“Phenotype match” algorithm for low and high glucose conditions.

<p>The simulated and observed ethanol yields matched well for low (A) and high (B) glucose conditions, respectively. Negative correlations between the weighting factors of minimizing the overall enzyme usage and the observed ethanol yield were found for low (C) and high (D) glucose conditions.</p

Effect of cutoff <i>p</i>-value on omFBA prediction using “small pool” of genes.

<p>Three cutoff <i>p</i>-values, i.e., 0.05, 0.67, and 0.95, were used to filter the transcriptomics data. For each cutoff <i>p</i>-value, we re-ran the omFBA algorithm for 40 times and calculated the percentage of matches between the predicted and the observed ethanol yields in the validation datasets.</p

OM-FBA: Integrate Transcriptomics Data with Flux Balance Analysis to Decipher the Cell Metabolism

<div><p>Constraint-based metabolic modeling such as flux balance analysis (FBA) has been widely used to simulate cell metabolism. Thanks to its simplicity and flexibility, numerous algorithms have been developed based on FBA and successfully predicted the phenotypes of various biological systems. However, their phenotype predictions may not always be accurate in FBA because of using the objective function that is assumed for cell metabolism. To overcome this challenge, we have developed a novel computational framework, namely omFBA, to integrate multi-omics data (e.g. transcriptomics) into FBA to obtain omics-guided objective functions with high accuracy. In general, we first collected transcriptomics data and phenotype data from published database (e.g. GEO database) for different microorganisms such as <i>Saccharomyces cerevisiae</i>. We then developed a “Phenotype Match” algorithm to derive an objective function for FBA that could lead to the most accurate estimation of the known phenotype (e.g. ethanol yield). The derived objective function was next correlated with the transcriptomics data via regression analysis to generate the omics-guided objective function, which was next used to accurately simulate cell metabolism at unknown conditions. We have applied omFBA in studying sugar metabolism of <i>S</i>. <i>cerevisiae</i> and found that the ethanol yield could be accurately predicted in most of the cases tested (>80%) by using transcriptomics data alone, and revealed valuable metabolic insights such as the dynamics of flux ratios. Overall, omFBA presents a novel platform to potentially integrate multi-omics data simultaneously and could be incorporated with other FBA-derived tools by replacing the arbitrary objective function with the omics-guided objective functions.</p></div

Correlation between phenotype-matched weighting factors and gene expressions.

<p>The absolute values of the correlation coefficients in one of the training datasets were ranked from high to low (only the top 30 genes were shown here). The top 3 genes were chosen as the genetic markers to derive the omics-guided objective function (blue bars).</p

Key flux ratio analysis.

<p>Four key flux ratios (PGI/G6PDH2, FBA/TKT1, ENO/PPCK, and PYK/PDC) were selected to be correlated with phenotype-matched weighting factors, observed ethanol yields, and the ratios of the corresponding gene expression levels for low and high glucose condition. All the values of the ratios were exponential. Abbreviations: PGI, glucose-6-phosphate isomerase; G6PDH2, glucose 6-phosphate dehydrogenase; FBA, fructose-bisphosphate aldolase; TKT1, transketolase; ENO, enolase; PPCK, phosphoenolpyruvate carboxykinase; PYK, pyruvate kinase; PYRDC, pyruvate decarboxylase.</p

Complex interactions of various components in cell metabolism.

<p>Multi-omics data has provided the quantitative readouts of these components, which helps us to elucidate the interactions among the multi-layer regulations.</p

Prediction accuracy of omFBA algorithm.

<p>Direct comparison of the predicted and observed ethanol yields in low (A) and high (B) glucose conditions. The omFBA algorithm was repeated for 40 times and the percentage of matched predictions of omFBA algorithm were calculated and ranked for low (C) and high (D) glucose conditions.</p

Key flux ratios compared with previous studies using ¹³C metabolic flux analysis.

<p>Key flux ratios compared with previous studies using ¹³C metabolic flux analysis.</p

Investigate the Metabolic Reprogramming of <i>Saccharomyces cerevisiae</i> for Enhanced Resistance to Mixed Fermentation Inhibitors via ¹³C Metabolic Flux Analysis

<div><p>The fermentation inhibitors from the pretreatment of lignocellulosic materials, e.g., acetic acid and furfural, are notorious due to their negative effects on the cell growth and chemical production. However, the metabolic reprogramming of the cells under these stress conditions, especially metabolic response for resistance to mixed inhibitors, has not been systematically investigated and remains mysterious. Therefore, in this study, ¹³C metabolic flux analysis (¹³C-MFA), a powerful tool to elucidate the intracellular carbon flux distributions, has been applied to two <i>Saccharomyces cerevisiae</i> strains with different tolerances to the inhibitors under acetic acid, furfural, and mixed (i.e., acetic acid and furfural) stress conditions to unravel the key metabolic responses. By analyzing the intracellular carbon fluxes as well as the energy and cofactor utilization under different conditions, we uncovered varied metabolic responses to different inhibitors. Under acetate stress, ATP and NADH production was slightly impaired, while NADPH tended towards overproduction. Under furfural stress, ATP and cofactors (including both NADH and NADPH) tended to be overproduced. However, under dual-stress condition, production of ATP and cofactors was severely impaired due to synergistic stress caused by the simultaneous addition of two fermentation inhibitors. Such phenomenon indicated the pivotal role of the energy and cofactor utilization in resisting the mixed inhibitors of acetic acid and furfural. Based on the discoveries, valuable insights are provided to improve the tolerance of <i>S</i>. <i>cerevisiae</i> strain and further enhance lignocellulosic fermentation.</p></div

MOESM1 of Metabolic engineering of Saccharomyces cerevisiae to produce 1-hexadecanol from xylose

Additional file 1: Table S1. Primers used in this study. Table S2. Promoters used in this study. Figure S1. DNA electrophoresis confirmed that all of the cassettes in both xylose pathway and 1-hexadecanol pathway existed in the evolved strains. Figure S2. The strengths of PDC1, TEF1, and ENO2 promoters in front of EGFP. Figure S3. Correlations between the promoter strengths in front of XR, XDH and XKS in xylose utilization pathways and the growth rates (blue dots) as well as 1-hexadecanol concentrations (red dots). Figure S4. Correlation between the growth rates and the 1-hexadecanol titers for the combinatorial promoter engineering. Figure S5. Flux balance analysis revealed correlation between the 1-hexadecanol production and ATP (A), NADH (B), NADPH (C), and growth rate (D