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Cho, J.-H.

Publications and source records attributed to Cho, J.-H..

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Distinct neuronal activity patterns induce different gene expression programs

Brief and sustained neuronal activity patterns can have opposite effects on synaptic strength that both require activity-regulated gene (ARG) expression. However, whether distinct patterns of activity induce different sets of ARGs is unknown. In genome-scale experiments, we reveal that a neurons activity-pattern history can be predicted from the ARGs it expresses. Surprisingly, brief activity selectively induces a small subset of the ARG program that that corresponds precisely to the first of three temporal waves of genes induced by sustained activity. These first-wave genes are distinguished by an open chromatin state, proximity to rapidly activated enhancers, and a requirement for MAPK/ERK signaling for their induction. MAPK/ERK mediates rapid RNA polymerase recruitment to promoters, as well as enhancer RNA induction but not histone acetylation at enhancers. Thus, the same mechanisms that establish the multi-wave temporal structure of ARG induction also enable different sets of genes to be induced by distinct activity patterns.

neuroscience

Metabolome Identification by Systematic Stable Isotope Labeling Experiments and False Discovery Analysis with a Target-Decoy Strategy

We introduce a formula-based strategy and algorithm (JUMPm) for global metabolite identification and false discovery analysis in untargeted mass spectrometry-based metabolomics. JUMPm determines the chemical formulas of metabolites from unlabeled and stable-isotope labeled metabolome data, and derives the most likely metabolite identity by searching structure databases. JUMPm also estimates the false discovery rate (FDR) with a target-decoy strategy based on the octet rule of chemistry. With systematic stable isotope labeling of yeast, we identified 2,085 chemical formulas (10% FDR), 892 of which were assigned with metabolite structures. We evaluated JUMPm with a library of synthetic standards, and found that 96% of the formulas were correctly identified. We extended the method to mammalian cells with direct isotope labeling and by heavy yeast spike-in. This strategy and algorithm provide a powerful a practical solution for global identification of metabolites with a critical measure of confidence.

biochemistry