(549e) Dynamic Transcriptomic Profiling Reveals Novel Short-Term and Long-Term Strategies to Cope with Oxygen Limitation in Scheffersomyces Stipitis

Authors: 
Hilliard, M., Auburn University
He, Q. P., Auburn University
Jeffries, T., University of Wisconsin-Madison
Wang, J., Auburn University
The rapid developments in “omics” technologies offer unprecedented opportunities to help understand cellular metabolisms at genome-scale. Among different “omics” data, RNA-seq based transcriptome profiling has been used to enhance the understanding of the genome-scale response of the organism to different stimuli. While these studies have provided insightful findings, they are most often limited to studying two steady-state conditions. From a control perspective, as cellular metabolism is a highly complex dynamic system, the transient response could offer significantly more information on the cellular metabolism, particularly on potential gene regulatory mechanisms.

In this work, using Scheffersomyces stipitis as the model organism, we examine whether the dynamic transcriptomic data obtained during a transition between two steady-states would provide more insight on the strain’s cellular metabolism. S. stipitis is an industrially relevant yeast species as it has one of the highest capacities to convert xylose into ethanol1. It has been suggested that redox balance plays a key role in the fermentative response employed by S. stipitis in response to reduced oxygen availability. Traditional analyses of S. stipitis for xylose fermentation has primarily focused on the steady-state responses to various oxygen conditions; however, these analyses have not been able to provide a systems level understanding of how the shift in redox balance contributes to the overall shift in metabolism utilized by the cells to cope with reduced oxygen availability. In this work, to gain better understanding on xylose fermentative metabolism in the strain, S. stipitis was cultivated in a controlled chemostat with xylose as the sole carbon source. Once aerobic steady state (aeroSS) growth was achieved in the reactor, the oxygen supply was significantly reduced and the cells were allowed to transition to the new micro aerobic steady state (microSS). Cultivation and RNAseq data were obtained from both steady states as well as the dynamic transition period to further investigate the metabolic shifts that occur in response to the induced oxygen limitation.

Our results demonstrate that the dynamic transcriptomic data obtained during the transient period between two steady-states provides significantly more information than the steady-states transcriptomic data alone. More importantly, the dynamic transcriptomic data reveals consistent regulatory behavior among different runs of experiment, while the steady-state transcriptomic data present inconsistent or conflicting results. Since very limited tools are available to analyze dynamic transcriptomic profiles, we developed our own data pre-processing and analysis pipe line by integrating available bioinformatics tools with system engineering tools. Finally, by integrating the analysis results obtained from dynamic transcriptomic data and the cultivation data with genome-scale modeling2, we were able to identify potential short term and long term strategies that the cells utilize to cope with oxygen limitation. Specifically, our results suggest that S. stipitis utilizes intracellularly stored sorbitol as a short-term response to the induced oxygen limitation which explains the observed overproduction of ethanol during the first half of the transition period 3,4. In addition, our results suggest that the upregulation of the glyoxylate shunt is involved in the long-term response cells utilize to cope with the oxygen limitation that persists in the reactor5,6.

(1) Jeffries, T. W.; Grigoriev, I. V.; Grimwood, J.; Laplaza, J. M.; Aerts, A.; Salamov, A.; Schmutz, J.; Lindquist, E.; Dehal, P.; Shapiro, H.; et al. Genome Sequence of the Lignocellulose-Bioconverting and Xylose-Fermenting Yeast Pichia Stipitis. Nat. Biotechnol. 2007. https://doi.org/10.1038/nbt1290.

(2) Hilliard, M.; Damiani, A.; He, Q. P.; Jeffries, T.; Wang, J. Elucidating Redox Balance Shift in Scheffersomyces Stipitis’ Fermentative Metabolism Using a Modified Genome-Scale Metabolic Model. Microb. Cell Fact. 2018, 17 (1), 140.

(3) Shen, B.; Hohmann, S.; Jensen, R. G.; Bohnert, and H. J. Roles of Sugar Alcohols in Osmotic Stress Adaptation. Replacement of Glycerol by Mannitol and Sorbitol in Yeast. Plant Physiol. 2002. https://doi.org/10.1104/pp.121.1.45.

(4) Diano, A.; Bekker-Jensen, S.; Dynesen, J.; Nielsen, J. Polyol Synthesis in Aspergillus Niger: Influence of Oxygen Availability, Carbon and Nitrogen Sources on the Metabolism. Biotechnol. Bioeng. 2006. https://doi.org/10.1002/bit.20915.

(5) Caspeta, L.; Nielsen, J. Toward Systems Metabolic Engineering of Aspergillus and Pichia Species for the Production of Chemicals and Biofuels. Biotechnol. J. 2013, 8 (5), 534–544. https://doi.org/10.1002/biot.201200345.

(6) Terabayashi, Y.; Shimizu, M.; Kitazume, T.; Masuo, S.; Fujii, T.; Takaya, N. Conserved and Specific Responses to Hypoxia in Aspergillus Oryzae and Aspergillus Nidulans Determined by Comparative Transcriptomics. Appl. Microbiol. Biotechnol. 2012, 93 (1), 305–317. https://doi.org/10.1007/s00253-011-3767-4.