Results

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Interactive Plasmid Map

The Totally [Fe]rocious project aims to develop an alternative to antibiotics in order to deal with antibiotic-resistant Klebsiella pneumoniae strains. We propose a Trojan horse approach using aerobactin, a siderophore used by pathogenic K. pneumoniae [1], coupled to a gold nanoparticle. The conjugate, once excited with a laser, should be able to induce the death of cells [2]. In the lab, our goal was to express the synthesis pathway of aerobactin in Escherichia coli. This pathway, a nonribosomal peptide synthetase-independent siderophore (NIS) synthesis pathway [3], is composed of four enzymes IucA-D [4]. We ordered the sequence for all four genes in the form of gBlocks from IDT. The gBlocks were assembled in the vector by Gibson assembly and transformed in E. coli. The siderophore would then be produced and purified using a chromatography-based method and the final product would be quantified by a colorimetric method. The purity would be assessed by mass spectroscopy. Once we have obtained aerobactin in a sufficient amount and at an acceptable level of purity, we would proceed with the coupling of the gold nanoparticles to the siderophore. Ultimately, we would test the conjugate between the siderophore and the nanoparticle to see if, once excited with a laser, it can cause cell death of pathogenic K. pneumoniae strains.

Dry-lab – GEM


Our modelling and experimental results together provide a comprehensive understanding of how environmental and genetic factors influence citrate and siderophore production in Escherichia coli. Using genome-scale metabolic models and constraint-based simulations, we observed clear trade-offs between growth and metabolite secretion. While the wild-type strain achieved high growth rates under neutral conditions, enhanced citrate export occurred primarily in acidic and phosphate-rich environments, and siderophore production remained remarkably stable across most tested conditions. These findings reveal how specific nutrient and pH conditions can be exploited to guide metabolite yields without necessarily improving biomass formation[5].

These results have important implications for our project goals. They confirm that citrate and siderophore pathways can be selectively optimized depending on the environmental context, and that siderophore biosynthesis is inherently resilient—a desirable feature for targeted delivery strategies such as our siderophore–gold nanoparticle conjugate. Moreover, the ICDHyr knockout simulations revealed that siderophore synthesis can persist even in the absence of growth, opening possibilities for controlled, non-proliferative production systems.

Not everything went as expected. Some simulation runs initially produced unrealistic flux distributions due to boundary condition errors or missing reaction constraints, forcing us to review model definitions and verify reaction reversibility. Through this process, we learned the importance of model validation, parameter sensitivity testing, and cross-checking with experimental data. These challenges strengthened our understanding of metabolic modelling and improved the reliability of our results.

Overall, these results emphasize the value of combining computational predictions with experimental insight. They also highlight the necessity of transparency, reproducibility, and scientific honesty in modelling—acknowledging uncertainty is as important as presenting strong results. Our work not only supports the feasibility of siderophore-based delivery strategies but also lays the foundation for more predictive and sustainable approaches to metabolic engineering.

Wet-lab - gBlock Amplification


Our first step was to obtain a sufficient concentration of each gBlock to proceed with the Gibson assembly into the extracted and digested pBluescript SK- plasmid. We performed standard PCR protocols following the DNA fragment manufacturer’s recommendations. The results of these PCR amplifications are shown in Figure 1.

The results were satisfactory enough for our needs in the cases of gBlocks 1 and 2. They were both obtained in a sufficient amount to proceed with the Gibson assembly. They were purified on magnetic beads to rid the mixture of the PCR reaction components and the non-specific products. These two were then ready for the Gibson assembly. For gBlock 3, there was too much non-specific amplification and not enough of our goal fragment (around 2,000 bp), meaning that we would not be able to get enough product after purification.

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Figure 1: Gel electrophoresis of our amplified gBlock fragments as well as Invitrogen’s 1kb+ ladder and High DNA mass ladder. Gel stained with EtBr and picture taken under UV.

