Scientists at the Medical Research Council’s Laboratory of Molecular Biology say they’ve engineered a bacteria whose genetic code is more efficient than any other lifeform on Earth.
They call their creation “Syn57,” a bioengineered strain of E. coli — yes, the same bad boy that can make you extremely sick if you eat an undercooked hot dog — which uses seven less codons than all life on earth. A codon, put simply, is a three-letter sequence found in DNA and RNA which delivers instructions for amino acids, a fundamental “building block” of life.
For the past billions years or so, all known life on earth has used 64 codons. Scientists cracked the code detailing which codons corresponded to which amino acids — mapping the standard genetic code, in other words — in 1966, revealing only 20 total amino acids.
Over the past 20 years, a class of cancer drugs called CD40 agonist antibodies have shown great promise—and induced great disappointment. While effective at activating the immune system to kill cancer cells in animal models, the drugs had limited impact on patients in clinical trials and caused dangerously systemic inflammatory responses, low platelet counts, and liver toxicity, among other adverse reactions—even at a low dose.
But in 2018, the lab of Rockefeller University’s Jeffrey V. Ravetch demonstrated it could engineer an enhanced CD40 agonist antibody so that it improved its efficacy and could be administered in a manner to limit serious side effects. The findings came from research on mice, genetically engineered to mimic the pathways relevant in humans. The next step was to have a clinical trial to see the drug’s impact on cancer patients.
Now the results from the phase 1 clinical trial of the drug, dubbed 2141-V11, have been published in Cancer Cell. Of 12 patients, six patients saw their tumors shrink, including two who saw them disappear completely.
The researchers demonstrate that an engineered antibody improves a class of drugs that has struggled to make good on its early promise.
Biohacking shouldn’t feel like a full-time job—or a scam. If you’re tired of chasing every shiny new trend without results to show for it, this session is your reset button. We’ll break down what’s worth your effort, what’s just a fad, and how to build a strategy that actually works for you.
One of the most fundamental processes in all of biology is the spontaneous organization of cells into clusters that divide and eventually turn into shapes—be they organs, wings or limbs.
Scientists have long explored this enormously complex process to make artificial organs or understand cancer growth—but precisely engineering single cells to achieve a desired collective outcome is often a trial-and-error process.
Harvard applied physicists consider the control of cellular organization and morphogenesis to be an optimization problem that can be solved with powerful new machine learning tools. In new research published in Nature Computational Science, researchers in the John A. Paulson School of Engineering and Applied Sciences (SEAS) have created a computational framework that can extract the rules that cells need to follow as they grow, in order for a collective function to emerge from the whole.
Having lived with an ALS diagnosis since 2018, Kate Nycz can tell you firsthand what it’s like to slowly lose motor function for basic tasks. “My arm can get to maybe 90 degrees, but then it fatigues and falls,” the 39-year-old said. “To eat or do a repetitive motion with my right hand, which was my dominant hand, is difficult. I’ve mainly become left-handed.”
People like Nycz who live with a neurodegenerative disease like ALS or who have had a stroke often suffer from impaired movement of the shoulder, arm or hands, preventing them from daily tasks like tooth-brushing, hair-combing or eating.
For the last several years, Harvard bioengineers have been developing a soft, wearable robot that not only provides movement assistance for such individuals but could even augment therapies to help them regain mobility.
But no two people move exactly the same way. Physical motions are highly individualized, especially for the mobility-impaired, making it difficult to design a device that works for many different people.
It turns out advances in machine learning can create a more personal touch. Researchers in the John A. Paulson School of Engineering and Applied Sciences (SEAS), together with physician-scientists at Massachusetts General Hospital and Harvard Medical School, have upgraded their wearable robot to be responsive to an individual user’s exact movements, endowing the device with more personalized assistance that could give users better, more controlled support for daily tasks.
Nature, particularly humans and other animals, has always been among the primary sources of inspiration for roboticists. In fact, most existing robots physically resemble specific animals and/or are engineered to tackle tasks by emulating the actions, movements and behaviors of specific species.
One innate ability of some animals that has so far been seldom replicated in robots is shape morphing and camouflaging. Some living organisms, including some insects, octopuses and chameleons, are known to reversibly change their appearance, form and shape in response to their surroundings, whether to hide from predators, move objects or simply while moving in specific environments.
Researchers at Jiangnan University, Technical University of Dresden, Laurentian University and the Shanghai International Fashion Education Center recently designed new flexible and programmable textile metasurfaces that could be used to develop robots exhibiting similar morphing and camouflaging capabilities. These materials, introduced in a paper published in Advanced Fiber Materials, essentially consist of knitted structures that can be carefully engineered by adapting the geometric arrangement of their underlying interlaced yarn loops.
Prosthetic heart valves (PHV) have been studied for around 70 years. They are the best alternative to save the life of patients with cardiac valve diseases. However, current PHVs may still cause significant disadvantages to patients. In general, native heart valves show complex structures and reproducing their functions challenges scientists. Valve repair and replacement are the options to heal heart valve diseases (VHDs), such as stenosis and regurgitation, which show high morbidity and mortality worldwide. Valve repair contributes to the performance of cardiac cycles. However, it fails to restore valve anatomy to its normal condition. On the other hand, replacement is the only alternative to treat valve degeneration. It may do so by mechanical or bioprosthetic valves. Although prostheses may restructure patients’ cardiac cycle, both prostheses may show limitations and potential disadvantages, such as mechanical valves causing thrombogenicity or bioprosthetic valves, calcification. Thus, prostheses require constant improvements to remedy these limitations. Although the design of mechanical valve structures has improved, their raw materials cause great disadvantages, and alternatives for this problem remain scarce. Cardiac valve tissue engineering emerged 30 years ago and has improved over time, e.g., xenografts and fabricated heart valves serving as scaffolds for cell seeding. Thus, this review describes cardiac valve substitutes, starting with the history of valvular prosthesis transplants and ending with some perspectives to alleviate the limitations of artificial valves.
A research team headed by the University of Zurich has developed a powerful new method to precisely edit DNA by combining cutting-edge genetic engineering with artificial intelligence. This technique opens the door to more accurate modeling of human diseases and lays the groundwork for next-generation gene therapies.
Precise and targeted DNA editing by small point mutations as well as the integration of whole genes via CRISPR/Cas technology has great potential for applications in biotechnology and gene therapy. However, it is very important that the so-called gene scissors do not cause any unintended genetic changes, but maintain genomic integrity to avoid unintended side effects. Normally, double-stranded breaks in the DNA molecule are accurately repaired in humans and other organisms. But occasionally, this DNA end joining repair results in genetic errors.
Gene editing with greatly improved precision Now, scientists from the University of Zurich (UZH), Ghent University in Belgium and the ETH Zurich have developed a new method which greatly improves the precision of genome editing. Using artificial intelligence (AI), the tool called Pythia predicts how cells repair their DNA after it is cut by gene editing tools such as CRISPR/Cas9. “Our team developed tiny DNA repair templates, which act like molecular glue and guide the cell to make precise genetic changes,” says lead author Thomas Naert, who pioneered the technology at UZH and is currently a postdoc at Ghent University.
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Synthetic cells are artificial constructs designed to mimic cellular functions, offering insights into fundamental biology, as well as promising impact in the fields of medicine, biotechnology, and bioengineering. In this perspective, the authors highlight major scientific hurdles, such as the integration of functional modules by ensuring compatibility across diverse synthetic subsystems, and propose strategies to advance the field.
