Majority sensing in synthetic microbial consortia

Synthetic microbial ecology: engineering habitats for modular consortia

Microbiomes, the complex networks of micro-organisms and the molecules through which they interact, play a crucial role in health and ecology. Over at least the past two decades, engineering biology has made significant progress, impacting the bio-based industry, health, and environmental sectors; but has only recently begun to explore the engineering of microbial ecosystems. The creation of synthetic microbial communities presents opportunities to help us understand the dynamics of wild ecosystems, learn how to manipulate and interact with existing microbiomes for therapeutic and other purposes, and to create entirely new microbial communities capable of undertaking tasks for industrial biology. Here, we describe how synthetic ecosystems can be constructed and controlled, focusing on how the available methods and interaction mechanisms facilitate the regulation of community composition and output. While experimental decisions are dictated by intended applications, the vast number of tools available suggests great opportunity for researchers to develop a diverse array of novel microbial ecosystems.

DyMMM-LEAPS: An ML-based framework for modulating evenness and stability in synthetic microbial communities

There have been a growing number of computational strategies to aid in the design of synthetic microbial consortia. A framework to identify regions in parametric space to maximize two essential properties, evenness and stability, is critical. In this study, we introduce DyMMM-LEAPS (dynamic multispecies metabolic modeling-locating evenness and stability in large parametric space), an extension of the DyMMM framework. Our method explores the large parametric space of genetic circuits in synthetic microbial communities to identify regions of evenness and stability. Due to the high computational costs of exhaustive sampling, we utilize adaptive sampling and surrogate modeling to reduce the number of simulations required to map the vast space. Our framework predicts engineering targets and computes their operating ranges to maximize the probability of the engineered community to have high evenness and stability. We demonstrate our approach by simulating five cocultures and one three-strain culture with different social interactions (cooperation, competition, and predation) employing quorum-sensing-based genetic circuits. In addition to guiding circuit tuning, our pipeline gives an opportunity for a detailed analysis of pockets of evenness and stability for the circuit under investigation, which can further help dissect the relationship between the two properties. DyMMM-LEAPS is easily customizable and can be expanded to a larger community with more complex interactions.

Synthetic Homoserine Lactone Sensors for Gram-Positive Bacillus subtilis Using LuxR-Type Regulators

A universal biochemical signal for bacterial cell-cell communication could facilitate programming dynamic responses in diverse bacterial consortia. However, the classical quorum sensing paradigm is that Gram-negative and Gram-positive bacteria generally communicate via homoserine lactones (HSLs) or oligopeptide molecular signals, respectively, to elicit population responses. Here, we create synthetic HSL sensors for Gram-positive Bacillus subtilis 168 using allosteric LuxR-type regulators (RpaR, LuxR, RhlR, and CinR) and synthetic promoters. Promoters were combinatorially designed from different sequence elements (-35, -16, -10, and transcriptional start regions). We quantified the effects of these combinatorial promoters on sensor activity and determined how regulator expression affects its activation, achieving up to 293-fold activation. Using the statistical design of experiments, we identified significant effects of promoter regions and pairwise interactions on sensor activity, which helped to understand the sequence-function relationships for synthetic promoter design. We present the first known set of functional HSL sensors (≥20-fold dynamic range) in B. subtilis for four different HSL chemical signals: p-coumaroyl-HSL, 3-oxohexanoyl-HSL, n-butyryl-HSL, and n-(3-hydroxytetradecanoyl)-HSL. This set of synthetic HSL sensors for a Gram-positive bacterium can pave the way for designable interspecies communication within microbial consortia.

Microbes and microbial strategies in carcinogenic polycyclic aromatic hydrocarbons remediation: a systematic review

Degradation, detoxification, or removal of the omnipresent polycyclic aromatic hydrocarbons (PAHs) from the ecosphere as well as their prevention from entering into food chain has never appeared simple. In this context, cost-effective, eco-friendly, and sustainable solutions like microbe-mediated strategies have been adopted worldwide. With this connection, measures have been taken by multifarious modes of microbial remedial strategies, i.e., enzymatic degradation, biofilm and biosurfactant production, application of biochar-immobilized microbes, lactic acid bacteria, rhizospheric-phyllospheric-endophytic microorganisms, genetically engineered microorganisms, and bioelectrochemical techniques like microbial fuel cell. In this review, a nine-way directional approach which is based on the microbial resources reported over the last couple of decades has been described. Fungi were found to be the most dominant taxa among the CPAH-degrading microbial community constituting 52.2%, while bacteria, algae, and yeasts occupied 37.4%, 9.1%, and 1.3%, respectively. In addition to these, category-wise CPAH degrading efficiencies of each microbial taxon, consortium-based applications, CPAH degradation-related molecular tools, and factors affecting CPAH degradation are the other important aspects of this review in light of their appropriate selection and application in the PAH-contaminated environment for better human-health management in order to achieve a sustainable ecosystem.

