iGEM 2021 — Team TAU_Israel

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# Team:TAU Israel/Notebook

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# Lab Notebook

* [Preparations](#handle2)
* [POC assay- calibration](#handle3)
* [ORF optimization for *B. subtilis*](#handle4)
* [ORF optimization for  *E. coli*](#handle5)
* [Promoters Optimization](#handle6)
* [GOI abundance test](#handle7)
* [Primers](#handle8)

## Introduction

On this page you can find all the procedures for all the experiments we performed, categorized into six categories: (1) preparations, (2) POC assay- calibration, (3) ORF optimization for *B. subtilis* , (4) ORF optimization for  *E. coli*, (5) Promoters optimization and (6) GOI abundance test. Additionally, you can find a document with all the primers we used. The experiment’s protocols can be found on the experiments page, while the adjustments to the protocols are addressed on this page in the lab notes of the experiment. The lab notebook was written using Benchling.

For raw data of POC assay obtained from the plate-reader [click here](https://static.igem.org/mediawiki/2021/9/98/T--TAU_Israel--Data.zip)

[ ]

## Preparations

If the pdf doesn't show, [click here](https://static.igem.org/mediawiki/2021/1/15/T--TAU_Israel--preparations.pdf) to download the pdf!

[ ]

## POC assay- calibration

If the pdf doesn't show, [click here](https://static.igem.org/mediawiki/2021/2/2d/T--TAU_Israel--fluorescence-_calibration.pdf) to download the pdf!

[ ]

## ORF optimization for *B. subtilis*

If the pdf doesn't show, [click here](https://static.igem.org/mediawiki/2021/e/e3/T--TAU_Israel--B.subtilis_optimized_ORF.pdf) to download the pdf!

[ ]

## ORF optimization for *E. coli*

If the pdf doesn't show, [click here](https://static.igem.org/mediawiki/2021/f/f0/T--TAU_Israel--E._coli_optimized_ORF.pdf) to download the pdf!

[ ]

## Promoters Optimization

If the pdf doesn't show, [click here](https://static.igem.org/mediawiki/2021/8/87/T--TAU_Israel--Promoters_optimization.pdf) to download the pdf!

[ ]

## GOI abundance test

If the pdf doesn't show, [click here](https://static.igem.org/mediawiki/2021/2/20/T--TAU_Israel--Fusion_PCR_Calibration.pdf) to download the pdf!

[ ]

## Primers

If the pdf doesn't show, [click here](https://static.igem.org/mediawiki/2021/2/2b/T--TAU_Israel--Primers.pdf) to download the pdf!

[![](https://static.igem.org/mediawiki/2021/0/0e/T--TAU_Israel--promega_logo.svg)](https://www.promega.com/)

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### Communique

Hungry for more? We would love to hear from you!

Haim Levanon 55, Tel Aviv, Israel

igem.tau.21@gmail.com

Communique © 2021

[![](https://static.igem.org/mediawiki/2021/8/8b/HQ_page_logo.jpg)](https://2021.igem.org)

# Team:TAU Israel/Engineering

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# Engineering Success

![](https://static.igem.org/mediawiki/2021/1/12/T--TAU_Israel--fig1-eng.png)
**Figure 1:** The engineering cycles

In our project, our long-term goal is to broaden the achievable horizons when it comes to microbiome engineering, and to progress biological development in the real world. Due to this ambitious ideology, we had to conduct extensive research on every building block and component inserted, then analyse them together, and finally design the engineering process in a user-friendly configurable manner.

Each one of these three engineering cycles was carefully designed according to the previous cycle, greatly impacting the following one and our general insights regarding this approach. In this section, we aim to introduce you to the engineering process that happened behind the scenes, and expose the various challenges while clarifying the background for our strategies.

## Part 1: Designing multi-organism optimizations

As previously stated, in order to achieve our premise, we decided to model and selectively optimize the core biophysical processes related to gene expression. Most of these processes have been modeled to some degree for a single organism, however this knowledge must be used for the following 3 consecutive goals:

1. **Learn**: Finding variability among the species in the microbiome
2. **Design**: Understanding multiorgnaism preferences and trends
3. **Build**: Re-designing genetic components according to the novel information - implementing both optimization and deoptimization simultaneously
4. **Test** out model

This process is demonstrated in all 3 main models:

## Entrance into the cells

![](https://static.igem.org/mediawiki/2021/5/5c/T--TAU_Israel--fig2-eng.png)
**Figure 2:** Interfering restriction sites

Our purpose was to optimally edit restriction sites. This problem can easily be solved for one organism, as the degrees of freedom within different genetic components are known.

However, in our approach, we wanted to insert sites present in the deoptimized organisms and avoid any sites present in the optimized organisms.

