Our aim is to design a probiotic platform maintaining the balance of metabolites in the gut microbe community, and furthermore the homeostasis of the human body. Leveraging the principle of synthetic biology, we constructed our platform containing sense-response modules based on biosensors and actuators to target two possible conditions: lack of beneficial metabolites and excess of harmful metabolites (Figure 1B). First, we consider butyrate as a beneficial metabolite for human health and constructed the butyrate sensing and bioproduction module. Then, we found indole-3-acetic acid (IAA) to be harmful to human body in excess amounts, and developed the IAA sensing and biodegredation module. Finally, we designed a logic module that uses food input as a prerequisite to activate all the other modules, which is achieved by implementing a bile acid biosensor and connecting the characterized biosensor and the other modules via an AND or NOR gate (Figure 1A). In addition to butyrate biosensing, we also decided to sense AHL 3-oxo-C12:2, which is shown to correlate with major butyrate-producing bacteria. There isn't any existing biosensor identified for this specific AHL, so we used mathematical modeling to screen for possible receptors. Last, we incorporated a safety module with a kill switch to avoid the potential release of the engineered E. coli. In conclusion, abnormal fluctuations in the gut microbe community can be detected and thus attenuated by the activation of specific outputs in our designed system. In this way, the balance of the gut is restored, and there is potential for alleviating gut dysbiosis and related diseases.

Figure 1. Our overall design shown in the form of logic gates. A, The beneficial metabolite module. B, The harmful metabolite module.

The Basic Platform

The basic platform consists of a food-sensing module and AND, NOR logic gate. The primary condition, or input, for the platform to operate is the presence of food. We designed two biosensors for deoxycholic acid (DCA), the main compound of bile acid, which is shown to have noticeable fluctuations before and after dining. In our platfrom, each output is produced under multiple conditions: the presence of bile acid and the presence or absence of the specific metabolites. Thus, we want to incorporate AND and NOR gates to trigger proper outputs in response to different conditions.

Deoxycholic Acid Sensing

Certain bacteria sense bile acid as signals for environmental adaption. We designed one bile acid sensor using TetR-family repressor VFA0359 from Vibrio fischeri and a second bile acid sensor using another TetR-family repressor BreR from Vibrio cholerae.

The VFA0359 bile acid biosensor system contains VFA0359 repressor and the regulated promoter pVFA0359. It has been shown to have low basal activity and 440-fold dynamic range in B. thetaiotaomicron upon induction by DCA (Taketani et al., 2020). In the system, VFA0359 is constitutively expressed and binds to its operator. When DCA is present, VFA0359 binds to DCA and releases the operator DNA, thus enabling downstream gene expression. However, this system has not been tested in E. coli previously. Thus, the challenge of implementing this biosensor is the incompatibility of B. thetaiotaomicron genetic parts in the E. coli genetic context. We need to replace the genetics parts (e.g., promoter, RBS, terminator, etc.) from B. thetaiotaomicron with reliable E. coli parts to adapt this system in E. coli.

As expected, the original sensor designed in B. Thetaiotaomicron shows problems such as undesired offsite transcription when expressed in E. coli as predicted by the Promoter Calculator (LaFleur et al., 2022) (Figure 2A). Additionally, the transcription level at the of the PcfxA promoter is low according to prediction (Figure 2A, blue boxed). Considering that VFA0359 is a TetR-family repressor, which shares homology with the previously reported "jungle express" system by EilR (Ruegg et al., 2018), we modified the jungle express system by replacing the EilR coding frame and EilO operator with VFA0359 CDS and the predicted operator sequences respectively. In our design, the expression of repressor VFA0359 is driven by a constitutive promoter apFAB254 (Mutalik et al., 2013). For the regulated promoter pVFA0359, we first predicted the palindromic operator sequence by SnowPrint (d’Oelsnitz et al., 2024) and inserted two operators in the phage early promoter used by jungle expression system: one overlaps the -35 and -10 hexamer and the other one downstream -10. This system showed onsite transcription and translation at desired rates (Figure 2B). As for the reporter gene, we inserted a GFP downstream pVFA0359, forming a complete biosensor (Figure 2C).

