ElectroPen: An ultra-low-cost, electricity-free, portable electroporator.

Electroporation is a basic yet powerful method for delivering small molecules (RNA, DNA, drugs) across cell membranes by application of an electrical field. It is used for many diverse applications, from genetically engineering cells to drug- and DNA-based vaccine delivery. Despite this broad utility, the high cost of electroporators can keep this approach out of reach for many budget-conscious laboratories. To address this need, we develop a simple, inexpensive, and handheld electroporator inspired by and derived from a common household piezoelectric stove lighter. The proposed "ElectroPen" device can cost as little as 23 cents (US dollars) to manufacture, is portable (weighs 13 g and requires no electricity), can be easily fabricated using 3D printing, and delivers repeatable exponentially decaying pulses of about 2,000 V in 5 ms. We provide a proof-of-concept demonstration by genetically transforming plasmids into Escherichia coli cells, showing transformation efficiency comparable to commercial devices, but at a fraction of the cost. We also demonstrate the potential for rapid dissemination of this approach, with multiple research groups across the globe validating the ease of construction and functionality of our device, supporting the potential for democratization of science through frugal tools. Thus, the simplicity, accessibility, and affordability of our device holds potential for making modern synthetic biology accessible in high school, community, and resource-poor laboratories.

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.

SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions.

BACKGROUND: The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms-two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR. RESULTS: We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6-27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. CONCLUSIONS: SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

Humoral immunity prevents clinical malaria during Plasmodium relapses without eliminating gametocytes.

Plasmodium relapses are attributed to the activation of dormant liver-stage parasites and are responsible for a significant number of recurring malaria blood-stage infections. While characteristic of human infections caused by P. vivax and P. ovale, their relative contribution to malaria disease burden and transmission remains poorly understood. This is largely because it is difficult to identify 'bona fide' relapse infections due to ongoing transmission in most endemic areas. Here, we use the P. cynomolgi-rhesus macaque model of relapsing malaria to demonstrate that clinical immunity can form after a single sporozoite-initiated blood-stage infection and prevent illness during relapses and homologous reinfections. By integrating data from whole blood RNA-sequencing, flow cytometry, P. cynomolgi-specific ELISAs, and opsonic phagocytosis assays, we demonstrate that this immunity is associated with a rapid recall response by memory B cells that expand and produce anti-parasite IgG1 that can mediate parasite clearance of relapsing parasites. The reduction in parasitemia during relapses was mirrored by a reduction in the total number of circulating gametocytes, but importantly, the cumulative proportion of gametocytes increased during relapses. Overall, this study reveals that P. cynomolgi relapse infections can be clinically silent in macaques due to rapid memory B cell responses that help to clear asexual-stage parasites but still carry gametocytes.

LK-DFBA: a linear programming-based modeling strategy for capturing dynamics and metabolite-dependent regulation in metabolism.

BACKGROUND: The systems-scale analysis of cellular metabolites, "metabolomics," provides data ideal for applications in metabolic engineering. However, many of the computational tools for strain design are built around Flux Balance Analysis (FBA), which makes assumptions that preclude direct integration of metabolomics data into the underlying models. Finding a way to retain the advantages of FBA's linear structure while relaxing some of its assumptions could allow us to account for metabolite levels and metabolite-dependent regulation in strain design tools built from FBA, improving the accuracy of predictions made by these tools. We designed, implemented, and characterized a modeling strategy based on Dynamic FBA (DFBA), called Linear Kinetics-Dynamic Flux Balance Analysis (LK-DFBA), to satisfy these specifications. Our strategy adds constraints describing the dynamics and regulation of metabolism that are strictly linear. We evaluated LK-DFBA against alternative modeling frameworks using simulated noisy data from a small in silico model and a larger model of central carbon metabolism in E. coli, and compared each framework's ability to recapitulate the original system. RESULTS: In the smaller model, we found that we could use regression from a dynamic flux estimation (DFE) with an optional non-linear parameter optimization to reproduce metabolite concentration dynamic trends more effectively than an ordinary differential equation model with generalized mass action rate laws when tested under realistic data sampling frequency and noise levels. We observed detrimental effects across all tested modeling approaches when metabolite time course data were missing, but found these effects to be smaller for LK-DFBA in most cases. With the E. coli model, we produced qualitatively reasonable results with similar properties to the smaller model and explored two different parameterization structures that yield trade-offs in computation time and accuracy. CONCLUSIONS: LK-DFBA allows for calculation of metabolite concentrations and considers metabolite-dependent regulation while still retaining many computational advantages of FBA. This provides the proof-of-principle for a new metabolic modeling framework with the potential to create genome-scale dynamic models and the potential to be applied in strain engineering tools that currently use FBA.

