Control points for design of taxonomic composition in synthetic human gut communities.

Microbial communities offer vast potential across numerous sectors but remain challenging to systematically control. We develop a two-stage approach to guide the taxonomic composition of synthetic microbiomes by precisely manipulating media components and initial species abundances. By combining high-throughput experiments and computational modeling, we demonstrate the ability to predict and design the diversity of a 10-member synthetic human gut community. We reveal that critical environmental factors governing monoculture growth can be leveraged to steer microbial communities to desired states. Furthermore, systematically varied initial abundances drive variation in community assembly and enable inference of pairwise inter-species interactions via a dynamic ecological model. These interactions are overall consistent with conditioned media experiments, demonstrating that specific perturbations to a high-richness community can provide rich information for building dynamic ecological models. This model is subsequently used to design low-richness communities that display low or high temporal taxonomic variability over an extended period. A record of this paper's transparent peer review process is included in the supplemental information.

Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics.

Predicting the dynamics and functions of microbiomes constructed from the bottom-up is a key challenge in exploiting them to our benefit. Current models based on ecological theory fail to capture complex community behaviors due to higher order interactions, do not scale well with increasing complexity and in considering multiple functions. We develop and apply a long short-term memory (LSTM) framework to advance our understanding of community assembly and health-relevant metabolite production using a synthetic human gut community. A mainstay of recurrent neural networks, the LSTM learns a high dimensional data-driven non-linear dynamical system model. We show that the LSTM model can outperform the widely used generalized Lotka-Volterra model based on ecological theory. We build methods to decipher microbe-microbe and microbe-metabolite interactions from an otherwise black-box model. These methods highlight that Actinobacteria, Firmicutes and Proteobacteria are significant drivers of metabolite production whereas Bacteroides shape community dynamics. We use the LSTM model to navigate a large multidimensional functional landscape to design communities with unique health-relevant metabolite profiles and temporal behaviors. In sum, the accuracy of the LSTM model can be exploited for experimental planning and to guide the design of synthetic microbiomes with target dynamic functions.

Under-Oil Autonomously Regulated Oxygen Microenvironments: A Goldilocks Principle-Based Approach for Microscale Cell Culture.

Oxygen levels in vivo are autonomously regulated by a supply-demand balance, which can be altered in disease states. However, the oxygen levels of in vitro cell culture systems, particularly microscale cell culture, are typically dominated by either supply or demand. Further, the oxygen microenvironment in these systems is rarely monitored or reported. Here, a method to establish and dynamically monitor autonomously regulated oxygen microenvironments (AROM) using an oil overlay in an open microscale cell culture system is presented. Using this method, the oxygen microenvironment is dynamically regulated via the supply-demand balance of the system. Numerical simulation and experimental validation of oxygen transport within multi-liquid-phase, microscale culture systems involving a variety of cell types, including mammalian, fungal, and bacterial cells are presented. Finally, AROM is applied to establish a coculture between cells with disparate oxygen demands-primary intestinal epithelial cells (oxygen consuming) and Bacteroides uniformis (an anaerobic species prevalent in the human gut).

Negative interactions determine Clostridioides difficile growth in synthetic human gut communities.

Understanding the principles of colonization resistance of the gut microbiome to the pathogen Clostridioides difficile will enable the design of defined bacterial therapeutics. We investigate the ecological principles of community resistance to C. difficile using a synthetic human gut microbiome. Using a dynamic computational model, we demonstrate that C. difficile receives the largest number and magnitude of incoming negative interactions. Our results show that C. difficile is in a unique class of species that display a strong negative dependence between growth and species richness. We identify molecular mechanisms of inhibition including acidification of the environment and competition over resources. We demonstrate that Clostridium hiranonis strongly inhibits C. difficile partially via resource competition. Increasing the initial density of C. difficile can increase its abundance in the assembled community, but community context determines the maximum achievable C. difficile abundance. Our work suggests that the C. difficile inhibitory potential of defined bacterial therapeutics can be optimized by designing communities featuring a combination of mechanisms including species richness, environment acidification, and resource competition.

Design of synthetic human gut microbiome assembly and butyrate production.