Stable States of a Microbial Community Are Formed by Dynamic Metabolic Networks with Members Functioning to Achieve Both Robustness and Plasticity

A more detailed understanding of the mechanisms underlying the formation of microbial communities is essential for the efficient management of microbial ecosystems. The stable states of microbial communities are commonly perceived as static and, thus, have not been extensively examined. The present study investigated stabilizing mechanisms, minority functions, and the reliability of quantitative ana-lyses, emphasizing a metabolic network perspective. A bacterial community, formed by batch transferred cultures supplied with phenol as the sole carbon and energy source and paddy soil as the inoculum, was analyzed using a principal coordinate ana-lysis (PCoA), mathematical models, and quantitative parameters defined as growth activity, community-changing activity, community-forming activity, vulnerable force, and resilience force depending on changes in the abundance of operational taxonomic units (OTUs) using 16S rRNA gene amplicon sequences. PCoA showed succession states until the 3rd transferred cultures and stable states from the 5th to 10th transferred cultures. Quantitative parameters indicated that the bacterial community was dynamic irrespective of the succession and stable states. Three activities fluctuated under stable states. Vulnerable and resilience forces were detected under the succession and stable states, respectively. Mathematical models indicated the construction of metabolic networks, suggesting the stabilizing mechanism of the community structure. Thirteen OTUs coexisted during stable states, and were recognized as core OTUs consisting of majorities, middle-class, and minorities. The abundance of the middle-class changed, whereas that of the others did not, which indicated that core OTUs maintained metabolic networks. Some extremely low abundance OTUs were consistently exchanged, suggesting a role for scavengers. These results indicate that stable states were formed by dynamic metabolic networks with members functioning to achieve robustness and plasticity.

Perspective on the development of synthetic microbial community (SynCom) biosensors

Synthetic microbial community (SynCom) biosensors are a promising technology for detecting and responding to environmental cues and target molecules. SynCom biosensors use engineered microorganisms to create a more complex and diverse sensing system, enabling them to respond to stimuli with enhanced sensitivity and accuracy. Here, we give a definition of SynCom biosensors, outline their construction workflow, and discuss current biosensing technology. We also highlight the challenges and future for developing and optimizing SynCom biosensors and the potential applications in agriculture and food management, biotherapeutic development, home sensing, urban and environmental monitoring, and the One Health foundation. We believe SynCom biosensors could be used in a real-time and remote-controlled manner to sense the chaos of constantly dynamic environments.

Signaling in microbial communities with open boundaries

Microbial communities such as swarms or biofilms often form at the interfaces of solid substrates and open fluid flows. At the same time, in laboratory environments these communities are commonly studied using microfluidic devices with media flows and open boundaries. Extracellular signaling within these communities is therefore subject to different constraints than signaling within classic, closed-boundary systems such as developing embryos or tissues, yet is understudied by comparison. Here, we use mathematical modeling to show how advective-diffusive boundary flows and population geometry impact cell-cell signaling in monolayer microbial communities. We reveal conditions where the intercellular signaling lengthscale depends solely on the population geometry and not on diffusion or degradation, as commonly expected. We further demonstrate that diffusive coupling with the boundary flow can produce signal gradients within an isogenic population, even when there is no flow within the population. We use our theory to provide new insights into the signaling mechanisms of published experimental results, and we make several experimentally verifiable predictions. Our research highlights the importance of carefully evaluating boundary dynamics and environmental geometry when modeling microbial cell-cell signaling and informs the study of cell behaviors in both natural and synthetic systems.

