Team:TAU Israel/Proof Of Concept

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Proof Of Concept


Communique’s software integrates critical genetic parameters controlling gene synthesis and expression, aiming to differentially express gene of interest (GOI) in a selected member of the microbial community. The software’s output is consists of an expressive-optimized version of genetic components for a chosen bacterium, which is also expressive-deoptimized to the other. In order to validate our software’s results for each gene component , we’ve established a simple and robust proof of concept assay to measure gene expression . Following conditions calibration, the assay was performed to determine variations in expression levels of a modified GOI (optimized/deoptimized) in two bacterial species. The results demonstrated a significant fold of change in GOI expression when the open reading frame (ORF) was modified according to the software’s analysis, in both optimized and deoptimized directions. Moreover, few ORFs modifications halted the growth of deoptimized bacteria, possibly due to ribosomal traffic jams [1]. Taken together, the results confirmed the competence of our algorithm to customize a target gene in selected bacteria, while reducing its expression and impairing the growth of chosen deoptimized bacteria. Applying these to natural or synthetic microbial communities might potentially reduce GOI expression in unintended bacterial members, that gained the gene through horizontal gene transfer (HGT), allowing safe microbial community engineering.

To further examine our approach in blocking HGT implications, we designed an experiment to assess GOI abundance in the microbial community by linking GOI to its host, based on the work of Peter J. Diebold et al. [2]. In this method, individual bacteria are encapsulated to obtain single-cell emulsion droplets, in which fusion-PCR is conducted to fuse GOI to 16s rRNA gene in its bacterial host. Then, fusion products are amplified by qPCR or examined via next-generation sequencing (NGS) in order to identify the GOI-bearing host. To date, we are calibrating the method to link our plasmid-encoding GOI to its bacterial host either when growing separately or in a binary community.

Experimental design

Proof of concept assay

Initially, we selected two common bacterial models to work with - E. coli and B. subtilis. The selection was based on the following factors: genomic data availability, growth in lab conditions, transformation competence, and taxonomic distance. We chose bacteria from distinct phylogenetic groups to validate if our model is able to optimize and deoptimize two distinct taxonomic clades before proceeding to related organisms with a lower degree of separation.   The tested bacteria were transformed with a shuttle vector capable of propagating in both species, that also carry measurable GOI- the mCherry gene.

The genetic components designed by the software to be optimally expressed in E. coli, while deoptimized for B. subtilis and vice versa, were cloned into the plasmid via the Gibson assembly method and replaced the original sequences (ORF or promoter). Then, transformed bacteria were grown separately for 17hrs in a 96-well plate, and every 20 minutes protein fluorescence intensity, as well as bacterial density (OD600 nm), were determined by a hybrid plate reader. Each gene component was tested in three biological replicates, with three technical replicates. Mean bacterial density and fluorescence intensity of each gene component being tested were plotted against time and compared to its control (unmodified gene component). In order to account for the alterations in fluorescence intensity to variations in gene components and not to differences in bacterial density, it was normalized by the ratio of fluorescence intensity per bacterial density. Higher values of this ratio represent greater GOI expression per bacteria and conversely. 

Each experiment included the following controls:

  • Wells with medium only for background subtraction at OD600 nm.
  • Wells with bacteria containing the same plasmid but lacking mCherry gene, for autofluorescence subtraction.
  • Wells with bacteria containing the original plasmid with unmodified gene component for baseline comparison. 

Figure 1: POC assay. A. An Illustration of assay steps, from cloning of optimized gene components to GOI measurement in a plate reader, and B. Example results of one of our POC assays demonstrating significant changes in GOI expression, as reflected by its fluorescence intensity. CAI, TAI-D, TAI-R, TDR-D, and TDR-R represents five versions of GOI’s ORFs that were expressive-optimized for B. subtilis and expressive-deoptimized for E. coli.

GOI abundance test

To investigate whether our technological approach is capable of restricting GOI expression in selected bacteria only, despite HGT events in microbial community, we implemented and designed the OIL-PCR method developed by Peter J. Diebold et al, with some modifications to fit it to the time constraints of iGEM. 

