Project description:Significant insight about biological networks arises from the study of network motifs –overly abundant network subgraphs, but such wiring patterns do not specify when and how potential routes within a cellular network are used. To address this limitation, we introduce activity motifs, which capture patterns in the dynamic use of a network. Using this framework to analyze transcription in Saccharomyces cerevisiae metabolism, we find that cells use different timing activity motifs to optimize transcription timing in response to changing conditions: forward activation to produce metabolic compounds efficiently, backward shutoff to rapidly stop production of a detrimental product and synchronized activation for co-production of metabolites required for the same reaction Measuring protein abundance over a time course reveals that mRNA timing motifs also occur at the protein level. Timing motifs significantly overlap with binding activity motifs, where genes in a linear chain have ordered binding affinity to a transcription factor, suggesting a mechanism for ordered transcription. Finely timed transcriptional regulation is therefore abundant in yeast metabolism, optimizing the organism's adaptation to new environmental conditions.
Project description:Reprogramming a non-methylotrophic industrial host, such as Saccharomyces cerevisiae, to a synthetic methylotroph reprents a huge challenge due to the complex regulation in yeast. Through TMC strategy together with ALE strategy, we completed a strict synthetic methylotrophic yeast that could use methanol as the sole carbon source. However, how cells respond to methanol and remodel cellular metabolic network on methanol were not clear. Therefore, genome-scale transcriptional analysis was performed to unravel the cellular reprograming mechanisms underlying the improved growth phenotype.
Project description:In response to carbon source switching from glucose to non-glucose, such as ethanol and galactose, yeast cells can directionally preprogram cellular metabolism to efficiently utilize the nutrients. However, the understanding of cellular responsive network to utilize a non-natural carbon source, such as xylose, is limited due to the incomplete knowledge on the xylose response mechanisms. Here, through optimization of the xylose assimilation pathway together with combinational evaluation of reported targets, we generated a series of mutants with varied growth ability. However, understanding how cells respond to xylose and remodel cellular metabolic network is far insufficient based on current information. Therefore, genome-scale transcriptional analysis was performed to unravel the cellular reprograming mechanisms underlying the improved growth phenotype.