A generic approach to infer community-level fitness of microbial genes.

The gene content in a metagenomic pool defines the function potential of a microbial community. Natural selection, operating on the level of genomes or genes, shapes the evolution of community functions by enriching some genes while depriving the others. Despite the importance of microbiomes in the environment and health, a general metric to evaluate the community-wide fitness of microbial genes remains lacking. In this work, we adapt the classic neutral model of species and use it to predict how the abundances of different genes will be shaped by selection, regardless of at which level the selection acts. We establish a simple metric that quantitatively infers the average survival capability of each gene in a microbiome. We then experimentally validate the predictions using synthetic communities of barcoded Escherichia coli strains undergoing neutral assembly and competition. We further show that this approach can be applied to publicly available metagenomic datasets to gain insights into the environment-function interplay of natural microbiomes.

Programming Dynamic Division of Labor Using Horizontal Gene Transfer.

The metabolic engineering of microbes has broad applications, including biomanufacturing, bioprocessing, and environmental remediation. The introduction of a complex, multistep pathway often imposes a substantial metabolic burden on the host cell, restraining the accumulation of productive biomass and limiting pathway efficiency. One strategy to alleviate metabolic burden is the division of labor (DOL) in which different subpopulations carry out different parts of the pathway and work together to convert a substrate into a final product. However, the maintenance of different engineered subpopulations is challenging due to competition and convoluted interstrain population dynamics. Through modeling, we show that dynamic division of labor (DDOL), which we define as the DOL between indiscrete populations capable of dynamic and reversible interchange, can overcome these limitations and enable the robust maintenance of burdensome, multistep pathways. We propose that DDOL can be mediated by horizontal gene transfer (HGT) and use plasmid genomics to uncover evidence that DDOL is a strategy utilized by natural microbial communities. Our work suggests that bioengineers can harness HGT to stabilize synthetic metabolic pathways in microbial communities, enabling the development of robust engineered systems for deployment in a variety of contexts.

The collapse of cooperation during range expansion of Pseudomonas aeruginosa.

Cooperation is commonly believed to be favourable in spatially structured environments, as these systems promote genetic relatedness that reduces the likelihood of exploitation by cheaters. Here we show that a Pseudomonas aeruginosa population that exhibited cooperative swarming was invaded by cheaters when subjected to experimental evolution through cycles of range expansion on solid media, but not in well-mixed liquid cultures. Our results suggest that cooperation is disfavoured in a more structured environment, which is the opposite of the prevailing view. We show that spatial expansion of the population prolongs cooperative swarming, which was vulnerable to cheating. Our findings reveal a mechanism by which spatial structures can suppress cooperation through modulation of the quantitative traits of cooperation, a process that leads to population divergence towards distinct colonization strategies.

Phase transition of GvpU regulates gas vesicle clustering in bacteria.

Gas vesicles (GVs) are microbial protein organelles that support cellular buoyancy. GV engineering has multiple applications, including reporter gene imaging, acoustic control and payload delivery. GVs often cluster into a honeycomb pattern to minimize occupancy of the cytosol. The underlying molecular mechanism and the influence on cellular physiology remain unknown. Using genetic, biochemical and imaging approaches, here we identify GvpU from Priestia megaterium as a protein that regulates GV clustering in vitro and upon expression in Escherichia coli. GvpU binds to the C-terminal tail of the core GV shell protein and undergoes a phase transition to form clusters in subsaturated solution. These properties of GvpU tune GV clustering and directly modulate bacterial fitness. GV variants can be designed with controllable sensitivity to GvpU-mediated clustering, enabling design of genetically tunable biosensors. Our findings elucidate the molecular mechanisms and functional roles of GV clustering, enabling its programmability for biomedical applications.

Duplicated antibiotic resistance genes reveal ongoing selection and horizontal gene transfer in bacteria.