1. **Learn**: We characterized the [restriction enzyme database](http://rebase.neb.com/rebase/rebase.html) in order to characterize the origin of different restriction sites and piece together organisms with their correspondingly recognized restriction sites.
2. **Design**: We designed a dual-organism model for one optimized and one deoptimized species, coordinating synonyms insertion and avoidance of restriction sites.
3. **Build**: Our problem was that different restriction sites interfere with each other, causing conflicts and dependencies between different sites and organisms. Thus, we decided to build a novel algorithm and implement it to take the variation in placing and number of sites from each organism into account.
4. **Test**: When applying this model, it did not insert sites from the optimized organisms (despite rare cases characterized in the software log files).

## Transcription

Different organisms utilize different transcription factors in order to promote the transcription initiation, as each of these factors recognises different sets of genomic sequences described as “motifs”.

1. **Learn**: We reviewed different motif-finding tools and characterized them in our future contribution.
2. **Design:** We picked the MEME-suite tools and built a dual-organism model, that crosses the set of motifs that are selectively found in one organism compared to the other, with the set found in promoters compared to intergenic regions
3. **Build**: We used the selective-motif principle in order to devise a novel algorithm described in the transcription optimization model that can take those two needed features (efficiency and selectivity) into account. This can be done using the motif set construction strategy.
4. **Test**: We tested our model in our POC; Go check our [results!](https://2021.igem.org/Team%3ATAU_Israel/Results)

## Translation

During evolution, cellular machinery has adapted to translate certain codons more optimally than others. Different genetic information can be used in order to infer the profile of preferred codons (also known as the codon usage bias, or CUB) of each organism.

1. **Learn:** Researched different CUB - we looked into traditional codon harmonization techniques such as CAI (codon adaptation index) and tAI (tRNA adaptation index), and even examined a new approach using ribo-seq profiling called TDR (typical decoding rate).
2. **Design**: We devised an initial selective optimization strategy similar to the single codon approach, and examined two different implementations- optimization of the score difference between the optimized and deoptimized organism, and optimization of the score ratio.
3. **Build**: We took the mCherry fluorescent gene and optimized translation in *B. subtilis* while simultaneously deoptimizing it for *E. coli*. We did this for all specified CUB measurements.
4. **Test**: Tested the results in the wet lab, and found out that tAI worked really well. (See [Results](https://2021.igem.org/Team%3ATAU_Israel/Results))

## Part 2: Analysis

Due to the computational and biological novelty of our two goals - whole microbiome design, and simultaneous optimization and deoptimization - this engineering cycle was not conducted perfectly; we had to revisit the steps according to feedback and due non satisfactory results. However, we recognise the importance of this imperfect learning and engineering cycle, as it exhibits the wide potential of analysis and advancements that can be made to and using our models.

Additionally, we hope that this thinking process will inspire future iGEM teams to not to stick to traditional methods and strive to develop basic assessment strategies for their innovations, as well as broaden the arsenal of computational synthetic biology analysis mechanisms to fit the rapid evolution of this field.

## Initial cycle

This engineering cycle bridges the individual models with the analysis process

1. **Learn**: when analysing our experimental results we examined the performance of the designed sequences in the lab, we performed many analysis processes which you can find in our results. A consistently evident conclusion was that the two best codon usage bias scores for our optimization were CAI and tAI (Figure 3):
   ![](https://static.igem.org/mediawiki/2021/4/47/T--TAU_Israel--fig3-eng.png)
   **Figure 3:** Optimized fluorescence ratio shows optimization success

   As seen in this graph, the fluorescence of *B. subtilis* was nearly 12 times better than *E. coli* for CAI optimization, and 9 times better in TAI-ration.

   After finding out that tRNA optimizations worked exceptionally well, we made an effort to find the optimal way to precisely calculate without impacting the number of organisms that can be optimized by the software. We compared the tGCN (tRNA gene copy numbers) from NCBI and the [GtRNAdb](http://gtrnadb.ucsc.edu/) only to find that the quality of the tRNA was worth it’s limitations.
2. **Design**: Devised comparison strategies between CUB measurements. Our initial optimization score was a traditional implementation of the statistical Z-score.

**Click here** to read more about our initial optimization analysis.

**Optimization index 1 - Z-score ratio:**

Z-score:

Z-score (standard score) describes the number of standard deviations by which a specific value is distant from the mean value of an interest population. Negative Z-score indicates the original value is below the mean, and positive Z-score indicates the original value is above the mean. The standardization is given by the formula:

![](https://static.igem.org/mediawiki/2021/3/3e/T--TAU_Israel--Eq1-eng.jpeg)
**Equation 1**

Where μ and σ  are the [mean](https://en.wikipedia.org/wiki/Mean) and the [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) of the population, correspondingly [ref].