Metabolic modeling helps interpret transcriptomic changes during malaria.

Disease represents a specific case of malfunctioning within a complex system. Whereas it is often feasible to observe and possibly treat the symptoms of a disease, it is much more challenging to identify and characterize its molecular root causes. Even in infectious diseases that are caused by a known parasite, it is often impossible to pinpoint exactly which molecular profiles of components or processes are directly or indirectly altered. However, a deep understanding of such profiles is a prerequisite for rational, efficacious treatments. Modern omics methodologies are permitting large-scale scans of some molecular profiles, but these scans often yield results that are not intuitive and difficult to interpret. For instance, the comparison of healthy and diseased transcriptome profiles may point to certain sets of involved genes, but a host of post-transcriptional processes and regulatory mechanisms renders predictions regarding metabolic or physiological consequences of the observed changes in gene expression unreliable. Here we present proof of concept that dynamic models of metabolic pathway systems may offer a tool for interpreting transcriptomic profiles measured during disease. We illustrate this strategy with the interpretation of expression data of genes coding for enzymes associated with purine metabolism. These data were obtained during infections of rhesus macaques (Macaca mulatta) with the malaria parasite Plasmodium cynomolgi or P. coatneyi. The model-based interpretation reveals clear patterns of flux redistribution within the purine pathway that are consistent between the two malaria pathogens and are even reflected in data from humans infected with P. falciparum. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

NS-kNN: a modified k-nearest neighbors approach for imputing metabolomics data.

INTRODUCTION: A common problem in metabolomics data analysis is the existence of a substantial number of missing values, which can complicate, bias, or even prevent certain downstream analyses. One of the most widely-used solutions to this problem is imputation of missing values using a k-nearest neighbors (kNN) algorithm to estimate missing metabolite abundances. kNN implicitly assumes that missing values are uniformly distributed at random in the dataset, but this is typically not true in metabolomics, where many values are missing because they are below the limit of detection of the analytical instrumentation. OBJECTIVES: Here, we explore the impact of nonuniformly distributed missing values (missing not at random, or MNAR) on imputation performance. We present a new model for generating synthetic missing data and a new algorithm, No-Skip kNN (NS-kNN), that accounts for MNAR values to provide more accurate imputations. METHODS: We compare the imputation errors of the original kNN algorithm using two distance metrics, NS-kNN, and a recently developed algorithm KNN-TN, when applied to multiple experimental datasets with different types and levels of missing data. RESULTS: Our results show that NS-kNN typically outperforms kNN when at least 20-30% of missing values in a dataset are MNAR. NS-kNN also has lower imputation errors than KNN-TN on realistic datasets when at least 50% of missing values are MNAR. CONCLUSION: Accounting for the nonuniform distribution of missing values in metabolomics data can significantly improve the results of imputation algorithms. The NS-kNN method imputes missing metabolomics data more accurately than existing kNN-based approaches when used on realistic datasets.

Precise control of lycopene production to enable a fast-responding, minimal-equipment biosensor.

Pigmented metabolites have great potential for use in biosensors that target low-resource areas, since sensor output can be interpreted without any equipment. However, full repression of pigment production when undesired is challenging, as even small amounts of enzyme can catalyze the production of large, visible amounts of pigment. The red pigment lycopene could be particularly useful because of its position in the multi-pigment carotenoid pathway, but commonly used inducible promoter systems cannot repress lycopene production. In this paper, we designed a system that could fully repress lycopene production in the absence of an inducer and produce visible lycopene within two hours of induction. We engineered Lac, Ara, and T7 systems to be up to 10 times more repressible, but these improved systems could still not fully repress lycopene. Translational modifications proved much more effective in controlling lycopene. By decreasing the strength of the ribosomal binding sites on the crtEBI genes, we enabled full repression of lycopene and production of visible lycopene in 3-4h of induction. Finally, we added the mevalonate pathway enzymes to increase the rate of lycopene production upon induction and demonstrated that supplementation of metabolic precursors could decrease the time to coloration to about 1.5h. In total, this represents over an order of magnitude reduction in response time compared to the previously reported strategy. The approaches used here demonstrate the disconnect between fluorescent and metabolite reporters, help enable the use of lycopene as a reporter, and are likely generalizable to other systems that require precise control of metabolite production.

Correction to: Integrative analysis associates monocytes with insufficient erythropoiesis during acute Plasmodium cynomolgi malaria in rhesus macaques.

After publication of the article [1], it was brought to our attention that several symbols were missing from Fig. 1, including some cited in the figure's key. The correct version of the figure is shown below and has now been updated in the original article.