Figure 2. The original and improved design of DCA sensor. A, De Novo DNA Promotor Calculator result of the bile acid sensor designed by Mao Taketani et al. in comparison with the sequence of the sensor; sequence obtained from plasmid pMT447 (Addgene Plasmid #139387). B, New design of the bile acid sensor utilizing the Jungle Express framework and its De Novo DNA Promotor Calculator result; the blue boxes and arrows highlight the desired regions of transcriptional activity. C, Plasmid design of VFA0359-pDCA; created by biorender.com.

The BreR system contains TetR-family transcription factor BreR and its corresponding promoter pBreR containing the operator sequence identified in Vibrio cholerae. We choose to test this as well because it has been validated in E. coli, with an about 230-fold dynamic range to DCA (Beabout et al., 2023), so it's a promising choice for our E. coli probiotic platform. In this system, BreR is constitutively expressed and binds to its operator. When DCA is present, it binds to BreR, releasing it from the operator and triggering the expression of downstream genes.

In our design, we used constitutive promoter J23106 for BreR expression. The pBreR is designed with a modified and optimized version of the J23101 promoter with BreO operator (Figure 3). We inserted GFP after the pBreR to enable characterization of the system.

The DCA sensor will be incorporated into the platform as a permanent part of the input.

Sensing and Producing Beneficial Metabolites

For the beneficial metabolites, we choose to mediate butyrate, an important anti-inflammatory short chain fatty acid that is shown to be low in IBD and depression patients (Grellier et al., 2022). To supplement butyrate when needed, we designed butyrate biosensors based on pHpdH/HpdR and pPcha-Lrp systems to detect conditions lacking of butyrate, they will then activiate expression of Tes4 enzyme from Bacteroides fragilis to enable butyrate bioproduction.

Butyrate sensing

We designed two butyrate biosensors. One is the pPcha-Lrp system from Escherichia coli strain O157:H7 (Bai et al., 2020), the other using pHpdH/HpdR system from Pseudomonas putida (Wang et al., 2023). Both systems are activated in the presence of butyrate. Since we want to identify conditions where butyrate is lacking, we added a pLam-cI NOT gate to reverse the logic in expressing the downstream genes.

The pPcha/Lrp system has already been tested by previous iGEM teams, confirming it could be activated in the presence of butyrate ( BBa_K4442001). It is also native to E.coli, making it a promising sensor. In this system, leucine-responsive regulatory protein (Lrp) forms a complex with butyrate and then binds to pPcha promoter, activating the transcription of downstream genes.

In our design, Lrp is constitutively expressed using the J23101 promoter, while pLam controls expression of desired output genes. When butyrate is not present, cI repressor won't be expressed, so pLam expresses the target genes. When Lrp binds to the pPcha promoter in the presence of butyrate, it activates the expression of cI regulator, which then inhibits promoter pLam, and stops the expression of output genes (Figure 4).

Figure 4. Plasmid design of PpchA-cI-GFP. Created by biorender.com.

However, we found the dynamic range of the pPcha promoter to be relatively small with only around 1-2 folds as reported previously (Serebrinsky-Duek et al., 2023), which which will not trigger significant response to butyrate for therapeutic purpose. Thus, we tested a second butyrate biosensor based on pHpdH/HpdR from Pseudomonas putida. It has been used by previous iGEM team in Bacillus subtilis and Vibrio natriegens (BBa_K2560304), and shown to have up to 41-fold induction in response to butyrate (Wang et al., 2023). When butyrate is present, it binds to HpdR, changing its conformation so it could bind and activate the pHpdH promoter, resulting in expression of downstream genes.

We first validated the butyrate induction of this system. HpdR is constitutively expressed by Plpp1 promoter to regulate the previously optimized pHpdH promoter with deletion of a palindromic sequence (Wang et al., 2021b)(Figure 5). Then, similar to the logic for pPcha, the cI inhibitor is placed downstream pHpdH, with pLam leading output gene expression to achieve the NOT gate (Figure 6).

Figure 5. Plasmid design of HpdR-pHpdH-GFP. Created by biorender.com.

Figure 6. Plasmid design of HpdR-pHpdH-cI-GFP. Created by biorender.com.

