Model-based transcriptome engineering promotes a fermentative transcriptional state in yeast
Creators: name: Michael, Drew
name: Maier, Ezekiel
name: Brent, Michael R.
Publisher: name: F1000Research
Year: 2017

description: Background The central premise of regulatory systems biology is that a systematic map of a cell’s regulatory machinery will enable us to understand, predict, and rationally manipulate the cell’s state or behavior. The ability to engineer specific behaviors into cells would have a significant impact on biomedicine and biotechnology, including applications to regenerative medicine and biofuels production. One way to coax cells to behave in a desired way is to globally modify their gene expression state, making it more like the state of cells with the desired behavior. This paper introduces a broadly applicable algorithm for transcriptome engineering—designing transcription factor deletions or overexpressions to move cells to a gene expression state that is associated with a desired behavior. Our algorithm, known as NetSurgeon, uses a qualitative transcription factor (TF) network map and gene expression profiles for an initial state and goal state. This paper also presents an approach to benchmarking and validating such algorithms. The availability of systematic, objective benchmarks for a computational task often stimulates increased effort and rapid progress on that task.<br><br>Results After validating NetSurgeon on existing genome-wide data sets, we applied it to improving ethanol production in a mixed glucose–xylose co-culture. These fermentations use <i>S. cerevisiae</i> (yeast) cells that have been modified with transgenes needed to metabolize xylose. The yeast produce ethanol while rapidly consuming the glucose, but they stop producing ethanol when only xylose remains. Yeast cells do not natively consume xylose, so we hypothesized that they were unable to recognize xylose as a fermentable carbon source and therefore entered a transcriptional state that was non-optimal for fermentation. We ran NetSurgeon using the transcriptional state of wild type (WT) cells growing in xylose alone as the initial state and that of WT cells in glucose-plus-xylose as the goal state. NetSurgeon produced a ranked list of regulators whose deletion was predicted to force the system toward the goal state. We tested the top eight NetSurgeon-selected deletions and one (<i>SNF1</i>) selected by a human expert. Six of the eight NetSurgeon-selected interventions lowered the number of genes that are differentially expressed (DE) between the wild-type strain in glucose plus-xylose and the wild type in xylose alone (Fig. 1A). The best intervention, cat8Δ, reduced the number of DE genes by 22%. These six also lowered the Euclidean distance to the goal state (avg. 21%, cat8Δ 28%, Fig. 1B). Among the 445 carbon metabolism genes that NetSurgeon targeted for optimization, cat8Δ reduced the Euclidean distance by 36% (Fig. 4C). Seven of the eight NetSurgeon-selected interventions reduced the Euclidean distance between the initial and goal state expression levels of the genes in at least one of the central carbon metabolism pathways (Fig. 1D). Deletion of <i>CAT8</i> moved all of the TCA cycle genes toward their expression levels in the goal state (Fig. 4E). Among genes encoding enzymes in the TCA cycle, cat8Δ reduced the Euclidean distance to the goal by 60%. Snf18Δ, selected by our human expert, performed worse than six of the eight deletions selected by NetSurgeon.<br><br>Conclusion These observations highlight the power of transcriptome-level interventions to modulate the expression of many more genes than is feasible by traditional, one-gene-at-a-time genetic engineering. NetSurgeon is able to successfully guide transcriptome engineering. It performs well on existing genome-wide data sets (not described above) and in a practical prospective task. However, it there many avenues by which it can be improved.