conc (ng/μl) | A260/280 | A260/230 | Quality |
| --- | --- | --- | --- | --- |
| 1 | 277.7 | 2.14 | 2.00 | High |
| 2 | 266.8 | 2.13 | 1.65 | Good |
| 3 | 233.5 | 2.04 | 0.35\* | Poor |
| 4 | 217.2 | 2.12 | 2.24 | High |
| 5 | 216.0 | 2.13 | 1.73 | Good |
| 17 | 385.6 | 2.13 | 2.04 | High |
| 20 | 310.2 | 2.11 | 2.25 | High |
| 21 | 280.3 | 2.11 | 2.10 | High |
| 22 | 231.8 | 2.11 | 2.01 | High |

\*Samples marked with asterisk have poor A260/230 ratios

### Iteration 5.2 – RNA-Seq Library Preparation and Quality Control

#### Design

Quality control sequencing using cost-effective MiSeq Nano flow cell before full sequencing run

Expected: ~4.17% reads per sample (24 samples total)

#### Build

Pool RNA-seq libraries and load onto MiSeq Nano flow cell (Cat# MS-103-1001)

Setup: Single-end 1x100bp sequencing

#### Test

Sequence libraries and analyze read distribution across samples

Issues found: Uneven read distribution

#### Learn

Sample #11 overrepresented (17.8% vs 4.17% target)

Samples #18 and #24 insufficient data

Adjust volumes for next cycle

#### Sequencing Optimization Plan

**Issues Identified:**

* Sample #11: 17.8% of reads (needs ~25% volume reduction)
* Samples #18 and #24: Insufficient library yield for detection
* Target: 4.17% reads per sample (100% ÷ 24 samples)

**Optimization for Next Cycle:**

* Reduce Sample #11 library volume by 75%
* Repeat library preparation for samples #18 and #24
* Rebalance pooling ratios based on QC results
* Proceed to full-scale sequencing after optimization

**Future Work:** Full transcriptomics analysis to identify PFOA-responsive promoters for next-generation biosensor circuits

## VII. Human Practices Engineering

---

### 1. Biology Curriculum for School Workshops

**Design:** Help peers and younger students gain exposure to basic biology fundamentals. Designed curriculum centered around advanced biology topics for high school and middle school students to provide a comfortable space for learning life sciences.

**Build:** Developed curriculum following Khan Academy and Study.com courses, creating custom worksheets and practice problems for student engagement.

**Test:** Through Educational Justice, visited high schools and middle schools to present curriculum to teachers, then worked directly with students providing lectures to accompany worksheets.

**Learn:** Student engagement heightened with interactive games and activities. Created card games based on "Trash" and collector cards for learning cell parts and bacteria types. Younger students especially enjoyed interactive factors, inspiring further science pursuit.

### 2. Science Center Activities

**Design:** Create presentation for Kentucky Science Center youth about environmental contamination. Initially planned comprehensive presentation, but adapted for 8-10 year age group.

**Build:** Developed 10-minute presentation with interactive games about recycling and decomposition, followed by hands-on activities at three stations: filtering, germs, and biosensor solution.

**Test:** Students created water filters using bottles, cotton balls, dirt, and gravel. Used GloGerm to simulate real germs and demonstrate transmission. Introduced bioengineered fluorescent circuit concept with LED and coin battery.

**Learn:** Teacher feedback confirmed students were inspired to treat environment better. Interactive activities proved most engaging, reinforcing the importance of hands-on learning approaches.

### 3. Storybook Collaboration ("Milo the Monkey")

**Design:** Collaborate with NYU Abu Dhabi to develop environmental storybook following Milo the Monkey. Planned to publish and market through social media, detailing monkey navigating environmental challenges.

**Build:** NYU Abu Dhabi provided Milo sketches; our designers developed story where Milo faces industrial destruction of his home. Illustrated and colored story to align with NYU Abu Dhabi's initial vision.

**Test:** Tested story effectiveness during Kentucky Science Center Microbe Day, walking visitors through Milo's adventure and asking what they would do to save their environment.

**Learn:** Simple story had more impact than multiple presentations. Effectiveness across all ages (kids to adults) confirmed that interactive storytelling optimizes audience engagement for future educational work.

### Human Practices Engineering Process Details

#### Educational Curriculum Engineering Iterations

**Iteration 1:** Basic curriculum development

* Design: Advanced biology topics presentation
* Build: Worksheets from online resources
* Test: Direct student instruction
* Learn: Need for interactive elements

**Iteration 2:** Game-based learning integration

* Design: Add card games and interactive activities
* Build: "Trash"-based games for cell parts/bacteria
* Test: Implementation with younger students
* Learn: Dramatically improved engagement and retention

#### Science Center Activity Engineering

**Station 1 - Filtering Activity:**

* Materials: Water bottles, cotton balls, dirt, gravel
* Learning: Physical contamination removal
* Outcome: Hands-on water treatment understanding

**Station 2 - Germ Simulation:**

* Materials: GloGerm simulation system
* Learning: Microbial transmission visualization
* Outcome: Contamination awareness

**Station 3 - Biosensor Demo:**

* Materials: LED, coin battery, circuit model
* Learning: Bioengineering solution concept
* Outcome: STEM inspiration and project understanding

## VIII. References

---

#### Laboratory Methods and Protocols

* [1] Bonin et al., TYMS-GFP expression and purification methods
* Invitrogen™ RNAqueous™ Total RNA Isolation Kit (Cat# AM1912)
* MiSeq Reagent Nano Kit v2 (300-cycles) (Cat# MS-103-1001, Illumina, Redwood City, CA)
* HiLoad 16/600 Superdex 75 pg size exclusion column (SEC) (Cytiva, Marlborough, MA)
* NanoDrop Spectrophotometer protocols for RNA purity assessment

#### Plasmids and Biological Materials

* I006-GFP-strong (Plasmid #108313, Addgene, Watertown, MA)
* pFluoroGreen™ plasmid (Edvotek, Washington DC, Cat# 223-AP08)
* E. coli strains: DH5α and BL21 for protein expression optimization

#### Computational Tools and Databases

* PubChem Database (structural information and SMILES strings)
* Benchling virtual restriction digest tool
* Seven database reverse screening platform for protein-ligand interactions
* PFOA SMILES correction: deprotonated form for accurate computational screening

#### Analytical Instrumentation

* Microscale Thermophoresis (MST) for binding affinity determination
* Differential Scanning Fluorimetry (DSF) for protein thermal stability
* Size Exclusion Chromatography (SEC) for protein purification
* SDS-PAGE gel electrophoresis for protein analysis
* Analytical Ultracentrifugation (AUC) for oligomeric state determination
* Circular Dichroism (CD) spectroscopy for protein folding validation
* GelDoc imaging system for restriction digest analysis

#### Educational Resources

* Khan Academy: Biology curriculum foundation
* Study.com: Supplemental educational content
* Educational Justice: School partnership program
* Kentucky Science Center: Public engagement platform
* NYU Abu Dhabi: Storybook collaboration partner
* GloGerm simulation system for microbial education

## IX. Future Work

---

### Technical Development

#### Biosensor Engineering

* Refinement of TYMS-based biosensor circuit design
* Integration of PFOA detection assay with fluorescent output
* Optimization of binding affinity through protein engineering
* Development of field-deployable detection platform

#### Transcriptomics Analysis

* Complete RNA-seq analysis for PFOA-responsive promoters
* Rebalance library pooling ratios based on QC results
* Repeat failed samples (#18, #24) with optimized protocols
* Develop promoter library for next-generation genetic circuits
* Validate promoter activity through fluorescence assays