GFP is used as the reporter gene for characterizing both systems. After comparing results of both system, we could choose one with induction fold matching our need, and change the output to butyrate-producing enzyme to form the complete module.

Sensing butyrate-producing bacteria

To further specify the condition to produce butyrate, we also considered using AHL molecule 3-oxo-C12:2 that correlates with major butyrate producing bacteria (Landman et al., 2018) as an additional input. Thus, when sensing low amounts of 3-oxo-C12, it indicates a low amount of butyrate-producing bacteria present, meaning more need for butyrate production.

However, there isn't any existing biosensor identified for this specific AHL. Thus, we used mathematical modeling to screen for possible receptors. Upon building this own biosensor, we will incorporate it into the whole circuit.

Butyrate production

Many bacteria have the ability to produce butyrate. We use the Tes4 enzyme, a bacterial acyl-ACP thioesterase from Bacteroides fragilis, for butyrate production. It has been tested by previous iGEM teams (BBa_K3838613), and studies showed that Tes4 expression in BL21 produces butyrate as main product. The metabolic pathway of butyrate production starts with the generation of butyryl-ACP from glucose via the native fatty acid biosynthesis pathway (FASII) in E. coli, followed by the conversion of butyryl-ACP into butyrate through Tes4 (Kallio P. et al., 2014).

Figure 8. Tes4 butyrate production pathway.

We first characterized the Tes4 expression and butyrate production using pET28a plasmid so that the Tes4 expression is induced by IPTG (Figure 9). After validating the function of Tes4 in producing butyrate, we will incorporate it into the food detection module and butyrate biosensor so that butyrate production will be triggered when the butyrate level is low and when the food is present.

Figure 9. Plasmid design of pet28a-Tes4. Created by biorender.com.

Genome Integration

In our platform design, Tes4 is intended to produce butyrate in response to the absence of a specific metabolite. We aim to model the expression of Tes4, which in turn leads to an increase in butyrate levels and its eventual impact on the colonization or growth of our probiotic strain. We want to achieve butyrate production without giving the bacteria too much burden, so we decided to try genome integration of the Tes4 enzyme. To achieve this, we utilized the Lambda Red recombination system to integrate Tes4 and a reporter gene, GFP, into the genome of E. coli MG1655. (Yu et al., 2008) This allows us to track the expression of Tes4 through GFP fluorescence and correlate it with butyrate production and its downstream effects on our probiotic growth and colonization.

This integration approach involves three plasmids. First, pRed-Aspink expresses the Lambda Red recombinase under the control of an arabinose-inducible promoter. The plasmid also constitutively expresses the pigment protein asPink, which serves as a visual marker to confirm the presence of the plasmid in the host cells (Figure 10).

Figure 10. Plasmid design of pRed-Aspink. Created by biorender.com.

The second plasmid, pDual-Select (Figure 11), was designed to facilitate both positive and negative selection. Initially, chloramphenicol resistance is used for positive selection, enabling the identification of colonies with successful transformation of pDual-Select. Following recombination by the Lambda Red system, the expression of SacB protein allows for negative selection, as SacB is lethal in the presence of sucrose. This step ensures that only colonies with the correct integration of the gene of interest into the genome survive.

Figure 11. Plasmid design of pDual-Select. Created by biorender.com.

The third plasmid we constructed was designed to facilitate the integration of the Gene of Interest (GOI) into the genome. This plasmid includes a barcode for colony PCR verification post-integration (Figure 12A).

After testing this system using the barcode, we designed plasmid pReplace-Tes4-GFP to be used with pDual-select and pRed-Aspink, so we could incorporate Tes4 enzyme encoding genes in the bacterial genome (Figure 12B). However, due to time limits, this part needs further construction and verification.

Figure 12. Plasmid design of pReplace and pReplace-Tes4-GFP. Created by biorender.com. A, pReplace. B, pReplace-Tes4-GFP.