The goal of this method is to identify and assess the abundance of bacteria that contain plasmid-encoded GOI in a microbial community. It’s based on a single-cell fusion-PCR in which the amplification product of GOI serves as a forward primer to amplify 16s rRNA gene, thus resulting in a fused product of GOI-16s rRNA. Bacterial identification is determined either through NGS of the fusion products for complex bacterial communities, or qPCR when a specified community with known bacterial members is tested. In both cases, targeting variable regions in 16s rRNA gene reveals bacterial identity. It’s noteworthy that monitoring HGT events might require sustained growth of a bacterial community. This could be achieved by automatic systems that allow co-culturing and maintaining bacterial growth by waste removal and fresh medium supply, such as Chi.Bio. However, to implement this method and calibrate its conditions, we first analyzed a single E. coli culture containing the plasmid-encoded GOI.

In our experiment calibration, ~200 cells/µl from cultures of E. coli with and without mCherry (GOI) that grew separately were tested. Fusion PCR reaction was performed using forward and reverse primers targeting GOI, with a tail attached to the reverse primer complementary to a mutual region of 16S rRNA gene of both bacteria. Then, the GOI amplicon served as a forward primer to amplify mutual region of 16S rRNA gene together with a reverse primer. 

During calibrations, we initially tested several options of primers combination, and whether GOI and 16s rRNA are amplified with the designed fusion primer.

Following calibration, we are planning to clean the fused product (GOI-16S rRNA) and amplify it with a specific set of forward primer targeting the fusion region and species-specific reverse primer, targeting a variable region of 16s rRNA gene, via qPCR to identify the bacterial species.

Figure 2: General principles of plasmid-encoded GOI abundance experiment. A. Fusion-PCR reaction, from left to right: GOI amplification with forward and reverse primers (in green), with a tail attached to the reverse primer (in orange) that complementary to a mutual region in 16s rRNA gene. Then, GOI amplicon servers as a forward primer in the amplification reaction of 16s rRNA gene, which results in the fused product of GOI-16s rRNA. B. Bacterial identification via qPCR: Fused product purified from the previous reaction (A) is amplified by qPCR with species-specific reverse primers (bacteria A and B, in pink and blue respectively) and forward primer complementary to the junction zone of GOI-16s rRNA (in red). On the right, qPCR results demonstrating the identification of the plasmid-encoded GOI in bacteria A only.

Currently, this method enables tracking of host-containing GOI, but not its expression levels (mRNA). Nevertheless, GOI abundance in the DNA level might be sufficient to conclude the validity of our strategy, mainly due to the tendency of bacteria to eliminate deoptimized plasmid-encoded gene during bacterial propagation as a consequence of its metabolic burden and segregational loss [3,4]. Meaning that if the relative fraction of deoptimized bacteria that contain the GOI is negligible following an HGT event (through plasmid elimination), while a fraction of optimized bacteria that carry GOI is maintained stable, then our POC would be confirmed. It is important to mention that even costly plasmids manage to stably persist in the bacterial population [5]. However, we believe that the synergistic effect of several deoptimized components constructed in a plasmid will strongly disfavor the plasmid, so compensatory adaptation would fail.   

Additionally, we are planning to expand this method to combine GOI abundance and its expression by applying the same principles of GOI-abundance method but based on fusion-PCR of mRNA products of GOI and 16s rRNA genes. For this purpose, the fusion-PCR step will include primers targeting GOI’s mRNA, with a reverse primer having a tail complementary to the mRNA of 16s rRNA and reverse transcriptase to establish the fused product. Potentially, this method can detect both the host identity and its GOI expression levels. 


  1. Zur, H., Cohen-Kupiec, R., Vinokour, S. et al. Algorithms for ribosome traffic engineering and their potential in improving host cells' titer and growth rate. Sci Rep10, 21202 (2020).
  2. Diebold PJ, New FN, Hovan M, Satlin MJ, Brito IL. Linking plasmid-based beta-lactamases to their bacterial hosts using single-cell fusion PCR. Elife. 2021 Jul 20;10:e66834.
  3. Rodríguez-Beltrán, J., DelaFuente, J., León-Sampedro, R. et al. Beyond horizontal gene transfer: the role of plasmids in bacterial evolution. Nat Rev Microbiol 19, 347–359 (2021).
  4. Alvaro San Millan,Evolution of Plasmid-Mediated Antibiotic Resistance in the Clinical Context,Trends in Microbiology,Volume 26, Issue 12,2018,Pages 978-985.
  5. Millan, A., Peña-Miller, R., Toll-Riera, M. et al. Positive selection and compensatory adaptation interact to stabilize non-transmissible plasmids. Nat Commun 5, 5208 (2014).