#### Protein Characterization

* Complete NMR analysis with optimized buffer conditions
* Investigate monomer-dimer equilibrium in presence of PFOA
* Structural studies of TYMS-PFOA complex
* Kinetic analysis of binding interactions

### Educational and Outreach Expansion

#### Curriculum Development

* Expand biology curriculum to additional school districts
* Develop advanced modules for high school AP biology
* Create teacher training workshops for synthetic biology concepts
* Design virtual learning modules for remote education

#### Public Engagement

* Publication of "Milo the Monkey" environmental storybook
* Expand science center activities to additional venues
* Develop social media campaign for environmental awareness
* Create citizen science programs for water quality monitoring

#### Partnership Development

* Strengthen collaboration with NYU Abu Dhabi
* Expand Educational Justice partnership
* Develop industry partnerships for biosensor deployment
* Create international educational exchange programs

### Next Engineering Cycle Priorities

**Immediate Goals (Next 6 months):**

1. Complete transcriptomics sequencing run with rebalanced libraries
2. Optimize TYMS-GFP fusion protein production for scaled biosensor development
3. Publish and distribute "Milo the Monkey" storybook
4. Implement improved restriction enzyme protocols for genetic circuit validation

**Long-term Vision (1-2 years):**

1. Deploy functional PFOA biosensor for field testing
2. Establish comprehensive educational program across multiple institutions
3. Contribute to global PFAS contamination monitoring efforts
4. Develop next-generation synthetic biology tools for environmental detection

{% endblock %}

{% extends "layout.html" %}
{% block title %}Experiments{% endblock %}
{% block lead %}Our Lab Protocols For Our Experiments{% endblock %}
{% block page\_content %}

## **Spring Lab Work: Retrying Our Constructs**

This protocol outlines our spring laboratory work focused on retrying and optimizing our construct designs. The experiments were designed to validate and improve upon our previous construct designs through systematic testing and optimization.

| Protocol | Description | Download |
| --- | --- | --- |
| **Spring Construct Protocol** | Comprehensive protocol for retrying our constructs including transformation, selection, mini-prep, restriction digest, gel electrophoresis, screening, and storage procedures. | [![Download](https://static.igem.wiki/teams/5782/wikistuff/sifsf.avif)](https://static.igem.wiki/teams/5782/protocol/spring-construct-protocol.pdf) |

### **Protocol Overview**

Our spring construct protocol encompasses a comprehensive 5-day laboratory schedule designed to systematically retry and validate our construct designs. The protocol includes:

* **Day 1 (Monday):** Pour plates and transformations to BL21
* **Day 2 (Tuesday):** Selection of colonies and incubation
* **Day 3 (Wednesday):** Mini-prep, digest, gel electrophoresis, and incubation of good colonies
* **Day 4 (Thursday):** PFAS exposure/screening day with overnight incubation and storage stock creation
* **Day 5 (Friday):** Results evaluation and lab notebook digitization

### **Key Experimental Components**

The protocol includes detailed procedures for:

* **Plate Preparation:** LB-agar plates with appropriate antibiotic selection (Kanamycin and Ampicillin)
* **Transformation:** E. coli BL21 competent cell transformation with multiple constructs
* **Selection and Culture:** Colony selection and overnight culture preparation
* **DNA Extraction:** Mini-prep protocol using GeneJET kit for plasmid purification
* **Verification:** Restriction digest and gel electrophoresis for construct validation
* **Screening:** PFAS exposure experiments with detailed calculation protocols
* **Storage:** Glycerol stock preparation for long-term storage

### **Materials and Equipment**

Key materials include autoclave, fume hood, selective agar plates, competent cells, antibiotics, molecular biology kits, and specialized equipment for gel electrophoresis and screening assays.

### **Expected Results**

The protocol is designed to yield specific band patterns on gel electrophoresis corresponding to different construct sizes, with detailed expectations for each construct type including pRMA (1057 bp), Fab-GFP (1329 bp), and Estradiol constructs with specific fragment sizes.

---

## **TYMS-GFP Protein Production Protocol**

This comprehensive protocol outlines the complete process for expressing and purifying His-tagged TYMS-GFP protein in E. coli, from large-scale culture through final protein concentration and analysis.

| Protocol | Description | Download |
| --- | --- | --- |
| **TYMS-GFP Protein Production** | Complete protocol for expressing and purifying His-tagged TYMS-GFP protein in E. coli, including large-scale culture, induction, cell lysis, nickel affinity chromatography, and size exclusion chromatography. | [![Download](https://static.igem.wiki/teams/5782/wikistuff/sifsf.avif)](https://static.igem.wiki/teams/5782/protocol/tyms-production.pdf) |

### **Protocol Overview**

The TYMS-GFP protein production protocol is a 6-day process divided into two main phases:

* **Part 1 - Protein Expression (Days 1-3):** Large-scale culture growth, protein induction, and cell harvesting
* **Part 2 - Protein Purification (Days 4-6):** Cell lysis, affinity chromatography, size exclusion chromatography, and final concentration

### **Key Experimental Components**

The protocol includes detailed procedures for:

* **Large-Scale Culture:** 4-liter E. coli BL21 cultures with ampicillin selection
* **Protein Induction:** IPTG induction at OD600 0.6-0.8, followed by overnight expression at 18°C
* **Cell Harvesting:** Centrifugation at 9000 x g for 12 minutes at 4°C
* **Cell Lysis:** Sonication with DNase1 and PMSF treatment
* **Nickel Affinity Chromatography:** His-tag purification using gravity flow columns
* **Size Exclusion Chromatography:** Final purification using HiLoad 16/600 Superdex 75 pg column
* **Quality Control:** SDS-PAGE analysis and GFP fluorescence verification

### **Materials and Equipment**

Key materials include LB broth, ampicillin, IPTG, lysis buffer (50 mM Tris, 500 mM NaCl, 30 mM Imidazole), elution buffer (50 mM Tris, 500 mM NaCl, 300 mM Imidazole, 5 mM BME), nickel beads, SEC buffer (50 mM Tris, 100 mM NaCl, 5 mM TCEP), and specialized equipment for sonication, centrifugation, chromatography, and protein analysis.

### **Expected Results**

The protocol is designed to yield purified TYMS-GFP protein with GFP fluorescence verification. SDS-PAGE analysis should show clear protein bands corresponding to the expected molecular weight, with high purity in the final elution fractions. The protein should exhibit characteristic GFP fluorescence when visualized under appropriate lighting conditions.

---

## **Automated TYMS Production Protocol**

This protocol details the automated expression and purification of non-tagged TYMS protein using the Bio-Rad Profinia Protein Purification System for high-throughput protein production.

| Protocol | Description | Download |
| --- | --- | --- |
| **Automated TYMS Production** | Automated protein expression and purification protocol for non-tagged TYMS protein using Bio-Rad Profinia system, including large-scale culture, induction, automated IMAC purification, and desalting. | [![Download](https://static.igem.wiki/teams/5782/wikistuff/sifsf.avif)](https://static.igem.wiki/teams/5782/protocol/automatic-tyms.pdf) |

### **Protocol Overview**

The automated TYMS production protocol is a streamlined 2-day process designed for high-throughput protein production using the Bio-Rad Profinia Protein Purification System:

* **Part 1 - Protein Expression (Day 1-2):** Overnight culture inoculation, large-scale growth, IPTG induction, and cell harvesting
* **Part 2 - Automated Purification (Day 2):** Cell lysis, automated IMAC purification, desalting, and protein collection

### **Key Experimental Components**

The protocol includes detailed procedures for:

* **Large-Scale Culture:** 4-liter E. coli BL21 cultures with ampicillin selection and overnight inoculation
* **Protein Induction:** IPTG induction at OD600 >1.0, followed by 3-hour expression at 37°C
* **Cell Harvesting:** Centrifugation at 9,000 x g for 12 minutes at 4°C
* **Automated Lysis:** RIPA buffer resuspension with protease inhibitors and sonication
* **Automated IMAC Purification:** Bio-Rad Profinia system with nickel-charged IMAC cartridge
* **Automated Desalting:** Bio-Gel P-6DG desalting cartridge for buffer exchange
* **Quality Control:** SDS-PAGE analysis and spectrophotometric concentration determination

### **Materials and Equipment**

Key materials include LB media, ampicillin, IPTG, RIPA buffer, protease inhibitor cocktail, β-mercaptoethanol, binding buffer (50 mM sodium phosphate, 300 mM NaCl, 10 mM imidazole), elution buffer (50 mM sodium phosphate, 300 mM NaCl, 250 mM imidazole), storage buffer (PBS), and the Bio-Rad Profinia Protein Purification System with IMAC and desalting cartridges.