Both pDual-Select and pReplace plasmids were assembled with an R6K origin of replication (ori), which can only replicate in E. coli DH5-alpha pir+ strain. This design ensures that when these plasmids are transformed into E. coli MG1655, they cannot replicate, preventing the presence of redundant plasmid copies and avoiding false-positive results during integration verification. In addition, the latter two plasmids contain homology arms to guide presice integration of the GOI into the specific, non-essential regions of the E. Coli MG1655's genome. This ensures that the integration occurs at safe sites, avoiding disruption of the organisms' basic functions and maintaining normal cellular processes. (Park et al., 2022) Finally, by sequentially transforming the three plasmids, it is possible for us to integrate the GOI into the E. Coli MG1655's genome. (See our protocol for detailed steps).

Combination

The complete circuit aiming at supplementing butyrate is as in the figure. The butyrate sensing and AHL sensing circuits are combined with food input module, where the output gene is Tes4, leading to production of butyrate (Figure 13).

Figure 13. The beneficial metabolite module.

Sensing and Degrading Harmful Metabolites

The alternative situation is the excess of harmful metabolites. For this, we decided to focus on indole-3-acetic acid (IAA) produced by L. murinus, which is shown to disrupt intestinal cell linage (Wei et al., 2024) and even cause a higher risk of tumor formation (Hezaveh et al., 2022) in excess amounts. We use the iacR system from Pseudomonas putida 1290 to sense the amount of IAA, and produce IAA-degrading enzymes using the iadCDE gene cluster from Variovorax paradoxus.

IAA sensing

As IAA is also a plant hormone, many bacteria have endogenous biosensors for IAA. Although other systems could also detect IAA, they have problems with ligand promiscuity. For example, the newly discovered MarR_73 from V. paradoxus can bind more than 10 compounds as determined by isothermal titration calorimetry and protein-ligand cocrystallization (Johan et al., 2008). The iacR is a MarR family repressor that is likely to be more specific to IAA due to a smaller ligand binding pocket, so we chose it to avoid interference by other metabolites. iacR loses its repressing function upon binding with IAA, enabling further gene expression.

In our project, we used a mutated version of J23119 to constitutively express iacR (Figure 14). For piacR, we used the SnowPrint website (d’Oelsnitz et al., 2024) to predict the iacO operator sequence, and inserted in lacUV5 derived promoter in place of the lacO operator, which has been proven to be effective in developing other MarR-family transcription factor-based biosensors (Liang et al., 2015). GFP is adopted as a reporter gene, and it will be changed to IAA degrading genes in the final construct to achieve real-time biodegradation of IAA.

Figure 14. Plasmid design of pIacR. Created by biorender.com.

IAA degradation

The iadCDE genes from V. paradoxus is the minimal set of genes capable of efficiently degrading IAA in relatively short amount of time. It has also been tested in E. coli and shown effective (Conway et al., 2022). Specifically, iadC encodes a putative ferredoxin subunit; iadD is a putative large terminal subunit of phenylpropionate dioxygenase, and iadE is a putative small subunit of 3-phenylpropionate dioxygenase.

We first characterized iadCDE expression and IAA degradation using pET28a plasmid, with the iadCDE expression induced by IPTG (Figure 15). Because transcription and translation rates are significantly small when we assess the original plasmid from the article using the RBS calculator, we separated the three genes with three stronger RBSs to enhance the gene expression level. We also designed a second version of this degradation module in which the T7 promoter is replaced with the J23119 constitutive promoter for an even higher gene expression level (Figure 16). After confirming the function of the IAA biosensor and degradation devices, we plan to combine them to form a complete circuit.

Figure 15. Plasmid design of pet28a-iadCDE. Created by biorender.com.

Figure 16. Plasmid design of J23119-iadCDE. Created by biorender.com.

Combination

The complete circuit aiming at degrading excess IAA is as in the figure. The IAA sensing circuit is combined with the IAA degradation module, where the output genes are iadCDE, leading to degradation of IAA in the presence of excessive IAA (Figure 17).

Figure 17. The harmful metabolite module.

Safety module

To ensure our engineered E. coli does not contaminate the environment upon accidental release, we aim to implement a cold-inducible kill switch system that activates self-lysis. Our bacteria is supposed to function in the human gut, which is approximately 37℃ in temprature. However, when it is excreted to the outer environment, there will be a significant change in temperature. Therefore, we designed our safety module based on the TEV ts-18 and TFts-2 proteins, which have corresponding temperature-transition points at 36.5℃, to sense the temperature change and trigger rapid response in killing the engineered strain (Zheng et al., 2019).

