iGEM 2025 — Team notredame

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## Notebook for Wet Lab Experiments

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Having trouble viewing?
[Open the notebook in a new tab](https://static.igem.wiki/teams/6036/wetlab/igem-notebook.pdf).

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###### On this page

* [Fleur Design](#fleur-design)
* [Engineered Plasmid Design](#plasmid)
* [Experimental Parts](#parts)
* [Challenges and Solutions](#challenges)
* [Computational Engineering](#computational)
* [Clinical Application & Future Vision](#clinical)
* [References](#references)

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## Fleur Design

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Fleur is a live biotherapeutic product (LBP): a genetically engineered probiotic designed to synthesize and secrete native, wild‑type human GLP‑1 directly within the gastrointestinal (GI) tract. By leveraging synthetic biology to transform a safe probiotic into a localized drug vehicle, Fleur aims to mimic the physiological pattern of GLP‑1 action (pulsatile, meal‑responsive, and locally concentrated) thereby preserving therapeutic efficacy while minimizing the systemic exposure that drives adverse effects. This approach represents a paradigm shift from conventional pharmacology to a new class of engineered, living medicines.

![Background image 5](https://static.igem.wiki/teams/6036/wetlab/project-eg.webp)

*Probiotics Design*

Fleur's core hypothesis is that by delivering wild‑type GLP‑1 locally within the gut, it is possible to achieve therapeutic benefit while avoiding the consequences of systemic overexposure. This is accomplished by engineering a probiotic chassis to function as a controllable, in‑situ biomanufacturing system.

![Background image 5](https://static.igem.wiki/teams/6036/project/glp-1.webp)

*Fleur Design*

### Chassis Selection

The choice of the bacterial host is paramount. We selected *Lactobacillus plantarum* and *Lactobacillus acidophilus*, lactic acid bacteria with several key advantages. Both have a long history of safe human consumption in fermented foods and are designated as Generally Recognized As Safe (GRAS) by the U.S. FDA [1,2]. Both possess natural tolerance to the low pH of the stomach and the bile salts of the small intestine [3]. Finally, a well‑established suite of genetic tools, protocols, and usage exists for the two species [4]. *Reference:* Optibac Probiotics.

![Background image 5](https://static.igem.wiki/teams/6036/project/project-17.webp)

*Optibac Probiotics*

### Theoretical Genetic Construct Design

The therapeutic function is encoded in a custom‑designed genetic expression cassette, stably integrated into the chromosome. Key components include a **promoter** (constitutive initially; inducible in advanced designs to couple expression to meals), a **codon‑optimized GLP‑1 gene** (for Lactobacillus translation efficiency), and a **secretion signal peptide** (e.g., Usp45 from *Lactococcus lactis*) that directs export of GLP‑1 to the gut lumen [5]. Chromosomal integration promotes genetic stability and reduces mobilization risk compared to plasmids [6].

Once consumed, engineered *L. plantarum* reaches the small intestine and secretes human GLP‑1, establishing a high local concentration at its primary site of action. Circulating GLP‑1 is rapidly degraded by DPP‑4, re‑creating a physiological gradient—high in the gut, low in the blood—thereby minimizing systemic side effects.

![Background image 5](https://static.igem.wiki/teams/6036/project/glp-1.webp)

*Fleur Design*

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## Engineered Plasmid Design

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![Background image 5](https://static.igem.wiki/teams/6036/project/project-16.webp)

*Plasmid Design*

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## Experimental Parts

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*Source: iGEM Distribution Kit*

| Plate | Well | Name | Description |
| --- | --- | --- | --- |
| 1 | I1 | amajLime | E. coli Protein Expression – fluorescent protein gene |
| 1 | G1 | tsPurple | E. coli Protein Expression – fluorescent protein gene |
| 1 | O1 | aeBlue | E. coli Protein Expression – fluorescent protein gene |
| 1 | C3 | eYFP | E. coli Protein Expression – fluorescent protein gene |
| 1 | M1 | asPink | E. coli Protein Expression – fluorescent protein gene |
| 1 | A15 | 3\_Colors\_ins\_BBa\_J06504 | E. coli Protein Expression – fluorescent protein gene |
| 1 | K1 | gfasPurple | E. coli Protein Expression – fluorescent protein gene |
| 1 | I15 | GFP (green) | E. coli Protein Expression – fluorescent protein gene |
| 1 | E3 | EiraCFP (cyan) | E. coli Protein Expression – fluorescent protein gene |
| 1 | M3 | mCerulean | E. coli Protein Expression – fluorescent protein gene |
| 2 | G1 | cjBlue | E. coli Protein Expression – fluorescent protein gene |
| 2 | C7 | amilRFP | E. coli Protein Expression – fluorescent protein gene |

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## Challenges and Solutions

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Developing a living medicine presents unique challenges not found in conventional drug development.