Bacterial-Mediated Tumor Therapy: Old Treatment in a New Context

Targeted therapy and immunotherapy have brought hopes for precision cancer treatment. However, complex physiological barriers and tumor immunosuppression result in poor efficacy, side effects, and resistance to antitumor therapies. Bacteria-mediated antitumor therapy provides new options to address these challenges. Thanks to their special characteristics, bacteria have excellent ability to destroy tumor cells from the inside and induce innate and adaptive antitumor immune responses. Furthermore, bacterial components, including bacterial vesicles, spores, toxins, metabolites, and other active substances, similarly inherit their unique targeting properties and antitumor capabilities. Bacteria and their accessory products can even be reprogrammed to produce and deliver antitumor agents according to clinical needs. This review first discusses the role of different bacteria in the development of tumorigenesis and the latest advances in bacteria-based delivery platforms and the existing obstacles for application. Moreover, the prospect and challenges of clinical transformation of engineered bacteria are also summarized.

The space between us: Modeling spatial heterogeneity in synthetic microbial consortia dynamics

A central endeavor in bioengineering concerns the construction of multistrain microbial consortia with desired properties. Typically, a gene network is partitioned between strains, and strains communicate via quorum sensing, allowing for complex behaviors. Yet a fundamental question of how emergent spatiotemporal patterning in multistrain microbial consortia affects consortial dynamics is not understood well. Here, we propose a computationally tractable and straightforward modeling framework that explicitly allows linking spatiotemporal patterning to consortial dynamics. We validate our model against previously published results and make predictions of how spatial heterogeneity impacts interstrain communication. By enabling the investigation of spatial patterns effects on microbial dynamics, our modeling framework informs experimentalists, helps advance the understanding of complex microbial systems, and supports the development of applications involving them.

Guided by the principles of microbiome engineering: Accomplishments and perspectives for environmental use

Although the accomplishments of microbiome engineering highlight its significance for the targeted manipulation of microbial communities, knowledge and technical gaps still limit the applications of microbiome engineering in biotechnology, especially for environmental use. Addressing the environmental challenges of refractory pollutants and fluctuating environmental conditions requires an adequate understanding of the theoretical achievements and practical applications of microbiome engineering. Here, we review recent cutting-edge studies on microbiome engineering strategies and their classical applications in bioremediation. Moreover, a framework is summarized for combining both top-down and bottom-up approaches in microbiome engineering toward improved applications. A strategy to engineer microbiomes for environmental use, which avoids the build-up of toxic intermediates that pose a risk to human health, is suggested. We anticipate that the highlighted framework and strategy will be beneficial for engineering microbiomes to address difficult environmental challenges such as degrading multiple refractory pollutants and sustain the performance of engineered microbiomes in situ with indigenous microorganisms under fluctuating conditions.

Calibrating spatiotemporal models of microbial communities to microscopy data: A review

Spatiotemporal models that account for heterogeneity within microbial communities rely on single-cell data for calibration and validation. Such data, commonly collected via microscopy and flow cytometry, have been made more accessible by recent advances in microfluidics platforms and data processing pipelines. However, validating models against such data poses significant challenges. Validation practices vary widely between modelling studies; systematic and rigorous methods have not been widely adopted. Similar challenges are faced by the (macrobial) ecology community, in which systematic calibration approaches are often employed to improve quantitative predictions from computational models. Here, we review single-cell observation techniques that are being applied to study microbial communities and the calibration strategies that are being employed for accompanying spatiotemporal models. To facilitate future calibration efforts, we have compiled a list of summary statistics relevant for quantifying spatiotemporal patterns in microbial communities. Finally, we highlight some recently developed techniques that hold promise for improved model calibration, including algorithmic guidance of summary statistic selection and machine learning approaches for efficient model simulation.

Combating biotic stresses in plants by synthetic microbial communities: Principles, applications, and challenges

Presently, agriculture worldwide is facing the major challenge of feeding the increasing population sustainably. The conventional practices have not only failed to meet the projected needs, but also led to tremendous environmental consequences. Hence, to ensure a food-secure and environmentally sound future, the major thrust is on sustainable alternatives. Due to challenges associated with conventional means of application of biocontrol agents in the management of biotic stresses in agro-ecosystems, significant transformations in this context is needed. The crucial role played by soil microbiomes in efficiently and sustainably managing the agricultural production has unfolded a newer approach of rhizospheric engineering that shows immense promise in mitigating biotic stresses in an eco-friendly manner. The strategy of generating synthetic microbial communities (SynCom), by integrating omics approaches with traditional techniques of enumeration and in-depth analysis of plant-microbe interactions, is encouraging. The review discusses the significance of the rhizospheric microbiome in plant's fitness, and its manipulation for enhancing plant attributes. The focus of the review is to critically analyze the potential tools for the design and utilization of SynCom as a sustainable approach for rhizospheric engineering to ameliorate biotic stresses in plants. Further, based on the synthesis of reports in the area, we have put forth possible solutions to some of the critical issues that impair the large-scale application of SynComs in agriculture.

