ENPP1 has long been linked with metabolic diseases, with the common ENPP1 K173Q variant conferring increased risk for type 2 diabetes. Here, we demonstrate that the K173Q variant has decreased cGAMP hydrolysis activity, suggesting that this loss of enzymatic function could contribute to its pathogenesis.
We introduce BioReason-Pro, the first multimodal reasoning large language model (LLM) for protein function prediction that integrates protein embeddings with biological context to generate structured reasoning traces.
Heterogeneity in cancer gene expression is typically linked to genetic and epigenetic alteration. Here, we systematically measured messenger RNA (mRNA) dynamics across diverse breast cancer models, revealing that mRNA stability substantially shapes gene expression variability.
Here, by charting a high-resolution map of microbiome ageing and its functional consequences throughout the lifespan of mice, we identify a mechanism by which inhibition of gut–brain signalling during ageing results in impaired neuronal activation in the hippocampus and loss of memory encoding.
The influence of lifestyle factors on the effectiveness of T cell-mediated cancer immunotherapies remains unclear. Here, we demonstrate that the ketogenic diet-induced ketone metabolite β-hydroxybutyrate (BHB) augments chimeric antigen receptor T cell function across multiple preclinical cancer models.
AI models that learn information from genomic sequences have advanced prediction and design capabilities. Here we introduce Evo 2, a biological foundation model trained on 9 trillion DNA base pairs from a highly curated genomic atlas spanning all domains of life to have a 1 million token context window with single-nucleotide resolution.
Vitamins are essential metabolites that must be obtained from external sources. In modern times, they have become widely available, leading to their ad hoc consumption. We developed a nutritional genomics framework to systematically identify monogenic diseases responsive to micronutrient modulation.
The clinical success of cancer drug candidates depends on efficacy across many different individuals, but we currently rely on a limited set of in vivo preclinical models. Here, to address this limitation, we introduce GENEVA, a scalable single-cell-resolution platform for measuring responses to drug perturbations.
Cells integrate exogenous and endogenous signals, likely through dynamic functional associations between genes, but measuring these relationships is non-trivial. Here, we evaluate genetic associations in response to cell-cycle interruption, genotoxic perturbation, and nutrient deprivation using genetic interaction mapping in human cells.
We present MULTI-evolve, a rapid evolution framework that systematically engineers multimutants. Our approach combines protein language models or existing functional data with epistatic modelling to predict synergistic combinations.