Peer review of: Engineering regulatory networks for complex phenotypes in E. Coli (2020)

Reviewers' Comments: 
 
Reviewer #1: 
Remarks to the Author: 
This manuscript focuses on identifying mutations in a high-throughput format in transcription 
regulators that can enhance strain tolerance and strain productivity. The researchers first 
identified a global regulatory network comprised of regulatory genes that regulate or interact with 
hundreds or thousands of other genes in E. coli. In order to do this, the PIs employed a previously 
developed CRISPR-based method called CREATE that was developed previously in the Gill lab. By 
using CREATE, it was possible for them to construct a mutagenesis library targeting some of the 
regulators in the global network. From this data, the researchers were able to identify different 
characteristics important for industrial applications such as mutations that improved production 
phenotypes. To construct the network library, 82 regulators were selected to regulate or interact 
with approximately 4000 genes, specifically the active sites of these genes. This library was used 
to demonstrate large fold improvements, showing that the regulatory network library is both 
universally applicable and is an effective method for engineering targeted phenotypes. 
 
Strengths: 
This is a very well written manuscript. When looking at the overall flow , it is both easy to follow 
and succinct. This makes following the overall experimental goals and methodology simple. 
 
In general, the experimental sections were well justified and easy to follow. For example, when 
designing the experiment to test the effect of alcohol-induced stress on the growth and survival 
rate of cells, the researchers explained their reasoning for choosing the mutant FliA_R94K as a 
way to improve cell stability under alcohol stress. 
 
One of the strongest sections of the manuscript was the discussion. The discussion section was 
able to summarize and provide relevant analysis for all of the experiments conducted. Additionally, 
the discussion section was able to explain what was learned from each experiment as well as some 
applications for what was discovered. 
 
The methods section was able to provide justification for why certain strains were used and 
protocols for growing all relevant cultures. 
 
Figures 2-10 are easy to follow, and are full of relevant information. In particular, figure 6 is able 
to take a lot of information and display it in a clear, concise way. I was able to follow this graphic 
very easily. Additionally, figure 2 did a good job of explaining how genome editing efficiency is 
calculated and what it means in terms of the experiments. 
 
Comments for improvement: 
 
Line 46: The authors may want to include the following reference as an example of using 
transcription regulation to improve phenotypes and production: doi: 10.1186/s12934-016-0623-3. 
 
While the grammar of the paper was mostly good, there were mistakes. 
Line 127, therefore should either be at the start of the sentence of eliminated all-together. 
 
Line 135 needs to be rewritten. 
 
Line 150, there is a comma before the word “however” and it needs to be a semicolon. 
 
Line 122: please explain how did you choose these concentrations. Would it be possible to use 
increasingly higher concentrations in order to identify the most critical and important mutations to 
improve tolerance and production? 
 
Line 172 is a run on sentence 
 
Line 298 “benefit” should be plural 
 
The abstract states the paper will discuss the understanding of global regulation in less-understood 
organisms. This is brought up once at the very end of the paper and isn’t really discussed how this 
library can be used in other organisms or even what these organisms could be. I think a small 
paragraph talking about this would make this claim more justified. 
 
The introduction states tolerance trials were used but it does not explain why tolerance trials were 
used. 
 
The section around line 303 is difficult to follow. There is a lot of information included there that 
may be easier to explain in a figure than written in the body of text. 
 
The section “Mutations in OmpR improve isobutanol production” does not make clear why genes 
related to stress resistance being down-regulated would lead to the three mutations not being 
enriched in the tolerance selection. 
 
Figure S1 looks interesting, but it is difficult to decipher any information other than there are many 
gene interactions between the regulators in the regulatory network. If this is all the figure is trying 
to show, it may make more sense to just have this as a sentence as following the lines in the 
figure is next to impossible. 
 
In the Discussion section the authors may want to discuss the possibility of using the identified 
regulatory changes as a guide for further strain improvement. 
 
 
 
Reviewer #2: 
Remarks to the Author: 
The authors present an impressive genetic engineering strategy for rewriting regulator networks. 
They use their previously described CREATE methodology to generate a designed library of 
109,480 mutations of 82 regulators that control the expression for ~4,000 genes. The resultant 
strains were screened or selected for desired phenotypes - e.g., growth in furfural, styrene, 
acetate, isopropanol, and isobutanol and production of isobutanol. 
 
I like the manuscript and think it is deserving of publication. I only have some minor suggestions 
for improvements. First, most of the figures have far too small font to read. Second, in figures like 
1b, individual genes are used as examples of different classes. I think it would be better to just 
use a colored bar or list all genes of that type. Third, some discussion of what types of mutations 
were generated and enriched would be useful - i.e., how many enriched mutations were gain-of-
function and how many were loss-of-function? Fourth, it would considerably strengthen the 
demonstration of this approach to show that mutations could be combined to either further 
improve a growth phenotype and/or metabolite production. Could the library be regenerated in the 
background of the high-performing first mutation? Similarly, or instead, would you pull out 
different mutants and/or still see improvements if you started with a pre-optimized strain from the 
phenotypes of interest? I wouldn’t need to see this, but would view it as a nice demonstration of 
how this strategy can be serially used to achieve further improved phenotypes. Lastly, the 
description of “large fold increases” doesn’t seem appropriate given that the improvement was 
only 1.4-fold in one case.