Bacillus subtilis engineered for topical delivery of an antifungal agent.

Fungal skin infections are a common condition affecting 20-25 percent of the world population. While these conditions are treatable with regular application of an antifungal medication, we sought to develop a more convenient, longer-lasting topical antifungal platform that could increase patient adherence to treatment regimens by using Bacillus subtilis, a naturally antifungal bacteria found on the skin, for drug production and delivery. In this study, we engineered B. subtilis for increased production of the antifungal lipopeptide iturin A by overexpression of the pleiotropic regulator DegQ. The engineered strain had an over 200% increase in iturin A production as detected by HPLC, accompanied by slower growth but the same terminal cell density as determined by absorbance measurements of liquid culture. In an in vitro antifungal assay, we found that despite its higher iturin A production, the engineered strain was less effective at reducing the growth of a plug of the pathogenic fungus Trichophyton mentagrophytes on an agar plate compared to the parent strain. The reduced efficacy of the engineered strain may be explained by its reduced growth rate, which highlights the need to address trade-offs between titers (e.g. measured drug production) and other figures of merit (e.g. growth rate) during metabolic engineering.

Plasmid Crosstalk in Cell-Free Expression Systems.

Although cell-free protein expression has been widely used for the synthesis of single proteins, cell-free synthetic biology has rapidly expanded to new, more complex applications. One such application is the prototyping or implementation of complex genetic networks involving the expression of multiple proteins at precise ratios, often from different plasmids. However, expression of multiple proteins from multiple plasmids may inadvertently result in unexpected, off-target changes to the levels of the proteins being expressed, a phenomenon termed plasmid crosstalk. Here, we show that the effects of plasmid crosstalk─even at the qualitative level of increases vs decreases in protein expression─depend on the concentration of plasmids in the reaction and the type of transcriptional machinery involved in the expression. This crosstalk can have a significant impact on genetic circuitry function and even interpretation of simple experimental results and thus should be taken into consideration during the development of cell-free applications.

Complex Dependence of Escherichia coli-based Cell-Free Expression on Sonication Energy During Lysis.

Cell lysis─by sonication or bead beating, for example─is a key step in preparing extracts for cell-free expression systems. To create high protein-production capacity extracts, standard practice is to lyse cells sufficiently to thoroughly disrupt the membrane and thus extract expression machinery but without degrading that machinery. Here, we investigate the impact of different sonication energy inputs on the protein-production capacity of Escherichia coli extracts. While the existence of operator-specific optimal sonication energy inputs is widely known, our findings show that the sonication energy input that yields maximal protein output from a given expression template may depend on plasmid concentration, transcriptional and translational features (e.g., promoter), and other expression vector components (e.g., origin of replication). These results indicate that sonication protocols cannot be standardized to a single optimum, suggest strategies for improving protein yields, and more broadly highlight the need for better metrics and protocols for characterizing cell extracts.

A Cell-Free Biosensor for Assessment of Hyperhomocysteinemia.

Hyperhomocysteinemia─a condition characterized by elevated levels of homocysteine in the blood─is associated with multiple health conditions including folate deficiency and birth defects, but there are no convenient, low-cost methods to measure homocysteine in plasma. A cell-free biosensor that harnesses the native homocysteine sensing machinery of Escherichia coli bacteria could satisfy the need for a detection platform with these characteristics. Here, we describe our efforts to engineer a cell-free biosensor for point-of-care, low-cost assessment of homocysteine status. This biosensor can detect physiologically relevant concentrations of homocysteine in plasma with a colorimetric output visible to the naked eye in under 1.5 h, making it a fast, convenient tool for point-of-use diagnosis and monitoring of hyperhomocysteinemia and related health conditions.

Introduction to Engineering Biology: A Conceptual Framework for Teaching Synthetic Biology.

As the impacts of engineering biology grow, it is important to introduce the field early and in an accessible way. However, teaching engineering biology poses challenges, such as limited representation of the field in widely used scientific textbooks or curricula, and the interdisciplinary nature of the subject. We have created an adaptable curriculum module that can be used by anyone to teach the basic principles and applications of engineering biology. The module consists of a versatile, concept-based slide deck designed by experts across engineering biology to cover key topic areas. Starting with the design, build, test, and learn cycle, the slide deck covers the framework, core tools, and applications of the field at an undergraduate level. The module is available for free on a public website and can be used in a stand-alone fashion or incorporated into existing curricular materials. Our aim is that this modular, accessible slide deck will improve the ease of teaching current engineering biology topics and increase public engagement with the field.

Rapid and Finely-Tuned Expression for Deployable Sensing Applications.