The capability to design microbiomes with predictable functions would enable new technologies for applications in health, agriculture, and bioprocessing. Towards this goal, we develop a model-guided approach to design synthetic human gut microbiomes for production of the health-relevant metabolite butyrate. Our data-driven model quantifies microbial interactions impacting growth and butyrate production separately, providing key insights into ecological mechanisms driving butyrate production. We use our model to explore a vast community design space using a design-test-learn cycle to identify high butyrate-producing communities. Our model can accurately predict community assembly and butyrate production across a wide range of species richness. Guided by the model, we identify constraints on butyrate production by high species richness and key molecular factors driving butyrate production, including hydrogen sulfide, environmental pH, and resource competition. In sum, our model-guided approach provides a flexible and generalizable framework for understanding and accurately predicting community assembly and metabolic functions.

Wellness: Combating Burnout and Its Consequences in Emergency Medicine.

Medicine recognizes burnout as a threat to quality patient care and physician quality of life. This issue exists throughout medicine but is notably prevalent in emergency medicine (EM). Because the concept of "wellness" lacks a clear definition, attempts at ameliorating burnout that focus on achieving wellness make success difficult to achieve and measure. Recent work within the wellness literature suggests that the end goal should be to achieve a culture of wellness by addressing all aspects of the physician's environment. A review of the available literature on burnout and wellness interventions in all medical specialties reveals that interventions focusing on individual physicians have varying levels of success. Efforts to compare these interventions are hampered by a lack of consistent endpoints. Studies with consistent endpoints do not demonstrate clear benefits of achieving them because improving scores on various scales may not equate to improvement in quality of care or physician quality of life. Successful interventions have uncertain, long-term effects. Outside of EM, the most successful interventions focus on changes to systems rather than to individual physicians. Within EM, the number of well-structured interventions that have been studied is limited. Future work to achieve the desired culture of wellness within EM requires establishment of a consistent endpoint that serves as a surrogate for clinical significance, addressing contributors to burnout at all levels, and integrating successful interventions into the fabric of EM.

Microbial Interaction Network Inference in Microfluidic Droplets.

Microbial interactions are major drivers of microbial community dynamics and functions but remain challenging to identify because of limitations in parallel culturing and absolute abundance quantification of community members across environments and replicates. To this end, we developed Microbial Interaction Network Inference in microdroplets (MINI-Drop). Fluorescence microscopy coupled to computer vision techniques were used to rapidly determine the absolute abundance of each strain in hundreds to thousands of droplets per condition. We showed that MINI-Drop could accurately infer pairwise and higher-order interactions in synthetic consortia. We developed a stochastic model of community assembly to provide insight into the heterogeneity in community states across droplets. Finally, we elucidated the complex web of interactions linking antibiotics and different species in a synthetic consortium. In sum, we demonstrated a robust and generalizable method to infer microbial interaction networks by random encapsulation of sub-communities into microfluidic droplets.

Light-optimized growth of cyanobacterial cultures: Growth phases and productivity of biomass and secreted molecules in light-limited batch growth.

Cyanobacteria are photosynthetic microorganisms whose metabolism can be modified through genetic engineering for production of a wide variety of molecules directly from CO2, light, and nutrients. Diverse molecules have been produced in small quantities by engineered cyanobacteria to demonstrate the feasibility of photosynthetic biorefineries. Consequently, there is interest in engineering these microorganisms to increase titer and productivity to meet industrial metrics. Unfortunately, differing experimental conditions and cultivation techniques confound comparisons of strains and metabolic engineering strategies. In this work, we discuss the factors governing photoautotrophic growth and demonstrate nutritionally replete conditions in which a model cyanobacterium can be grown to stationary phase with light as the sole limiting substrate. We introduce a mathematical framework for understanding the dynamics of growth and product secretion in light-limited cyanobacterial cultures. Using this framework, we demonstrate how cyanobacterial growth in differing experimental systems can be easily scaled by the volumetric photon delivery rate using the model organisms Synechococcus sp. strain PCC7002 and Synechococcus elongatus strain UTEX2973. We use this framework to predict scaled up growth and product secretion in 1L photobioreactors of two strains of Synechococcus PCC7002 engineered for production of l-lactate or L-lysine. The analytical framework developed in this work serves as a guide for future metabolic engineering studies of cyanobacteria to allow better comparison of experiments performed in different experimental systems and to further investigate the dynamics of growth and product secretion.

High-CO2 Requirement as a Mechanism for the Containment of Genetically Modified Cyanobacteria.

