ACS Synthetic Biology最新文献

Inspired by the properties of natural protein-based biomaterials, protein nanomaterials are increasingly designed with natural or engineered peptides or with protein building blocks. Few examples describe the design of functional protein-based materials for biotechnological applications that can be readily manufactured, are amenable to functionalization, and exhibit robust assembly properties for macroscale material formation. Here, we designed a protein-scaffolding system that self-assembles into robust, macroscale materials suitable for in vitro cell-free applications. By controlling the coexpression in Escherichia coli of self-assembling scaffold building blocks with and without modifications for covalent attachment of cross-linking cargo proteins, hybrid scaffolds with spatially organized conjugation sites are overproduced that can be readily isolated. Cargo proteins, including enzymes, are rapidly cross-linked onto scaffolds for the formation of functional materials. We show that these materials can be used for the in vitro operation of a coimmobilized two-enzyme reaction and that the protein material can be recovered and reused. We believe that this work will provide a versatile platform for the design and scalable production of functional materials with customizable properties and the robustness required for biotechnological applications.

Ribosomally synthesized lanthionine-containing peptides (lanthipeptides) have emerged as a promising source of antimicrobials against multidrug resistance pathogens. An effective way to discover and engineer lanthipeptides is through heterologous expression of their biosynthetic gene clusters (BGCs) in a host of choice. Here we report a plug-and-play pathway refactoring strategy for rapid evaluation of lanthipeptide BGCs in Bacillus subtilis based on the T7 expression system. As a proof of concept, we used this strategy to not only observe the successful production of a known lanthipeptide haloduracin β but also discover two new human-microbiota-derived lanthipeptides that previously failed to be produced in Escherichia coli. The resulting B. subtilis plug-and-play T7 expression system should enable the genome mining of new lanthipeptides in a high-throughput manner.

Robots can be powerful tools to advance basic scientific discovery.

Ginsenosides are major active components of Panax ginseng, which are generally glycosylated at C3–OH and/or C20–OH of protopanaxadiol (PPD) and C6–OH and/or C20–OH of protopanaxatriol. However, the glucosides of dammarenediol-II (DM), which is the direct precursor of PPD, have scarcely been separated from P. ginseng. Because different positions and numbers of the hydroxyl and glycosyl groups lead to a diversity of structure and function of the ginsenosides, it can be inferred that DM glucosides may have different pharmacological activities compared with natural ginsenosides. Herein, we first constructed the cell factory for de novo biosynthesis of 3-O-(β-D-glucopyranosyl-(1→2)-β-D-glucopyranosyl)-dammar-24-ene-3β,20S-diol (3β-O-Glc²-DM) by introducing the codon-optimized genes encoding dammarenediol-II synthase, two UDP-glycosyltransferases (UGTs) including UGT74AC1-M7 from Siraitia grosvenorii and UGTPg29 from P. ginseng in Saccharomyces cerevisiae via the CRISPR/Cas9 system. The titer of 3β-O-Glc²-DM was then increased from 18.9 to 148.0 mg/L by several metabolic engineering strategies including overexpressing the rate-limiting enzymes of triterpenoid biosynthesis, balancing carbon flux of biosynthetic pathways of triterpenoid and ergosterol, and engineering endoplasmic reticulum. Furthermore, the 3β-O-Glc²-DM titer of 766.3 mg/L was achieved through fed-batch fermentation in a 3-L bioreactor. Finally, in vitro assays demonstrated that 3β-O-Glc²-DM exhibited a protective effect on H/R-induced cardiomyocyte damage. This work provides a feasible approach for production of 3β-O-Glc²-DM as a potential cardioprotective drug candidate.

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Robotics can play a useful role in the scientific understanding of the sense of self, both through the construction of embodied models of the self and through the use of robots as experimental probes to explore the human self. In both cases, the embodiment of the robot allows us to devise and test hypotheses about the nature of the self, with regard to its development, its manifestation in behavior, and the diversity of selves in humans, animals, and, potentially, machines. This paper reviews robotics research that addresses the topic of the self-the minimal self, the extended self, and disorders of the self-and highlights future directions and open challenges in understanding the self through constructing its components in artificial systems. An emerging view is that key phenomena of the self can be generated in robots with suitably configured sensor and actuator systems and a layered cognitive architecture involving networks of predictive models.

Protein–protein interactions (PPIs) regulate signalling pathways and cell phenotypes, and the visualization of spatially resolved dynamics of PPIs would thus shed light on the activation and crosstalk of signalling networks. Here we report a method that leverages a sequential proximity ligation assay for the multiplexed profiling of PPIs with up to 47 proteins involved in multisignalling crosstalk pathways. We applied the method, followed by conventional immunofluorescence, to cell cultures and tissues of non-small-cell lung cancers with a mutated epidermal growth-factor receptor to determine the co-localization of PPIs in subcellular volumes and to reconstruct changes in the subcellular distributions of PPIs in response to perturbations by the tyrosine kinase inhibitor osimertinib. We also show that a graph convolutional network encoding spatially resolved PPIs can accurately predict the cell-treatment status of single cells. Multiplexed proximity ligation assays aided by graph-based deep learning can provide insights into the subcellular organization of PPIs towards the design of drugs for targeting the protein interactome.

Rendezvous with sperm whales for biological observations is made challenging by their prolonged dive patterns. Here, we propose an algorithmic framework that codevelops multiagent reinforcement learning-based routing (autonomy module) and synthetic aperture radar-based very high frequency (VHF) signal-based bearing estimation (sensing module) for maximizing rendezvous opportunities of autonomous robots with sperm whales. The sensing module is compatible with low-energy VHF tags commonly used for tracking wildlife. The autonomy module leverages in situ noisy bearing measurements of whale vocalizations, VHF tags, and whale dive behaviors to enable time-critical rendezvous of a robot team with multiple whales in simulation. We conducted experiments at sea in the native habitat of sperm whales using an "engineered whale"-a speedboat equipped with a VHF-emitting tag, emulating five distinct whale tracks, with different whale motions. The sensing module shows a median bearing error of 10.55° to the tag. Using bearing measurements to the engineered whale from an acoustic sensor and our sensing module, our autonomy module gives an aggregate rendezvous success rate of 81.31% for a 500-meter rendezvous distance using three robots in postprocessing. A second class of fielded experiments that used acoustic-only bearing measurements to three untagged sperm whales showed an aggregate rendezvous success rate of 68.68% for a 1000-meter rendezvous distance using two robots in postprocessing. We further validated these algorithms with several ablation studies using a sperm whale visual encounter dataset collected by marine biologists.

Aerial robots assisted with artificial intelligence improve real-time wildlife monitoring of sperm whales.