Horizontal gene transfer (HGT) and gene duplication are often considered as separate mechanisms driving the evolution of new functions. However, the mobile genetic elements (MGEs) implicated in HGT can copy themselves, so positive selection on MGEs could drive gene duplications. Here, we use a combination of modeling and experimental evolution to examine this hypothesis and use long-read genome sequences of tens of thousands of bacterial isolates to examine its generality in nature. Modeling and experiments show that antibiotic selection can drive the evolution of duplicated antibiotic resistance genes (ARGs) through MGE transposition. A key implication is that duplicated ARGs should be enriched in environments associated with antibiotic use. To test this, we examined the distribution of duplicated ARGs in 18,938 complete bacterial genomes with ecological metadata. Duplicated ARGs are highly enriched in bacteria isolated from humans and livestock. Duplicated ARGs are further enriched in an independent set of 321 antibiotic-resistant clinical isolates. Our findings indicate that duplicated genes often encode functions undergoing positive selection and horizontal gene transfer in microbial communities.

Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics.

The ability to effectively represent microbiome dynamics is a crucial challenge in their quantitative analysis and engineering. By using autoencoder neural networks, we show that microbial growth dynamics can be compressed into low-dimensional representations and reconstructed with high fidelity. These low-dimensional embeddings are just as effective, if not better, than raw data for tasks such as identifying bacterial strains, predicting traits like antibiotic resistance, and predicting community dynamics. Additionally, we demonstrate that essential dynamical information of these systems can be captured using far fewer variables than traditional mechanistic models. Our work suggests that machine learning can enable the creation of concise representations of high-dimensional microbiome dynamics to facilitate data analysis and gain new biological insights.

Global control of cellular physiology by biomolecular condensates through modulation of electrochemical equilibria.

Control of the electrochemical environment in living cells is typically attributed to ion channels. Here we show that the formation of biomolecular condensates can modulate the electrochemical environment in cells, which affects processes globally within the cell and interactions of the cell with its environment. Condensate formation results in the depletion or enrichment of certain ions, generating intracellular ion gradients. These gradients directly affect the electrochemical properties of a cell, including the cytoplasmic pH and hyperpolarization of the membrane potential. The modulation of the electrochemical equilibria between the intra- and extra-cellular environments by biomolecular condensates governs charge-dependent uptake of small molecules by cells, and thereby directly influences bacterial survival under antibiotic stress. The shift of the intracellular electrochemical equilibria by condensate formation also drives a global change of the gene expression profile. The control of the cytoplasmic environment by condensates is correlated with their volume fraction, which can be highly variable between cells due to the stochastic nature of gene expression at the single cell level. Thus, condensate formation can amplify cell-cell variability of the environmental effects induced by the shift of cellular electrochemical equilibria. Our work reveals new biochemical functions of condensates, which extend beyond the biomolecules driving and participating in condensate formation, and uncovers a new role of biomolecular condensates in cellular regulation.

Programming dynamic division of labor using horizontal gene transfer.

The metabolic engineering of microbes has broad applications, including in biomanufacturing, bioprocessing, and environmental remediation. The introduction of a complex, multi-step pathway often imposes a substantial metabolic burden on the host cell, restraining the accumulation of productive biomass and limiting pathway efficiency. One strategy to alleviate metabolic burden is division of labor (DOL), in which different subpopulations carry out different parts of the pathway and work together to convert a substrate into a final product. However, the maintenance of different engineered subpopulations is challenging due to competition and convoluted inter-strain population dynamics. Through modeling, we show that dynamic division of labor (DDOL) mediated by horizontal gene transfer (HGT) can overcome these limitations and enable the robust maintenance of burdensome, multi-step pathways. We also use plasmid genomics to uncover evidence that DDOL is a strategy utilized by natural microbial communities. Our work suggests that bioengineers can harness HGT to stabilize synthetic metabolic pathways in microbial communities, enabling the development of robust engineered systems for deployment in a variety of contexts.