As researchers engineer cyanobacteria for biotechnological applications, we must consider potential environmental release of these organisms. Previous theoretical work has considered cyanobacterial containment through elimination of the CO2-concentrating mechanism (CCM) to impose a high-CO2 requirement (HCR), which could be provided in the cultivation environment but not in the surroundings. In this work, we experimentally implemented an HCR containment mechanism in Synechococcus sp. strain PCC7002 (PCC7002) through deletion of carboxysome shell proteins and showed that this mechanism contained cyanobacteria in a 5% CO2 environment. We considered escape through horizontal gene transfer (HGT) and reduced the risk of HGT escape by deleting competence genes. We showed that the HCR containment mechanism did not negatively impact the performance of a strain of PCC7002 engineered for L-lactate production. We showed through coculture experiments of HCR strains with ccm-containing strains that this HCR mechanism reduced the frequency of escape below the NIH recommended limit for recombinant organisms of one escape event in 108 CFU.

Engineering photosynthetic production of L-lysine.

L-lysine and other amino acids are commonly produced through fermentation using strains of heterotrophic bacteria such as Corynebacterium glutamicum. Given the large amount of sugar this process consumes, direct photosynthetic production is intriguing alternative. In this study, we report the development of a cyanobacterium, Synechococcus sp. strain PCC 7002, capable of producing L-lysine with CO2 as the sole carbon-source. We found that heterologous expression of a lysine transporter was required to excrete lysine and avoid intracellular accumulation that correlated with poor fitness. Simultaneous expression of a feedback inhibition resistant aspartate kinase and lysine transporter were sufficient for high productivities, but this was also met with a decreased chlorophyll content and reduced growth rates. Increasing the reductant supply by using NH4+, a more reduced nitrogen source relative to NO3-, resulted in a two-fold increase in productivity directing 18% of fixed carbon to lysine. Given this advantage, we demonstrated lysine production from media formulated with a municipal wastewater treatment sidestream as a nutrient source for increased economic and environmental sustainability. Based on our results, we project that Synechococcus sp. strain PCC 7002 could produce lysine at areal productivities approaching that of sugar cane to lysine via fermentation using non-agricultural lands and low-cost feedstocks.

A metabolic pathway for catabolizing levulinic acid in bacteria.

Microorganisms can catabolize a wide range of organic compounds and therefore have the potential to perform many industrially relevant bioconversions. One barrier to realizing the potential of biorefining strategies lies in our incomplete knowledge of metabolic pathways, including those that can be used to assimilate naturally abundant or easily generated feedstocks. For instance, levulinic acid (LA) is a carbon source that is readily obtainable as a dehydration product of lignocellulosic biomass and can serve as the sole carbon source for some bacteria. Yet, the genetics and structure of LA catabolism have remained unknown. Here, we report the identification and characterization of a seven-gene operon that enables LA catabolism in Pseudomonas putida KT2440. When the pathway was reconstituted with purified proteins, we observed the formation of four acyl-CoA intermediates, including a unique 4-phosphovaleryl-CoA and the previously observed 3-hydroxyvaleryl-CoA product. Using adaptive evolution, we obtained a mutant of Escherichia coli LS5218 with functional deletions of fadE and atoC that was capable of robust growth on LA when it expressed the five enzymes from the P. putida operon. This discovery will enable more efficient use of biomass hydrolysates and metabolic engineering to develop bioconversions using LA as a feedstock.

A roadmap for the synthesis of separation networks for the recovery of bio-based chemicals: Matching biological and process feasibility.

Microbial conversion of renewable feedstocks to high-value chemicals is an attractive alternative to current petrochemical processes because it offers the potential to reduce net CO2 emissions and integrate with bioremediation objectives. Microbes have been genetically engineered to produce a growing number of high-value chemicals in sufficient titer, rate, and yield from renewable feedstocks. However, high-yield bioconversion is only one aspect of an economically viable process. Separation of biologically synthesized chemicals from process streams is a major challenge that can contribute to >70% of the total production costs. Thus, process feasibility is dependent upon the efficient selection of separation technologies. This selection is dependent on upstream processing or biological parameters, such as microbial species, product titer and yield, and localization. Our goal is to present a roadmap for selection of appropriate technologies and generation of separation schemes for efficient recovery of bio-based chemicals by utilizing information from upstream processing, separation science and commercial requirements. To achieve this, we use a separation system comprising of three stages: (I) cell and product isolation, (II) product concentration, and (III) product purification and refinement. In each stage, we review the technology alternatives available for different tasks in terms of separation principles, important operating conditions, performance parameters, advantages and disadvantages. We generate separation schemes based on product localization and its solubility in water, the two most distinguishing properties. Subsequently, we present ideas for simplification of these schemes based on additional properties, such as physical state, density, volatility, and intended use. This simplification selectively narrows down the technology options and can be used for systematic process synthesis and optimal recovery of bio-based chemicals.