Z-score ratio:

The calculation is performed according to the following algorithm:

The following steps should be performed for each one of the organisms in the community:

I.                    Creating the population by calculating the CUB for each gene of the organism.

II.                  Calculating the CUB of the original sequence, and standardizing it according to the population.

III.                Calculating the CUB of the optimized sequence, and standardizing it according to the population.

Let Y be an organism in which we want to optimize the gene expression, and let Z be an organism in which we want to deoptimize the gene expression. The calculation is given by the following formula:

Optimization index 1 =

![](https://static.igem.org/mediawiki/2021/f/f7/T--TAU_Israel--eq2-eng.png)
**Equation 2**

Where:

ZY' is the Z-score of the CAI of the optimized sequence according to organism Y population,

ZZ' is the Z-score of the CAI of the optimized sequence according to organism Z population,

ZY is the Z-score of the CAI of the original sequence according to organism Y population,

ZZ is the Z-score of the CAI of the original sequence according to organism Z population.

In case of multiple organisms, step (2) should be performed for each pair of Y and Z organisms, and the returned index would be the mean value.

1. **Build:** multi organism optimization functions based on single codon optimizations.
2. **Test:** we performed novel whole-microbiome performance analysis of the *Arabidopsis* soil microbiome, provided in our software analysis.

At this point, we were very puzzled - our experimental POC was beyond our expectations, but somehow the scale-up from two organisms to a complete microbiome has completely failed. The performance test results were disappointing, and different consultations have made it clear that although our score is statistically accepted, it is counter-intuitive and hard to analyse.

This was one of the major challenges we faced during our project, as our computational results simply did not align with the traditional codon harmonization method. We decided to revisit step 2:

## Design - second attempt

We re-designed our optimization score (which is the current version) to be more user friendly and biologically correct, by fitting the mathematical nature of the measurement to the behavior of selective translation efficiency optimization.

## Test - second attempt

After testing the new statistical optimization score on our *Arabidopsis* microbiome for the correlation between evolutionary distance and model performance, we still received problematic results:

![](https://static.igem.org/mediawiki/2021/7/71/T--TAU_Israel--fig4-eng.png)
**Figure 4:** Correlation between optimization score and evolutionary distance (measured as number of different positions in the sequence alignment of the 16S rRNA).

For more information about this analysis revisit the software analysis page.

In Figure 4:

* Red points = optimization failed
* Green point = optimization succeeded

It is clear from this graph that the optimization is not very successful in most cases, and optimization chances are relatively low for closely related organisms.

After this encounter, we understood that the single codon optimization does not take into account the significance of the codon usage bias score change of the protein in the context of the whole microbiome. I.e., if an organism has very close codon usage bias scores for all proteins, even a small change in CUB can be significant.

For example (**Figure 5**):

![](https://static.igem.org/mediawiki/2021/1/1b/T--TAU_Israel--fig5-eng.png)
**Figure 5:** The standard deviation of CAI scores of the proteome of the bacteria in A.thaliana root microbiome which was used for software analysis.

As seen in Figure 5, there is a order of magnitude difference in the standard deviation of CAI scores of the endogenous proteins for different bacteria present in the *A. thaliana* root microbiome that was analysed.

We the returned to the third step:

## Build - third attempt

We devised a whole microbiome hill climbing optimization approach in order to achieve our goal.

## Test - third attempt

Once we finished implementing the new optimization strategy, we analysed the results and were able to both improve correlation between the performance and evolutionary distance and improve overall optimization capabilities, especially for closely related organisms. Review our results for all optimization strategies in the software analysis page.

[![](https://static.igem.org/mediawiki/2021/0/0e/T--TAU_Israel--promega_logo.svg)](https://www.promega.com/)

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### Communique

Hungry for more? We would love to hear from you!

Haim Levanon 55, Tel Aviv, Israel

igem.tau.21@gmail.com

Communique © 2021

[![](https://static.igem.org/mediawiki/2021/8/8b/HQ_page_logo.jpg)](https://2021.igem.org)

# Team:TAU Israel/Experiments

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  + [Sponsors](https://2021.igem.org/Team%3ATAU_Israel/Sponsors)
  + [Medals](https://2021.igem.org/Team%3ATAU_Israel/Medals)

Layout for Experiments page

# Experiments

Experiments

Protocols

## Experiments

On this page, you can read about the two main experiments we conducted:

(1) POC assay

(2) GOI abundance test via fusion-PCR

You can go through the main steps and procedures we performed in each experiment. (for more detailed information go to our Protocols or [Lab notebook](https://2021.igem.org/Team%3ATAU_Israel/Notebook) page).