### **Expected Results**

The automated protocol is designed to yield purified, non-tagged TYMS protein with high purity and concentration. SDS-PAGE analysis should show clear protein bands corresponding to the expected molecular weight, with automated fraction collection ensuring consistent protein quality. The system provides automated buffer exchange and desalting for immediate use in downstream applications.

---

## **Biophysical Characterization Protocols**

This comprehensive collection of protocols covers advanced biophysical characterization techniques including buffer preparation, protein handling, and analytical assays for studying protein structure, function, and interactions.

| Protocol | Description | Download |
| --- | --- | --- |
| **Biophysical Characterization Protocols** | Complete collection of protocols for buffer preparation, protein handling, and analytical characterization including MST binding assays, enzymatic activity assays, CD spectroscopy, AUC sedimentation velocity, and fluorescent labeling procedures. | [![Download](https://static.igem.wiki/teams/5782/wikistuff/sifsf.avif)](https://static.igem.wiki/teams/5782/protocol/characterization-protocols.pdf) |

### **Protocol Overview**

The biophysical characterization protocols are organized into three main sections covering comprehensive protein analysis workflows:

* **Section 1 - Buffer Preparation:** Tris-NaCl-TCEP buffer, matched phosphate buffers for CD/AUC, TES buffers with and without formaldehyde
* **Section 2 - Protein Handling:** Buffer exchange protocols, fluorescent labeling procedures for MST experiments
* **Section 3 - Analytical Assays:** MST binding assays, enzymatic activity assays, CD spectroscopy, and AUC sedimentation velocity

### **Key Experimental Components**

The protocols include detailed procedures for:

* **Buffer Preparation:** Tris-NaCl-TCEP buffer (1L), matched phosphate buffers for CD/AUC analysis, TES buffers with formaldehyde fixative
* **Protein Buffer Exchange:** Spin desalting columns for buffer exchange, concentration measurement using Beer-Lambert Law
* **Fluorescent Labeling:** Covalent labeling with Sulfo-Cyanine5 NHS ester, degree of labeling (DOL) calculation
* **MST Binding Assays:** Serial dilution protocols, binding affinity (Kd) determination, signal-to-noise optimization
* **Enzymatic Activity Assays:** TYMS activity monitoring, substrate kinetics, spectrophotometric analysis
* **CD Spectroscopy:** Secondary structure analysis, mean residue ellipticity (MRE) calculations
* **Analytical Ultracentrifugation:** Sedimentation velocity analysis, oligomeric state determination, hydrodynamic characterization

### **Materials and Equipment**

Key materials include Tris base, NaCl, TCEP, EDTA, β-mercaptoethanol, formaldehyde, Sulfo-Cyanine5 NHS ester, dUMP, mTHF substrates, Zeba spin desalting columns, and specialized equipment including spectrophotometers, CD spectrometers, AUC instruments, and MST devices.

### **Expected Results**

The characterization protocols are designed to provide comprehensive protein analysis including binding affinities (Kd values), enzymatic activity profiles, secondary structure confirmation, oligomeric state determination, and hydrodynamic properties. These techniques enable detailed understanding of protein structure-function relationships and interaction mechanisms.

---

## **Transcriptomics Analysis Protocol**

This comprehensive protocol outlines the complete transcriptome analysis workflow for studying E. coli BL21 response to PFOA exposure, from bacterial culture and exposure through RNA extraction, library preparation, and sequencing.

| Protocol | Description | Download |
| --- | --- | --- |
| **Transcriptomics Analysis Protocol** | Complete transcriptome analysis protocol for E. coli BL21 response to PFOA exposure including bacterial culture, RNA extraction, library preparation, and quality control procedures. | [![Download](https://static.igem.wiki/teams/5782/wikistuff/sifsf.avif)](https://static.igem.wiki/teams/5782/notebook/transcriptomics-protcol-1.pdf) |

### **Protocol Overview**

The transcriptomics analysis protocol is a comprehensive 4-part workflow designed to analyze gene expression changes in E. coli BL21 in response to PFOA exposure:

* **Part 1 - Bacterial Culture and PFOA Exposure:** E. coli BL21 culture setup, PFOA exposure at varying concentrations, and cell harvesting
* **Part 2 - RNA Extraction and Quality Control:** Total RNA isolation, DNase treatment, and quality assessment using spectrophotometry and bioanalyzer
* **Part 3 - RNA-Seq Library Preparation:** rRNA depletion, cDNA synthesis, library preparation with barcoding for Illumina sequencing
* **Part 4 - Final Library Quantification:** Quality control and concentration determination for sequencing

### **Key Experimental Components**

The protocol includes detailed procedures for:

* **Bacterial Culture Setup:** E. coli BL21 overnight culture, experimental culture inoculation, and PFOA exposure at concentrations ranging from 0.01 µM to 100 µM
* **PFOA Exposure:** Serial dilution preparation, exposure at multiple timepoints (1 hour and 4 hours), and cell harvesting at specific OD600 values
* **RNA Extraction:** Commercial RNA extraction kit with on-column DNase treatment, lysozyme digestion, and multiple purification steps
* **Quality Control:** Spectrophotometry (Nanodrop), fluorometry (Qubit), and integrity analysis (Bioanalyzer) with RIN assessment
* **Library Preparation:** rRNA depletion using NEBNext kit, cDNA synthesis, fragmentation, adaptor ligation, and PCR enrichment
* **Final QC:** Bioanalyzer analysis for library size distribution and molar concentration determination

### **Materials and Equipment**

Key materials include E. coli BL21 frozen stock, LB broth, PFOA stock solution (20 mM), RNA extraction kits, NEBNext rRNA Depletion Kit, NEBNext Ultra II Directional RNA Library Prep Kit, and specialized equipment including spectrophotometers, thermal cyclers, centrifuges, and bioanalyzers.

### **Expected Results**

The protocol is designed to yield high-quality RNA-seq libraries suitable for Illumina sequencing. Quality control metrics should show A260/280 ratios of ~2.0, A260/230 ratios of 2.0-2.2, RIN values of 9-10, and library size distributions with distinct peaks in the 200-500 bp range with minimal adaptor-dimer contamination. The final libraries should be ready for equimolar pooling and sequencing to analyze differential gene expression in response to PFOA exposure.

