Elucidating redox balance shift in Scheffersomyces stipitis’ fermentative metabolism using a modified genome-scale metabolic model

Abstract

BackgroundScheffersomyces stipitis is an important yeast species in the field of biorenewables due to its desired capacity for xylose utilization. It has been recognized that redox balance plays a critical role in S. stipitis due to the different cofactor preferences in xylose assimilation pathway. However, there has not been any systems level understanding on how the shift in redox balance contributes to the overall metabolic shift in S. stipitis to cope with reduced oxygen uptake. Genome-scale metabolic network models (GEMs) offer the opportunity to gain such systems level understanding; however, currently the two published GEMs for S. stipitis cannot be used for this purpose, as neither of them is able to capture the strain’s fermentative metabolism reasonably well due to their poor prediction of xylitol production, a key by-product under oxygen limited conditions.ResultsA system identification-based (SID-based) framework that we previously developed for GEM validation is expanded and applied to refine a published GEM for S. stipitis, iBB814. After the modified GEM, named iDH814, was validated using literature data, it is used to obtain genome-scale understanding on how redox cofactor shifts when cells respond to reduced oxygen supply. The SID-based framework for GEM analysis was applied to examine how the environmental perturbation (i.e., reduced oxygen supply) propagates through the metabolic network, and key reactions that contribute to the shifts of redox and metabolic state were identified. Finally, the findings obtained through GEM analysis were validated using transcriptomic data.ConclusionsiDH814, the modified model, was shown to offer significantly improved performance in terms of matching available experimental results and better capturing available knowledge on the organism. More importantly, our analysis based on iDH814 provides the first genome-scale understanding on how redox balance in S. stipitis was shifted as a result of reduced oxygen supply. The systems level analysis identified the key contributors to the overall metabolic state shift, which were validated using transcriptomic data. The analysis confirmed that S. stipitis uses a concerted approach to cope with the stress associated with reduced oxygen supply, and the shift of reducing power from NADPH to NADH seems to be the center theme that directs the overall shift in metabolic states.Electronic supplementary materialThe online version of this article (10.1186/s12934-018-0983-y) contains supplementary material, which is available to authorized users.

Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

“…Again, if only the transcriptome profiles at the two steady states were compared, important information would have been missed. Finally, by integrating the analysis results obtained from dynamic transcriptomic data and the cultivation data with genome-scale modelling [117], we were able to identify potential shortand long-term strategies that the cells utilize to cope with oxygen limitation. More details can be found in [109,110,117].…”

“…Finally, by integrating the analysis results obtained from dynamic transcriptomic data and the cultivation data with genome-scale modelling [117], we were able to identify potential shortand long-term strategies that the cells utilize to cope with oxygen limitation. More details can be found in [109,110,117]. This study highlights the importance of studying the dynamic transient response of RNA-Seq data in order to understand the global response triggered by the perturbation and potential generegulatory mechanism.…”

“…The conventional approach for biological model validation is to compare model predictions with experimental data under different conditions [117,169,170]. For example, for genome-scale metabolic network model (GEM) validation, most often the experimental data consist of measured cross-membrane fluxes, i.e., various substrate uptake rates, product excretion rates, and cell growth rate.…”

“…To address the shortcomings of point-matching validation approaches, we proposed a system identification (SID)-based framework for GEM validation. In the SID framework, biological knowledge embedded in a GEM is first extracted from a series of designed in silico experiments through multivariate analysis methods such as principal component analysis (PCA); next, the extracted knowledge, such as how cells respond under a given stimulus, is visualized and compared with the existing knowledge for model validation and analysis [117,171]. We term the proposed approach "knowledge-matching" as the simulation results are not directly compared with experimental data; instead, the knowledge captured by the model is compared with available knowledge.…”

Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species

“…A modified version of iBB814, the model iDH814, was published in 2016 and used to elucidate the redox balance shift response to reduced oxygen supply conditions (Hilliard et al . 2018 ). As these four reconstructions just account for the cytoplasm, mitochondria and peroxisome as cellular compartments, a fully compartmentalized model for this relevant organism is still missing.…”