### Challenge 1: Dosage Control

**Challenge:** How can one control the dose of a drug that is produced by a self-replicating organism? The population dynamics of the engineered probiotic in the gut are complex.

**Solution – Transient Colonization & Regular Dosing:** Most probiotics, including *L. plantarum*, are transient colonizers cleared within days to weeks. A stable therapeutic effect relies on regular consumption (e.g., daily), which creates a predictable steady‑state population. Dose is controlled by viable cell concentration and dose frequency.

**Solution – Engineered Biocontainment:** Advanced containment circuits (e.g., engineered auxotrophy) constrain bacterial proliferation by requiring a nutrient supplied only in the formulation, absent in the gut [7].

### Challenge 2: Genetic Stability & HGT

**Challenge:** Genetic Stability and Horizontal Gene Transfer (HGT): A primary safety concern is the stability of the engineered gene cassette and the potential for it to be transferred to other resident gut microbes.

**Solution:** Chromosomal integration at safe‑harbor loci reduces horizontal transfer risk and increases stability versus plasmids [6]. Integration sites are chosen away from mobile genetic elements to minimize HGT potential.

### Challenge 3: Host–Microbe Interaction

**Challenge:** Host-Microbe Interaction and Immune Response: Introducing a genetically modified organism could potentially alter its interaction with the host immune system.

**Solution:** Lactobacilli generally trigger tolerogenic/anti‑inflammatory responses [8], but preclinical validation (co‑culture, animal immunology) is required to confirm that human GLP‑1 expression does not provoke adverse inflammatory or allergic responses.

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## Computational Engineering

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We prioritize GLP‑1 amino‑acid positions amenable to mutation using multiple sequence alignment (MSA) and Shannon entropy to improve stability and receptor binding while preserving structure.

#### Identifying Target Sites for Mutation

* **Low entropy (< 1.0):** Highly conserved and critical – avoid modifying.
* **High entropy (> 2.0):** Variable and tolerant – preferred engineering targets.

Our initial focus is on the four most abundant amino acids in GLP‑1: Leucine, Serine, Glycine, and Alanine.

![Entropy Analysis](https://static.igem.wiki/teams/6036/drylab/fig4.webp)

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## Clinical Application and Future Vision

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Fleur will follow the LBP regulatory pathway from preclinical safety/efficacy through phased clinical trials. Beyond GLP‑1 for diabetes and obesity, the platform can deliver other proteins in the gut: IL‑10 for IBD [9], PAL for PKU [10], or anti‑infective peptides targeting pathogens such as *C. difficile* while sparing the broader microbiome [11].

![Background image 5](https://static.igem.wiki/teams/6036/wetlab/project-eg-2.webp)

*Microbiota Impact on Physiology*

Ultimately, the platform enables personalized microbiome medicine: profiling an individual's gut ecosystem to design a bespoke LBP that restores missing functions (Cani, 2017 [12]).

![Background image 5](https://static.igem.wiki/teams/6036/assets/fleur-home-page.webp)

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## References

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1. Murali, S. K., & Mansell, T. J. (2024). Next generation probiotics: Engineering live biotherapeutics. *Biotechnology Advances*, 72, 108336. https://doi.org/10.1016/j.biotechadv.2024.108336
2. Optibac Probiotics. Company resources (accessed 2025).
3. Behera, S. S., El Sheikha, A. F., Hammami, R., & Kumar, A. (2018). *BioMed Research International*, 2018, 9361614. https://doi.org/10.1155/2018/9361614
4. Kleerebezem, M. et al. (2003). Complete genome sequence of *Lactobacillus plantarum* WCFS1. *PNAS*, 100(4), 1990‑1995. https://doi.org/10.1073/pnas.0337704100
5. Kok, J. et al. (2005). Comparative and functional genomics of lactococci. *FEMS Microbiology Reviews*, 29(3), 411‑433. https://doi.org/10.1016/j.femsre.2005.04.010
6. van Asseldonk, M. et al. (1990). Cloning of *usp45*, a gene encoding a secreted protein from *Lactococcus lactis*. *Gene*, 95(1), 155‑160. https://doi.org/10.1016/0378-1119(90)90428-T
7. Steidler, L. et al. (2003). Biological containment of genetically modified *L. lactis* for IL‑10 delivery. *Nature Biotechnology*, 21(7), 785‑789. https://doi.org/10.1038/nbt840
8. Wells, J. M. (2011). Immunomodulatory mechanisms of lactobacilli. *Microbial Cell Factories*, 10(S1), S17. https://doi.org/10.1186/1475-2859-10-S1-S17
9. Steidler, L. et al. (2000). Treatment of murine colitis by *L. lactis* secreting IL‑10. *Science*, 289(5483), 1352‑1355. https://doi.org/10.1126/science.289.5483.1352
10. Isabella, V. M. et al. (2018). Synthetic live bacterial therapeutic for PKU. *Nature Biotechnology*, 36(9), 857‑864. https://doi.org/10.1038/nbt.4222
11. Kurtz, C. B. et al. (2019). Engineered *E. coli* Nissle improves hyperammonemia. *Science Translational Medicine*, 11(475), eaau7975. https://doi.org/10.1126/scitranslmed.aau7975
12. Cani, P. (2017). Gut microbiota — at the intersection of everything? *Nat Rev Gastroenterol Hepatol*, 14, 321–322. https://doi.org/10.1038/nrgastro.2017.54