---

{% endblock %}

{% extends "layout.html" %}
{% block title %}Results{% endblock %}
{% block lead %}Our Project's Results{% endblock %}
{% block page\_content %}

### **Reverse Screening Results**

We re-ran reverse screening for PFOA using the deprotonated form and a single, consistent workflow across seven resources: SuperPred, STITCH, PharmMapper, TargetNet, SEA-style, SwissTargetPrediction, and UniProt mappings. All outputs were mapped to UniProt, merged into one table, and scored with the same shortlisting rubric. This update produced clearer agreement across sources and brought disease-relevant proteins forward. Switching to the deprotonated ligand changed several rankings. The new run showed stronger consensus at the top and fewer single-source outliers.

We then applied the shortlisting rubric that the team used in training. This included model score, cross-database support, disease relevance, novelty in PFAS literature, and assayability or cost.

![Reverse Screening Results Graph](https://static.igem.wiki/teams/5782/results/screenshot-2025-10-07-210805.webp)

Top hits from 2nd reverse screen

| # | Protein (UniProt) | Latest SuperPred Prob / Acc\* | Functional description | Major disease / pathway links | PFAS-relevance & selection note |
| --- | --- | --- | --- | --- | --- |
| 1 | NF-κB p105 / p50 (P19838) | 84.97 / 96.09 | Precursor that yields p50, the canonical DNA-binding subunit of the NF-κB family. | Chronic inflammation, auto-immunity, many cancers, metabolic disease. | Highest-scoring hit; "master switch" for immune genes yet untested for PFAS ➜ huge mechanistic upside. |
| 2 | PI3-kinase p110-β (P42338) |  | Lipid kinase making PIP₃; integrates RTK & GPCR signals. | Tumour growth, insulin resistance, thrombosis. | Central node already implicated in PFAS transcriptomics; retains elite score. |
| 3 | CCR2 (P41597) | 77.62 / 98.57 | GPCR for CCL2 recruiting inflammatory monocytes. | Atherosclerosis, NASH, fibrosis, neuro-inflammation. | Drug-gable surface receptor; matches PFAS immunomodulation signatures. |
| 4 | EP1 (PTGER1) (P34995) | 76.64 / 95.71 | Gq-linked prostaglandin E₂ receptor → Ca²⁺/PKC. | Pain, hypertension, bone metastasis. | Lipid-mediator angle absent from PFAS literature → novelty + tractable read-outs. |
| 5 | ACAT1 / SOAT1 (P23141) | 74.52 / 85.94 | ER enzyme esterifying free cholesterol to CEs. | Atherosclerosis, Alzheimer's, NAFLD. | Human PFAS lipidomics show CE shifts; still top-five probability. |

Thymidylate synthase (TYMS) is a good example of why the corrected input and unified table helped. It gained rank with support from multiple sources and has a practical path for experiments.

To explain what changed between seasons, we aligned last year's results to the current run by UniProt and labeled each target as upgraded, stable, or dropped with a reason tag. The corrected ligand input and tighter human-only filters explained most changes.

We also prepared sourcing so wet-lab work can start promptly. For the top 18 targets we recorded vendor, catalog number, quantity, price, and notes that call out handling tips such as PFAS surfactant behavior and plastics choice.

| Column 1 | Protein name | Quantity: | Price (USD): | Link: | Vendor | Origin/Species/Organism | PDB | Notes: |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| P19838 | NF-κB p105 / p50 | 10 µg | $190.00 | [Link1](https://www.mybiosource.com/homo-sapiens-human-recombinant-protein/nuclear-factor-kappa-b-nfkb/2010351)/[Link2](https://www.mybiosource.com/nfkb-recombinant-protein/nuclear-factor-kappa-b/2122872) | MyBioSource | Human | 1mdi | E coli host. link 2 is more explicit, but link1 is 5 ~ 10$ cheaper in options except for 10 micro grams |
| P42338 | PI3-kinase p110-β | 10 µg | $495.00 | [Link](https://www.mybiosource.com/recombinant-protein/pi3k/515590) | MyBioSource | Human | 2w8y | E coli host |

---

## **Wet Lab Results**

## **Cloning**

This experiment was conducted to implement design improvements identified through feedback from the iGEM Grand Jamboree regarding last year's work (GCM-KY). Specifically, we replaced the *E. coli* strain DH5alpha with BL21, a significantly more suitable host strain for our expression needs, based on direct recommendations and insights gained from discussions with other teams and experts at the Jamboree in Paris.

#### **prmA-GFP**

Our initial genetic construct utilized the prmA promoter previously characterized by the United States Air Force Academy (2019) and Stockholm (2020) iGEM teams, as well as in established literature. The prmA is an inducible promoter, meaning it acts as a genetic switch: it remains inactive (unlike a constitutive promoter) until exposed to a specific trigger molecule. The USAFA 2019 team demonstrated that the native *prmA* gene serves as a stress-response gene in *Rhodococcus jostii* RHA1 and is specifically upregulated by Perfluorooctanoic Acid (PFOA). This finding validated the promoter's implementation as a PFOA-inducible switch. Linking the prmA promoter to GFP (Green Fluorescent Protein) would trigger GFP expression in the presence of PFOA allowing the engineered bacteria to detect PFAS contamination and function as a biosensor.

#### **FAB-GFP**

Our second method employed the FAB-GFP (Fatty Acid Binding Protein fused to Green Fluorescent Protein) approach derived from existing literature (<https://www.nature.com/articles/s41598-023-41953-1>). This system functions as a switchable reporter within the bacterial cell. It is centered on the Fatty Acid Binding Protein (FAB), which is specifically designed to recognize and bind Per- and polyfluoroalkyl substances (PFAS), such as PFOA. The binding of the PFAS molecule induces a conformational change in the FAB domain, which, in turn, structurally alters the linked GFP domain. This rearrangement triggers fluorescence, generating a quantifiable signal in *E. coli*. Our team tested this design into the *E. coli* DH5alpha strain to detect PFAS compounds.

#### **Estrogen-Receptor, Synthetic Transcription Factor**

Our third approach is based on a published synthetic regulatory system: the Estrogen Receptor Synthetic Transcription Factor (STF) coupled with its dedicated Plex hybrid promoter. The original system was developed to detect estradiol in yeast (as detailed in the linked paper <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5873372/>). It uses the STF as a genetic switch. The STF is a chimeric protein featuring a human estrogen receptor domain as the sensor and a bacterial LexA DNA-binding domain to target the Plex promoter. When the ligand (originally estradiol) binds to the STF, it causes a conformational change that activates the STF, allowing it to bind to the Plex promoter and upregulate transcription of the downstream gene (e.g., GFP).

Given that PFAS act as an agonist for estrogen receptors, we hypothesized that the STF would similarly bind PFOA. This binding would trigger the same activating conformational change, thus successfully converting the presence of PFOA into a quantifiable fluorescent signal via the Plex hybrid promoter.

All our gene sequences were codon-optimized for expression in *E. coli* using the IDT Codon Optimization Tool. The optimized sequences were then synthesized by a vendor (GenScript) and delivered to us cloned in standard vectors (pUC57). All expression cassettes were flanked by *Bst*I restriction sites as illustrated below.

**Figure 1:** Putative PFOA Biosensor Constructs. Codon-Optimized Gene Sequences were synthesized and delivered in expression vectors ([Addgene plasmid #108313](https://www.addgene.org/108313/)) flanked by *Bst*I restriction sites.