Dynamic cybergenetic control of bacterial co-culture composition via optogenetic feedback

Communities of microbes play important roles in natural environments and hold great potential for deploying division-of-labor strategies in synthetic biology and bioproduction. However, the difficulty of controlling the composition of microbial consortia over time hinders their optimal use in many applications. Here, we present a fully automated, high-throughput platform that combines real-time measurements and computer-controlled optogenetic modulation of bacterial growth to implement precise and robust compositional control of a two-strain E. coli community. In addition, we develop a general framework for dynamic modeling of synthetic genetic circuits in the physiological context of E. coli and use a host-aware model to determine the optimal control parameters of our closed-loop compositional control system. Our platform succeeds in stabilizing the strain ratio of multiple parallel co-cultures at arbitrary levels and in changing these targets over time, opening the door for the implementation of dynamic compositional programs in synthetic bacterial communities.

“Unity and Struggle of Opposites” as a Basis for the Functioning of Synthetic Bacterial Immobilized Consortium That Continuously Degrades Organophosphorus Pesticides

This work was aimed at the development of an immobilized artificial consortium (IMAC) based on microorganisms belonging to the Gram-positive and Gram-negative bacterial cells capable of jointly carrying out the rapid and effective degradation of different organophosphorus pesticides (OPPs): paraoxon, parathion, methyl parathion, diazinon, chlorpyrifos, malathion, dimethoate, and demeton-S-methyl. A cryogel of poly(vinyl alcohol) was applied as a carrier for the IMAC. After a selection was made between several candidates of the genera Rhodococcus and Pseudomonas, the required combination of two cultures (P. esterophilus and R. ruber) was found. A further change in the ratio between the biomass of the cells inside the granules of IMAC, increasing the packing density of cells inside the same granules and decreasing the size of the granules with IMAC, gave a 225% improvement in the degradation activity of the cell combination. The increase in the velocity and the OPP degradation degree was 4.5 and 16 times greater than the individual P. esterophilus and R. ruber cells, respectively. Multiple uses of the obtained IMAC were demonstrated. The increase in IMAC lactonase activity confirmed the role of the cell quorum in the action efficiency of the synthetic biosystem. The co-inclusion of natural strains in a carrier during immobilization strengthened the IMAC activities without the genetic enhancement of the cells.

Ratiometric control of cell phenotypes in monostrain microbial consortia

We address the problem of regulating and keeping at a desired balance the relative numbers between cells exhibiting a different phenotype within a monostrain microbial consortium. We propose a strategy based on the use of external control inputs, assuming each cell in the community is endowed with a reversible, bistable memory mechanism. Specifically, we provide a general analytical framework to guide the design of external feedback control strategies aimed at balancing the ratio between cells whose memory is stabilized at either one of two equilibria associated with different cell phenotypes. We demonstrate the stability and robustness properties of the control laws proposed and validate them in silico, implementing the memory element via a genetic toggle-switch. The proposed control framework may be used to allow long-term coexistence of different populations, with both industrial and biotechnological applications. As a representative example, we consider the realistic agent-based implementation of our control strategy to enable cooperative bioproduction of a dimer in a monostrain microbial consortium.

Engineered Living Hydrogels

Living biological systems, ranging from single cells to whole organisms, can sense, process information, and actuate in response to changing environmental conditions. Inspired by living biological systems, engineered living cells and nonliving matrices are brought together, which gives rise to the technology of engineered living materials. By designing the functionalities of living cells and the structures of nonliving matrices, engineered living materials can be created to detect variability in the surrounding environment and to adjust their functions accordingly, thereby enabling applications in health monitoring, disease treatment, and environmental remediation. Hydrogels, a class of soft, wet, and biocompatible materials, have been widely used as matrices for engineered living cells, leading to the nascent field of engineered living hydrogels. Here, the interactions between hydrogel matrices and engineered living cells are described, focusing on how hydrogels influence cell behaviors and how cells affect hydrogel properties. The interactions between engineered living hydrogels and their environments, and how these interactions enable versatile applications, are also discussed. Finally, current challenges facing the field of engineered living hydrogels for their applications in clinical and environmental settings are highlighted.

