Engineering Escherichia coli for constitutive production of monophosphoryl lipid A vaccine adjuvant.

During the COVID-19 pandemic, expedient vaccine production has been slowed by the shortage of safe and effective raw materials, such as adjuvants, essential components to enhance the efficacy of vaccines. Monophosphoryl lipid A (MPLA) is a potent and safe adjuvant used in human vaccines, including the Shingles vaccine, Shingrix. 3-O-desacyl-4'-monophosphoryl lipid A (MPL), a representative MPLA adjuvant commercialized by GSK, was prepared via chemical conversion of precursors isolated from Salmonella typhimurium R595. However, the high price of these materials limits their use in premium vaccines. To combat the scarcity and high cost of safe raw materials for vaccines, we need to develop a feasible MPLA production method that is easily scaled up to meet industrial requirements. In this study, we engineered peptidoglycan and outer membrane biosynthetic pathways in Escherichia coli and developed a Escherichia coli strain, KHSC0055, that constitutively produces EcML (E. coli-produced monophosphoryl lipid A) without additives such as antibiotics or overexpression inducers. EcML production was optimized on an industrial scale via high-density fed-batch fermentation, and obtained 2.7 g of EcML (about 135,000 doses of vaccine) from a 30-L-scale fermentation. Using KHSC0055, we simplified the production process and decreased the production costs of MPLA. Then, we applied EcML purified from KHSC0055 as an adjuvant for a COVID-19 vaccine candidate (EuCorVac-19) currently in clinical trial stage III in the Philippines. By probing the efficacy and safety of EcML in humans, we established KHSC0055 as an efficient cell factory for MPLA adjuvant production.

Compressive recovery of smartphone RGB spectral sensitivity functions.

Spectral response (or sensitivity) functions of a three-color image sensor (or trichromatic camera) allow a mapping from spectral stimuli to RGB color values. Like biological photosensors, digital RGB spectral responses are device dependent and significantly vary from model to model. Thus, the information on the RGB spectral response functions of a specific device is vital in a variety of computer vision as well as mobile health (mHealth) applications. Theoretically, spectral response functions can directly be measured with sophisticated calibration equipment in a specialized laboratory setting, which is not easily accessible for most application developers. As a result, several mathematical methods have been proposed relying on standard color references. Typical optimization frameworks with constraints are often complicated, requiring a large number of colors. We report a compressive sensing framework in the frequency domain for accurately predicting RGB spectral response functions only with several primary colors. Using a scientific camera, we first validate the estimation method with direct spectral sensitivity measurements and ensure that the root mean square errors between the ground truth and recovered RGB spectral response functions are negligible. We further recover the RGB spectral response functions of smartphones and validate with an expanded color checker reference. We expect that this simple yet reliable estimation method of RGB spectral sensitivity can easily be applied for color calibration and standardization in machine vision, hyperspectral filters, and mHealth applications that capitalize on the built-in cameras of smartphones.

Metabolic engineering of Escherichia coli to produce a monophosphoryl lipid A adjuvant.

Monophosphoryl lipid A (MPLA) species, including MPL (a trade name of GlaxoSmithKline) and GLA (a trade name of Immune Design, a subsidiary of Merck), are widely used as an adjuvant in vaccines, allergy drugs, and immunotherapy to boost the immune response. Even though MPLA is a derivative of lipopolysaccharide (LPS), a component of the outer membrane of Gram-negative bacteria, bacterial strains producing MPLA have not been found in nature nor engineered. In fact, MPLA generation involves expensive and laborious procedures based on synthetic routes or chemical transformation of precursors isolated from Gram-negative bacteria. Here, we report the engineering of an Escherichia coli strain for in situ production and accumulation of MPLA. Furthermore, we establish a succinct method for purifying MPLA from the engineered E. coli strain. We show that the purified MPLA (named EcML) stimulates the mouse immune system to generate antigen-specific IgG antibodies similarly to commercially available MPLA, but with a dramatically reduced manufacturing time and cost. Our system, employing the first engineered E. coli strain that directly produces the adjuvant EcML, could transform the current standard of industrial MPLA production.

Rapid Self-Healing Hydrogel with Ultralow Electrical Hysteresis for Wearable Sensing.