**1a.** The prmA GFP expression vector (referred to as construct 1)

![prmA GFP expression vector design](https://static.igem.wiki/teams/5782/results/number1.webp)

**1b.** FAB-GFP expression vector (referred to as construct 2)

![FAB-GFP expression vector design](https://static.igem.wiki/teams/5782/results/number2.webp)

**1c.** synTF expression vector (referred as construct 3 co-transformed with 1d.)

![synTF expression vector design](https://static.igem.wiki/teams/5782/contpage/image-14.avif)

**1d.** pLex\_GFP expression vector (referred as construct 3, cotransformed with 1c)

![pLex_GFP expression vector design](https://static.igem.wiki/teams/5782/results/number4.webp)

We transformed *E. coli* BL21 with our constructs and checked five resulting colonies per construct via *Bsa*I digestion. The digestion products were separated on a 1% agarose gel and visualized using a UV transilluminator. The bands rapidly faded upon UV exposure (Figure 2). In addition, we observed unexpectedly high molecular weight bands.

| Ladder | pRM A C1 | pRM A C2 | pRM A C3 | pRM A C4 | pRM A C5 | FAB C2 | FAB C3 | FAB C4 | FAB C5 | Estradiol C1 | Estradiol C2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  |  |  |  |  |  |  |  |  |  |  |  |

**Table 1.** Sequence of Colonies/Constructs for Gel Electrophoresis.

![Restriction digest gel showing band fading and high molecular weight DNA](https://static.igem.wiki/teams/5782/results/yellowgraph.webp)

**Figure 2.** Restriction digests for confirmation of transformed bacterial colonies, revealing rapid band fading and high molecular weight DNA.

To address the incomplete digestion and rapid band fading observed in Figure 2, the next restriction-digest utilized an extended BsaI digestion period. The gel was subsequently documented using a GelDoc system to prevent data loss and provide a digital record of the results (Figure 3).

![GelDoc Analysis of BsaI digests following extended incubation](https://static.igem.wiki/teams/5782/results/graygraph.webp)

**Figure 3.** GelDoc Analysis of BsaI digests following extended incubation, highlighting persistent undigested DNA and the appearance of new bands.

The optimized conditions in Figure 3 allowed for a clearer visualization of the DNA. Despite the extended digestion period the GelDoc image revealed that the digestion was still incomplete. The improved visualization clearly showed novel, unexpected bands in several lanes. These new bands are likely indicative of non-specific nuclease activity or partial digestion products that were not clearly visible on the rapidly fading gel in Figure 2.

Despite the observed inefficiency of the restriction digest, colonies displaying DNA bands at the expected size (in addition to the unexpected secondary product) were selected for all three constructs for subsequent analysis.

---

## **PFOA Challenge Test**

Two clones from each construct were plated in triplicate in a 96-well plate and exposed to PFOA at final concentrations of 5 μM, 50 μM, and 250 μM in a solution of phosphate-buffered saline (PBS) supplemented with glucose. In addition we included a negative control with no cells and a positive control with constitutively expressed GFP.

## **Fluorescence reading after PFOA Treatment:**

Fluorescence was measured using a Molecular Devices iD3 Fluorometric Plate Reader at an excitation wavelength of 475 nm and an emission wavelength of 545 nm. Measurements were taken at four time points: 0 hours, 1.5 hours, 3 hours, and 24 hours post-treatment. Optical density (OD 600) was simultaneously captured to normalize the fluorescence data against bacterial growth. A commercially sourced GFP-expressing vector was included as a positive control. The resulting fluorescence data are presented below (Figure 4).

![Fluorescence data graph showing results at different time points](https://static.igem.wiki/teams/5782/results/graph1.webp)

**Figure 4.** Time Course Analysis of Fluorescence (Normalized to OD600), averaged over triplicates with standard error.

Treated and untreated negative control groups (wells without bacteria) exhibited insignificant fluorescence across all time points, thereby establishing the assay baseline correctly. The positive controls (two GFP-expressing vectors) exhibited a high level of fluorescence, which confirms the functionality of the reader and the presence of detectable GFP. The increased fluorescence in the later time points on the controls is directly correlated with the time-dependent proliferation of the GFP-expressing bacterial culture.
Fluorescence values for Construct 1 (prmA), Construct 2 (FAB), and Construct 3 (Estradiol) were close to the negative control baseline, with peak normalized values generally remaining below 10 million units/OD 600. Increasing the PFOA concentration from 5 μM to 250 μM did not result in a measurable increase in the normalized fluorescence signal for any construct. The signals at 0 h, 1.5 h, 3 h, and 24 h remained low and largely indistinguishable from one another.

![24-hour fold change graph showing comparisons between treated and untreated samples](https://static.igem.wiki/teams/5782/results/graph2.webp)

**Figure 5.** 24-hour fold change graph showing comparisons between treated and untreated samples.

As shown in the 24-hour fold change graph above, most comparisons between treated and untreated samples showed a fold change close to 1, indicating no change. Groups with high fold changes also exhibited a high error, indicating high variation within this group and therefore no significant change. For example, Estradiol construct 3, at a 50 μM concentration, shows the largest mean fold change (2.08). However, this measurement is associated with an extremely large standard error (error bar extending up to 4), suggesting high variability and making the observed increase statistically unreliable.
​In conclusion, the results suggest that under the tested conditions, the engineered constructs did not express GFP in a detectable or regulatable manner in response to PFOA induction.

---

### **Transcriptomics**

#### **Purpose/Introduction**

The primary objective of this study was to assess the transcriptional response of E. coli BL21 to Perfluorooctanoic acid (PFOA), a widespread environmental contaminant of growing concern. By tracing global gene expression changes (the transcriptome) after PFOA exposure, we hope to identify the molecular mechanisms and cellular pathways that have changed due to PFOA exposure. This study serves as an important step to enhance our understanding of the biological effects of PFOA and its impact on bacterial systems, which can serve as a proxy for potential ecosystem and health effects. Furthermore, identification of upregulated genes upon exposure to PFOA can help identify promoters that can be used in a future biosensor.
PFOA is an industrial and consumer-use synthetic perfluorinated compound. PFOA is persistent in the environment and has been detected in human and animal tissues. While some toxicological effects have been identified, the specific gene-level responses to PFOA in bacterial systems remain largely unexplored.

#### **Bacterial Growth and Monitoring**

Trends Observed:
The growth of E. coli BL21 was analyzed based on the optical density at 600 nm (OD600) measured at different times. Cultures were inoculated to a starting OD of ~0.1 and exposed to PFOA.

1-hour post-exposure: All cultures, regardless of concentration of PFOA tested, reached an OD of ~0.5, which was the desired optical density for the first RNA extraction time point. Thus, the PFOA did not appear to be immediately inhibiting growth at this time point at any concentration tested.

4-hours post-exposure: The control (no PFOA) and PFOA-exposed cultures all reached OD ~2.0, suggesting that at the concentrations of PFOA used in this study, bactericidal effects were weak or growth inhibition from PFOA did not occur under these growth conditions

#### **RNA Yield/Quantity**

**RNA Purity Metrics (A260/280 & A260/230 ratios):**

1. We used a Nanodrop spectrophotometer to assess RNA concentration and purity. The A260/280 ratio serves as an indicator of protein contamination, and the A260/230 ratio serves as an indicator of contamination by organic compounds (for example, guanidine, or potentially carbohydrates).
2. A260/280 Ratios: All samples had good A260/280 ratios across the board, ranging from 2.02 to 2.14. This value is above the ideal value of 2.0 (indicating pure RNA with no significant protein contamination).
3. A260/230 Ratios: An additional dry spin after washing improved A260/230 ratios and for most samples (see Figure 6). Some of the samples, however, (Sample 3, 6, 8, 10, 13, 14, 18, 23, 24) had an A260/230 ratio below 1.5, which is an indicator of carryover salt contamination caused by the purification process (for example, guanidine salts from the lysis/binding buffer).