Promotion of plasmid maintenance by heterogeneous partitioning of microbial communities.

Transferable plasmids play a critical role in shaping the functions of microbial communities. Previous studies suggested multiple mechanisms underlying plasmid persistence and abundance. Here, we focus on the interplay between heterogeneous community partitioning and plasmid fates. Natural microbiomes often experience partitioning that creates heterogeneous local communities with reduced population sizes and biodiversity. Little is known about how population partitioning affects the plasmid fate through the modulation of community structure. By modeling and experiments, we show that heterogeneous community partitioning can paradoxically promote the persistence of a plasmid that would otherwise not persist in a global community. Among the local communities created by partitioning, a minority will primarily consist of members able to transfer the plasmid fast enough to support its maintenance by serving as a local plasmid haven. Our results provide insights into plasmid maintenance and suggest a generalizable approach to modulate plasmid persistence for engineering and medical applications.

Interface of biomolecular condensates modulates redox reactions.

Biomolecular condensates mediate diverse cellular processes. The density transition process of condensate formation results in selective partitioning of molecules, which define a distinct chemical environment within the condensates. However, the fundamental features of the chemical environment and the mechanisms by which such environment can contribute to condensate functions have not been revealed. Here, we report that an electric potential gradient, thereby an electric field, is established at the liquid-liquid interface between the condensate and the bulk environment due to the density transition of ions and molecules brought about by phase separation. We find that the interface of condensates can drive spontaneous redox reactions in vitro and in living cells. Our results uncover a fundamental physicochemical property of the interface of condensates and the mechanism by which the interface can modulate biochemical activities.

Engineering synthetic biomolecular condensates.

The concept of phase-separation-mediated formation of biomolecular condensates provides a new framework to understand cellular organization and cooperativity-dependent cellular functions. With growing understanding of how biological systems drive phase separation and how cellular functions are encoded by biomolecular condensates, opportunities have emerged for cellular control through engineering of synthetic biomolecular condensates. In this Review, we discuss how to construct synthetic biomolecular condensates and how they can regulate cellular functions. We first describe the fundamental principles by which biomolecular components can drive phase separation. Next, we discuss the relationship between the properties of condensates and their cellular functions, which informs the design of components to create programmable synthetic condensates. Finally, we describe recent applications of synthetic biomolecular condensates for cellular control and discuss some of the design considerations and prospective applications.

Programmable synthetic biomolecular condensates for cellular control.

The formation of biomolecular condensates mediated by a coupling of associative and segregative phase transitions plays a critical role in controlling diverse cellular functions in nature. This has inspired the use of phase transitions to design synthetic systems. While design rules of phase transitions have been established for many synthetic intrinsically disordered proteins, most efforts have focused on investigating their phase behaviors in a test tube. Here, we present a rational engineering approach to program the formation and physical properties of synthetic condensates to achieve intended cellular functions. We demonstrate this approach through targeted plasmid sequestration and transcription regulation in bacteria and modulation of a protein circuit in mammalian cells. Our approach lays the foundation for engineering designer condensates for synthetic biology applications.

Toward predictive engineering of gene circuits.

Many synthetic biology applications rely on programming living cells using gene circuits - the assembly and wiring of genetic elements to control cellular behaviors. Extensive progress has been made in constructing gene circuits with diverse functions and applications. For many circuit functions, however, it remains challenging to ensure that the circuits operate in a predictable manner. Although the notion of predictability may appear intuitive, close inspection suggests that it is not always clear what constitutes predictability. We dissect this concept and how it can be confounded by the complexity of a circuit, the complexity of the context, and the interplay between the two. We discuss circuit engineering strategies, in both computation and experiment, that have been used to improve the predictability of gene circuits.

Vertical and horizontal gene transfer tradeoffs direct plasmid fitness.