In our system, the TEVts is controlled by TFts-repressed PR promoter, while TFts also represses the expression of endolysin Spn1S. At lower temperatures, TEVts cleaves and inactivates TFts, making it unable to bind and repress the PR promoter. As a result, endolysin is expressed and the E. coli suicides (Figure 18).

Figure 18. Plasmid design of safety module. Created by biorender.com.

Reference

Bai, Y., & Mansell, T. J. (2020). Production and Sensing of Butyrate in a Probiotic E.coli Strain. International Journal of Molecular Sciences, 21(10), 3615. https://doi.org/10.3390/ijms21103615

Beabout, K., Ehrenworth Breedon, A. M., Blum, S. M., Miklos, A. E., Lux, M. W., Chávez, J. L., & Goodson, M. S. (2022). Detection of Bile Acids in Complex Matrices Using a Transcription Factor-Based Biosensor. ACS Biomaterials Science & Engineering, 9(9), 5151–5162. https://doi.org/10.1021/acsbiomaterials.2c01006

Byung Jo Yu, Kui Hyeon Kang, Jun Hyoung Lee, Bong Hyun Sung, Mi Sun Kim, & Sun Chang Kim. (2008). Rapid and efficient construction of markerless deletions in the Escherichia coli genome. Nucleic Acids Research, 36(14), e84–e84. https://doi.org/10.1093/nar/gkn359

Conway, J. M., Walton, W. G., Salas-González, I., Law, T. F., Lindberg, C. A., Crook, L. E., Kosina, S. M., Fitzpatrick, C. R., Lietzan, A. D., Northen, T. R., Jones, C. D., Finkel, O. M., Redinbo, M. R., & Dangl, J. L. (2022). Diverse MarR bacterial regulators of auxin catabolism in the plant microbiome. Nature Microbiology, 7(11), 1817–1833. https://doi.org/10.1038/s41564-022-01244-3

d’Oelsnitz, S., Stofel, S. K., Love, J. D., & Ellington, A. D. (2024). Snowprint: a predictive tool for genetic biosensor discovery. Communications Biology, 7(1). https://doi.org/10.1038/s42003-024-05849-8

Grellier, N., Suzuki, M., Brot, L., Rodrigues, A., Humbert, L., Escoubeyrou, K., Rainteau, D., Grill, J.-P., Lami, R., & Philippe Seksik. (2022). Impact of IBD-Associated Dysbiosis on Bacterial Quorum Sensing Mediated by Acyl-Homoserine Lactone in Human Gut Microbiota. International Journal of Molecular Sciences, 23(23), 15404–15404. https://doi.org/10.3390/ijms232315404

Hezaveh, K., Shinde, R. S., Klötgen, A., Halaby, M. J., Lamorte, S., Ciudad, M. T., Quevedo, R., Neufeld, L., Liu, Z. Q., Jin, R., Grünwald, B. T., Foerster, E. G., Chaharlangi, D., Guo, M., Makhijani, P., Zhang, X., Pugh, T. J., Pinto, D. M., Co, I. L., . . . McGaha, T. L. (2022). Tryptophan-derived microbial metabolites activate the aryl hydrocarbon receptor in tumor-associated macrophages to suppress anti-tumor immunity. Immunity, 55(2), 324-340.e8. https://doi.org/10.1016/j.immuni.2022.01.006

Johan H.J. Leveau, Saskia Gerards, Discovery of a bacterial gene cluster for catabolism of the plant hormone indole 3-acetic acid, FEMS Microbiology Ecology, Volume 65, Issue 2, August 2008, Pages 238–250, https://doi.org/10.1111/j.1574-6941.2008.00436.x

Kallio, P., Pásztor, A., Thiel, K., Akhtar, M. K., & Jones, P. R. (2014). An engineered pathway for the biosynthesis of renewable propane. Nature Communications, 5(1). https://doi.org/10.1038/ncomms5731

Kineret Serebrinsky-Duek, Barra, M., Danino, T., & Garrido, D. (2023). Engineered Bacteria for Short-Chain-Fatty-Acid-Repressed Expression of Biotherapeutic Molecules. Microbiology Spectrum, 11(2). https://doi.org/10.1128/spectrum.00049-23

