Peer review of: Majority sensing in synthetic microbial consortia (2020)

Reviewers' Comments: 
 
Reviewer #1: 
 
Remarks to the Author: 
 

Review of the paper: 

Majority Sensing in synthetic microbial consortia 

Summary 
This paper focuses on the development of novel tools for gene expression in microbial co-cultures. The 
authors built a majority-win system and a minority-win system, both of which can vary their gene 
expression profiles based on the proportion of the two co-cultured strains. The paper shows that the 
expression of a fluorescent protein can be either turned ON or OFF by a communication molecule 
produced by a different strain at a given ratio in the consortium. Additionally, the behaviours of the 
consortia can be finely controlled by externally adding inducers. The behaviours of the various consortia 
have been demonstrated across three growth conditions: on solid agar, in small batch culture in 96-well 
plates and in a continuous culture in a microfluidic chamber. The paper also presents a simple model 
that is able to predict the fluorescence levels of both strains when the consortium is seeded with 
different initial strain ratios or supplemented with different inducers. Although the standard deviations 
of the systems appear to be quite large, the means taken across all biological replicates agree with the 
modelling results. 

General comments 
Each figure would benefit from displaying the number of biological replicates used for each experiment. 
Whenever more than 3 biological replicates were used, it would be interesting to know why and how 
this reflects on the robustness and reproducibility of the tools developed in the paper. Providing more 
details on how the system was built would be useful for the reader to comprehend the properties of the 
systems. Currently no information is given on how the constructs were assembled, and how the 
different parts were selected for the final design. Sequences of the genetic parts used in the circuits are 
not provided. It is imperative for reproducibility that all sequences are provided and properly annotated. 

To understand how usable the system proposed actually is for consortia-based expression of different 
genes or metabolic pathways, it is important to investigate whether the co-repressive consortia 
presented in the paper would work if the genes of interest differ more than just by a few point 
mutations. I would thus suggest that the author test the system on one or two genes of interests or 
pathways that are substantially different beyond those currently considered. 

Specific Comments 
The following points and questions are brought to the attention of the reviewer and should be discussed 
as part of the main paper and/or supplementary material before the paper can be further considered for 
publication. 
1. 

Page 3 - Results: 

– 

How were C4-HSL and C14-HSL selected as suitable candidate for the system? Data from 
Kylilis et al. (2018) [1] showed that C4-HSL and C14-HSL have different input-output static 
response curves, which could be a problem for tuning the system. Was this a problem for 
building up the systems that this paper is presenting? If so, was the problem addressed by 
tuning the regulatory elements of the quorum sensing components of the circuit? Please 
provide an explanation for this to help justify the design and tuning of the system. 

– 

How do you justify the need for using strong degradation tags and a lactonase degrading 
C4-HSL and C14-HSL in the system? Was the choice informed by the modelling or previous 
experimental work? Please provide an explanation for this to help justify the design and 
tuning of the system. 

[1] Kylilis, N., Tuza, Z.A., Stan, G.B. and Polizzi, K.M., 2018. Tools for engineering coordinated system 
behaviour in synthetic microbial consortia. Nature communications, 9(1), pp.1-9. 
2. 

Page 5 - Figure 2: How was the normalisation for the strain fraction applied? Perhaps displaying 
the formula could help the reader with the understanding of the (c) and (f) plots more easily. 
Page 6 (lines 124-125) - Majority Wins Consortium: Did you expect the fluorescent protein 
expression to be different for the co-repressive consortium and the control consortium? Did the 
authors compare the strength of the pLac and the pIQ promoters? Again, please provide an 
explanation for this to help justify the design and tuning of the system. 
Page 7 - Figure 3: 

Please include the number of biological replicates in the figure caption (n=…)? 

– 
–  Would it be more representative to display the relative fluorescence/strain fraction instead 

of the relative fluorescence as you did in Figure 2 (c) and (f)? This would show that again 
the increase or decrease of fluorescence is not due to a change in cell numbers. 
It would be useful and informative to add the reference to Supplementary Figure S1 to 
show how the model developed in the paper can predict the cross-over point by tuning the 
level of inducer. 

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– 
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Page 9 - Figure 4: 

3. 

4. 

5. 

6. 

7. 

8. 

9. 

Please the number of biological replicates in the figure caption (n=…)? 
How do you explain that the cyan relative fluorescence always collapses to 0 while the 
model predicts that it should settle to a positive value when the yellow strain fraction is 
small? Please explain. 

Page 11 - Figure 5 (b): How does the paper explain the large standard deviation in the biological 
replicates in (b)? Please include the number of biological replicates in the figure caption (n=…)? 
Page 12: Why does the ratio of cells change through time if you have shown in Figure S3 that the 
different strains have a similar growth rates? Have you been able to observe ratio fluctuations 
during the time course of the plate-reader experiments in 96-well plates? Please explain. 
Page 11 - Co-repressive consortium plasmids: 

–  Why was the sdiA knock-out required for building the co-repressive consortium? Please 

– 

provide an explanation for this to help justify the design and tuning of the system. 
Please include the plasmid maps of the constructs in the supplementary alongside with the 
sequences of the genetic parts used in this paper. 