Both of the experiments were conducted on two species of bacteria - *E. coli* and *B. subtilis* and the tested GOI was mCherry.

### POC assay

We initially calibrated our system conditions, such as the type of minimal medium in which both bacterial species grow up to the stationary phase, bacterial density, experiment duration, and measurements parameters. In parallel, the modeling team designed several optimized and deoptimized gene components to be tested.

Once the system was calibrated and new versions of ORF and promoters arrived, we were ready to go!

First, the plasmid containing the original mCherry gene was purified from the bacteria we got from Avigdor Eldar’s lab. Then, we performed PCR to linearize the plasmid with primers complementary to the gBlocks (software-designed gene components), and that exclude the sequence to be modified (figure 1).

![](https://static.igem.org/mediawiki/2021/e/e6/T--TAU_Israel--Fig1-experiments.png)
**Figure 1:** Construction of the tested gene components into our plasmid.

Original GOI or its promoter (marked in blue) was substituted with their modified versions that were obtained by our software (gBlocks), via PCR and Gibson assembly.

Next, a portion of PCR products was examined by DNA electrophoresis, and the rest has treated with DpnI in order to remove the remaining of the original plasmid.

Then, Gibson assembly was performed with the linearized plasmid and its matching gBlock to establish the final plasmid. This plasmid was transformed into competent bacteria.

Colony PCR was conducted to screen transformed colonies for the positive inserts, which was finally confirmed by Sanger sequencing. Following sequence validation, plasmids were prepared from positive colonies and then transformed into our bacterial strains. Transformed colonies were picked and glycerol stocks were prepared. When completed, we proceed with the assay to measure whether our software-designed sequences alter gene expression as predicted. See the experimental design [here.](https://2021.igem.org/Team%3ATAU_Israel/Proof_Of_Concept)

It is important to note that we successfully managed to construct plasmids with optimized ORFs for *B. subtilis*, which are deoptimized for *E. coli*. Plasmid construction for the reciprocal modification- ORFs optimization for *E. coli* and deoptimization for *B. subtilis*, was failed. When we screened for insert-positive colonies, we found only colonies containing the original vector, except for one ORF version. We suspected that DpnI treatment didn’t work. Due to iGEM’s time frame constraints, we decided to continue with another gene component, and we constructed plasmids with new promoters. In this case, even if DpnI treatment was useless, the original vector was eliminated by extracting the linearized vector directly from the gel, see the [notebook.](https://2021.igem.org/Team%3ATAU_Israel/Notebook)

![](https://static.igem.org/mediawiki/2021/e/ef/T--TAU_Israel--Fig2NEW-experiments.jpg)
**Figure 2:** Main steps of the fluorescence assay experiment

### GOI abundance test

To fully verify if our technological approach is capable of restricting plasmid abundance within microbial communities despite HGT, we implemented the fusion-PCR method. To date, we are still calibrating the primers sets necessary for the fusion reaction. For detailed in-silico primers and fused-product design go to protocols.

* [Bacterial transformation](#handle2)
* [Chemically competent cells](#handle3)
* [Cloning](#handle4)
* [Fluorescence measurement assay](#handle5)
* [Fusion-PCR](#handle6)
* [General protocols](#handle7)

## Protocols

On this page you can find all the protocols for all the experiments we performed.

[ ]

## Bacterial transformation

If the pdf doesn't show, [click here](https://static.igem.org/mediawiki/2021/f/f4/T--TAU_Israel--Bacterial_transformation.pdf) to download the pdf!

[ ]

## Chemically competent cells

If the pdf doesn't show, [click here](https://static.igem.org/mediawiki/2021/8/8b/T--TAU_Israel--Chemically_competent_cells.pdf) to download the pdf!

[ ]

## Cloning

If the pdf doesn't show, [click here](https://static.igem.org/mediawiki/2021/8/87/T--TAU_Israel--Cloning.pdf) to download the pdf!

[ ]

## Fluroscence measurement assay

If the pdf doesn't show, [click here](https://static.igem.org/mediawiki/2021/e/e1/T--TAU_Israel--Fluorescence_measurement_assay.pdf) to download the pdf!

[ ]

## Fusion-PCR

If the pdf doesn't show, [click here](https://static.igem.org/mediawiki/2021/d/d8/T--TAU_Israel--Fusion-PCR.pdf) to download the pdf!

[ ]

## General Protocols

If the pdf doesn't show, [click here](https://static.igem.org/mediawiki/2021/4/4a/T--TAU_Israel--General_protocols.pdf) to download the pdf!

