The global rise in antibiotic resistance is making bacterial infections harder to treat and increasing the risk of disease spread, severe illness, and death. Once considered miracle drugs, antibiotics are now losing their effectiveness against ever-evolving bacteria. One company is aiming to treat infections with a different strategy: arming tiny viruses called bacteriophages with Crispr. Known as phages for short, these viruses naturally infect and kill bacteria. Locus Biosciences of North Carolina is adding the gene-editing tool Crispr to the phages' armory to boost their killing ability. The company is testing the approach against urinary tract infections, or UTIs, caused by E. coli bacteria. Results from a small trial published in August suggest the experimental treatment has promise, but larger studies will be needed to confirm its benefits. Phages exist everywhere that bacteria do, including sewers and soil, and there are thousands of different types. Whereas antibiotics kill bacteria indiscriminately'including the beneficial kind'phages have evolved to be selective in the strains or species of bacteria they target. This makes them an attractive alternative for treating infections....

In India, a group of researchers raced to develop a CRISPR-based genome editing therapy to save the life of a young woman with a rare neurodegenerative disease. Despite a valiant effort, the pace of research was ultimately too slow to save her life. While many are convinced that these therapies could offer hope to those with overlooked genetic conditions, it will likely take years to develop the techniques needed to quickly create bespoke treatments, something people in need don't have....

Ghosh and his collaborators were racing to design a one-off treatment that would edit the DNA in the 20-year-old woman's brain cells and get them to stop producing toxic proteins. It was an approach that had never been tried before, with a long list of reasons for why it might not work. But the team was making swift progress. The researchers were maybe six months away from being ready to give Uditi the therapy, Ghosh told her parents over breakfast at their home outside New Delhi last June. Even so, Uditi's mother was not satisfied. Work faster, she urged him. Then, Uditi was carried to the breakfast table, and Ghosh understood her urgency. Once a gregarious and energetic child and teenager, with a quick laugh and a mischievous streak, Uditi was now unable to walk or feed herself. She had become nearly blind and deaf. Her family tried to talk to her: 'These are the people who are making a therapy for you,' they said loudly. Shaken, Ghosh returned to his gene-therapy laboratory at Narayana Nethralaya Eye Hospital in Bengaluru, India, and got to work. 'If you need to put up tents in the lab, then we can do so,' he told his students. 'I'm not going to sleep.'...

In the never-ending quest to discover previously unknown CRISPR gene-editing systems, researchers have scoured microbes in everything from hot springs and peat bogs, to poo and even yogurt. Now, thanks to advances in generative artificial intelligence (AI), they might be able to design these systems with the push of a button. This week, researchers published details of how they used a generative AI tool called a protein language model ' a neural network trained on millions of protein sequences ' to design CRISPR gene-editing proteins, and were then able to show that some of these systems work as expected in the laboratory1. And in February, another team announced that it had developed a model trained on microbial genomes, and used it to design fresh CRISPR systems, which are comprised of a DNA or RNA-cutting enzyme and RNA molecules that direct the molecular scissors as to where to cut2. 'It's really just scratching the surface. It's showing that it's possible to design these complex systems with machine-learning models,' says Ali Madani, a machine-learning scientist and chief executive of the biotechnology firm Profluent, based in Berkeley, California. Madani's team reported what it says is 'the first successful editing of the human genome by proteins designed entirely with machine learning' in a 22 April preprint1 on bioRxiv.org (which hasn't been peer-reviewed)....