Organisms from across the tree of life have evolved highly efficient mechanisms for sensing molecules of interest using biomolecular machinery that can in turn be quite valuable for the development of biosensors. However, purification of such machinery for use in in vitro biosensors is costly, while the use of whole cells as in vivo biosensors often leads to long sensor response times and unacceptable sensitivity to the chemical makeup of the sample. Cell-free expression systems overcome these weaknesses by removing the requirements associated with maintaining living sensor cells, allowing for increased function in toxic environments and rapid sensor readout at a production cost that is often more reasonable than purification. Here, we focus on the challenge of implementing cell-free protein expression systems that meet the stringent criteria required for them to serve as the basis for field-deployable biosensors. Fine-tuning expression to meet these requirements can be achieved through careful selection of the sensing and output elements, as well as through optimization of reaction conditions via tuning of DNA/RNA concentrations, lysate preparation methods, and buffer conditions. Through careful sensor engineering, cell-free systems can continue to be successfully used for the production of tightly regulated, rapidly expressing genetic circuits for biosensors.

Short Activators and Repressors of RNA Toehold Switches.

RNA toehold switches are a widely used class of molecule to detect specific RNA "trigger" sequences, but their design, intended function, and characterization to date leave it unclear whether they can function properly with triggers shorter than 36 nucleotides. Here, we explore the feasibility of using standard toehold switches with 23-nucleotide truncated triggers. We assess the crosstalk of different triggers with significant homology and identify a highly sensitive trigger region where just one mutation from the consensus trigger sequence can reduce switch activation by 98.6%. However, we also find that triggers with as many as seven mutations outside of this region can still lead to 5-fold induction of the switch. We also present a new approach using 18- to 22-nucleotide triggers as translational repressors for toehold switches and assess the off-target regulation for this strategy as well. The development and characterization of these strategies could help enable applications like microRNA sensors, where well-characterized crosstalk between sensors and detection of short target sequences are critical.

Towards inferring absolute concentrations from relative abundance in time-course GC-MS metabolomics data.

Metabolomics, the large-scale study of metabolites, has significant appeal as a source of information for metabolic modeling and other scientific applications. One common approach for measuring metabolomics data is gas chromatography-mass spectrometry (GC-MS). However, GC-MS metabolomics data are typically reported as relative abundances, precluding their use with approaches and tools where absolute concentrations are necessary. While chemical standards can be used to help provide quantification, their use is time-consuming, expensive, or even impossible due to their limited availability. The ability to infer absolute concentrations from GC-MS metabolomics data without chemical standards would have significant value. We hypothesized that when analyzing time-course metabolomics datasets, the mass balances of metabolism and other biological information could provide sufficient information towards inference of absolute concentrations. To demonstrate this, we developed and characterized MetaboPAC, a computational framework that uses two approaches-one based on kinetic equations and another using biological heuristics-to predict the most likely response factors that allow translation between relative abundances and absolute concentrations. When used to analyze noiseless synthetic data generated from multiple types of kinetic rate laws, MetaboPAC performs significantly better than negative control approaches when 20% of kinetic terms are known a priori. Under conditions of lower sampling frequency and high noise, MetaboPAC is still able to provide significant inference of concentrations in 3 of 4 models studied. This provides a starting point for leveraging biological knowledge to extract concentration information from time-course intracellular GC-MS metabolomics datasets, particularly for systems that are well-studied and have partially known kinetic structures.

MaHPIC malaria systems biology data from Plasmodium cynomolgi sporozoite longitudinal infections in macaques.

Plasmodium cynomolgi causes zoonotic malarial infections in Southeast Asia and this parasite species is important as a model for Plasmodium vivax and Plasmodium ovale. Each of these species produces hypnozoites in the liver, which can cause relapsing infections in the blood. Here we present methods and data generated from iterative longitudinal systems biology infection experiments designed and performed by the Malaria Host-Pathogen Interaction Center (MaHPIC) to delve deeper into the biology, pathogenesis, and immune responses of P. cynomolgi in the Macaca mulatta host. Infections were initiated by sporozoite inoculation. Blood and bone marrow samples were collected at defined timepoints for biological and computational experiments and integrative analyses revolving around primary illness, relapse illness, and subsequent disease and immune response patterns. Parasitological, clinical, haematological, immune response, and -omic datasets (transcriptomics, proteomics, metabolomics, and lipidomics) including metadata and computational results have been deposited in public repositories. The scope and depth of these datasets are unprecedented in studies of malaria, and they are projected to be a F.A.I.R., reliable data resource for decades.

Harnessing Escherichia coli's Native Machinery for Detection of Vitamin C (Ascorbate) Deficiency.