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### Communique

Hungry for more? We would love to hear from you!

Haim Levanon 55, Tel Aviv, Israel

igem.tau.21@gmail.com

Communique © 2021

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# Team:TAU Israel/Results

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Layout for Results page

# Results

* [Translation Optimization Results](#handle2)
* [Transcription Optimization Results](#handle3)
* [Discussion and future prospects](#handle4)

[ ]

## Translation Optimization Results

## Background

We re-coded the open reading frame of the optimized gene in order to consider the microbiome’s codon preferences, thus enhancing the translation efficiency of the optimized organisms while decreasing translation efficiency for the deoptimized organisms. You can read more about this in the Translation Optimization Modeling Section. We then constructed each of the modified sequences into a plasmid, which was transformed into our chassis organisms to test their effect on protein expression and growth rates.

All the presented data is the average of three biological replicates,  performed with three technical replicates of each ORF.

## Computational Analysis

As explained in our engineering cycle, the sequences for this analysis were produced using a previous version of our software.

We engineered the fluorescent mCherry gene to optimize The translation for *B. subtilis* while deoptimizing it for *E. coli*, and test different CUB measurements along with optimization techniques.

Specifically, we calculated 3 CUB profiles: CAI, tAI, TDR.

We then optimized the sequences according to 2 different optimization strategies:

* **R optimization:** the new score for each codon is calculated as the ratio between the CUB score in *B. subtilis* and the CUB score in *E. coli*. The synonymous codon with the highest ratio score is selected.
* **D optimization:** the new score for each codon is calculated as the difference between the CUB score in *B. subtilis* and the CUB score in *E. coli*. The synonymous codon with the highest difference score is selected.

In order to visualize the differences between the sequences, we created the following plots:

![](https://static.igem.org/mediawiki/2021/f/f7/T--TAU_Israel--fig1-trans.png)
**Figure 1:** Multiple sequence alignment, highlighting different positions

![](https://static.igem.org/mediawiki/2021/9/97/T--TAU_Israel--Figure_2a-trans.png)
**Figure 1.2a:** The number of different positions (out of 711 in the full coding sequence)

\*Note: E stands for *E. coli* and B for *B. subtilis*

During our measurement process, both OD and fluorescence were measured once every 20 minutes using a plate reader. This rate satisfied our need in finding the growth curve, however, it was not frequent enough to receive derivations of manageable qualities due to random noise in measurements. Thus, we had to devise a new automatic strategy to find the logarithmic growth phase.

During the logarithmic growth phase, the growth is optimal and linear, thus the growth rate is maximal. In order to find that region, in each iteration, a point from one of the edges of the graph was removed, and a linear trendline was fitted to the curve (Fig. 1.3). If the removal of the point caused the slope of the linear curve to increase, it was considered not part of the log phase. These iterations were conducted continuously until:

1. Only ⅛ of the graph is left.
2. Two iterations did not change the slope

![](https://static.igem.org/mediawiki/2021/4/49/T--TAU_Israel--Figure_3-trans.PNG)
**Figure 1.3:** Two examples of automatic-detected logarithmic growth phase, for growth curves of *B. subtilis* with ORF version of TAI-R and *E. coli* with TDR-D. Calculated slopes indicate the growth rate values.

## Statistical analysis

We calculated p-values with the student’s t-test and permutation test. Since t-tests are not traditionally performed on probability spaces of this size, we also calculated an experimental p-value using a permutation test. In the latter, we fit the traditional permutation test to our data; for every optimization, the three experiments from the same organism were averaged and a difference between *E. coli* and *B. subtilis* was calculated. Then, splitting, averaging, and distance calculations were performed for all random splits of the six results, to see if the separation between *E. coli* and *B. subtilis* is significant. Or, in other words, how many times can a random split be more different from each other than the difference between the two organisms (initial split). The p-value is considered the percent of splits in which the difference between the two is larger than the difference between the original split. Larger p-values would mean that many random cases performed better than our optimization, thus the optimization is not significant, In other words, the significance of our results increases as the p-value decreases). [Download summary of results here.](%E2%80%9Dhttps%3A//static.igem.org/mediawiki/2021/b/bb/T--TAU_Israel--Statistical_analysis-_ORF.xlsx%E2%80%9D)

**Note**: Statistically, the p-value can never be 0, it is only under the detection threshold of the analysis, which is in this case 0.0083.

## Experimental Results

### Altering ORF sequences hinders the growth of deoptimized bacteria:

Growth curves obtained from three biological repeats for each bacteria and GOI version were plotted against time (Fig. 1.4A). Detection of logarithmic phase for each type of ORF was performed as mentioned (see computational analysis), and growth rates depicted by the slopes of linear trendline were calculated.

