The evolution of the human brain may owe as much to fruit and honey as to meat, according to a new Analytical Review. Its findings challenge one of the dominant ideas about our ancestors' diet. “Although animal foods were clearly instrumental, changes in intake of sugars and starches also shed light on human development, health, and disease,” write the authors.  “Our modeling provides new perspectives on evolution of the human diet over the course of 4 million years, highlighting the important role of carbohydrates, sugars, and sweetness.” Human evolution is often framed around the growing dietary importance of meat, protein, and omega-3 fatty acids in driving brain growth and development of human traits. However, early hominins likely also consumed substantial amounts of carbohydrates, like natural sugars from fruits and honey, which supplied glucose needed by the increasingly energy-hungry brain and other glucose-dependent tissues. The development of cooking later added starch-rich foods to this carbohydrate supply, while animal foods supported adaptations such as smaller teeth, jaws, and digestive tracts. However, the role of sugars in diets of early humans remains poorly understood.

In this Analytical Review, Jennie Brand-Miller and colleagues argue that carbohydrates were equally as important to our ancestors’ development as the consumption of animal-based foods. By modeling brain and body size alongside metabolic demands over 4 million years of human evolution, Brand-Miller et al. estimate that natural sugars provided roughly 20-35% of total dietary energy intake for much of human evolution. Human anatomy, physiology, and genetic adaptations related to glucose metabolism further suggest that carbohydrate-rich foods helped sustain the exceptionally high energy demands of a growing brain and increasingly active body, with cooked starch becoming important later. Moreover, the authors suggest that seasonal and climatic fluctuations in carbohydrate availability may also have shaped foraging strategies, brain development, and even population survival.

Podcast: A segment of Science's weekly podcast with Jennie Brand-Miller, related to this research, will be available on the Science.org podcast landing page after the embargo lifts. Reporters are free to make use of the segments for broadcast purposes and/or quote from them – with appropriate attribution (i.e., cite "Science podcast"). Please note that the file itself should not be posted to any other Web site.

Using AI, researchers have designed complete, functional bacteriophage genomes from scratch and tested them against bacteria that had evolved resistance to a natural bacteriophage. Their work marks a step toward generative systems capable of engineering entire biological systems rather than individual genes or small genetic systems, while also raising important biosafety and biosecurity concerns. “[The authors] engage with biosafety and biosecurity questions more deliberately than most developers of powerful biological AI models,” write Thomas Inglesby and Moritz Hanke in a related Perspective. Advances in DNA sequencing and synthesis have made it increasingly possible to read and write entire genomes, but designing a functional genome from scratch remains extraordinarily difficult because genes, regulatory sequences, and other elements interact in highly complex ways. Here, Samuel King and colleagues introduce an approach to designing functional genomes that combines the Evo genomic language models they’d previously built (including Evo 1), computational biology, and experimental screening. They used this approach to generate complete bacteriophage genomes; bacteriophages have substantial utility as biotechnologies and have therapeutic relevance as treatments for bacterial infections. Using the well-studied ΦX174 bacteriophage as a model, King et al. computationally designed hundreds of candidate genomes and experimentally identified 16 functional phages, whose genetic sequences and structures differed substantially from one another. Some of the engineered phages performed comparably to naturally occurring relatives, they report, and notably, some combinations of the new phages overcame resistance in two strains of E. coli that resisted ΦX174-like phages. The findings demonstrate that AI-guided generative genomics could eventually enable the design of more durable phage-based therapies.

King et al. emphasize that the ability to design and synthesize functional AI-generated genomes introduces important biosafety and biosecurity concerns and underscores the need for expert oversight and robust safeguards throughout the design process. “Groups conducting future whole-genome design work should consult both safety and security professionals throughout the project lifecycle,” they say. They argue that existing safety frameworks can be adapted to generative genomics, while model-level protections, such as excluding sensitive viral sequences from training data, may provide an additional layer of risk mitigation. “The question is no longer whether generative viral genome design will exist,” say Inglesby and Hanke in the Perspective. “It is whether society can build oversight that allows its benefits to unfold while preventing it from enabling serious harm.”