Vitamin C (l-ascorbate) deficiency is a global public health issue most prevalent in resource-limited regions, creating a need for an inexpensive detection platform. Here, we describe efforts to engineer whole-cell and cell-free ascorbate biosensors. Both sensors used the protein UlaR, which binds to a metabolite of ascorbate and regulates transcription. The whole-cell sensor could detect lower, physiologically relevant concentrations of ascorbate, which we attributed to intact functionality of a phosphotransferase system (PTS) that transports ascorbate across the cell membrane and phosphorylates it to form UlaR's ligand. We used multiple strategies to enhance cell-free PTS functionality (which has received little previous attention), improving the cell-free sensor's performance, but the whole-cell sensor remained more sensitive. These efforts demonstrated an advantage of whole-cell sensors for detection of molecules─like ascorbate─transformed by a PTS, but also proof of principle for cell-free sensors requiring membrane-bound components like the PTS. In addition, the cell-free sensor was functional in plasma, setting the stage for future implementation of ascorbate sensors for clinically relevant biofluids in field-deployable formats.

Corrigendum: Effective use of linear DNA in cell-free expression systems.

[This corrects the article DOI: 10.3389/fbioe.2021.715328.].

Low-cost, point-of-care biomarker quantification.

Low-cost, point-of-care (POC) devices that allow fast, on-site disease diagnosis could have a major global health impact, particularly if they can provide quantitative measurement of molecules indicative of a diseased state (biomarkers). Accurate quantification of biomarkers in patient samples is already challenging when research-grade, sophisticated equipment is available; it is even more difficult when constrained to simple, cost-effective POC platforms. Here, we summarize the main challenges to accurate, low-cost POC biomarker quantification. We also review recent efforts to develop and implement POC tools beyond qualitative readouts, and we conclude by identifying important future research directions.

Systems biology-based analysis of cell-free systems.

Cell-free expression systems are becoming increasingly widely used due to their diverse applications in biotechnology. Despite this rapid expansion in adoption, many aspects of cell-free systems remain surprisingly poorly understood. Systems biology approaches make it possible to characterize cell-free systems deeply and broadly to better understand their underlying complexity. Here, we review recent systems biology studies that have provided insight into cell-free systems. We focus on characterization of the cell-free proteome, including its dependence on preparation protocol and host strain, as well as the cell-free metabolome and the relationship of endogenous metabolism to system performance. We conclude by highlighting promising future research directions.

Metabolomics Analysis of Cell-Free Expression Systems Using Gas Chromatography-Mass Spectrometry.

Metabolomics is the systems-scale study of the biochemical intermediates of metabolism. This approach has great potential to uncover how metabolic intermediates are used and generated in cell-free expression systems, something that is to date not well understood. Here, we present a detailed metabolomics protocol for characterization of the small molecules in cell-free systems. We specifically focus on the analysis of Escherichia coli lysate-based cell-free systems using gas chromatography coupled to mass spectrometry. Measuring and monitoring the metabolic changes in cell-free systems can provide insight into the ways that metabolites affect the productivity of cell-free reactions, ultimately allowing for more informed engineering and optimization efforts for cell-free systems.

Diverse classes of constraints enable broader applicability of a linear programming-based dynamic metabolic modeling framework.

Current metabolic modeling tools suffer from a variety of limitations, from scalability to simplifying assumptions, that preclude their use in many applications. We recently created a modeling framework, Linear Kinetics-Dynamic Flux Balance Analysis (LK-DFBA), that addresses a key gap: capturing metabolite dynamics and regulation while retaining a potentially scalable linear programming structure. Key to this framework's success are the linear kinetics and regulatory constraints imposed on the system. However, while the linearity of these constraints reduces computational complexity, it may not accurately capture the behavior of many biochemical systems. Here, we developed three new classes of LK-DFBA constraints to better model interactions between metabolites and the reactions they regulate. We tested these new approaches on several synthetic and biological systems, and also performed the first-ever comparison of LK-DFBA predictions to experimental data. We found that no single constraint approach was optimal across all systems examined, and systems with the same topological structure but different parameters were often best modeled by different types of constraints. However, we did find that when genetic perturbations were implemented in the systems, the optimal constraint approach typically remained the same as for the wild-type regardless of the model topology or parameterization, indicating that just a single wild-type dataset could allow identification of the ideal constraint to enable model predictivity for a given system. These results suggest that the availability of multiple constraint approaches will allow LK-DFBA to model a wider range of metabolic systems.

Nucleic acid partitioning in PEG-Ficoll protocells.

The phase separation of aqueous polymer solutions is a widely used method for producing self-assembled, membraneless droplet protocells. Non-ionic synthetic polymers forming an aqueous two-phase system (ATPS) have been shown to reliably form protocells that, when equipped with biological materials, are useful for applications such as analyte detection. Previous characterization of an ATPS-templated protocell did not investigate the effects of its biological components on phase stability. Here we report the phase diagram of a PEG 35k-Ficoll 400k-water ATPS at baseline and in the presence of necessary protocell components. Because the stability of an ATPS can be sensitive to small changes in composition, which in turn impacts solute partitioning, we present partitioning data of a variety of nucleic acids in response to protocell additives. The results show that the additives-particularly a mixture of salts and small organic molecules-have profound positive effects on ATPS stability and nucleic acid partitioning, both of which significantly contribute to protocell function. Our data uncovers several new areas of optimization for future protocell engineering.