While growth rates of *B. subtilis*, to which ORFs were optimized, growth rates and maximal OD600 nm values were comparable to the original ORF (Fig. 1.4B). In *E. coli,* to which ORFs were deoptimized, growth rates as well as maximal OD600nm values were both significantly reduced in the ORF versions of TAI-D and TDR-D (Fig. 1.4B). In particular, TAI-D version largely restricted *E. coli’s* growth rateby approximately 7-fold, possibly due to ribosomal traffic jams (Fig. 1.4C). In this case, ribosomes are stalled during mRNA translation while waiting for the binding of low abundance tRNA anticodon. As a result, endogenous protein synthesis is diminished, which in turn restricts cellular growth. To assess the degree of optimization in*B. subtilis* relative to the deoptimization in  *E. coli,* growth rates folds of *B. subtilis* were divided by those of *E. coli* as the following formula indicates:

![](https://static.igem.org/mediawiki/2021/4/4b/T--TAU_Israel--Equation-trans.jpg)
**Equation 1**

As the graph clearly shows, TAI-D is by far optimized for *B. subtilis* and deoptimized for *E. coli* in terms of growth rates (Fig. 1.4D).

![](https://static.igem.org/mediawiki/2021/3/30/T--TAU_Israel--FIG4-trans.PNG)
![](https://static.igem.org/mediawiki/2021/e/e6/T--TAU_Israel--FIG4-_D-trans.PNG)
**Figure 1.4:** **A.** Representative growth curves for *E. coli* and *B. subtilis* from one of the biological repeats. Control (black dashed curve) stands for bacteria containing the same plasmid backbone that lacks the mCherry gene. mCherry (red dashed curve) is the original (unmodified) version of the gene, and CAI, TAI-D, TDA-R, TDR-D, and TDR-R are modified versions of mCherry gene. **B.** Right: fold change in bacterial growth rates of each ORF version relative to the growth rates in mCherry. Left: the same as the right but calculated for the average maximal density. **C.** Illustration of stalled ribosomes during mRNA translation, which hinders bacterial growth. **D.** Fold of growth rates in *B. subtilis* relative to *E. coli.* Values more than one confirm success in ORF version in terms of bacterial growth rates.

## Significant variations in GOI expression corresponds with ORF modifications:

MCherry’s fluorescence intensity reflects that its expression levels increased or decreased substantially in all modified versions, in both *B. subtilis* and *E. coli* respectively (Fig. 5A). In *B. subtilis,* the TAI-D version exhibited the highest average maximal fluorescence intensity compared to the original version, while in *E. coli,* the TDR-R version exhibited the lowest (Fig. 5B). In order to account for the alterations in fluorescence intensity to variations in ORF’s sequence code and not to differences in bacterial density, it was normalized to the ratio of fluorescence intensity per bacterial density (OD600 nm values). Then, the average of normalized fluorescence intensity was determined. Higher values of this ratio represent greater GOI expression per bacteria and vice versa. When normalized, TAI-D was still ranked highest as expressive-optimized version in *B. subtilis*. However, in *E. coli*, CAI was ranked as the most deoptimized version, just ahead of TAI-R (Fig. 5C).

Finally, we evaluated the optimization degree (*B. subtilis* relative to *E. coli*) in terms of GOI expression by using the formula mentioned above, with the average normalized fluorescence intensity values. All five ORF versions, but especially CAI, TAI-R, and TDR-D, showed successful optimization in *B. subtilis* (Fig. 1.5D).

![](https://static.igem.org/mediawiki/2021/8/8b/T--TAU_Israel--FIG5-trans.PNG)
**Figure 1.5:** ***A.*** Representative fluorescence intensity plots of all ORF variants in *B. subtilis* (left) and in *E. coli* (right) from one of the biological repeats. Note that the control lacked mCherry gene, and thus wasn’t exhibited fluorescence, and served for background subtraction. **B**. Fold change in average maximal fluorescence intensity of each ORF version relative to mCherry. **C.** the same as the right but calculated for the average normalized fluorescence. **D.** Fold of average normalized fluorescence in *B. subtilis* relative to *E. coli.* Values more than one confirm success in the ORF version in terms of GOI expression.

[ ]

## Transcription Optimization Results

## Background

We used our software in order to find endogenous *E. coli* and B. subtilis promoters that can be selectively transcribed only in one organism and not in the other.

We scanned the endogenous promoters of *B. subtilis* and *E. coli* searching for 2 different types of genetic motifs:

* Motifs that have enhanced presence in one organism compared to the other, i.e. selective motifs
* Motifs that are found in highly expressed genes, i.e. transcription motifs

These two sets of motifs are then joined together, and only selective motifs that had similar representation in the transcription motif set were used, creating a final set of motifs that are transcription enhancing in a selective manner.