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###### On this page

* [Overview](#overview)
* [Experimental Strategy](#strategy)
* [Materials & Methods](#materials)
* [Transformation Protocols](#transformations)
* [E. coli Validation](#ecoli)
* [Analytical Methods](#analytics)
* [Quality Control](#qc)
* [Results Summary](#results)
* [Future Directions](#future)
* [References](#references)

## Overview

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The Fleur wet‑lab program developed robust transformation protocols for probiotic *Lactobacillus* species to enable GLP‑1 expression. We combined systematic protocol optimization with rigorous documentation and iterative troubleshooting to establish reliable workflows that underpin our therapeutic goals.

## Experimental Strategy

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##### Research Objectives

1. Optimize *Lactobacillus acidophilus* and *L. plantarum* as GLP‑1 hosts.1
2. Establish reliable transformation methods for probiotics.1
3. Systematically optimize parameters for maximal efficiency.1
4. Implement validation systems for reproducibility.2

##### Target Organisms

* **Primary:** *L. acidophilus* (GRAS), *L. plantarum* (GLP‑1 precedent).3
* **Control:** *E. coli* for protocol validation.1

## Materials and Methods

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##### Media Preparation

###### LB Broth

**Per liter:** 10 g casein peptone, 5 g yeast extract, 10 g NaCl. Dissolve 25 g/L, autoclave 121°C 15 min. For agar, add 15 g agar before autoclave.4

###### MRS Broth

**Per liter:** 10 g proteose peptone no.3; 10 g beef extract; 5 g yeast extract; 20 g dextrose; 1 g polysorbate 80; 2 g ammonium citrate; 5 g sodium acetate; 0.1 g MgSO4; 0.05 g MnSO4; 2 g K2HPO4. Suspend 55 g/L, boil 1 min, autoclave 121°C 15 min. For agar, add 15 g agar.4

##### Antibiotic Stocks

Chloramphenicol: stock 10 mg/mL in ethanol; working 0.5–34 μg/mL; filter sterilize; store −20°C.2

##### Competent Cell Preparation

###### Chemically Competent

Key reagents: 1 M KCl, Glucose, MgCl2, MOPS, 0.1 M acetic acid, 1 M KOH. Target OD600 0.659–0.750; ice ≥30 min; 0.5 M sucrose + 10% glycerol modification.1,2

###### Electrocompetent

1. Grow to mid‑log (OD600 0.4–0.6); chill.
2. Spin 4°C, 4000 rpm, 10 min; wash 3× with ice‑cold 10% glycerol.
3. Resuspend 100–200 μL per aliquot; flash freeze; store −80°C.1

##### Glycerol Stocks

Prepare 50% glycerol (1:1 with culture) and autoclave; use for long‑term storage.4

## Transformation Protocols

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##### Heat Shock (baseline)

1. Thaw competent cells on ice 20–30 min; warm plates.
2. Add 1–5 μL DNA to 20–50 μL cells; mix gently; ice 20–30 min.
3. Heat shock 42°C for 30–60 s; ice 2 min.
4. Recover in LB/SOC 45 min at 37°C; plate 50 μL and remainder; incubate overnight.2

**Optimization outcome:** 20 μL cells + 1 μL DNA + 10 μg/mL Cm; however, *Lactobacillus* transformations were unsuccessful → pivot to electroporation.1

##### Electroporation

| Organism | Voltage | Capacitance | Resistance | Cuvette |
| --- | --- | --- | --- | --- |
| *E. coli* | 1800 V | 25 μF | 200 Ω | 1 mm |
| *L. plantarum* | 2000 V | 25 μF | 400 Ω | 2 mm |
| *L. acidophilus* | 2000 V | 25 μF | 400 Ω | 2 mm |

###### Optimized Procedure

1. Pre‑chill new cuvettes; ensure DNA in low‑salt buffer; thaw cells on ice.
2. Use 100–200 μL cells + 1–3 μL DNA; transfer to chilled cuvette.
3. Pulsing per species; record time constant and voltage.
4. Immediate recovery: +500 μL media; shake 1–2 h; plate 25–300 μL at multiple antibiotic levels; incubate 37°C overnight.1