–  What motivated the choice of using bicistronic RBSs? Was it to increase enzyme 

production? Please provide an explanation for this to help justify the design and tuning of 
the system. 

Page 17 - Confirming strain ratios: The use of a flow cytometer could help quantifying the ratio of 
the two strains in the consortium in a more rapid and high-throughput manner. Does the method 
described in the paper for quantifying strain ratios present advantages compared to using a flow 
cytometer, assuming that as Figure S3 shows, the cells do not suffer from toxicity effects caused by 
the genes being expressed? 

– 

– 

How was the normalized fluorescence calculated in both graphs? Please explain this more 
thoroughly. 
The fluorescence level of the yellow strain (when induced) seems to be around 8 times 
higher than that of the cyan strain. Why are the normalised fluorescence levels so different 
in the yellow and cyan strains? Please explain. 

10.  Supp. Page 9 - Figure S5: 

 
 
Reviewer #2: 
Remarks to the Author: 
In this paper, Razan et al. developed a two-strain co-repressive gene circuit that can sense 
the majority in microbial consortia. The system is composed of two strains, each of which 
produces an intercellular signaling molecule to tune down the production of its orthogonal 
molecules. The circuits are carried by two plasmids. Each plasmid contain three elements: an 
acyl-homoserine lactone synthase (either RhlI or CinI), a transcriptional repressor (LacI or 
RbsR-L), and a fluorescent reporter (sfCFP or sfYFP). The transcriptional repressor RbsR-L 
responds to the quorum sensing (QS) molecules C14-HSL, while LacI responds to C4-HSL. 
Therefore, when the two stains are cultured together, the more numerous strain will produce 
more QS molecules overall and shut down the QS production in the other strain. 
 
First, Razan et al. tested them in monoculture, where addition of the opposite QS molecule 
turned the fluorescence of each strain `OFF'. Then, they mixed two strains in 10% increments 
on agar plate or in liquid media, and quantify the fluorescence of the mixture. The results 
suggested the dominance of majority in the fluorescence pattern. Next, they showed that the 
crossover point at which one strain wins can be tuned by external inducer IPTG or ribose. They 
developed a mathematical model to describe the dynamic behavior of this system. They also 
changed the promoters of the co-repressive consortium to engineer a ‘minority win’ 
consortium. In this consortium, the fluorescence was directly induced by the QS molecules 
produced by the opposite strain. At last, using microfluidic devices, they showed that both 
systems responded to the fluctuating strain fractions. 
 
General comments 
Quorum sensing has been well-established as a mechanism by which bacteria sense and 
respond to changes in their population sizes. This study exploits this property and uses density 
sensing as an important parameter to amplify the dynamics of gene circuits. For instance, in 
the majority sensing system, the relative fluorescence (per unit biomass) is critically 
dependent on the strength of mutual inhibition, which in turn is sensitive to the densities of 
the two strains. 
 
Overall, the results (both modeling and experiments) are comprehensive and convincing in 
terms of demonstrating the concept of majority sensing (or minority sensing). I believe the 
paper can be strengthened by better articulating when and why the majority sensing (or 
minority sensing) can be important or useful. For instance, as noted by authors, at the 
consortium level, the total fluorescence of the consortium depends on the ratio of two strains, 
with or without the co-repressive switch. The key difference is that the single-gene expression 
in the co-repressive switch responds to the change in the ratio; single-cell gene expression in 
the consortium without the co-repressive switch is independent of the strain ratio. As such, it 
is important to explain why this difference is valuable in a broader context. 
 
Other points 
(1) As noted above, a defining feature of the majority (or minority) sensing that the authors 
emphasize is the average response from single cells (e.g., the contrast between FIgure 2c and 
Figure 2f). Thus, it seems more consistent by presenting the data in Figures 3 & 4 in the same 
manner as Figure 2c and Figure 2f. 
 
(2) As it stands, the modeling component of the work seems to primarily provide a 
consistency check for experimental data interpretation. I wonder if the additional insights can 
be gained about certain quantitative properties of the ratio sensing. 

 
(3) For the microfluidics experiments, it was tricky to determine the identity of the cells when 
the fluorescence is off. Here, the author used the following strategy: ’when cyan is ON, 
thresholds are set so that any pixel above a set cyan threshold belongs to the cyan strain and 
any pixel below that threshold belongs to the yellow strain. When both strains are dim, a 
threshold ratio of yellow to cyan fluorescence is set to label the pixels”, which can induce a lot 
of errors. Will it be better to label one the strains with constitutively expressed fluorescence 
like mCherry, so that the two strains can be better distinguished from each other. 
 
(3) I am curious whether this idea of design can be generalized to more complex communities 
that are composed of multiple members instead of two.