Cell therapies show encouraging results for a deadly brain infection, advanced kidney cancer

Tumor immune microenvironment studies reveal clues to colorectal cancer recurrence, multiple myeloma and oral cancer progression, and how T cells eliminate AML

KRAS inhibitors demonstrate promise as targeted therapy for rare appendix cancer, novel epigenetic therapy displays activity in treatment-resistant AML

Researchers develop a new way to isolate the strongest cancer-fighting immune cells

New insights reveal how fasting improves intestinal recovery after radiation

The remarkable diversity of mammalian carnivores may be built on a surprisingly limited set of dental solutions - teeth that are good at slicing meat cannot also excel at crushing hard foods, and vice versa. According to a new study, this inherent tradeoff between cutting and crushing performance has repeatedly steered the evolution of carnassial teeth toward two recurring forms, helping explain how predators evolved to exploit different diets. Teeth are among the most informative features in vertebrate evolution because they directly reflect how animals obtain and process their food. This, combined with their durability in the fossil record, makes them an indispensable tool to understand the ecology and evolutionary history of mammals. A defining innovation in mammals is heterodont dentition, or the presence of specialized tooth types that perform different tasks. One of these specializations is the tribosphenic molar, a tooth whose structure enables both cutting and crushing in a single bite – an adaptation that allows mammals to eat a wide range of foods. This innovation, however, came with a fundamental trade-off: teeth specialized for cutting sacrifice crushing efficiency, while those adapted for grinding reduce cutting performance. How mammals have balanced these competing functions to shape ecological adaptability and evolutionary diversification remains unknown.

 

To address this question, Narimane Chatar and colleagues analyzed the three-dimensional shape of the lower carnassial tooth in 250 living and extinct carnivorous mammals, revealing two recurring evolutionary designs. One is a highly specialized, blade-like tooth with a reduced grinding surface, characteristic of obligate meat-eaters such as cats. The other has a larger grinding region that supports a broader, more omnivorous diet, as seen in dogs and bears. These patterns suggest that relatively small developmental changes can produce substantial functional differences while remaining constrained by underlying genetic mechanisms. Experimental tests with 3D-printed teeth showed a clear tradeoff between slicing and crushing performance. Fewer than 1% of species approached optimal performance in both functions. In the vast majority of species, teeth optimized for slicing flesh were poor at crushing hard materials, whereas teeth adapted for crushing sacrificed slicing efficiency. For example, the blade-like carnassials of hypercarnivores closely matched the theoretically optimal shape for cutting, indicating that natural selection has repeatedly favored an efficient design despite diverse evolutionary histories.

Artificial intelligence (AI)-designed proteins can now expand the CRISPR toolbox beyond what evolution has produced. In a new study, researchers report the design of synthetic RNA-guided nucleases that match or exceed the activity of natural enzymes, revealing how structure-guided design can generate functional genome-editing proteins with substantially different sequences. CRISPR-Cas technologies have transformed genetic engineering by allowing scientists to target and modify DNA and RNA precisely. Researchers are now exploring whether protein design methods can create new RNA-guided nucleases with novel properties beyond those found in nature. However, this remains challenging because multi-domain proteins are complex and small changes can easily disrupt enzyme activity. Although AI models have shown success in the design of new nucleases, they often produce versions that closely resemble the reference sequences used to train the models themselves. Here, Petr Skopintsev and colleagues present a protein design strategy that combines the ESM Inverse Folding (ESM-IF1) model with evolution-informed residue constraints to generate new variants of TnpB, a minimal CRISPR-Cas12-like nuclease, termed SynTnpBs. Skopintsev et al. then screened these AI-designed proteins for activity in bacterial, plant, and human cells, and used cryo-electron microscopy (cryo-EM) to determine the structure of the most divergent variants. The authors found that many AI-designed nucleases retained or surpassed the activity of the natural enzyme across multiple cell types. Cryo-EM revealed that the engineered proteins formed new stabilizing interactions at the RNA-DNA interface across different conformations, providing the first experimentally determined structures of AI-designed RNA-guided nucleases. Together, these findings demonstrate how the design strategy can produce active, non-natural genome-editing enzymes.

In a new study, Mass General Brigham researchers unveil ALADYNOULLI, a generative model that identifies hidden disease signatures, predicts future health outcomes and helps reveal the biology underlying human disease.