Self-healing hydrogels are in high demand for wearable sensing applications due to their remarkable deformability, high ionic and electrical conductivity, self-adhesiveness to human skin, as well as resilience to both mechanical and electrical damage. However, these hydrogels face challenges such as delayed healing times and unavoidable electrical hysteresis, which limit their practical effectiveness. Here, we introduce a self-healing hydrogel that exhibits exceptionally rapid healing with a recovery time of less than 0.12 s and an ultralow electrical hysteresis of less than 0.64% under cyclic strains of up to 500%. This hydrogel strikes an ideal balance, without notable trade-offs, between properties such as softness, deformability, ionic and electrical conductivity, self-adhesiveness, response and recovery times, durability, overshoot behavior, and resistance to nonaxial deformations such as twisting, bending, and pressing. Owing to this unique combination of features, the hydrogel is highly suitable for long-term, durable use in wearable sensing applications, including monitoring body movements and electrophysiological activities on the skin.

Spongy Ag Foam for Soft and Stretchable Strain Gauges.

Strain gauges, particularly for wearable sensing applications, require a high degree of stretchability, softness, sensitivity, selectivity, and linearity. They must also steer clear of challenges such as mechanical and electrical hysteresis, overshoot behavior, and slow response/recovery times. However, current strain gauges face challenges in satisfying all of these requirements at once due to the inevitable trade-offs between these properties. Here, we present an innovative method for creating strain gauges from spongy Ag foam through a steam-etching process. This method simplifies the traditional, more complex, and costly manufacturing techniques, presenting an eco-friendly alternative. Uniquely, the strain gauges crafted from this method achieve an unparalleled gauge factor greater than 8 × 103 at strains exceeding 100%, successfully meeting all required attributes without notable trade-offs. Our work includes systematic investigations that reveal the intricate structure-property-performance relationship of the spongy Ag foam with practical demonstrations in areas such as human motion monitoring and human-robot interaction. These breakthroughs pave the way for highly sensitive and selective strain gauges, showing immediate applicability across a wide range of wearable sensing applications.

A machine-learning-enabled smart neckband for monitoring dietary intake.

The increasing need for precise dietary monitoring across various health scenarios has led to innovations in wearable sensing technologies. However, continuously tracking food and fluid intake during daily activities can be complex. In this study, we present a machine-learning-powered smart neckband that features wireless connectivity and a comfortable, foldable design. Initially considered beneficial for managing conditions such as diabetes and obesity by facilitating dietary control, the device's utility extends beyond these applications. It has proved to be valuable for sports enthusiasts, individuals focused on diet control, and general health monitoring. Its wireless connectivity, ergonomic design, and advanced classification capabilities offer a promising solution for overcoming the limitations of traditional dietary tracking methods, highlighting its potential in personalized healthcare and wellness strategies.

mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics.

Hyperspectral imaging acquires data in both the spatial and frequency domains to offer abundant physical or biological information. However, conventional hyperspectral imaging has intrinsic limitations of bulky instruments, slow data acquisition rate, and spatiospectral trade-off. Here we introduce hyperspectral learning for snapshot hyperspectral imaging in which sampled hyperspectral data in a small subarea are incorporated into a learning algorithm to recover the hypercube. Hyperspectral learning exploits the idea that a photograph is more than merely a picture and contains detailed spectral information. A small sampling of hyperspectral data enables spectrally informed learning to recover a hypercube from a red-green-blue (RGB) image without complete hyperspectral measurements. Hyperspectral learning is capable of recovering full spectroscopic resolution in the hypercube, comparable to high spectral resolutions of scientific spectrometers. Hyperspectral learning also enables ultrafast dynamic imaging, leveraging ultraslow video recording in an off-the-shelf smartphone, given that a video comprises a time series of multiple RGB images. To demonstrate its versatility, an experimental model of vascular development is used to extract hemodynamic parameters via statistical and deep learning approaches. Subsequently, the hemodynamics of peripheral microcirculation is assessed at an ultrafast temporal resolution up to a millisecond, using a conventional smartphone camera. This spectrally informed learning method is analogous to compressed sensing; however, it further allows for reliable hypercube recovery and key feature extractions with a transparent learning algorithm. This learning-powered snapshot hyperspectral imaging method yields high spectral and temporal resolutions and eliminates the spatiospectral trade-off, offering simple hardware requirements and potential applications of various machine learning techniques.