Synthetic nonlinear computation for genetic circuit design

Computation frameworks have been studied in synthetic biology to achieve biosignals integration and processing, for biosensing and therapeutics applications. Biological systems exhibit nonlinearity across scales from the molecular level, to biochemical network and intercellular systems. At the molecular level, cooperative bindings contribute to nonlinear molecular signal processing in a way similar to weight variables. At the intracellular network level, feedback and feedforward regulations result in cell behaviors such as multistability and adaptation. When biochemical networks are distributed in different cell groups, intercelluar networks can generate population dynamics. Here, we review works that highlight nonlinear computations in synthetic biology. We group the works according to the scale of implementations, from the cis-transcription level, to biochemical circuit level and cellular networks.

Management of abiotic stresses by microbiome-based engineering of the rhizosphere

Abiotic stresses detrimentally affect both plant and soil health, threatening food security in an ever-increasing world population. Sustainable agriculture is necessary to augment crop yield with simultaneous management of stresses. Limitations of conventional bioinoculants has shifted the focus on more effective alternatives. With the realisation of the potential of rhizospheric microbiome engineering in enhancing plant's fitness under stresses, efforts have accelerated in this direction. Though still in its infancy, microbiome-based engineering has gained popularity because of its advantages over microbe-based approach. This review briefly presents major abiotic stresses afflicting arable land, followed by introduction to the conventional approach of microbe-based enhancement of plant attributes and stress mitigation with its inherent limitations. It then focusses on the significance of rhizospheric microbiome, and harnessing its potential by its strategic engineering for stress management. Further, success stories related to two major approaches of microbiome engineering (generation of synthetic microbial community/consortium, and host-mediated artificial selection) pertaining to stress management have been critically presented. Together with bringing forth the challenges associated with wide application of rhizospheric microbiome engineering in agriculture, the review proposes the adoption of combinatorial scheme for the same, bringing together ecological and reductionist approaches for improvised sustainable agricultural practices.

Characterizing chemical signaling between engineered "microbial sentinels" in porous microplates

Living materials combine a material scaffold, that is often porous, with engineered cells that perform sensing, computing, and biosynthetic tasks. Designing such systems is difficult because little is known regarding signaling transport parameters in the material. Here, the development of a porous microplate is presented. Hydrogel barriers between wells have a porosity of 60% and a tortuosity factor of 1.6, allowing molecular diffusion between wells. The permeability of dyes, antibiotics, inducers, and quorum signals between wells were characterized. A “sentinel” strain was constructed by introducing orthogonal sensors into the genome of Escherichia coli MG1655 for IPTG, anhydrotetracycline, L‐arabinose, and four quorum signals. The strain’s response to inducer diffusion through the wells was quantified up to 14 mm, and quorum and antibacterial signaling were measured over 16 h. Signaling distance is dictated by hydrogel adsorption, quantified using a linear finite element model that yields adsorption coefficients from 0 to 0.1 mol m⁻³. Parameters derived herein will aid the design of living materials for pathogen remediation, computation, and self‐organizing biofilms.

Design of stable and self-regulated microbial consortia for chemical synthesis

Microbial coculture engineering has emerged as a promising strategy for biomanufacturing. Stability and self-regulation pose a significant challenge for the generation of intrinsically robust cocultures for large-scale applications. Here, we introduce the use of multi-metabolite cross-feeding (MMCF) to establish a close correlation between the strains and the design rules for selecting the appropriate metabolic branches. This leads to an intrinicially stable two-strain coculture where the population composition and the product titer are insensitive to the initial inoculation ratios. With an intermediate-responsive biosensor, the population of the microbial coculture is autonomously balanced to minimize intermediate accumulation. This static-dynamic strategy is extendable to three-strain cocultures, as demonstrated with de novo biosynthesis of silybin/isosilybin. This strategy is generally applicable, paving the way to the industrial application of microbial cocultures.

Stability and tunability are two desirable properties of microbial consortia-based bioproduction. Here, the authors integrate a caffeate-responsive biosensor into two and three strains coculture system to achieve autonomous regulation of strain ratios for coniferol and silybin/isosiltbin production, respectively.

Microbial associations for bioremediation. What does "microbial consortia" mean?