We tweaked the PCR protocol for gBlock 3 trying Touchdown (TD) and Inverse-Touchdown (iTD) methods as well as removing the PCR Enhancer from the master mix (see Figure 2). Touchdown PCR consists in hybridizing the primers with the template at a higher temperature during the first cycles and gradually lowering it to Tm - 5°C. This technically leads to more specific amplification in the first few cycles. Inverse-Touchdown PCR, which is the opposite of TD, begins the PCR cycles at a lower hybridizing temperature to make sure the primers hybridize to the template and rising to Tm-5°C. This leads to less specific but easier primer hybridization. The aim of this method was to make sure to obtain an amplicon after the first few cycles. In the end, we found that the iTD protocol without enhancer yielded the best results, so we did multiple reactions with that protocol and pooled the results together after a gel-based purification (we still had too much non-specific amplification to do a column purification).

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Figure 2: Gel electrophoresis of our different gBlock 3 protocols as well as Invitrogen’s 1kb+ ladder and High DNA mass ladder. Gel stained with EtBr and picture taken under UV.

Wet-lab - Gibson Assembly & Transformation


With all gBlocks in sufficient concentration, we proceeded with the Gibson assembly into pBluescript SK-, followed by a chemical transformation into competent NEB5α E. coli cells. The transformed cells were plated on XGAL medium for white/blue screening. A few different positive mutants were selected and they were incubated overnight. We then isolated their plasmid and digested each of them using BamHI and EcoRI. When we did the gel electrophoresis to verify the results, the fragments did not correspond with the ones we were expecting (Figure 3). However, we had multiple repeated digestion patterns indicating that we were able to obtain a certain product in a repeatable fashion.

In our interpretation, this means that during the Gibson assembly, a part of our fragments was successfully integrated to the plasmid in a repeatable fashion. However, somewhere in our gBlock sequences was an element that interfered with the designed assembly and led to an unwanted final product that does not contain the full sequence of all three gBlocks. As we also had trouble with the amplification of gBlock 3, we suspect that that fragment is our problem child. To get some insight into what is happening, we decided to send one of our repeated digestions to Flow Genomics for DNA sequencing. The plasmid from clone 4 was chosen for sequencing.

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Figure 3: Gel electrophoresis of our different clone digestions with EcoRI (left), BamHI (middle) and no enzyme (right) as well as Invitrogen’s 1kb+ ladder. Gel stained with EtBr and picture taken under UV.

We suspected that it might be ambitious to attempt a Gibson assembly of three 2kb gBlocks at once on an unestablished protocol on limited time, so we also attempted a different assembly method, opting for a PCR Stitch. However, we only had time for one attempt with this method and the stitch of gBlocks 1 and 2 yielded very little product, and not at our desired length of 4 kb (see Figure 4). The protocol we followed for this stitch is the exact same as the protocol for the amplification of gBlocks 1 and 2, except we used the forward primer of gBlock 1 and the reverse primer of gBlock 2.

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Figure 4: Gel electrophoresis of our PCR Stitch of gBLocks 1 and 2 as well as Invitrogen’s 1kb+ ladder. Gel stained with EtBr and picture taken under UV.

Wet-lab - DNA sequencing


Once the sequencing results came in, we were not shocked to see that the sequenced plasmid was not the expected construction containing all three gBlocks. However, the sequencing did give us some insight on what could have gone wrong. The plasmid extracted from clone 4 comprised 5514bp whereas the expected construction was around 8750bp long. Upon closer inspection, we realized that the sequenced plasmid contained the beginning of gBlock 2 and the beginning of gBlock 3 (see interactive map below). The gBlock 1 and the beginning of gBlock 2 were inserted successfully, but something went wrong and the beginning of gBlock 3 was inserted in the middle of gBlock 2. Since the PCR products used in the Gibson Assembly were all of the right size, the assembly itself was probably not the problem. What could have happened is a recombination event after the transformation of the assembly. The presence of repeated sequences long enough to induce homologous recombination could explain the loss of gBlock 2 and gBlock 3’s sequences.