LaFleur, T. L., Hossain, A., & Salis, H. M. (2022). Automated model-predictive design of synthetic promoters to control transcriptional profiles in bacteria. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-32829-5

Landman, C., Grill, J. P., Mallet, J. M., Marteau, P., Humbert, L., Le Balc’h, E., Maubert, M. A., Perez, K., Chaara, W., Brot, L., Beaugerie, L., Sokol, H., Thenet, S., & Saint Antoine IBD Network. (2018). Inter-kingdom effect on epithelial cells of the N-acyl homoserine lactone 3-oxo-C12:2, a major quorum-sensing molecule from gut microbiota. PLOS ONE, 13(8), e0202587. https://doi.org/10.1371/journal.pone.0202587

Liang, C., Xiong, D., Zhang, Y., Mu, S., & Tang, S.-Y. (2014). Development of a novel uric-acid-responsive regulatory system in Escherichia coli. Applied Microbiology and Biotechnology, 99(5), 2267–2275. https://doi.org/10.1007/s00253-014-6290-6

Mutalik, V. K., Guimaraes, J. C., Cambray, G., Lam, C., Christoffersen, M. J., Mai, Q.-A., Tran, A. B., Paull, M., Keasling, J. D., Arkin, A. P., & Endy, D. (2013). Precise and reliable gene expression via standard transcription and translation initiation elements. Nature Methods, 10(4), 354–360. https://doi.org/10.1038/nmeth.2404

Park, Y., Borujeni, A. E., Gorochowski, T. E., Shin, J. & Voigt, C. A. (2020). Precision design of stable genetic circuits carried in highly-insulated E. coli genomic landing pads. Molecular Systems Biology, 16(8), e9584. https://doi.org/10.15252/msb.20209584

Ruegg, T. L., Pereira, J. H., Chen, J. C., DeGiovanni, A., Novichkov, P., Mutalik, V. K., Tomaleri, G. P., Singer, S. W., Hillson, N. J., Simmons, B. A., Adams, P. D., & Thelen, M. P. (2018). Jungle Express is a versatile repressor system for tight transcriptional control. Nature Communications, 9, 3617. Jungle Express is a versatile repressor system for tight transcriptional control | Nature Communications

Taketani, M., Zhang, J., Zhang, S., Triassi, A. J., Huang, Y. J., Griffith, L. G., & Voigt, C. A. (2020). Genetic circuit design automation for the gut resident species Bacteroides thetaiotaomicron. Nature Biotechnology, 38(9), 962–969. Genetic circuit design automation for the gut resident species Bacteroides thetaiotaomicron | Nature Biotechnology

Wang, J., Li, C., Jiang, T., & Yan, Y. (2023). Biosensor-assisted titratable CRISPRi high-throughput (BATCH) screening for over-production phenotypes. Metabolic Engineering, 75, 58–67. https://doi.org/10.1016/j.ymben.2022.11.004

Wang, J., Zhang, R., Zhang, J., Gong, X., Jiang, T., Sun, X., Shen, X., Wang, J., Yuan, Q., & Yan, Y. (2021). Tunable hybrid carbon metabolism coordination for the carbon-efficient biosynthesis of 1,3-butanediol in_Escherichia coli_. Green Chemistry, 23(21), 8694–8706. https://doi.org/10.1039/d1gc02867g

Wei, W., Liu, Y., Hou, Y., Cao, S., Chen, Z., Zhang, Y., Cai, X., Yan, Q., Li, Z., Yuan, Y., Wang, G., Zheng, X., & Hao, H. (2023). Psychological stress-induced microbial metabolite indole-3-acetate disrupts intestinal cell lineage commitment. Cell Metabolism. Redirecting

Zheng, Y., Meng, F., Zhu, Z., Wei, W., Sun, Z., Chen, J., Yu, B., Lou, C., & Chen, G.-Q. (2019). A tight cold-inducible switch built by coupling thermosensitive transcriptional and proteolytic regulatory parts. Nucleic Acids Research, 47(21), e137–e137. https://doi.org/10.1093/nar/gkz785