Plasmid fitness is directed by two orthogonal processes-vertical transfer through cell division and horizontal transfer through conjugation. When considered individually, improvements in either mode of transfer can promote how well a plasmid spreads and persists. Together, however, the metabolic cost of conjugation could create a tradeoff that constrains plasmid evolution. Here, we present evidence for the presence, consequences, and molecular basis of a conjugation-growth tradeoff across 40 plasmids derived from clinical Escherichia coli pathogens. We discover that most plasmids operate below a conjugation efficiency threshold for major growth effects, indicating strong natural selection for vertical transfer. Below this threshold, E. coli demonstrates a remarkable growth tolerance to over four orders of magnitude change in conjugation efficiency. This tolerance fades as nutrients become scarce and horizontal transfer attracts a greater share of host resources. Our results provide insight into evolutionary constraints directing plasmid fitness and strategies to combat the spread of antibiotic resistance.

Engineering living materials by synthetic biology.

Natural biological materials are programmed by genetic information and able to self-organize, respond to environmental stimulus, and couple with inorganic matter. Inspired by the natural system and to mimic their complex and delicate fabrication process and functions, the field of engineered living materials emerges at the interface of synthetic biology and materials science. Here, we review the recent efforts and discuss the challenges and future opportunities.

Bringing cells to the edge.

A network of open channels allows cells and molecular cargo to travel from the center to the periphery of lab-grown colonies of Pseudomonas aeruginosa, helping to eradicate competing species.

Distributed information encoding and decoding using self-organized spatial patterns.

Dynamical systems often generate distinct outputs according to different initial conditions, and one can infer the corresponding input configuration given an output. This property captures the essence of information encoding and decoding. Here, we demonstrate the use of self-organized patterns that generate high-dimensional outputs, combined with machine learning, to achieve distributed information encoding and decoding. Our approach exploits a critical property of many natural pattern-formation systems: in repeated realizations, each initial configuration generates similar but not identical output patterns due to randomness in the patterning process. However, for sufficiently small randomness, different groups of patterns that arise from different initial configurations can be distinguished from one another. Modulating the pattern-generation and machine learning model training can tune the tradeoff between encoding capacity and security. We further show that this strategy is scalable by implementing the encoding and decoding of all characters of the standard English keyboard.

Horizontal gene transfer enables programmable gene stability in synthetic microbiota.

The functions of many microbial communities exhibit remarkable stability despite fluctuations in the compositions of these communities. To date, a mechanistic understanding of this function-composition decoupling is lacking. Statistical mechanisms have been commonly hypothesized to explain such decoupling. Here, we proposed that dynamic mechanisms, mediated by horizontal gene transfer (HGT), also enable the independence of functions from the compositions of microbial communities. We combined theoretical analysis with numerical simulations to illustrate that HGT rates can determine the stability of gene abundance in microbial communities. We further validated these predictions using engineered microbial consortia of different complexities transferring one or more than a dozen clinically isolated plasmids, as well as through the reanalysis of data from the literature. Our results demonstrate a generalizable strategy to program the gene stability of microbial communities.

Engineering consortia by polymeric microbial swarmbots.

Synthetic microbial consortia represent a new frontier for synthetic biology given that they can solve more complex problems than monocultures. However, most attempts to co-cultivate these artificial communities fail because of the winner-takes-all in nutrients competition. In soil, multiple species can coexist with a spatial organization. Inspired by nature, here we show that an engineered spatial segregation method can assemble stable consortia with both flexibility and precision. We create microbial swarmbot consortia (MSBC) by encapsulating subpopulations with polymeric microcapsules. The crosslinked structure of microcapsules fences microbes, but allows the transport of small molecules and proteins. MSBC method enables the assembly of various synthetic communities and the precise control over the subpopulations. These capabilities can readily modulate the division of labor and communication. Our work integrates the synthetic biology and material science to offer insights into consortia assembly and serve as foundation to diverse applications from biomanufacturing to engineered photosynthesis.