Future Outlook


These results represent what we were able to obtain in the Wet-Lab for this year’s iGEM cycle. The team’s objective was to tackle the antibiotic-resistant Klebsiella pneumoniae strain using a Trojan horse approach. To further develop the analogy, aerobactin was our wooden horse and gold nanoparticles the soldiers hidden inside it. Our unsuccessful attempts at building an expression vector for the enzymes responsible for aerobactin’s synthesis halted us in our steps. The expression of the enzymes and purification of the siderophore represented the first steps in the Totally [Fe]rocious project. Although we did not manage to meet all our objectives, we managed to gain insight on what went wrong and what can be changed in order to go forward with the next steps for this project. As we mentioned earlier, maintaining the expression vector in E. coli is most likely where we tripped. Because we were not able to express the enzymes to produce the siderophore, we could not try the purification protocol nor the nanoparticle coupling method. We remain hopeful that we will eventually manage to produce aerobactin and that we will get to try to couple the nanoparticles to the siderophore. Ultimately, the goal would be to test to see whether or not the conjugate between the siderophore and the nanoparticle can act as an alternative to antibiotics in the fight against multiresistant K. pneumoniae.

In terms of our next steps, the first thing we will have to solve is the expression system. The Gibson assembly method did not allow us to build a plasmid containing the genes coding for the enzymes required for in vivo synthesis of aerobactin. Since the assembly of all three gBlocks in the vector at once was complicated, we think that assembling them two by two is a more promising way to deal with our issue than redesigning a Gibson assembly from scratch. This is why a PCR stitching method seems like a viable option to us. We tried this method with our current set of primers but it did not work. The use of this method would require us to redesign primers for this specific use. We would assemble the gBlocks two by two, eventually leading to a final assembly step where we would have all three gBlocks lined up in a single fragment and the backbone. This final assembly step can be done by Gibson assembly. The assembly of two fragments instead of four increases our odds of success. Another possible avenue would be to assemble the gBlocks in the backbone by utilizing the recombination ability of Saccharomyces cerevisiae. Although this method could work, it would require us to utilize a backbone compatible with the yeast. Once the assembly is done, we would extract the plasmid, amplify the region containing the genes iucA-D to clone it in a vector compatible with E. coli and transform it into E. coli. If our problem really is related to recombination, we would try to transform the assembly in a cloning strain that has limited recombination activity, such as Stbl from Thermo Fischer or NEB Stable from New England Biolab [6,7].

Interactive Plasmid Map - pBluescript iGEM


Size: 5,514 bp

Feature Information:

Click on a feature to view details

Promoter
CDS/Gene
RBS/Primer
Operator
Origin
Resistance
Misc

If you are interested, here is the complete plasmidic sequence from Flow Genomics:

Plasmid Sequence Box
Plasmid Sequence

Molecular dynamics


Once the various trajectories have been computed, the first step is to assess their stability and convergence towards an equilibrium. Two complementary approaches can be used to evaluate the state of the system. The first focuses on global system properties, monitoring the energy (Fig 6), temperature (Fig 7) and volume (Fig 8). Both the total energy and volume exhibit convergence towards an equilibrium while the temperature remains constant throughout the trajectory. The observed differences in total energy can be largely, if not entirely, attributed to the plug domain (PD), as a greater number of residues naturally contributes to higher structural and interaction energy. This is reflected in a lower potential energy, indicating a more favorable energetic state.

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Figure 6: Evolution of the total energy of the system during the trajectory. The darker central line represents the smoothed trend of the data, while the lighter background points correspond to the raw computed values.
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Figure 7: Evolution of the temperature of the system during the trajectory. The darker central line represents the smoothed trend of the data, while the lighter background points correspond to the raw computed values.
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Figure 8: Evolution of the total volume throughout the trajectory. The darker central line represents the smoothed trend of the data, while the lighter background points correspond to the raw computed values.

The second approach relates to the root mean square deviation (RMSD), a metric that quantifies the average distance between atoms of two superimposed structures, effectively measuring conformational differences within the same protein over time [8]. By focusing exclusively on the TBDT, we can compute the various conformational states observed by the protein. A persistent positive RMSD value is expected due to the inherent flexibility of residues with fewer structural constraints. In this case, all trajectories of the systems containing the plug domain converge towards a RMSD of 2 Å, whereas the trajectory lacking the same domain stabilizes around 1.5 Å. The latter thus remains slightly closer to initial conformation, although both display minimal structural dviation from the predicted model. This behavior is consistent with convergence towards a stable state. Only the first replicate of the system with a plug domain shows an early spike in RMSD, which nevertheless remains below 3 Å.