In Situ Spray Polymerization of Conductive Polymers for Personalized E-textiles.

E-textiles, also known as electronic textiles, seamlessly merge wearable technology with fabrics, offering comfort and unobtrusiveness and establishing a crucial role in health monitoring systems. In this field, the integration of custom sensor designs with conductive polymers into various fabric types, especially in large areas, has presented significant challenges. Here, we present an innovative additive patterning method that utilizes a dual-regime spray system, eliminating the need for masks and allowing for the programmable inscription of sensor arrays onto consumer textiles. Unlike traditional spray techniques, this approach enables in situ, on-the-fly polymerization of conductive polymers, enabling intricate designs with submillimeter resolution across fabric areas spanning several meters. Moreover, it addresses the nozzle clogging issues commonly encountered in such applications. The resulting e-textiles preserve essential fabric characteristics such as breathability, wearability, and washability while delivering exceptional sensing performance. A comprehensive investigation, combining experimental, computational, and theoretical approaches, was conducted to examine the critical factors influencing the operation of the dual-regime spraying system and its role in e-textile fabrication. These findings provide a flexible solution for producing e-textiles on consumer fabric items and hold significant implications for a diverse range of wearable sensing applications.

Edible Matrix Code with Photogenic Silk Proteins.

Counterfeit medicines are a healthcare security problem, posing not only a direct threat to patient safety and public health but also causing heavy economic losses. Current anticounterfeiting methods are limited due to the toxicity of the constituent materials and the focus of secondary packaging level protections. We introduce an edible, imperceptible, and scalable matrix code of information representation and data storage for pharmaceutical products. This matrix code is digestible as it is composed of silk fibroin genetically encoded with fluorescent proteins produced by ecofriendly, sustainable silkworm farming. Three distinct fluorescence emission colors are incorporated into a multidimensional parameter space with a variable encoding capacity in a format of matrix arrays. This code is smartphone-readable to extract a digitized security key augmented by a deep neural network for overcoming fabrication imperfections and a cryptographic hash function for enhanced security. The biocompatibility, photostability, thermal stability, long-term reliability, and low bit error ratio of the code support the immediate feasibility for dosage-level anticounterfeit measures and authentication features. The edible code affixed to each medicine can serve as serialization, track and trace, and authentication at the dosage level, empowering every patient to play a role in combating illicit pharmaceuticals.

Erratum: Multicolor fluorescence imaging using a single RGB-IR CMOS sensor for cancer detection with smURFP-labeled probiotics: publisher's note.

[This corrects the article on p. 2951 in vol. 11, PMID: 32637234.].

Dendritic cell activation by an E. coli-derived monophosphoryl lipid A enhances the efficacy of PD-1 blockade.

Cancer immunotherapy is a powerful approach for cancer treatment, but its clinical effects rely on the tumor's immune conditions. In particular, low response rates to PD-1 blockades are highly correlated with impaired T cell priming. Here, we demonstrate that E. coli-derived monophosphoryl lipid A (EcML) activates dendritic cells in a toll-like receptor-4 (TLR-4)-dependent manner and increases the sensitivity of cancer cells to anti-PD-1 immunotherapy. EcML is a mixture of 4'-monophosphoryl lipids A (MPLAs) produced directly by an engineered Escherichia coli strain; it has a unique congener composition that differentiates it from the well-established MPLA adjuvants, 3-O-desacyl-4'-monophosphoryl lipid A and glucopyranosyl lipid A. Given that active dendritic cells initiate adaptive immune responses, we investigated the anti-tumor activity of an aqueous formulation of EcML. Upon sensing EcML via TLR-4, dendritic cells matured into powerful antigen-presenting cells that could stimulate naïve T cells. EcML reduced tumor growth in the B16F10 mouse model via dendritic cell activation and potentiated PD-1 blockade therapy in the B16F10-OVA melanoma model. These data identify EcML as a promising TLR-4 agonist that can induce anti-tumor immune responses and potentiate PD-1 blockade therapy against tumors.