(174bx) Engineering Horizontal Gene Transfer Systems to Control Microbial Populations of Increasing Complexity

Engineering microbial consortia has been a leading research direction in systems and synthetic biology for the last two decades. However, achieving long-term multi-population stability remains a primary challenge in the field, largely due to genetic instability of engineered members and/or competitive exclusion effects of the population overall. To address these challenges, horizontal gene transfer (HGT) of plasmids, specifically through the process of conjugation, could be a potential solution for engineering stable microbial communities. Indeed, HGT by conjugation enables the transfer of plasmid DNA through direct cell-to-cell contact; it is highly prevalent in natural microbial communities, playing a large role in population compositions and structures over time. Thus, conjugation may serve as an ecological control strategy to program stability into microbial communities. However, whether HGT could be used to achieve multi-population stability in general, and the specific parameters necessary to achieve such control, are not currently known. Here we investigated conjugation as a novel ecological control strategy for establishing stable microbial consortia. Stability, specifically can be achieved from the mechanistic manipulation of conjugation parameters, where single-recipient conjugation parameters can be tuned to predict and control population dynamics in heterogenous communities.

To investigate this question, we developed a general kinetic model of conjugation dynamics involving two populations (referred herein as “species”) each carrying one of two plasmids. We primarily focused on strictly orthogonal systems, where each plasmid was only allowed to uniquely conjugate within its own host. The resulting “plasmid-free” or “plasmid-carrying” subfractions of each species can be represented as a system of four ordinary differential equations (ODEs). This model was characterized by relevant conjugation parameters including growth rates, fitness costs, conjugation efficiencies and plasmid loss rates.

We pursued two approaches in parallel to determine the parameter regions wherein conjugation facilitated species-level coexistence. Here, co-existence is defined as the condition where there is a steady state existence of the given two species. First, we analytically derived mathematical criteria predicting the conditions required for establishing stable co-existence of the system. The analytically derived criteria formed a preliminary basis for providing a set of constraints and conservative estimates for realizing the two-species co-existence. Second, we used numerical simulations with randomly distributed parameters, constrained by experimentally estimated values, to generate the steady-state species fractions; the model parameter sets driving steady state co-existence could then be identified. Finally, in all cases, results were compared to the same model in the absence of any conjugation, as a control (i.e., two species no-plasmid system).

Comparison of analytical and numerical simulations allowed us to group conjugation parameters into distinct regimes corresponding to species-level coexistence. Specifically, we found that tradeoffs between species growth rates, plasmid transfer rates, and plasmid loss rates, can allow both species to persist even if one of the species grows at a significantly slower rate than the second. More generally, compared to the control setup, results revealed that incorporating conjugative plasmids clearly and significantly increased the long-term stability of two species under a wider range of parameter distributions.

Overall, this hybrid approach combining first principles and data-driven modeling enabled us to identify specific conjugation parameters governing the stable population fractions at precise levels, providing mechanistic insights into our results. Immediate next steps include experimental verification, and integration with engineered communities applied to diverse areas of interest, including bioremediation, bioprocessing, biomedical diagnostics and therapeutics and high-value chemical products.