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Figure 9: Root mean square deviation (RMSD), of all six trajectories, grouped according to their initial system (with or without the plug domain). All trajectories converge toward a low and stable RMSD value, indicating overall structural stability of the complex. The darker central line represents the smoothed trend of the data, while the lighter background points correspond to the raw computed values.

While the available metrics suggest that the protein complex is stable, the duration of each trajectories, with a minimum of 350 ns, may not be sufficient to fully capture the system in its most stable conformation. Indeed, longer computation, on the order of a 1 μs, would likely provide enough data to more confidently assert that an equilibrium has been reached. By focusing subsequent analysis on the second half of the trajectory, the first half is effectively considered as the adaptation period during which the protein complex would adjust to the various lipids in the membrane.

Plug domain: structural implication


Once the system’s stability has been confirmed, a segment from each trajectories exhibiting a consistent low and stable RMSD was selected as the region of interest for subsequent analysis. From these segments, the corresponding average structures were computed to evaluate the root mean square fluctuation (RMSF), a metric similar to RMSD assessing the fluctuation of each residue throughout a trajectory relative to a mean structure [9]. This analysis identifies regions of the protein complex exhibiting higher mobility. In our case, it provides insight into how the PD influences the overall structure stability. Figure 10 presents the RMSF profiles for both systems, along with their associated standard errors. The removal of the PD leads to increased fluctuations within the β-strands of the β-barrel, reflected by higher RMSF values. Additionally, the cytoplasmic regions, particularly the β-turns, exhibit higher RMSF values. Altogether, these results illustrate the crucial role of the PD in stabilizing the overall structure, with a particularly strong influence on the cytoplasmic regions.

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Figure 10: Root mean square fluctuation (RMSF) of the TBDT for systems with (green) and without (purple) the plug domain. The structural annotations corresponding to the α-helices (αX), the β-strands of the β-barrel (βXX) and other β-strands (SX), are also shown. The top row shows domain and structural annotations from the uniprot entry Q6U607, including the Signal Peptide (SP), TonB Box (TBB), Plug domain (PD), Ligand binding site (LBS), TonB Dependant Receptor β-barrel (TBDR β-barrel) and TonB C-terminal Box (TBCB). The second row shows predicted residue positions in the membrane based on PPM [10], the same method used by CHARMM-GUI [11], with green, orange, and blue indicating cytoplasmic, membrane, and extracellular regions, respectively. Overall, removal of the PD increases fluctuations of the various β-strand of the β-barrel, highlighting PD’s role in structural stability.

Comparison of Predicted and Empirical Siderophore-TBDT Interactions


The interaction between the siderophore-Fe3+ complex and the TBDT represent the first step in the uptake of both the iron ions and the siderophore. The initial analysis involves docking the siderophore complex to the TBDT to assess the ligand’s affinity for binding. Figure 11 compares our predicted interaction (second from the top-left) with experimentally determined TBDT-siderophore complexes. Among the structures shown, only 6Q5E represents an interaction between an NRPS-type siderophore and its TBDT, whereas all others correspond to NIS-type siderophores bound to their respective TBDT. Our predicted complex aligns well with the experimentally observed interactions of other NIS siderophores, with the ligand deeply embedded at the base of the ligand binding site (LBS), while the NRPS interaction (6Q5E) appears to occur near the surface of the TBDT structure.

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Figure 11: Predicted and experimentally determined interactions between a siderophore and its corresponding TBDT domain. The predicted complex is shown second from the top-left. The remaining structures, arranged from top-left to bottom-right, are taken from the PDB (6I97, 1BY5, 6Q5E, 2W6T, 3QLB et 6E4V).