In order to this set, we had to define what promoter is considered highly expressed:

**a.**
![](https://static.igem.org/mediawiki/2021/5/5c/T--TAU_Israel--fig1a-transcription.png)
**b.**
![](https://static.igem.org/mediawiki/2021/b/b1/T--TAU_Israel--Fig_1b-transcription.png)
**c.**
![](https://static.igem.org/mediawiki/2021/4/43/T--TAU_Israel--Fig_1c-transcription.png)
**d.**
![](https://static.igem.org/mediawiki/2021/d/d4/T--TAU_Israel--Fig_1d-transcription.png)

**Figure 2.1:** Selective and transcription enhancing motif sets are compared using pairwise motif correlation, for different definitions of highly expressed promoters. In each subplot, the columns are the selective and transcription enhancing motifs of *E. coli*, and the rows are the  transcription enhancing and selective  motifs of *B. subtilis*. Brighter rubrics represent motifs that are more similar to each other.

* Fig. 2.1a: only promoters belonging to the top 5% of expression are taken
* Fig. 2.1b: only promoters belonging to the top 10% of expression are taken
* Fig. 2.1c: only promoters belonging to the top 25% of expression are taken
* Fig. 2.1d: only promoters belonging to the top 50% of expression are taken

In order to decide which definition is optimal, two factors have been taken into account:

1. **The total number of motifs in each set**: More significant motifs allows us to scan and tailor the endogenous promoters in an improved manner, as explained in the transcription model.
2. **The correlation patterns**: Generally speaking, the correlation between selective and transcription enhancing motifs from the same organism are higher than for the other organism, however in all cases the transcription enhancing motifs had good correlations across organisms, which shows that a large proportion of transcription-related motifs are non-selective and emphasizing the need to intersect the two motif sets.

We decided to choose option d, filtering out the lowest 50% of our promoters. These motifs were then used to rank optional promoters according to both selectivity and expression abilities and point synthetic changes were introduced to them to enhance their activity. You can read more about how we did this on the Transcription Optimization Modeling Page.

We then constructed each of the modified promoters into a plasmid, which was transformed into our chassis organisms to test their effect on protein expression and growth rates.

It is important to mention that the new promoters replaced the existing P43 promoter in our plasmid. However, while inserting the new promoters, a synthetic ribosome binding site (SynthRBS) (Fig. 2.2) wasn’t excluded while the vector was linearized by PCR reaction (see cloning protocols). We suspect that having a synthetic RBS in addition to equivalent sites in our promoters gave poor protein expression and impaired our results. Nevertheless, we repeated our POC assay twice and built graphs, and calculated growth rates as described in the translation tab.

![](https://static.igem.org/mediawiki/2021/c/c5/T--TAU_Israel--Fig2-transcription1.jpg)
**Figure 2.2:** Schematic representation of the modified region in our plasmid: P43 is the original promoter that was excluded by PCR and replaced with new promoters. Downstream to P43 and conjugated to the mCherry gene is the synthetic ribosomal binding site that was accidentally retained in the newly constructed plasmids.

The following table summarizes promoters’ names and their predicted bacteria to be optimization, hence are deoptimized to the other bacteria:

|  |  |  |
| --- | --- | --- |
| **Promoter** | **Name** | **Optimized to** |
| A2 | Ribosomal protein L4 | *B. subtilis* |
| C | cAMP phosphodiesterase | *E. coli* |
| D1 | L-lactate permease | *B. subtilis* |
| D2 | Synthetic cAMP phosphodiesterase | *E. coli* |
| E | Synthetic L-lactate permease | *B. subtilis* |
| K | 50S ribosomal subunit protein L27 | *E. coli* |
| M | RNA polymerase transcription factor DK | *E. coli* |
| T | 16S rRNA pseudouridine synthase | *E. coli* |

## Experimental Results

### Subtle deviations in growth rates regardless of the optimization direction:

As indicated from growth curves and slopes of logarithmic phases, all promoter versions in *E. coli* had no significant effect on its growth compared to the P43 original promoter (Fig. 2.3A, B). However, the duration of the lag phase seems to be shortened compared to the P43 original promoter. In *B. subtilis* these measures were increased in most of the promoter's versions (fig. 2.3A, B), regardless of the promoter optimization direction.

