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Circular ribonucleic acids (circRNAs) are a promising platform for gene expression studies as a stable and prevalent ribonucleic acid in eukaryotic cells, which arise from back-splicing. In a new report now published in Nature Biotechnology, Robert Chen and a team of interdisciplinary researchers at Stanford University, California, U.S., developed a systematic approach to rapidly assemble and test features affecting protein production based on synthetic circular RNAs. The team maximized translation of the circRNA by optimizing fine elements to implement design principles to improve circular RNA yield by several hundred-fold. The outcomes facilitated an increased translation of the RNA of interest, when compared to messenger RNA (mRNA) levels, to provide durable translation in vivo.
Developing circular RNA (circRNA) in the lab
Therapeutics based on ribonucleic acids span across messenger RNA (mRNA), small interfering RNAs (siRNA) and microRNAs (miRNA) with expansion into modern medicine including small molecules, biologics and cell therapeutics. For example, the lately popular mRNA vaccines can be designed in the lab and developed at a rapid pace to respond to evolving and urgent medical crises. Coding RNAs can be circularized into circRNAs to extend the duration of protein translation, based on RNA molecules that covalently join head-to-tail. Bioengineers have also advanced the synthesis of circular long transcripts into circRNAs. However, the fundamental mechanisms of initiating translation to form circular RNA or messenger RNA differ due to the lack of a 7-methylguanylate (M7G) cap on the circular RNAs. As a result of this, researchers need to thoroughly examine the principles of circular RNA translation to build better therapies and potentially surpass the translational capacities of mRNA.
The chip is an artificial neuron, but nothing like previous chips built to mimic the brain’s electrical signals. Rather, it adopts and adapts the brain’s other communication channel: chemicals.
Called neurotransmitters, these chemicals are the brain’s “natural language,” said Dr. Benhui Hu at Nanjing Medical University in China. An artificial neuron using a chemical language could, in theory, easily tap into neural circuits—to pilot a mouse’s leg, for example, or build an entirely new family of brain-controlled prosthetics or neural implants.
A new study led by Hu and Dr. Xiaodong Chen at Nanyang Technological University, Singapore, took a lengthy stride towards seamlessly connecting artificial and biological neurons into a semi-living circuit. Powered by dopamine, the setup wasn’t a simple one-way call where one component activated another. Rather, the artificial neuron formed a loop with multiple biological counterparts, pulsing out dopamine while receiving feedback to change its own behavior.
Somerville and John Gibbons, a genomicist at the University of Massachusetts, Amherst, independently focused on food fermentation, which helped early farmers and herders transform fresh produce and milk into products that can last months or years. Gibbons took a close look at the genome of Aspergillus oryzae, the fungus that jump-starts production of sake from rice and soy sauce and miso from soybeans.
When farmers cultivate A. oryzae, the fungus—a eukaryote, with its DNA enclosed in a nucleus—reproduces on its own. But when humans take a little finished sake and transfer it to a rice mash to begin fermentation anew, they also transfer cells of the fungal strains that evolved and survived best during the first round of fermentation.
Gibbons compared the genomes of scores of A. oryzae strains with those of their wild ancestor, A. flavus. Over time, he found, selection by humans had boosted A. oryzae’s ability to break down starches and to tolerate the alcohol produced by fermentation. “The restructuring of metabolism appears to be a hallmark of domestication in fungi,” he reported last week at Microbe 2022, the annual meeting of the American Society for Microbiology. For example, domesticated Aspergillus strains may have up to five times more copies of a gene for metabolizing starches as their ancestor—“a brilliant way for evolution to turn up this enzyme,” Wolfe says.
Engineers have created intelligent 3D printers that can quickly detect and correct errors, even in previously unseen designs, or unfamiliar materials like ketchup and mayonnaise, by learning from the experiences of other machines.
The engineers, from the University of Cambridge, developed a machine learning algorithm that can detect and correct a wide variety of different errors in real time, and can be easily added to new or existing machines to enhance their capabilities. 3D printers using the algorithm could also learn how to print new materials by themselves. Details of their low-cost approach are reported in the journal Nature Communications.
3D printing has the potential to revolutionize the production of complex and customized parts, such as aircraft components, personalized medical implants, or even intricate sweets, and could also transform manufacturing supply chains. However, it is also vulnerable to production errors, from small-scale inaccuracies and mechanical weaknesses through to total build failures.
Down the line, the researchers hope to develop an injectable protein solution that forms a gel inside the body. The ability to design hydrogels with specific functions opens up for a range of possible applications. Such a gel could, for example, be used to achieve a controlled release of drugs into the body. In the chemical industry, it could be fused to enzymes, a form of proteins used to speed up various chemical processes.
“In the slightly longer term, I think injectable gels can become very useful in regenerative medicine,” says the study’s first author Tina Arndt, a PhD student in Anna Rising’s research group at Karolinska Institute. “We have a long way to go, but the fact that the protein solution quickly forms a gel at body temperature and that the spider silk has been shown to be well tolerated by the body is promising.”
The ability of spiders to spin incredibly strong fibers from a silk protein solution in fractions of a second has sparked an interest in the underlying molecular mechanisms. The researchers at KI and SLU have been particularly interested in the spiders’ ability to keep proteins soluble so that they do not clump together before the spinning of the spider silk. They have previously developed a method for the production of valuable proteins which mimics the process the spider uses to produce and store its silk proteins.