A closer examination of the predicted interaction between the siderophore by PyMOL [12] and the TBDT is shown in Figure 12. Among the identified contacts, residue Q343 is the only interaction site originating from the β-barrel and located near to the starting residues annotated for the LBS. The other predicted interactions involve residues from the extracellular loop regions (R403, R404 and S707) while residues Y90, R112 and R116 belong to the β2-β3 loop, the polypeptide segment connecting strands β2 and β3. Both of these strands are part of the central β-sheet of the plug domain. Owing to their proximity to the siderophore complex, residues R112 , R403, R404 in multiple interactions. These residues, through their spatial positioning within the TBDT, their sequential arrangement, and their intrinsic chemical nature, likely play a key role in the initial stages of the uptake mechanism.

Interaction
Figure 12: Predicted contact interactions between the siderophe-Fe3+ complex and the TBDT. Residues TYR90, ARG112, ARG116, GLN343 , TYR403, TYR404, SER707 are predicted to interact with the backbone of the siderophore.

References

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  2. Ha, S., Kim, J., Seo, H. W., Kim, L., Yi, Y. S., Seo, S. E., Kim, K. H., Kim, S., An, J. E., Kim, G. J., Ko, K. C., Jun, S., Ryu, C. M., & Kwon, O. S. (2025). Siderophore-Functionalized Nanodrug for Treating Antibiotic-Resistant Bacteria. ACS nano, 19(5), 5131–5145. https://doi.org/10.1021/acsnano.4c06501
  3. Xie, B., Wei, X., Wan, C., Zhao, W., Song, R., Xin, S., & Song, K. (2024). Exploring the Biological Pathways of Siderophores and Their Multidisciplinary Applications: A Comprehensive Review. Molecules, 29(10), 2318. https://doi.org/10.3390/molecules29102318
  4. Bailey, D. C., Alexander, E., Rice, M. R., Drake, E. J., Mydy, L. S., Aldrich, C. C., & Gulick, A. M. (2018). Structural and functional delineation of aerobactin biosynthesis in hypervirulent Klebsiella pneumoniae. The Journal of biological chemistry, 293(20), 7841–7852. https://doi.org/10.1074/jbc.RA118.002798
  5. Du, B., Yang, L., Lloyd, C. J., Fang, X., & Palsson, B. O. (2019). Genome-scale model of metabolism and gene expression provides a multi-scale description of acid stress responses in Escherichia coli. PLoS Computational Biology, 15(12), e1007525. https://doi.org/10.1371/journal.pcbi.1007525
  6. Thermo Fisher Scientific. (n.d.). Competent cells for cloning unstable DNA. Retrieved October 6, 2025, from https://www.thermofisher.com/us/en/home/life-science/cloning/competent-cells-for-transformation/competent-cells-applications/competent-cells-for-cloning-unstable-dna.html
  7. New England Biolabs. (n.d.). NEB® Stable Competent E. coli (High Efficiency). Retrieved October 6, 2025, from https://www.neb.com/en-us/products/c3040-neb-stable-competent-e-coli-high-efficiency
  8. Cohen, F. E., & Sternberg, M. J. (1980). On the prediction of protein structure: the significance of the root-mean-square deviation. Journal of molecular biology, 138(2), 321-333. https://doi.org/10.1016/0022-2836(80)90289-2
  9. Badar, M. S., Shamsi, S., Ahmed, J., & Alam, M. A. (2022). Molecular dynamics simulations: concept, methods, and applications. In Transdisciplinarity (pp. 131-151). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-94651-7_7
  10. Lomize, A. L., Todd, S. C., & Pogozheva, I. D. (2022). Spatial arrangement of proteins in planar and curved membranes by PPM 3.0. Protein Science, 31(1), 209-220. https://doi.org/10.1002/pro.4219
  11. Jo, S., Kim, T., Iyer, V. G., & Im, W. (2008). CHARMM-GUI: a web-based graphical user interface for CHARMM. Journal of computational chemistry, 29(11), 1859-1865 https://doi.org/10.1002/jcc.20945
  12. Schrodinger, L. L. C. (2015). The PyMOL molecular graphics system. Version, 1, 8.