###### Troubleshooting/Optimization

* Arcing eliminated by **new** cuvettes and correct gap settings.
* Optimal recovery window 1–2 h; >2 h caused lawns; <1 h reduced colonies.
* Best results: *L. plantarum* 200 μL cells, 1989 V, 6.9 ms; *L. acidophilus* 100 μL cells, 1977 V, 8.7 ms.1

## E. coli Validation Experiments

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Validated electroporation hardware and workflow using fluorescent plasmids (M3, C3). Settings: 2500 V, 2 mm, 250 Ω, 25 μF; OD600 0.695–0.750. Initial lawns indicated over‑recovery; tuning to 1–2 h and fresh competent cells improved outcomes.1

## Analytical Methods

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##### Miniprep

1. Pick single colonies to selective media; grow overnight.
2. Harvest, alkaline lysis, and quantify DNA.

##### Restriction Digest

Master mix per sample: 12 μL H2O, 2 μL CutSmart, 0.5 μL ApaI, 0.5 μL HindIII; +5 μL DNA; 37°C for 1–3 h; analyze by gel. Use fresh agarose/TAE to avoid dissolution.1

##### Gel Electrophoresis

1% agarose in 1× TAE; pour ~60°C; include stain. Replace aged reagents to prevent gel failure.

##### Colony Screening

* UV visualization for fluorescence; pick positives.
* Antibiotic‑based selection at 10 and 34 μg/mL.
* Extended incubation for slow‑growing *Lactobacillus*; *L. acidophilus* prefers 42–50°C.

## Quality Control and Validation

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##### Controls

* **Negative:** no‑DNA, untransformed on antibiotic, heat‑inactivated DNA.
* **Positive:** known plasmid, commercial controls, cjBlue (P2‑G1) kit.1

##### Documentation

* Record efficiency, time constants, voltages, counts, morphology, temperature/time optimizations; maintain troubleshooting logs.

## Results Summary

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##### Key Achievements

1. Established electroporation protocols for both target species.
2. Identified optimal parameters for transformation efficiency.
3. Implemented comprehensive control/validation procedures.
4. Built robust troubleshooting methodology.

##### Critical Success Factors

* Species‑specific voltages/capacitance; fresh cuvettes; recovery 1–2 h; extended incubation.

##### Current Status

* Transformations confirmed by growth/resistance; scale‑up prepared for GLP‑1 expression.3

## Future Experimental Directions

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1. **GLP‑1 expression validation:** Western, ELISA, time‑course.3
2. **Secretion optimization:** signal peptides, protease resistance, fermentation.
3. **Functional characterization:** GLP‑1R binding, cAMP, stability studies.
4. **Advanced:** glucose‑responsive regulation, co‑culture/scale‑up, formulation.3

⁂

## References

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1. iGEM‑Summer‑Write‑Up.pdf
2. Bacterial‑Transformation‑Heat‑Shock.pdf
3. Fleur‑Summer‑Wet‑Lab‑Protocols‑Outline‑1.pdf
4. Solutions‑Document.pdf

Additional: AddGene Bacterial Transformation Protocol (2024).

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###### On this page

Dry Lab

[Summary](#dry-lab)
[Data statistics](#dry-0)
[Shannon Entropy Profile](#dry-1)
[Conservation Score Profile](#dry-2)
[Gap Percentage Distribution](#dry-3)
[Sequence Variability](#dry-4)
[Entropy vs Conservation](#dry-5)
[Entropy Histogram](#dry-6)
[Conservation Heatmap](#dry-7)
[Top 10 Conserved vs Variable](#dry-8)
[Gap vs Variability](#dry-9)
[Cumulative Entropy](#dry-10)

Wet Lab

[Cycle 1](#wet-1)
[Cycle 2](#wet-2)
[Cycle 3](#wet-3)
[Cycle 4](#wet-4)
[Cycle 5](#wet-5)
[Cycle 6](#wet-6)

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## Dry Lab Results 💻

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### Summary

The glucagon sequence analysis provides a comprehensive view of evolutionary conservation, variability, and functional constraints within the glucagon family. Using entropy, conservation, and alignment-based metrics, our study characterizes how selective pressure has shaped key structural and receptor-binding residues. Conserved sites correspond to functional anchors necessary for receptor activation, while variable regions likely represent positions that can tolerate mutations without compromising function. These insights help distinguish essential residues from adaptable ones, guiding peptide engineering and comparative evolutionary studies across species.

Overall, our analysis demonstrates that glucagon maintains a tightly conserved structural and functional core with limited sequence flexibility. Variable regions, mostly located near terminal or loop positions, may enable functional fine-tuning across homologs like GLP-1 and GLP-2. Together, these results reinforce the idea that evolutionary conservation reflects the structural and biochemical constraints required for glucagon’s hormone activity, while sequence diversity reflects adaptive optimization across species.