Point-of-Care Analyte Quantification and Digital Readout via Lysate-Based Cell-Free Biosensors Interfaced with Personal Glucose Monitors.

Field-deployable diagnostics based on cell-free systems have advanced greatly, but on-site quantification of target analytes remains a challenge. Here we demonstrate that Escherichia coli lysate-based cell-free biosensors coupled to a personal glucose monitor (PGM) can enable on-site analyte quantification, with the potential for straightforward reconfigurability to diverse types of analytes. We show that analyte-responsive regulators of transcription and translation can modulate the production of the reporter enzyme ß-galactosidase, which in turn converts lactose into glucose for PGM quantification. Because glycolysis is active in the lysate and would readily deplete converted glucose, we decoupled enzyme production and glucose conversion to increase the end point signal output. However, this lysate metabolism did allow for one-pot removal of glucose present in complex samples (like human serum) without confounding target quantification. Taken together, our results show that integrating lysate-based cell-free biosensors with PGMs enables accessible target detection and quantification at the point of need.

Dramatic transcriptomic differences in Macaca mulatta and Macaca fascicularis with Plasmodium knowlesi infections.

Plasmodium knowlesi, a model malaria parasite, is responsible for a significant portion of zoonotic malaria cases in Southeast Asia and must be controlled to avoid disease severity and fatalities. However, little is known about the host-parasite interactions and molecular mechanisms in play during the course of P. knowlesi malaria infections, which also may be relevant across Plasmodium species. Here we contrast P. knowlesi sporozoite-initiated infections in Macaca mulatta and Macaca fascicularis using whole blood RNA-sequencing and transcriptomic analysis. These macaque hosts are evolutionarily close, yet malaria-naïve M. mulatta will succumb to blood-stage infection without treatment, whereas malaria-naïve M. fascicularis controls parasitemia without treatment. This comparative analysis reveals transcriptomic differences as early as the liver phase of infection, in the form of signaling pathways that are activated in M. fascicularis, but not M. mulatta. Additionally, while most immune responses are initially similar during the acute stage of the blood infection, significant differences arise subsequently. The observed differences point to prolonged inflammation and anti-inflammatory effects of IL10 in M. mulatta, while M. fascicularis undergoes a transcriptional makeover towards cell proliferation, consistent with its recovery. Together, these findings suggest that timely detection of P. knowlesi in M. fascicularis, coupled with control of inflammation while initiating the replenishment of key cell populations, helps contain the infection. Overall, this study points to specific genes and pathways that could be investigated as a basis for new drug targets that support recovery from acute malaria.

Protocell arrays for simultaneous detection of diverse analytes.

Simultaneous detection of multiple analytes from a single sample (multiplexing), particularly when done at the point of need, can guide complex decision-making without increasing the required sample volume or cost per test. Despite recent advances, multiplexed analyte sensing still typically faces the critical limitation of measuring only one type of molecule (e.g., small molecules or nucleic acids) per assay platform. Here, we address this bottleneck with a customizable platform that integrates cell-free expression (CFE) with a polymer-based aqueous two-phase system (ATPS), producing membrane-less protocells containing transcription and translation machinery used for detection. We show that multiple protocells, each performing a distinct sensing reaction, can be arrayed in the same microwell to detect chemically diverse targets from the same sample. Furthermore, these protocell arrays are compatible with human biofluids, maintain function after lyophilization and rehydration, and can produce visually interpretable readouts, illustrating this platform's potential as a minimal-equipment, field-deployable, multi-analyte detection tool.

Metabolic Dynamics in Escherichia coli-Based Cell-Free Systems.

The field of metabolic engineering has yielded remarkable accomplishments in using cells to produce valuable molecules, and cell-free expression (CFE) systems have the potential to push the field even further. However, CFE systems still face some outstanding challenges, including endogenous metabolic activity that is poorly understood yet has a significant impact on CFE productivity. Here, we use metabolomics to characterize the temporal metabolic changes in CFE systems and their constituent components, including significant metabolic activity in central carbon and amino acid metabolism. We find that while changing the reaction starting state via lysate preincubation impacts protein production, it has a comparatively small impact on metabolic state. We also demonstrate that changes to lysate preparation have a larger effect on protein yield and temporal metabolic profiles, though general metabolic trends are conserved. Finally, while we improve protein production through targeted supplementation of metabolic enzymes, we show that the endogenous metabolic activity is fairly resilient to these enzymatic perturbations. Overall, this work highlights the robust nature of CFE reaction metabolism as well as the importance of understanding the complex interdependence of metabolites and proteins in CFE systems to guide optimization efforts.