Microbial associations arise as useful tools in several biotechnological processes. Among them, bioremediation of contaminated environments usually takes advantage of these microbial associations. Despite being frequently used, these associations are indicated using a variety of expressions, showing a lack of consensus by specialists in the field. The main idea of this work is to analyze the variety of microbial associations referred to as "microbial consortia" (MC) in the context of pollutants biodegradation and bioremediation. To do that, we summarize the origin of the term pointing out the features that an MC is expected to meet, according to the opinion of several authors. An analysis of related bibliography was done seeking criteria to rationalize and classify MC in the context of bioremediation. We identify that the microbe's origin and the level of human intervention are usually considered as a category to classify them as natural microbial consortia (NMC), artificial microbial consortia (AMC), and synthetic microbial consortia (SMC). In this sense, NMC are those associations composed by microorganisms obtained from a single source while AMC members come from different sources. SMC are a class of AMC in which microbial composition is defined to accomplish a certain specific task. We propose that the effective or potential existence of the interaction among MC members in the source material should be considered as a category in the classification as well, in combination with the origin of the source and level of intervention. Cross-kingdom MC and new developments were also considered. Finally, the existence of grey zones in the limits between each proposed microbial consortia category is addressed. KEY POINTS: • Microbial consortia for bioremediation can be obtained through different methods. • The use of the term "microbial consortia" is unclear in the specialized literature. • We propose a simplified classification for microbial consortia for bioremediation.

Reprogramming microbial populations using a programmed lysis system to improve chemical production

Microbial populations are a promising model for achieving microbial cooperation to produce valuable chemicals. However, regulating the phenotypic structure of microbial populations remains challenging. In this study, a programmed lysis system (PLS) is developed to reprogram microbial cooperation to enhance chemical production. First, a colicin M -based lysis unit is constructed to lyse Escherichia coli. Then, a programmed switch, based on proteases, is designed to regulate the effective lysis unit time. Next, a PLS is constructed for chemical production by combining the lysis unit with a programmed switch. As a result, poly (lactate-co-3-hydroxybutyrate) production is switched from PLH synthesis to PLH release, and the content of free PLH is increased by 283%. Furthermore, butyrate production with E. coli consortia is switched from E. coli BUT003 to E. coli BUT004, thereby increasing butyrate production to 41.61 g/L. These results indicate the applicability of engineered microbial populations for improving the metabolic division of labor to increase the efficiency of microbial cell factories.

Detection of pks Island mRNAs Using Toehold Sensors in Escherichia coli

Synthetic biologists have applied biomolecular engineering approaches toward the goal of novel biological devices and have shown progress in diverse areas of medicine and biotechnology. Especially promising is the application of synthetic biological devices towards a novel class of molecular diagnostics. As an example, a de-novo-designed riboregulator called toehold switch, with its programmability and compatibility with field-deployable devices showed promising in vitro applications for viral RNA detection such as Zika and Corona viruses. However, the in vivo application of high-performance RNA sensors remains challenging due to the secondary structure of long mRNA species. Here, we introduced ‘Helper RNAs’ that can enhance the functionality of toehold switch sensors by mitigating the effect of secondary structures around a target site. By employing the helper RNAs, previously reported mCherry mRNA sensor showed improved fold-changes in vivo. To further generalize the Helper RNA approaches, we employed automatic design pipeline for toehold sensors that target the essential genes within the pks island, an important target of biomedical research in connection with colorectal cancer. The toehold switch sensors showed fold-changes upon the expression of full-length mRNAs that apparently depended sensitively on the identity of the gene as well as the predicted local structure within the target region of the mRNA. Still, the helper RNAs could improve the performance of toehold switch sensors in many instances, with up to 10-fold improvement over no helper cases. These results suggest that the helper RNA approaches can further assist the design of functional RNA devices in vivo with the aid of the streamlined automatic design software developed here. Further, our solutions for screening and stabilizing single-stranded region of mRNA may find use in other in vivo mRNA-sensing applications such as cas13 crRNA design, transcriptome engineering, and trans-cleaving ribozymes.

Engineered microbial consortia: strategies and applications

Many applications of microbial synthetic biology, such as metabolic engineering and biocomputing, are increasing in design complexity. Implementing complex tasks in single populations can be a challenge because large genetic circuits can be burdensome and difficult to optimize. To overcome these limitations, microbial consortia can be engineered to distribute complex tasks among multiple populations. Recent studies have made substantial progress in programming microbial consortia for both basic understanding and potential applications. Microbial consortia have been designed through diverse strategies, including programming mutualistic interactions, using programmed population control to prevent overgrowth of individual populations, and spatial segregation to reduce competition. Here, we highlight the role of microbial consortia in the advances of metabolic engineering, biofilm production for engineered living materials, biocomputing, and biosensing. Additionally, we discuss the challenges for future research in microbial consortia.