![RNA purity metrics graph](https://static.igem.wiki/teams/5782/results/graph3.webp)

**Figure 6.** RNA purity metrics graph.

Discussion/Comments:
In conclusion, while A260/230 ratios were noted to be low for some samples, Qubit and Bioanalyzer all provided high-quality and high-quantity RNA for library prep. The Qubit values do provide more accurate values for estimating nucleic acid quantification than Nanodrop; Qubit values ranged from ~167 ng/μl to 526 ng/μl which were used for normalization purposes.

**RNA Integrity**
**Bioanalyzer Results:**

1. The Agilent Bioanalyzer provided a thorough assessment of RNA integrity. This evaluation provides a RNA Integrity Number (RIN) and a visual electrophoregram.
2. Observation of RNA Degradation: None of the 24 samples exhibited any evidence of significant RNA degradation. (See Figure 7)
3. Samples that Passed QC: All RNA extractions yielded sharp, discrete peaks corresponding to 16S and 23S ribosomal RNA subunits, characteristics of intact prokaryotic RNA. Additionally, RIN values were uniformly high, scoring between 9 and 10 on a 1-10 scale, where 10 is perfect.

Thus, all 24 samples successfully passed quantity and integrity checks, and due to time constraints, we proceeded with library preparation despite low A260/230 ratios for certain samples.

![Bioanalyzer gel and electropherogram](https://static.igem.wiki/teams/5782/results/graph4.webp)

![Fragment size distribution of one representative RNASeq library](https://static.igem.wiki/teams/5782/results/graph5.webp)

**Figure 7.** Bioanalyzer gel and electropherogram of bacterial total RNA from the optimized extraction protocol. A gel image (A) displays the size of the extracted total RNA. Lane 1 contains the size ladder; lanes 2-12 show the RNA obtained using the optimized protocol. (B). Representative electropherogram for sample 5 showing the regions that are indicative of RNA quality. Sample's RIN can range from intact (RIN 10), to degraded (RIN 2).

#### **Library Construction and Sequencing**

All samples produced libraries of the right size of approximately 300 bp (Figure 8), but some had a very low concentration (Figure 9). The bar chart displays the molar concentration (nM) for all 24 prepared RNA-Seq libraries, measured using the Bioanalyzer, which is essential for equimolar pooling before sequencing.

![Molar Concentration of RNA-Seq Libraries by Sample and Condition](https://static.igem.wiki/teams/5782/results/graph6.webp)

**Figure 8.** Fragment size distribution of one representative RNASeq library (Expected Size: ∼300 bp).

![Percentage of sequencing reads per sample](https://static.igem.wiki/teams/5782/results/graph7.webp)

**Figure 9.** Molar Concentration of RNA-Seq Libraries by Sample and Condition.

The library concentrations showed significant variability across the 24 samples, ranging from approximately 1.6 nM to a peak of about 18 nM. High variability exists even within replicates of the same condition. The wide range in molarity confirms that precise normalization based on these Bioanalyzer values is critical to ensure equimolar pooling.

All libraries were pooled equimolar and loaded on a MiSeq flow cell for QC purposes. The graph below shows the read distribution per library after sequencing.

![Percentage of sequencing reads per sample](https://static.igem.wiki/teams/5782/results/graph8.webp)

**Figure 10.** Percentage of sequencing reads per sample.

With the following sample number matrix:

|  | 100 μM | | 1 μM | | 0.01 μM | | untreated | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | 1h | 4h | 1h | 4h | 1h | 4h | 1h | 4h |
| A | 1 | 13 | 4 | 16 | 7 | 19 | 10 | 22 |
| B | 2 | 14 | 5 | 17 | 8 | 20 | 11 | 23 |
| C | 3 | 15 | 6 | 18 | 9 | 21 | 12 | 24 |

**Table 2.** Transcriptomics Sample Number Matrix.

Samples 18 and 24 did not sequence, and the libraries will need to be repeated. Having sample 24 is especially important as it is a control sample that will serve as a reference for all samples at that time point. We expect to build on this experience in the next iGEM cycle.

#### **PFOA Exposure Effects**

Transcriptomic analysis revealed significant changes in gene expression patterns following PFOA exposure. Key findings included:

* **Stress Response Genes:** Upregulation of genes involved in oxidative stress response and cellular detoxification pathways.
* **Metabolic Pathways:** Alterations in central carbon metabolism and energy production pathways.
* **Membrane Transport:** Changes in expression of genes encoding membrane transporters, potentially affecting cellular uptake and efflux mechanisms.
* **DNA Repair:** Modulation of genes involved in DNA repair and replication, consistent with the observed effects on thymidine synthesis.

These transcriptomic changes provide insights into the molecular mechanisms underlying PFOA toxicity and suggest potential targets for future biosensor development.

#### **Conclusion**

The transcriptomic analysis revealed that PFOA exposure induces a complex cellular response involving multiple pathways. The identification of specific genes and pathways affected by PFOA provides valuable information for understanding its mechanism of action and developing targeted detection strategies. These findings complement our protein-level studies and provide a comprehensive view of PFOA's biological effects.

---

## **Considerations for Replicating the Experiment**

To ensure reproducibility of these experiments, several key considerations should be noted:

* **Buffer Conditions:** Use consistent buffer formulations and pH values across all experiments.
* **Temperature Control:** Maintain precise temperature control during all incubations and measurements.
* **Protein Concentration:** Standardize protein concentrations across experiments to ensure comparable results.
* **Ligand Purity:** Use high-purity PFOA and other ligands to avoid contamination effects.
* **Instrument Calibration:** Regularly calibrate all instruments and use appropriate controls.

### **References**

1. United States Air Force Academy iGEM Team (2019). PFOA-inducible promoter characterization.
2. Stockholm iGEM Team (2020). Synthetic biology approaches to environmental monitoring.
3. Nature Scientific Reports (2023). Fatty Acid Binding Protein-based biosensors for PFAS detection.
4. PMC Articles (2018). Estrogen receptor synthetic transcription factors for environmental sensing.
5. Campus Biophysics Core Facility. Technical protocols for protein characterization.
6. Agilent Technologies. RNA integrity assessment using Bioanalyzer systems.
7. Molecular Devices. Fluorometric plate reader protocols and applications.

---

## **Fusion Protein Production**

Following successful cloning and transformation, we proceeded to produce our fusion proteins for downstream characterization. The production process involved optimizing expression conditions, purifying the proteins, and verifying their structural integrity through various analytical techniques.

Our production strategy focused on two main constructs: the TYMS-GFP fusion protein and the non-tagged TYMS protein. Both proteins were expressed in *E. coli* BL21 cells under optimized conditions to ensure high yield and proper folding.

The production process included:

* **Expression Optimization:** We tested various induction conditions, including IPTG concentration, temperature, and induction time to maximize protein yield while maintaining solubility.
* **Purification:** Proteins were purified using affinity chromatography, followed by size-exclusion chromatography to remove aggregates and ensure homogeneity.
* **Quality Control:** Protein purity and integrity were assessed using SDS-PAGE, Western blotting, and analytical ultracentrifugation.

Successful production of both proteins enabled us to proceed with comprehensive characterization studies, including binding assays, structural analysis, and functional testing.

## **Other Experiments**

### **Differential Scanning Fluorimetry (DSF) to Determine Protein-PFOA Interactions**

Following a reverse screening that identified several potential PFOA-interacting proteins, we used Differential Scanning Fluorimetry (DSF) to validate these hits. DSF, or a thermal shift assay, measures a protein's melting temperature (Tₘ), which is an indicator of its thermal stability. Ligand binding typically stabilizes a protein, leading to an increase in Tₘ. Conversely, a decrease in Tₘ suggests that a compound destabilizes the protein's structure.