![Data statistics figure 1](https://static.igem.wiki/teams/6036/drylab/fig2.webp)

*GLP-1 Structure*

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### Data statistics

![Data statistics figure 1](https://static.igem.wiki/teams/6036/drylab/result-fig-1.webp)
![Data statistics figure 3](https://static.igem.wiki/teams/6036/drylab/result-fig-3.webp)
![Data statistics figure 4](https://static.igem.wiki/teams/6036/drylab/result-fig-4.webp)

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### I. Shannon Entropy Profile

This plot is fundamental for identifying functionally critical regions in glucagon. Since glucagon is a hormone with specific receptor-binding requirements, positions with low entropy (<1.0 bits) likely represent conserved residues essential for maintaining the peptide's structural integrity or biological activity. The high entropy positions (>2.0 bits) may indicate regions that tolerate variation across species or potentially contribute to species-specific functional adaptations. Understanding this entropy landscape helps prioritize which residues to target for mutagenesis studies or drug design, as conserved positions are likely critical for function while variable positions might offer opportunities for engineering modified glucagon analogs with improved properties.

![Entropy supporting figure 2](https://static.igem.wiki/teams/6036/drylab/result-fig-2.webp)
![Shannon entropy profile figure 5](https://static.igem.wiki/teams/6036/drylab/result-fig-5.webp)

*Shannon Entropy Profile Across Glucagon Alignment*

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### II. Conservation Score Profile

The conservation score provides a normalized measure that directly relates to functional importance. In our glucagon project, positions with conservation scores ≥0.8 are prime candidates for being part of the receptor-binding interface or structurally essential core. The moderate conservation regions (0.5–0.8) might represent positions that maintain the overall fold but allow some evolutionary flexibility. This profile is particularly valuable for comparative studies across different glucagon-family peptides (GLP-1, GLP-2) to identify shared conserved motifs versus unique specificity-determining residues that differentiate their biological functions and receptor specificities.

![Conservation score profile figure 6](https://static.igem.wiki/teams/6036/drylab/result-fig-6.webp)

*Conservation Score Profile (1 - Normalized Entrophy)*

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### III. Gap Percentage Distribution

Gap analysis reveals the quality and reliability of the multiple sequence alignment. Positions with high gap percentages (>50%) indicate regions where sequences are poorly aligned or have insertions/deletions, which could complicate structural interpretations. For glucagon, which has a relatively conserved length across species, high gap regions might represent genuine biological variations or alignment artifacts. This information helps determine which regions of the alignment are most reliable for downstream analyses and which might require manual inspection or exclusion from certain analyses to avoid misleading conclusions.

![Gap percentage distribution figure 7](https://static.igem.wiki/teams/6036/drylab/result-fig-7.webp)

*Gap Distribution Across Glucagon Alignment*

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### IV. Sequence Variability

This visualization directly shows the diversity of amino acid substitutions tolerated at each position. In glucagon research, positions with only 1–2 unique amino acids across 372 sequences represent absolutely conserved features likely critical for function. The highly variable positions (≥10 unique AAs) indicate regions that can accommodate substantial sequence variation, which could be exploited for engineering glucagon analogs with modified properties like increased stability, altered receptor affinity, or reduced immunogenicity. This analysis helps identify both the constraints and opportunities for glucagon protein engineering.

![Sequence variability figure 8](https://static.igem.wiki/teams/6036/drylab/result-fig-8.webp)

*Space Variability - Unique Amino Acids per Position*

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### V. Entropy vs Conservation Correlation

The expected strong negative correlation between entropy and conservation validates the reliability of our alignment and conservation metrics. Any deviations from this expected relationship could indicate interesting biological phenomena. For glucagon, positions that show unexpectedly high conservation despite moderate entropy might indicate co-evolution or compensatory mutations. This analysis provides quality control for conservation calculations and can reveal complex evolutionary patterns that simple conservation scores might miss, potentially uncovering important structural or functional relationships within the glucagon sequence.

![Entropy vs conservation correlation figure 9](https://static.igem.wiki/teams/6036/drylab/result-fig-9.webp)

*Entropy vs Conservation Correlation Analysis*

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### VI. Entropy Distribution Histogram

The overall distribution pattern reveals whether glucagon is predominantly conserved (left-skewed histogram) or variable (right-skewed). A bimodal distribution might indicate distinct functional domains with different evolutionary constraints. Understanding the global conservation pattern helps set appropriate thresholds for defining conserved versus variable positions and informs decisions about how stringent to be when selecting conserved positions for functional studies or designing consensus sequences for synthetic glucagon variants.

