Team:TAU Israel/Description

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Project Description

Introduction – Microbiomes

Bacteria are one of the most ubiquitous organisms on earth. They are usually organized in interconnected communities present in both natural and synthetic environments, including the human body (mainly gut and skin), soil and plant roots, and various forms of food products (probiotics and fermented foods such as dairy, bread, and alcohol).

Microbiome engineering

One may question the necessity or benefit in engineering bacterial consortiums, and reasonably so — the applications of microbiome engineering may not always be obvious: some examples may relate to the treatment of diseases using gut microbiome treatment [5, 6], bioremediation of natural disasters (primarily oil spills and other chemical-related incidents) [1-4], or agricultural applications relating to soil bacterial population, to name a few.

The Problem

In a community, bacteria naturally transfer genetic material between themselves in a process called “horizontal gene transfer”, essentially trading and sharing information. Genetically modifying microbiomes requires modification of specific species in the community while leaving others untouched.

Therefore, trying to engineer species within a microbiome selectively proves difficult, also creating a biosafety issue that can have unintended consequences with a significant ecological impact caused by the spreading of genes within a community.

The following may serve as a helpful analogy: Imagine a company in which any type of information can only be sent to all organization members and not directly to particular individuals or teams, creating immense confusion and exposing all members to potentially confidential or classified data. While this might not be the case in human communities, the selectivity of information is one of the primary obstacles that prevent modern science from holistically engineering bacterial communities (instead of a single species in lab conditions). No current generic technology is able to provide selectivity while engineering microbial communities, which limits any attempt to expand the use of GMOs beyond the borders of the supervised lab.

Our solution

Our approach allows us to take the genetic information passed on to the bacterial communities and modify the sequence such that it will only be expressible by the pre-specified species selected by the user. This will selectively target specific species to express the gene of interest, while avoiding successful transfer of the genes to unwanted species within the community. Using this approach, we can increase productivity in the utilization of bacterial communities for various applications while providing higher biosafety standards and new possibilities for engineering microbiomes.

Our solution is designed as a pipeline that automatically designs a plasmid for environment and species-specific expression. The model integrates species-specific modifications into the sequence, each affecting different aspects of gene expression (transcription, translation, ORI, restriction enzymes, and others), enabling the creation of a tailor-made sequence that is fine-tuned such that any gene of interest will only be expressed by the desired bacteria species, and optimizing the expression in the desired species (maximizing efficiency) while simultaneously preventing expression in other species.

Figure 1: A general Outline of the model and different aspects that it targets.

But why do it this way?

When looking for possible ways to attack the problem, one of our primary concerns was finding an approach that will lead to a solution that will be applicable to as many use cases and situations as possible, which led us to look at the most fundamental trait of a vector – its genetic sequence.

We figured that building a solution that engineers specificity into the vector sequence would be the most universally applicable, as it does not depend on traits/capabilities specific to certain environments/certain species of bacteria. Additionally, we agreed that an algorithm-based sequence optimization using a software model would be the most efficient and versatile method of optimizing the sequence.

Our solution, being a software model that is not based on any bacteria/environment-specific traits, can potentially be used to engineer any gene of interest into any community of bacteria, growing in any environment, in a safe and species-specific manner.

In conclusion, the ease-of-deployment of a software-based solution and the universality of the concepts it is based on lead us to believe that the approach we chose was the most optimal one.


  1. Dvořák, P., Nikel, P. I., Damborský, J., & de Lorenzo, V. (2017). Bioremediation 3.0: Engineering pollutant-removing bacteria in the times of systemic biology. Biotechnology Advances, 35(7), 845–866.
  2. Gilbert, E. S., Walker, A. W., & Keasling, J. D. (2003). A constructed microbial consortium for biodegradation of the organophosphorus insecticide parathion. Applied Microbiology and Biotechnology, 61(1), 77–81.
  3. Liu, H., Yao, J., Yuan, Z., Shang, Y., Chen, H., Wang, F., Masakorala, K., Yu, C., Cai, M., Blake, R. E., & Choi, M. M. F. (2014). Isolation and characterization of crude-oil-degrading bacteria from oil-water mixture in Dagang oilfield, China. International Biodeterioration & Biodegradation, 87, 52–59.
  4. Nitschke, M., & Pastore, G. M. (2006). Production and properties of a surfactant obtained from Bacillus subtilis grown on cassava wastewater. Bioresource Technology, 97(2), 336–341.
  5. Soria, L. R., Ah Mew, N., & Brunetti-Pierri, N. (2019). Progress and challenges in development of new therapies for urea cycle disorders. Human Molecular Genetics, 28(R1), R42–R48.
  6. Vivarelli, S., Salemi, R., Candido, S., Falzone, L., Santagati, M., Stefani, S., Torino, F., Banna, G. L., Tonini, G., & Libra, M. (2019). Gut Microbiota and Cancer: From Pathogenesis to Therapy. Cancers, 11(1), 38.