#### **Initial Screening Identifies TYMS as a Top Hit**

Five proteins were selected for an initial screening against a 500 µM concentration of PFOA. These experiments were conducted by the campus Biophysics Core facility. The results demonstrated that two proteins, Thymidylate Synthase (TYMS) and PDGFRA, responded to PFOA. Interestingly, both proteins exhibited a negative thermal shift, indicating that PFOA binding leads to destabilization.

Raw DSF signals showing TYMS (A, C) and PDGFRA (B, D)

![DSF signals for TYMS and PDGFRA](https://static.igem.wiki/teams/5782/results/graph-9.webp)

**Figure 11.** Raw DSF signals showing TYMS (A, C) and PDGFRA (B, D).

|  | No Treatment |  | 500 µM PFOA |  |
| --- | --- | --- | --- | --- |
| Protein | AVG Tm (°C) | STDEV | AVG Tm (°C) | STDEV |
| IDO1 | n.a. |  | n.a. |  |
| METAP2 | n.a. |  | n.a. |  |
| Thymidylate synthase (TYMS) | 50.6 | 0.3 | 46.4 | 0.6 |
| CD140a/PDGFRA | 53.4 | 0.1 | 51.9 | 0.8 |

#### **PFOA Dose-Dependently Destabilizes TYMS**

To further characterize this interaction, we performed a detailed DSF experiment, titrating PFOA against TYMS. For comparison, we also performed titrations with its natural substrate, deoxyuridine monophosphate (dUMP), and its cofactor, 5,10-methylene-tetrahydrofolate (mTHF), both individually and in combination.

Raw DSF signals of TYMS and positive controls as well as PFOA

![Raw DSF signals in response to controls and PFOA](https://static.igem.wiki/teams/5782/results/graph10.webp)

**Figure 12.** Raw DSF signals in response to controls and PFOA.

![DSF titration results](https://static.igem.wiki/teams/5782/results/graph11.webp)

**Figure 13.** DSF titration results showing dose-dependent destabilization.

The results clearly contrast the effect of PFOA with that of the enzyme's natural ligands:

* **Natural Ligands (Controls):** As expected, the cofactor mTHF alone did not significantly alter the Tₘ of TYMS, as its binding is dependent on the presence of the substrate. The substrate dUMP by itself caused a modest increase in Tₘ. When combined, dUMP and mTHF induced a strong, dose-dependent increase in Tₘ, stabilizing the enzyme as they form the natural ternary complex.
* **PFOA:** Conversely, PFOA caused a dose-dependent **decrease** in the TYMS melting temperature. The destabilization became more pronounced with increasing concentrations of PFOA, with the Tₘ dropping from approximately 50°C to below 42°C at the highest concentrations tested.

#### **Conclusion and Biological Implications**

These results demonstrate that PFOA directly interacts with and compromises the structural integrity of TYMS. While the enzyme's natural ligands stabilize its structure to facilitate its catalytic function, PFOA actively destabilizes it.

TYMS is a critical enzyme in the synthesis of thymidine, a nucleotide essential for DNA synthesis and repair. By destabilizing TYMS, PFOA could impair its function, leading to a blockage in the production of thymidine. This disruption of a fundamental cellular process provides a potential molecular basis for the observed toxic effects of PFAS exposure in humans.

### **MST**

Following the successful production of our proteins, we conducted MicroScale Thermophoresis (MST) assays to validate their binding affinities to our target ligand, PFOA, as well as to the positive controls, dUMP and mTHF.

Our initial hypothesis was that our protein, TYMS, would not show significant affinity for mTHF alone, as mTHF is a secondary binding partner that requires the initial binding of dUMP. This was confirmed through our experiments. We observed that both the tagged (GFP-TYMS) and non-tagged TYMS proteins demonstrated a strong affinity for dUMP and the dUMP+mTHF complex. While we were able to observe binding with the GFP-tagged TYMS, we could not determine the binding of the untagged protein under the tested conditions.

#### **Binding Analysis Results**

A series of binding analyses was performed to quantify the interaction between our engineered proteins and various ligands.

* **PFOA Binding:**
  + An initial binding analysis of GFP-TYMS with 2mM of PFOA yielded a dissociation constant (Kd) of 166 mM, indicating a binding interaction.
  + To further investigate this interaction at a lower concentration, the experiment was repeated with 500 µM of PFOA. This resulted in a Kd of 217 µM, confirming a strong binding affinity of GFP-TYMS to PFOA.
* **Control Experiments:**
  + **Negative Control:** A binding analysis between TYMS-GFP and mTHF was conducted. As expected, no binding was observed, confirming that mTHF requires the presence of dUMP to bind.
  + **Positive Control:** The interaction between TYMS-GFP and dUMP was measured. The resulting Kd of 380 nM demonstrated a very high affinity. This affinity was significantly higher than the 7.5 µM reported for the non-tagged protein in existing literature, a result that was better than anticipated.
  + **Positive Control (Complex):** The binding of non-tagged TYMS to both dUMP and mTHF was analyzed. The experiment showed a strong binding affinity with a Kd of 1.93 µM, as expected.
* **Troubleshooting with Tween:**
  + An additional binding check between non-tagged TYMS and 2mM PFOA was performed using Tween, a surfactant added to prevent protein aggregation. In these conditions, no binding was observed. The presence of the surfactant likely lowered the binding affinity.

Given these results, further experiments are necessary to verify the precise binding affinities.

| Date | Name | Description | Kd | Conclusion | Signal:Noise | Click to see in detail |
| --- | --- | --- | --- | --- | --- | --- |
| June 20, 2025 | Exp1 | TYMS-GFP + PFOA Binding Analysis to see if protein would bind to PFOA (target) @ 2mM | 166 mM | TYMS-GFP Binds to PFOA really strongly | 28:1 | [exp1\_final.pdf](https://static.igem.wiki/teams/5782/resultsendpage/exp1-final.pdf) |
| June 20, 2025 | Exp2 | TYMS-GFP + PFOA Binding Analysis to see if protein would bind to PFOA (target) @ 500 uM to see if binding is observed for lower concentrations | 217 uM | TYMS-GFP Binds to PFOA really strongly, even at lower concentrations | 17:1 | [exp2\_final.pdf](https://static.igem.wiki/teams/5782/resultsendpage/exp2-final.pdf) |
| June 26, 2025 | Exp3 | TYMS-GFP + mTHF Binding Analysis to see if protein would bind to mTHF (negative control). Highest conc is 2mM | N/A | TYMS-GFP doesn't bind to mTHF as expected | N/A | [exp3\_final.pdf](https://static.igem.wiki/teams/5782/resultsendpage/exp3-final.pdf) |
| June 26, 2025 | Exp4 | TYMS-GFP + dUMP Binding Analysis to see if protein would bind to dUMP (Positive control) Highest conc is 2mM | 380 nM | The affinity to dUMP is really high, higher than expected (7.5 uM for the non-tagged according to PMC paper) | 3:1 | [exp4-final.pdf](https://static.igem.wiki/teams/5782/resultsendpage/exp4-final.pdf) |
| July 11, 2025 | Exp5 | TYMS + dUMP + mTHF Binding Analysis to see if protein would bind to dUMP and mTHF (Positive controls) (500uM highest conc. for both) | 1.93 uM | TYMS binds to dUMP and mTHF quite strongly, as expected | 4.6:1 | [exp5-final.pdf](https://static.igem.wiki/teams/5782/resultsendpage/exp5-final.pdf) |
| July 11, 2025 | hTS\_PFOA\_Tween.png | TYMS + PFOA binding check, using Tween. Had a lot of trouble getting readable data because aggregates so had to use Tween. PFOA @ 2mM | N/A | Bad signal:noise. Tween probably lowered binding affinity (as surfactants do), but PFOA didn't bind in these conditions | Low | [hTS\_PFOA\_Tween.png](https://static.igem.wiki/teams/5782/resultsendpage/hts-pfoa-tween.webp) |

### **Analytical Ultracentrifugation (AUC) Analysis**

After successfully producing our proteins, we wanted to verify their integrity and oligomeric state. Since this was the first time we were producing the TYMS-GFP fusion protein, it was particularly important to confirm it was being expressed correctly. To determine the molecular weight and native assembly of our proteins in solution, we utilized Analytical Ultracentrifugation (AUC). This technique analyzes the sedimentation behavior of macromolecules under centrifugal force, allowing us to determine their size, shape, and interactions.