![Entropy distribution histogram figure 10](https://static.igem.wiki/teams/6036/drylab/result-fig-10.webp)

*Distribution of Shannon Entropy Values*

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### VII. Conservation Heatmap

The heatmap provides a color-coded overview that quickly identifies conserved blocks and variable regions along the glucagon sequence. Clusters of highly conserved positions likely represent the structural core or critical functional residues, while variable regions might correspond to surface-exposed loops that can tolerate variation. This view helps generate hypotheses about structure–function relationships.

![Conservation heatmap figure 11](https://static.igem.wiki/teams/6036/drylab/result-fig-11.webp)

*Conservation Heatmap Visualization*

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### VIII. Top 10 Conserved vs Variable Positions

Focusing on the extremes of conservation identifies the most promising candidates for experimental investigation. The top conserved positions in glucagon are almost certainly critical for function and should be preserved in engineered variants. Conversely, the most variable positions represent the best opportunities for introducing modifications to create novel glucagon analogs. This targeted analysis efficiently prioritizes residues for site-directed mutagenesis.

![Top conserved vs variable positions figure 12](https://static.igem.wiki/teams/6036/drylab/result-fig-12.webp)

*Top 10 Conserved vs Variable Positions*

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### IX. Gap vs Variability Analysis

This quadrant analysis distinguishes between genuine biological diversity and potential alignment artifacts. Positions in the upper-left quadrant (low gaps, high variability) represent authentic sequence diversity that evolution has tolerated, making them ideal targets for engineering studies. Positions in the lower-right quadrant (high gaps, low variability) suggest alignment problems in otherwise conserved regions, which might require manual correction. This helps ensure that conclusions about sequence conservation and variability are based on reliable alignment data.

![Gap vs variability analysis figure 13](https://static.igem.wiki/teams/6036/drylab/result-fig-13.webp)

*Gap Percentage vs Sequence Variability*

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### X. Cumulative Entropy Distribution

This statistical tool helps set evidence-based thresholds for conservation analysis. By showing what percentage of positions fall below specific entropy values, it provides a quantitative basis for defining conservation cutoffs. For example, if targeting the most conserved 25% of positions for functional studies, this plot gives the entropy threshold corresponding to that percentile, ensuring design decisions are data-driven.

![Cumulative entropy distribution figure 14](https://static.igem.wiki/teams/6036/drylab/result-fig-14.webp)

*Cumulative Distribution of Entrophy Values*

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## Wet Lab Results 🧪

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### Cycle 1: Foundational Skills & Initial Design (August - May 2025)

**Design:** The initial goal was to engineer *Lactobacillus* species. The first step involved designing a plasmid by recodonizing the human GLP-1 gene to have a GC content suitable for expression in both *Lactobacillus* and *E. coli*. Students explored modular cloning techniques and alternative BioBrick designs using the Benchling software.

**Build:** Team members were trained in fundamental microbiology skills, including aseptic technique and how to perform a quadrant streak to isolate single bacterial colonies.

**Learn:** Students learned to navigate the Benchling software and apply principles of synthetic biology to plasmid design. This phase was focused on acquiring the theoretical and practical skills necessary for the project.

**Test:** This initial cycle was primarily focused on design and training, with no formal experimental testing conducted.

![Wet lab cycle 1](https://static.igem.wiki/teams/6036/wetlab/results-1.webp)

*iGEM Engineering Cycle*

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### Cycle 2: Preparing Media & Acquiring Strains (June 2025)

**Design:** The plan was to create chemically competent *Lactobacillus* cells. This required designing and preparing the correct growth media and chemical solutions based on established protocols. The team ordered two key strains: *Lactobacillus acidophilus* (ATCC 4356) and *Lactiplantibacillus plantarum* (ATCC 8014).

**Build:** The team prepared several chemical solutions (1M MgCl₂, 1M KCl, 1M MOPS), specialized *Lactobacillus* MRS broth and agar plates, standard LB agar plates, and a 50% glycerol solution for long-term bacterial storage.

![Wet lab cycle 2](https://static.igem.wiki/teams/6036/wetlab/results-2.webp)

*Compounds Chemcial Structure*

**Test:** Upon arrival, the *Lactobacillus* cultures were found at room temperature. When streaked on standard LB plates, they failed to grow.

**Learn:** The initial failure led to two key insights. First, the viability of the bacteria was compromised, likely due to improper shipping temperatures. Second, *Lactobacillus* has specific nutritional requirements that are not met by standard LB medium. This prompted the switch to the specialized MRS medium, which was successful for growing the bacteria.