Integrative analysis associates monocytes with insufficient erythropoiesis during acute Plasmodium cynomolgi malaria in rhesus macaques.

BACKGROUND: Mild to severe anaemia is a common complication of malaria that is caused in part by insufficient erythropoiesis in the bone marrow. This study used systems biology to evaluate the transcriptional and alterations in cell populations in the bone marrow during Plasmodium cynomolgi infection of rhesus macaques (a model of Plasmodium vivax malaria) that may affect erythropoiesis. RESULTS: An appropriate erythropoietic response did not occur to compensate for anaemia during acute cynomolgi malaria despite an increase in erythropoietin levels. During this period, there were significant perturbations in the bone marrow transcriptome. In contrast, relapses did not induce anaemia and minimal changes in the bone marrow transcriptome were detected. The differentially expressed genes during acute infection were primarily related to ongoing inflammatory responses with significant contributions from Type I and Type II Interferon transcriptional signatures. These were associated with increased frequency of intermediate and non-classical monocytes. Recruitment and/or expansion of these populations was correlated with a decrease in the erythroid progenitor population during acute infection, suggesting that monocyte-associated inflammation may have contributed to anaemia. The decrease in erythroid progenitors was associated with downregulation of genes regulated by GATA1 and GATA2, two master regulators of erythropoiesis, providing a potential molecular basis for these findings. CONCLUSIONS: These data suggest the possibility that malarial anaemia may be driven by monocyte-associated disruption of GATA1/GATA2 function in erythroid progenitors resulting in insufficient erythropoiesis during acute infection.

Precision metabolic engineering: The design of responsive, selective, and controllable metabolic systems.

Metabolic engineering is generally focused on static optimization of cells to maximize production of a desired product, though recently dynamic metabolic engineering has explored how metabolic programs can be varied over time to improve titer. However, these are not the only types of applications where metabolic engineering could make a significant impact. Here, we discuss a new conceptual framework, termed "precision metabolic engineering," involving the design and engineering of systems that make different products in response to different signals. Rather than focusing on maximizing titer, these types of applications typically have three hallmarks: sensing signals that determine the desired metabolic target, completely directing metabolic flux in response to those signals, and producing sharp responses at specific signal thresholds. In this review, we will first discuss and provide examples of precision metabolic engineering. We will then discuss each of these hallmarks and identify which existing metabolic engineering methods can be applied to accomplish those tasks, as well as some of their shortcomings. Ultimately, precise control of metabolic systems has the potential to enable a host of new metabolic engineering and synthetic biology applications for any problem where flexibility of response to an external signal could be useful.

Precise metabolic engineering of carotenoid biosynthesis in Escherichia coli towards a low-cost biosensor.

Micronutrient deficiencies, including zinc deficiency, are responsible for hundreds of thousands of deaths annually. A key obstacle to allocating scarce treatment resources is the ability to measure population blood micronutrient status inexpensively and quickly enough to identify those who most need treatment. This paper develops a metabolically engineered strain of Escherichia coli to produce different colored pigments (violacein, lycopene, and ß-carotene) in response to different extracellular zinc levels, for eventual use in an inexpensive blood zinc diagnostic test. However, obtaining discrete color states in the carotenoid pathway required precise engineering of metabolism to prevent reaction at low zinc concentrations but allow complete reaction at higher concentrations, and all under the constraints of natural regulator limitations. Hence, the metabolic engineering challenge was not to improve titer, but to enable precise control of pathway state. A combination of gene dosage, post-transcriptional, and post-translational regulation was necessary to allow visible color change over physiologically relevant ranges representing a small fraction of the regulator's dynamic response range, with further tuning possible by modulation of precursor availability. As metabolic engineering expands its applications and develops more complex systems, tight control of system components will likely become increasingly necessary, and the approach presented here can be generalized to other natural sensing systems for precise control of pathway state.

Applications of metabolomics in cancer research.

The first discovery of metabolic changes in cancer occurred almost a century ago. While the genetic underpinnings of cancer have dominated its study since then, altered metabolism has recently been acknowledged as a key hallmark of cancer and metabolism-focused research has received renewed attention. The emerging field of metabolomics - which attempts to profile all metabolites within a cell or biological system - is now being used to analyze cancer metabolism on a system-wide scale, painting a broad picture of the altered pathways and their interactions with each other. While a large fraction of cancer metabolomics research is focused on finding diagnostic biomarkers, metabolomics is also being used to obtain more fundamental mechanistic insight into cancer and carcinogenesis. Applications of metabolomics are also emerging in areas such as tumor staging and assessment of treatment efficacy. This review summarizes contributions that metabolomics has made in cancer research and presents the current challenges and potential future directions within 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.

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.

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.

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.

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.

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.

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.