Analysis of gut microbiota in three species belonging to different genera (Hemitragus, Pseudois, and Ovis) from the subfamily Caprinae in the absence of environmental variance

This study aimed to identify the effects of host species on the gut microbial flora in three species (Hemitragus jemlahicus, Pseudois nayaur, and Ovis orientalis) from the subfamily Caprinae, by excluding the impact of environment factors. We investigated the differences in intestinal flora of three species belonging to Caprinae, which were raised in identical conditions. Fecal samples were collected from tahr, mouflon, and bharal, and the V3-V4 region of the 16S ribosomal RNA gene was analyzed by high-throughput sequencing. The analysis of 16S rRNA gene sequences reveals that fecal samples were mainly composed of four phyla: Firmicutes, Bacteroidetes, Spirochaetes, and Proteobacteria. The most abundant phyla included Firmicutes and Bacteroidetes accounting for >90% of the bacteria, and a higher Firmicutes/Bacteroidetes ratio was observed in tahrs. Moreover, significant differences existed at multiple levels of classifications in the relative abundance of intestinal flora, differing greatly between species. Phylogenetic analyses based on 16S rRNA gene indicated that mouflon is closely related to bharal, and it is inconsistent with previous reports in the species evolutionary relationships. In this study, we demonstrated that the gut microbiota in tahr had a stronger ability to absorb and store energy from the diet compared with mouflon and bharal, and the characteristics of host-microbiome interactions were not significant.

Emergent spatiotemporal population dynamics with cell-length control of synthetic microbial consortia

The increased complexity of synthetic microbial biocircuits highlights the need for distributed cell functionality due to concomitant increases in metabolic and regulatory burdens imposed on single-strain topologies. Distributed systems, however, introduce additional challenges since consortium composition and spatiotemporal dynamics of constituent strains must be robustly controlled to achieve desired circuit behaviors. Here, we address these challenges with a modeling-based investigation of emergent spatiotemporal population dynamics using cell-length control in monolayer, two-strain bacterial consortia. We demonstrate that with dynamic control of a strain's division length, nematic cell alignment in close-packed monolayers can be destabilized. We find that this destabilization confers an emergent, competitive advantage to smaller-length strains-but by mechanisms that differ depending on the spatial patterns of the population. We used complementary modeling approaches to elucidate underlying mechanisms: an agent-based model to simulate detailed mechanical and signaling interactions between the competing strains, and a reductive, stochastic lattice model to represent cell-cell interactions with a single rotational parameter. Our modeling suggests that spatial strain-fraction oscillations can be generated when cell-length control is coupled to quorum-sensing signaling in negative feedback topologies. Our research employs novel methods of population control and points the way to programming strain fraction dynamics in consortial synthetic biology.

An evolutionary algorithm for designing microbial communities via environmental modification

Despite a growing understanding of how environmental composition affects microbial communities, it remains difficult to apply this knowledge to the rational design of synthetic multispecies consortia. This is because natural microbial communities can harbour thousands of different organisms and environmental substrates, making up a vast combinatorial space that precludes exhaustive experimental testing and computational prediction. Here, we present a method based on the combination of machine learning and metabolic modelling that selects optimal environmental compositions to produce target community phenotypes. In this framework, dynamic flux balance analysis is used to model the growth of a community in candidate environments. A genetic algorithm is then used to evaluate the behaviour of the community relative to a target phenotype, and subsequently adjust the environment to allow the organisms to approach this target. We apply this iterative process to thousands of in silico communities of varying sizes, showing how it can rapidly identify environments that yield desired taxonomic compositions and patterns of metabolic exchange. Moreover, this combination of approaches produces testable predictions for the assembly of experimental microbial communities with specific properties and can facilitate rational environmental design processes for complex microbiomes.