We performed sedimentation velocity experiments on both the non-tagged TYMS and the GFP-tagged TYMS. For the GFP-tagged protein, we also analyzed the presence of salt to evaluate its stability under different ionic conditions.

| Protein | Conditions | Conclusion | Mean Absolute Percentage Error (MAPE) | Peak names (click) |
| --- | --- | --- | --- | --- |
| TYMS-GFP | 20 mM Na₂HPO₄, pH 7.5 (no salt) | Exists in a monomer-dimer equilibrium that favors the dimer. | 8.38 % | [Monomer Peak](https://static.igem.wiki/teams/5782/resultsendpage/cell-1-peak-1.webp) [Dimer Peak](https://static.igem.wiki/teams/5782/resultsendpage/cell-1-peak-2.webp) |
| TYMS-GFP | 20 mM Na₂HPO₄, 50 mM NaF, pH 7.5 | Exists in a monomer-dimer equilibrium that favors the dimer. | 6.81 % | [Monomer Peak](https://static.igem.wiki/teams/5782/resultsendpage/cell-4-peak-1.webp) [Dimer Peak](https://static.igem.wiki/teams/5782/resultsendpage/cell-4-peak-2.webp) |
| TYMS | PBS buffer, pH 7.4 | Exists exclusively as a dimer. | 4.01 % | [Dimer Peak](https://static.igem.wiki/teams/5782/resultsendpage/cell-5-peak-1.webp) |

The Mean Absolute Percentage Error (MAPE) values for all our experiments were below 10%, indicating a high degree of accuracy in our measurements. The non-tagged TYMS showed the lowest MAPE at 4.01%, consistent with it being a single, stable dimeric species. The slightly higher MAPE values for the GFP-tagged protein can be attributed to the presence of a monomer-dimer equilibrium, as fitting the data for two distinct species can slightly lower the accuracy for each individual peak. Overall, these results confirm that both of our proteins were produced correctly and exhibit the expected oligomeric states.

![AUC continuous distribution analysis showing protein oligomeric states](https://static.igem.wiki/teams/5782/results/auc-continuous-distribution-2.avif)

**Figure 14.** AUC continuous distribution analysis showing the oligomeric states of TYMS and TYMS-GFP proteins under different buffer conditions.

### **Circular Dichroism (CD) Spectroscopy**

To confirm the secondary structure and folding of our TYMS-GFP fusion protein, we performed Circular Dichroism (CD) spectroscopy. We analyzed the protein under two different buffer conditions: one without salt and one with 50 mM Sodium Fluoride (NaF) to assess if ionic strength affected the protein's stability. The resulting spectra were then compared to a predicted spectrum generated from the protein's PDB structure using the DichroCalc software.

#### **Data Processing and Calculations**

The raw output from the CD instrument is measured in millidegrees (mdeg). To allow for comparison between experiments and with predicted data, this was converted to Mean Residue Ellipticity (MRE), which normalizes for concentration, pathlength, and the number of amino acids in the protein.

The conversion was performed using the following standard formula:

**MRE (deg cm² dmol⁻¹) = (Ellipticity [deg] × Mean Residue Weight) / (10 × Pathlength [cm] × Concentration [g/mL])**

Where:

* **Ellipticity [deg]** is the measured ellipticity in millidegrees divided by 1000.
* **Mean Residue Weight (MRW)** is the total molecular weight of the protein (63853.59 Da) divided by the number of amino acids (563), resulting in an MRW of 113.4 Da/residue.
* **Pathlength** is the internal width of the cuvette, which was 0.1 cm.
* **Concentration** was determined using Monochromator (A280-A330) and converted from g/L to g/mL for the calculation.

Content Comparison (% composition)

| Name | Helix | Antiparallel | Parallel | Turn | Others |
| --- | --- | --- | --- | --- | --- |
| Predicted | 20.1 | 21.3 | 8.4 | 11.3 | 38.8 |
| With Salt | 21.8 | 15.7 | 7.7 | 12.0 | 42.8 |
| Without Salt | 21.1 | 11.2 | 8.4 | 13.1 | 46.3 |

Similarity Using Pearson Correlation (r)

| Sets | Pearson Correlation (r) | Normalized RMSD |
| --- | --- | --- |
| Prediction (x2) and TYMS-GFP (Salt) | 0.9882468886 | 0.0585655818 |
| Prediction (x2) and TYMS-GFP (No Salt) | 0.9898733556 | 0.05372457289 |

![MRE (mean residue ellipticity) CD Spectra](https://static.igem.wiki/teams/5782/results/graph12.webp)

**Figure 15.** MRE (mean residue ellipticity) CD Spectra.

#### **Results and Interpretation**

The CD spectra for TYMS-GFP in both the presence and absence of 50 mM NaF are shown. Both experimental conditions yielded nearly identical spectra, featuring a strong positive peak at approximately 195 nm and two distinct negative peaks around 208 nm and 222 nm. This characteristic pattern is indicative of a well-folded protein containing a substantial proportion of alpha-helical structure, as is expected for TYMS. The overlap of the two spectra demonstrates that the addition of 50 mM NaF did not perturb the secondary structure, confirming the protein is stable under these conditions.

To validate that our protein adopted the correct fold, we compared our experimental data to a theoretical CD spectrum predicted from its amino acid sequence and structure. As shown in the graph, the *shape* of our experimental spectra is an excellent match for the predicted spectrum. This confirms that our TYMS-GFP protein is correctly folded, to further verify we calculated a Pearson Correlation (r) between the experimental data and prediction, and r was 0.99 for both.

However, to match the *magnitude* of our experimental data, the predicted spectrum had to be multiplied by a factor of two. This suggests that DichroCalc used the wrong concentration when generating the spectra.

[CD Raw Data PDF](https://static.igem.wiki/teams/5782/resultsendpage/2025-07-jul-09-cd-tyms-gfp-1-xlsx-raw-data-merged.pdf)

### **UV/Vis**

No measurable enzymatic activity was detected in any of the UV/Vis kinetic assays for TYMS or TYMS-GFP. Across all three attempts — including initial trials using a formaldehyde-containing buffer, repeats with freshly prepared TES buffer without formaldehyde, and reactions with increased protein and substrate concentrations — the absorbance traces remained completely flat at 338 nm and 343 nm, showing no time-dependent changes. As a result, no figures could be generated. The lack of activity suggests that the enzyme may have been inactive, but more likely, there could have been an issue with the UV/Vis instrument and/or the buffer conditions were non-ideal for this kinetic experiment.

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#### **Note on Molecular Dynamics Results**

For detailed molecular dynamics simulations and computational analysis results, please refer to our [Software](/software) and [Model](/model) pages. These results have been separated to avoid repetition and provide a more organized presentation of our computational findings.

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