![Wet lab cycle 3](https://static.igem.wiki/teams/6036/wetlab/results-3.webp)

*Date of experiment: 6/24/2025*

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### Cycle 3: Chemical Transformation of Lactobacillus (Late June - Early July 2025)

**Design:** The team planned to transform *Lactobacillus* using a standard heat-shock protocol (Prest RbCl₂ method) that is common for *E. coli*. They designed a multi-faceted troubleshooting experiment to test several variables simultaneously: varying DNA concentrations; varying antibiotic (chloramphenicol) concentrations on the plates; using different plasmids from the iGEM distribution kit.

**Build:** Chemically competent cells of both *L. acidophilus* and *L. plantarum* were prepared. Multiple transformation experiments were performed using the heat-shock protocol with different amounts of plasmid DNA and plated on MRS agar with varying drug concentrations.

**Test:** All heat-shock transformation attempts failed. No colonies grew on any of the selective plates.

**Learn:** The standard *E. coli* heat-shock protocol was ineffective for *Lactobacillus*. The cell wall is likely too thick for chemical transformation. Other potential issues identified were incorrect antibiotic dosage, incompatible plasmid origins of replication, or insufficient recovery time. This prompted a strategic shift toward electroporation.

![Wet lab cycle 4](https://static.igem.wiki/teams/6036/wetlab/results-4.webp)

*Date of experiment: 7/9/2025*

| Bacteria | Plasmid | DNA Volume | Bacterial Volume | Plated Volume |
| --- | --- | --- | --- | --- |
| L. plantarum | Plate 1, A15 | 1 ul | 20 ul | 20 ul |
| L. plantarum | Plate 1, A15 | 1 ul | 20 ul | 480 ul |
| L. plantarum | Plate 1, A15 | 3 ul | 20 ul | 20 ul |
| L. plantarum | Plate 1, A15 | 3 ul | 20 ul | 480 ul |
| L. plantarum | Plate 1, A15 | 1 ul | 10 ul | 20 ul |
| L. plantarum | Plate 1, A15 | 1 ul | 10 ul | 480 ul |
| L. plantarum | Plate 1, A15 | 3 ul | 10 ul | 20 ul |
| L. plantarum | Plate 1, A15 | 3 ul | 10 ul | 480 ul |

| Bacteria | Plasmid | DNA Volume | Bacterial Volume | Plated Volume |
| --- | --- | --- | --- | --- |
| L. acidophilus | Plate 1, K1 | 1 ul | 20 ul | 20 ul |
| L. acidophilus | Plate 1, K1 | 1 ul | 20 ul | 480 ul |
| L. acidophilus | Plate 1, K1 | 3 ul | 20 ul | 20 ul |
| L. acidophilus | Plate 1, K1 | 3 ul | 20 ul | 480 ul |
| L. acidophilus | Plate 1, K1 | 1 ul | 10 ul | 20 ul |
| L. acidophilus | Plate 1, K1 | 1 ul | 10 ul | 480 ul |
| L. acidophilus | Plate 1, K1 | 3 ul | 10 ul | 20 ul |
| L. acidophilus | Plate 1, K1 | 3 ul | 10 ul | 480 ul |

*Date of experiment: 7/10/2025*

---

### Cycle 4: Electroporation of Lactobacillus (July 2025)

**Design:** The new plan was to use electroporation, a method that uses an electrical pulse to create temporary pores in the cell membrane. The team researched and designed an experiment with specific electroporation parameters (voltage, resistance, capacitance) for both *Lactobacillus* strains. They also planned to verify the integrity of their plasmids via restriction digest.

**Build:** The team prepared electrocompetent *Lactobacillus* cells. They then attempted numerous transformations using different voltage settings and DNA concentrations. In parallel, they set up restriction digests of their plasmids and attempted to pour an agarose gel for analysis.

**Test:** The electroporation attempts were plagued by technical failures. The use of old cuvettes caused electrical arcing, ruining the experiments. The attempts to prepare an agarose gel also failed, with the gel dissolving in the running buffer. While some later attempts did not arc, the resulting time constants were very low, and initial "colonies" were identified as defects in the petri dishes. Eventually, a few very small colonies of *L. acidophilus* were observed after a long incubation period.

**Learn:** This cycle revealed significant technical hurdles. The equipment (cuvettes) was a major source of failure. The protocol for making agarose gels needed to be revised. Most importantly, the electroporation parameters were still not optimized. The appearance of a few small colonies was a crucial, albeit small, sign of potential success, leading to a proposal to isolate and verify these potential transformants via MiniPrep or to just restart from *E. coli*.

| Setting (Name) | Performed on | Voltage Set | Time Constant | Voltage Read |
| --- | --- | --- | --- | --- |
| L. acidophilus | L. acidophilus | 2000V | 7.8 ms | 1977V |
| L. acidophilus | L. plantarum | 2000V | 5.8 ms | 1986V |
| L. acidophilus | L. acidophilus | 2000V | 7.9 ms | 1976V |
| L. acidophilus | L. acidophilus | 1800V | 4.5 ms | 1783V |
| L. plantarum | L. acidophilus | 1800V | 4.4 ms | 1781V |