Synthetic neural-like computing in microbial consortia for pattern recognition

Complex biological systems in nature comprise cells that act collectively to solve sophisticated tasks. Synthetic biological systems, in contrast, are designed for specific tasks, following computational principles including logic gates and analog design. Yet such approaches cannot be easily adapted for multiple tasks in biological contexts. Alternatively, artificial neural networks, comprised of flexible interactions for computation, support adaptive designs and are adopted for diverse applications. Here, motivated by the structural similarity between artificial neural networks and cellular networks, we implement neural-like computing in bacteria consortia for recognizing patterns. Specifically, receiver bacteria collectively interact with sender bacteria for decision-making through quorum sensing. Input patterns formed by chemical inducers activate senders to produce signaling molecules at varying levels. These levels, which act as weights, are programmed by tuning the sender promoter strength Furthermore, a gradient descent based algorithm that enables weights optimization was developed. Weights were experimentally examined for recognizing 3 × 3-bit pattern.

Vertical and horizontal quorum-sensing-based multicellular communications

Quorum sensing (QS) plays an important role in both natural and synthetic microbial systems. The complexity of QS entails multilayer controls, biomolecular crosstalk, and population-based interactions. In this review, we divide complex QS-based interactions into vertical and horizontal interactions. With respect to the former, we discuss QS-based interactions among phages, bacteria, and hosts in natural microbial systems, which are based on various QS signals and hormones. With regard to the latter, we highlight manipulations of QS-based interactions for multicomponent synthetic microbial consortia. We further present the recent and emerging applications of manipulating these interactions (collectively referred to as 'QS communication networks') in natural and synthetic microbiota. Finally, we identify key challenges in engineering diverse QS communication networks for various future applications.

Control of synthetic microbial consortia in time, space, and composition

While synthetic microbial systems are becoming increasingly complicated, single-strain systems cannot match the complexity of their multicellular counterparts. Such complexity, however, is much more difficult to control. Recent advances have increased our ability to control temporal, spatial, and community compositional organization, including modular adhesive systems, strain growth relationships, and asymmetric cell division. While these systems generally work independently, combining them into unified systems has proven difficult. Once such unification is proven successful we will unlock a new frontier of synthetic biology and open the door to the creation of synthetic biological systems with true multicellularity.

Autonomous and Assisted Control for Synthetic Microbiology

The control of microbes and microbial consortia to achieve specific functions requires synthetic circuits that can reliably cope with internal and external perturbations. Circuits that naturally evolved to regulate biological functions are frequently robust to alterations in their parameters. As the complexity of synthetic circuits increases, synthetic biologists need to implement such robust control "by design". This is especially true for intercellular signaling circuits for synthetic consortia, where robustness is highly desirable, but its mechanisms remain unclear. Cybergenetics, the interface between synthetic biology and control theory, offers two approaches to this challenge: external (computer-aided) and internal (autonomous) control. Here, we review natural and synthetic microbial systems with robustness, and outline experimental approaches to implement such robust control in microbial consortia through population-level cybergenetics. We propose that harnessing natural intercellular circuit topologies with robust evolved functions can help to achieve similar robust control in synthetic intercellular circuits. A "hybrid biology" approach, where robust synthetic microbes interact with natural consortia and-additionally-with external computers, could become a useful tool for health and environmental applications.

A Systematic Review of the Criminogenic Potential of Synthetic Biology and Routes to Future Crime Prevention

Synthetic biology has the potential to positively transform society in many application areas, including medicine. In common with all revolutionary new technologies, synthetic biology can also enable crime. Like cybercrime, that emerged following the advent of the internet, biocrime can have a significant effect on society, but may also impact on peoples' health. For example, the scale of harm caused by the SARS-CoV-2 pandemic illustrates the potential impact of future biocrime and highlights the need for prevention strategies. Systematic evidence quantifying the crime opportunities posed by synthetic biology has to date been very limited. Here, we systematically reviewed forms of crime that could be facilitated by synthetic biology with a view to informing their prevention. A total of 794 articles from four databases were extracted and a three-step screening phase resulted in 15 studies that met our threshold criterion for thematic synthesis. Within those studies, 13 exploits were identified. Of these, 46% were dependent on technologies characteristic of synthetic biology. Eight potential crime types emerged from the studies: bio-discrimination, cyber-biocrime, bio-malware, biohacking, at-home drug manufacturing, illegal gene editing, genetic blackmail, and neuro-hacking. 14 offender types were identified. For the most commonly identified offenders (>3 mentions) 40% were outsider threats. These observations suggest that synthetic biology presents substantial new offending opportunities. Moreover, that more effective engagement, such as ethical hacking, is needed now to prevent a crime harvest from developing in the future. A framework to address the synthetic biology crime landscape is proposed.