Variability of bacterial behavior in the mammalian gut captured using a growth-linked single-cell synthetic gene oscillator
Preprint posted on November 17, 2018 https://www.biorxiv.org/content/early/2018/11/17/472720
To keep life at a right pace, many biological systems display periodic fluctuations. Some well-known natural biological oscillators include the cell cycle, somite segmentation clock and circadian rhythms. Theoretically, a gene circuit composed of a negative feedback loop would be sufficient to generate periodic behaviors. This is well demonstrated by the first synthetic oscillator – termed repressilator. Repressilator was built in E.coli cells, and is made up of only three proteins that antagonize each other in turn (Figure 1) – LacI inhibits the transcription of the second repressor TetR, which then inhibits CI. Finally, CI inhibits lacI expression to complete the circle. Each bacterium also carries a reporter plasmid which produces GFP upon the repression of tetR. In this way, the progress of the oscillation can be read out by GFP signal intensity.
Figure 1. Repressilator system design. Repressilator consists of three basic elements that repress each other in turn. A reporter plasmid is used to monitor repressilator dynamics. From Elowitz and Leibler., 2000 (see further reading).
Repressilator has been later modified by adding several repressor binding sites (and is therefore termed repressilator v2.0). These modifications significantly improved the regularity and stability of the waves and so massively increased its potential for biological engineering or biomedicine applications. In this preprint, the authors proposed to use repressilator to monitor the growth rate of bacteria in the mammalian gut, and demonstrated the feasibility of this idea by using the mouse as a model. This work offers one of many possibilities that the repressilator system can be used for medical applications.
When a bacterium expressing repressilator v2.0 system is seeded on a plate, only the cells on the periphery will divide. In this manner, the oscillatory expression of fluorescent proteins during bacterial growth will be propagated on the plate radially as the colony expands. Importantly, the phase information can be read out by the radius of the rings. This property would allow one to read the initial phase of the seeding bacterium. In addition, the fact that roughly 14 generations are needed to complete an oscillation enables the growth rate to be theoretically calculated. Practically, the authors first developed a Repressilator-based Inference of Growth at Single-cell level (RINGS) system to analyze the fluorescence ring radius of each colony (Figure 2). By comparing the growth measurements generated by classical ways or by RINGS, the authors validated that the phase of the bacteria can indeed be a good readout for growth, and furthermore, they found that the oscillation periods of repressilator are insensitive to either strain or environmental variations, therefore allowing to apply this system to a wide range of conditions.
Figure 2. RINGs analysis has been used to measure the phase of the colony. Figure 1c from the preprint.
The authors decided to use this workflow to test the bacteria growth rate in the mouse gut. Considering the critical function of the mammalian gut microbiota’s composition in maintaining proper adult physiological status, this system would certainly have great application potential. Experimentally, the authors fed the mice with the repressilator v2.0 expressing bacteria and collected the bacteria back from the fecal samples with roughly every 6 hours over a day, and analyzed the bacteria phase by RINGS. As the first test of the system, the authors compared the growth rate of the bacteria from the mice treated or untreated with streptomycin, with two different bacteria strains, and different time durations. As good news, the repressilator v2.0 system works relatively stable: control and streptomycin groups share similar bacteria growth rate and this behavior is conserved between two bacteria strains, and moreover, the bacteria seem to remain functional for over 16 days.
Next, the authors compared the bacterial growth in mice treated or untreated with Dextran Sulfate Sodium (DSS) which is used as an inflammation model. To increase the sensitivity of the system, the authors synchronized the bacteria before feeding. Interestingly, the authors found that, albeit displaying a similar growth rate to control at the beginning of the experiments (within 5hrs), the DSS treated bacteria population display stronger heterogeneity in the long term (>17hrs). This result suggests that the inflammation condition make the bacteria population become desynchronized.
Specific questions to the authors:
Overall, I think this is a very nice work showing a case of how an artificial oscillatory network can be used for medical treatments. Of course, the application potential of this system can be much broader and more possibilities could be tested. For example, as a developmental biologist in training, I am interested in seeing how this system can be used for developmental contexts. For example, can we adapt the design of this system to generate synthetic somites?
But coming back to the preprint I have three specific questions:
- What do the authors think are the possible explanations for the de-synchronization seen in the inflammation experiment? Could it be the differential niches that the bacteria encounter? Could this result somehow actually reflect the low robustness of this system? In fact, no in vitro experiments have been performed to test whether the DSS treatment would impair the synchronization of the oscillation in vitro.
- Do the authors think that this pipeline can be applied to monitor the other health issues related to the gut? (for example, parasite?)
- Are there any potential risks of using this system? Although there is no doubt that using repressilator v2.0 to measure the growth of the bacteria, especially in the way demonstrated by the authors, is truly feasible, feeding the bacteria to healthy individuals sounds quite scary. What potential risks do the authors think this proposed method can confront?
- Elowitz, M. B. & Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature 403, 335-338 (2000).
- Potvin-Trottier, L., Lord, N. D., Vinnicombe, G. & Paulsson, J. Synchronous long-term oscillations in a synthetic gene circuit. Nature 538, 514-517 (2016).
Posted on: 25th January 2019 , updated on: 26th January 2019Read preprint
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