![](https://static.igem.org/mediawiki/2021/0/0b/T--TAU_Israel--fig3-transcription.PNG)
**Figure 2.3: A.** Representative growth curves for *E. coli* and *B. subtilis* from one of the biological repeats. Control (black dashed curve) stands for bacteria containing the same plasmid backbone that lacks the mCherry gene and P43 promoter. P43 (red dashed curve) is the original (unmodified) version of the promoter, and A2, D1, and E are optimized promoters for *B. subtilis* ( *E. coli* deoptimized). C, D2, K, M, and T are optimized promoters for *E. coli* (*B. subtilis* deoptimized). **B.** Right: fold change in bacterial growth rates of each promoter version relative to the growth rates in the P43 variant.

### Reduction in GOI expression in all promoter versions

Fluorescence intensity in both bacteria was dropped by a factor of approximately 10-fold for all promoter versions (Fig. 2.4A). It is possible that retaining the synthetic RBS (see explanation in the background above) largely an impaired translation of GOI, hence diminished protein expression. Not surprisingly, the average normalized fluorescence showed a dramatic decrease compared to the original promoter. In a try to find optimization preferences, folds of average normalized fluorescence of *B. subtilis* were divided by those of *E. coli* (as explained in translation results). Values more than one indicating optimization preferences toward *B. subtilis* and vice versa.  Indeed, D1 and E showed optimization toward *B. subtilis*. Where D1 worked better than its synthetic version E. Only promoter versions A2 and D2 had optimization preference to *E. coli*. Thus A2 promoter failed since it was predicted to be deoptimized in *E. coli.*

![](https://static.igem.org/mediawiki/2021/1/1b/T--TAU_Israel--fig4-transcription.PNG)
**Figure 2.4:*****A.*** Representative fluorescence intensity plots of all promoters variants in *B. subtilis* (left) and in *E. coli* (right) from one of the biological repeats. **B**. Fold change in average normalized fluorescence intensity of each promoter versiosn relative to P43. **C.** Fold of average normalized fluorescence in *B. subtilis* relative to *E. coli.*

[ ]

## Discussion and future prospects

## Discussion

The Communique project aims to develop software that allows safe and secure bioengineering of microbial communities in a selective manner.

The process was particularly challenging for several reasons:

First, a microbiome consists of tens and even hundreds of organisms - how does one create a model of biological processes for a whole microbiome? How does one then selectively optimize these in specific members of the community?

Second, optimization in the desired species is not sufficient for effective expression isolation - gene expression has to be deoptimized in the undesired species group. This is especially important in this model as ribosomes are a limited resource in the cell, so the introduction of non-optimal codons will create an mRNA sequence that is translated very inefficiently and slowly, decreasing the number of ribosomes free to translate other sequences. This actually slows down the cell’s growth rate, impairing fitness and creating innate selection against the presence of the gene in the deoptimized organisms.

Despite these obstacles, we managed to tackle these goals and implement them into one comprehensive software tool, including in silico analysis of a potential implementation and experimental tests. Within this software, we were also able to include appropriate plans for biosafety measures, our yet-to-be-released safety scan based on the SafetyNet software of the Heidelberg 2017 iGEM team.

Experimental results show that our strategy indeed works, as we were able to successfully optimize the ORF selectively. Our ORF variants robustly increased protein expression in the optimized bacteria, while substantially decreasing it in the deoptimized bacteria.

Surprisingly, growth rates were relatively unhindered in the optimized organisms, while we noticed a decrease in growth rate in the deoptimized organisms. While we would have expected the expression of a non-essential gene (such as mCherry) to have a negative effect on growth as it adds a metabolic burden on the cell and its machinery, increased fluorescence in comparison to the control did not cause a decrease in growth rate in *B. subtilis****,*** which points to the possibility of successful engineering of essential genes as well.

In addition, the decrease in growth rates of the deoptimized *E. coli* were interesting as well, as it may lead to a drop in abundance of the deoptimized organisms in the population, and thus also lower the possibility of plasmids reaching them via HGT.

In some cases, a user may want to use our software to optimize and deoptimize phylogenetically proximal species: As we saw in our [software analysis,](https://2021.igem.org/Team%3ATAU_Israel/Software#section3)optimization becomes less effective as phylogenetic proximity increases. We would, however, be happy to take this challenge on in the future. It should be noted that a scenario is unlikely to occur in any real-world application as very similar species tend to not occupy the same niches due to resource competition [1]. Were there to be a microbiome like this, we can use the same induced toxicity method described in the [Safety](https://2021.igem.org/Team%3ATAU_Israel/Safety)section, which would cause the deoptimized organism to die.

## References

[1] N. Tromas, Z. E. Taranu, B. D. Martin, A. Willis, N. Fortin, C. W. Greer, and B. J. Shapiro, “Niche separation increases with genetic distance among bloom-forming cyanobacteria,” *Frontiers in Microbiology*, vol. 9, 2018.