Steady-state and dynamic gene expression programs in Saccharomyces cerevisiae in response to variation in environmental nitrogen.

Cell growth rate is regulated in response to the abundance and molecular form of essential nutrients. InSaccharomyces cerevisiae(budding yeast), the molecular form of environmental nitrogen is a major determinant of cell growth rate, supporting growth rates that vary at least threefold. Transcriptional control of nitrogen use is mediated in large part by nitrogen catabolite repression (NCR), which results in the repression of specific transcripts in the presence of a preferred nitrogen source that supports a fast growth rate, such as glutamine, that are otherwise expressed in the presence of a nonpreferred nitrogen source, such as proline, which supports a slower growth rate. Differential expression of the NCR regulon and additional nitrogen-responsive genes results in >500 transcripts that are differentially expressed in cells growing in the presence of different nitrogen sources in batch cultures. Here we find that in growth rate-controlled cultures using nitrogen-limited chemostats, gene expression programs are strikingly similar regardless of nitrogen source. NCR expression is derepressed in all nitrogen-limiting chemostat conditions regardless of nitrogen source, and in these conditions, only 34 transcripts exhibit nitrogen source-specific differential gene expression. Addition of either the preferred nitrogen source, glutamine, or the nonpreferred nitrogen source, proline, to cells growing in nitrogen-limited chemostats results in rapid, dose-dependent repression of the NCR regulon. Using a novel means of computational normalization to compare global gene expression programs in steady-state and dynamic conditions, we find evidence that the addition of nitrogen to nitrogen-limited cells results in the transient overproduction of transcripts required for protein translation. Simultaneously, we find that that accelerated mRNA degradation underlies the rapid clearing of a subset of transcripts, which is most pronounced for the highly expressed NCR-regulated permease genesGAP1,MEP2,DAL5,PUT4, andDIP5 Our results reveal novel aspects of nitrogen-regulated gene expression and highlight the need for a quantitative approach to study how the cell coordinates protein translation and nitrogen assimilation to optimize cell growth in different environments.

Long-term persistence and development of induced pancreatic beta cells generated by lineage conversion of acinar cells.

Direct lineage conversion is a promising approach to generate therapeutically important cell types for disease modeling and tissue repair. However, the survival and function of lineage-reprogrammed cells in vivo over the long term has not been examined. Here, using an improved method for in vivo conversion of adult mouse pancreatic acinar cells toward beta cells, we show that induced beta cells persist for up to 13 months (the length of the experiment), form pancreatic islet-like structures and support normoglycemia in diabetic mice. Detailed molecular analyses of induced beta cells over 7 months reveal that global DNA methylation changes occur within 10 d, whereas the transcriptional network evolves over 2 months to resemble that of endogenous beta cells and remains stable thereafter. Progressive gain of beta-cell function occurs over 7 months, as measured by glucose-regulated insulin release and suppression of hyperglycemia. These studies demonstrate that lineage-reprogrammed cells persist for >1 year and undergo epigenetic, transcriptional, anatomical and functional development toward a beta-cell phenotype.

Finding the rules for successful drug optimisation.

Drug discovery is a process of multiparameter optimisation, with the objective of finding compounds that achieve multiple, project-specific property criteria. These criteria are often based on the subjective opinion of the project team, but analysis of historical data can help to find the most appropriate profile. Computational 'rule induction' approaches enable an objective analysis of complex data to identify interpretable, multiparameter rules that distinguish compounds with the greatest likelihood of success for a project. Each property criterion highlights the most critical data that enable effective compound prioritisation decisions. We illustrate this with two applications: determining rules for simple, drug-like properties; and exploring experimental target inhibition data to find rules to reduce the risk of toxicity.