Xi Xing

The paper uses in vivo isotope tracing and AI to identify biosynthetic origins of unknown metabolites, revealing new metabolic pathways and age-related changes.

This study maps spatial metabolic gradients in murine liver and intestine using integrated experimental-computational methods, revealing distinct metabolic patterns and fructose-induced metabolic derangements.

This paper introduces DeepMet, a chemical language model that predicts and discovers unknown metabolites using mass spectrometry data.

This study shows that supplementing one-carbon units enhances anti-PD-1 checkpoint blockade by supporting purine synthesis in tumor-infiltrating T cells, leading to increased durable tumor regression.

The paper quantifies nutrient preferences of gut bacteria in mice using isotope tracing and finds dietary fiber, protein, and host-derived metabolites drive microbial growth.

The paper introduces NetID, a global network optimization method for annotating untargeted metabolomics data, identifying novel metabolites in yeast and mouse datasets.