*Date of experiment: 7/24/2025*

| Cell Line | Amount DNA | Voltage Set (V) | Time Constant | Voltage Output (V) |
| --- | --- | --- | --- | --- |
| L. plantarum | 5 ul | 2000 | ARC | ARC |
| L. acidophilus | 5 ul | 2000 | 0.1 ms | 1995V\* |
| L. acidophilus | 1 ul | 1800 | 1.0 ms | 1782V |
| L. acidophilus | 2 ul | 1400 | 0.1 ms | 1584V |
| L. acidophilus | 3 ul | 1250 | 0.5 ms | 1242V |
| L. plantarum | 1 ul | 1400 | 1.0 ms | 1378V |

*Date of experiment: 7/29/2025*

![Wet lab cycle 5](https://static.igem.wiki/teams/6036/wetlab/results-5.webp)

*Date: 8/1/2025*

---

### Cycle 5: Shifting to E. coli as a Control (August 2025)

**Design:** Faced with continued difficulties in transforming *Lactobacillus*, the team decided to validate materials and methods using a standard, easy-to-transform lab strain: *E. coli*. The goal was to confirm that the iGEM plasmids and the general transformation protocol were sound. A series of electroporation experiments were designed using a panel of different plasmids from the kit.

**Build:** New batches of electrocompetent *E. coli* were prepared. Transformations were performed with multiple different plasmids, and recovery times were carefully controlled.

**Test:** Nearly all transformation attempts with *E. coli* failed, yielding either no colonies or lawns of untransformed bacteria. This was a critical negative result.

**Learn:** The failure to transform even a simple *E. coli* strain indicated a fundamental problem not specific to *Lactobacillus*. The issue was likely one of the core components: plasmid DNA, cell competency, transformation protocol, or the electroporator itself. This shifted the project into a deep troubleshooting phase.

| # | Voltage | Time Constant | Expected Color | Transformants |
| --- | --- | --- | --- | --- |
| G1 | 5.0 | 2481 | tsPurple | 0 |
| I1 | 5.0 | 2481 | amajLime | 0 |
| K1 | 5.0 | 2481 | gfasPurple | 0 |
| M1 | 5.0 | 2479 | asPink | 0 |
| O1 | 5.1 | 2480 | aeBlue | 0 |
| E3 | 5.1 | 2481 | EiraCFP (cyan) | 0 |
| I15 | 5.1 | 2481 | GFP (green) | 0 |

*Date of experiment: 8/13/2025*

![Wet lab cycle 10](https://static.igem.wiki/teams/6036/wetlab/results-10.webp)

*Date of experiment: 8/16/2025*

---

### Cycle 6: Systematic Troubleshooting of E. coli Transformation (September-October 2025)

**Design:** The team designed a systematic approach to isolate the point of failure. The plan involved:

* Using a known "gold-standard" GFP plasmid from another lab to rule out problems with the iGEM kit DNA.
* Testing multiple different electroporators in other labs to rule out a faulty machine.
* Switching from ampicillin to the more stable carbenicillin antibiotic to rule out degraded selection agents.
* Testing various heat-shock protocols as an alternative to electroporation.
* Using professionally prepared, commercially available competent cells to rule out errors in their cell preparation protocol.
* Trying different recovery broths (SOC medium).

**Build:** A series of transformations were performed, systematically changing one variable at a time: different plasmids, different machines, different antibiotics, different competent cells (both homemade and commercial), and different protocols (electroporation and heat shock).

**Test:** Despite exhaustive efforts, attempts at transformation yielded negligible results. No chromoproteins were being expressed by the bacteria, and while we tested commercial grade competent cells, we sadly did not have any successful transformants.

**Learn:** Due to timing, this cycle demonstrated that a persistent issue was present in our protocol that requires a more thorough exploration of protocols, parameters, and variables. Though systematic elimination of variables did provide momentary solace, the vast difference between having lawns of bacteria that were untransformed and plates with no colonies at all concerned us. Our team must take some time to regroup to assess protocol success and establish a rigorous and complete method for future teams.

![Wet lab cycle 6](https://static.igem.wiki/teams/6036/wetlab/results-6.webp)

*Date of experiment: 8/29/2025*

![Wet lab cycle 7](https://static.igem.wiki/teams/6036/wetlab/results-7.webp)

*Date of experiment: 9/11/2025*

![Wet lab cycle 8](https://static.igem.wiki/teams/6036/wetlab/results-8.webp)

*Date of experiment: 9/19/2025*

![Wet lab cycle 9](https://static.igem.wiki/teams/6036/wetlab/results-9.webp)

*Date of experiment: 9/30/2025*

{% endblock %}