Conjugative Baseline Assay
One of our goals was to estimate and quantify the rate of conjugation
between a selected donor and recipient cell pair. Understanding how the
cells act in a laboratory setting would serve to give us a baseline for
comparison at which the conjugation rates change when incorporating our
biocontainment measures. With the time given we were able to identify key
information regarding the rate of conjugation between two cells. Future
directions would include incorporating all our biocontainment parts to
effectively support the effectiveness of our scientific model.
Experimental trials revealed the ratio of donor, WM6065, to recipient, C41 at varying ratios showed no significant difference in conjugation rate. This is crucial for understanding other conjugative research, and may be applicable in other research including Estimating the rate of plasmid transfer and Understanding optimal conjugation conditions. By performing these tests for conjugation, we were able to come up with these Results.
Our experimental design has potential to be used by researchers performing similar experiments, as well as companies who want to test for gene transfer in their products. Experimenting with different ratios of donor and recipient cells, and exploring rates of conjugation with and preventative measures, may be useful for saving others time when working towards biocontainment in the future. See Results for more.
Synterception, our comprehensive biocontainment strategy, was evaluated
and challenged computationally through two conceptual models: kinetics and
community. The first model, Horizontal Gene Transfer Kinetics, refers to the
Markov Modeling comparing the baseline conjugation rate based on experimental
values and pseudo numbers. Together, these Markov Models help the team
understand how cells are behaving at a transitional level. Understanding
the rates at which cells change between states reveals where biocontainment
strategies will be most effective. The second method is a Conjugative
Agent-Based Model that simulates the conjugative behavior of hypothetical
bacterial communities. This program reveals how cells may behave in various
environmental settings, and can visualize the effects on the system as the
donor to recipient ratio, conjugative rate, and transformational rate differ.
Again, both experimental data and pseudo numbers show the potential Synterception
has as a biocontainment strategy, as well as other manipulations of conjugative
The models were developed as tools for conjugative simulations beyond use just from Synterception. Both models provide significant insight into the physics of biocontainment which can be applied to bacterial species beyond the scope of our project goals. For example, the Agent-Based Model can be used to compute communities in wastewater environments.
The models can be developed further to include more strain and environment specific conditions to provide more accurate outputs. The End-Point Markov Model computation brings to Synterception. This model provides the tools and basis for understanding the kinetics and agents of end-point transformation via analysis of OD600, rates of plasmid transfer and cell growth, total cell density (in liquid culture), and time. See Modeling for more.
Multiple Sequence Alignment
Horizontal gene transfer complicates the use of genetically modified organisms as solutions to biological problems in nature because of possible unknown consequences and unpredictability when introducing foreign DNA to new environments. Therefore, biocontainment procedures using CRISPR-Cas9 to prevent foriegn DNA from leaving the host cells in the first place is essential. To do so we created a multiple sequence alignment of conjugative plasmids to identify homology sites for gene knockout (See Bioinformatics). This allows our CRISPR-Cas9 based “immune system” to knock out conjugative plasmids, essentially inhibiting our system from ever passing on this information to its environment. Ultimately, we created a list of 10 sequences that should reliably cleave IncP conjugative plasmids in the gene coding for their conjugative relaxase. Future work on this subject could identify ideal cleavage sites in other conjugative plasmid families, or extend this method beyond just the relaxase enzyme to other significant contributing conjugation genes.