The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid metabolism

Background: Up to now, there have been three published versions of a yeast genome-scale metabolic model: iFF708, iND750 and iLL672. All three models, however, lack a detailed description of lipid metabolism and thus are unable to be used as integrated scaffolds for gaining insights into lipid metabolism from multilevel omic measurement technologies (e.g. genome-wide mRNA levels). To overcome this limitation, we reconstructed a new version of the Saccharomyces cerevisiae genome-scale model, ilN800 that includes a more rigorous and detailed descrition of lipid metabolism. Results: The reconstructed metabolic model comprises 1446 reactions and 1013 metabolites. Beyond incorporating new reactions involved in lipid metabolism, we also present new biomass equations that improve the predictive power of flux balance analysis simulations. Predictions of both growth capability and large scale in silico single gene deletions by ilN800 were consistent with experimental data. In addition, 13C-labeling experiments validated the new biomass equations and calculated intracellular fluxes. To demonstrate the applicability of ilN800, we show that the model can be used as a scaffold to reveal the regulatory importance of lipid metabolism precursors and intermediates that would have been missed in previous models from transcriptome datasets. Conclusions: Performing integrated analyses using ilN800 as a network scaffold is shown to be a valuable tool for elucidating the behavior of complex metabolic networks, particularly for identifying regulatory targets in lipid metabolism that can be used for industrial applications or for understanding lipid disease states

Gene Co-Expression Analysis Inferring the Crosstalk of Ethylene and Gibberellin in Modulating the Transcriptional Acclimation of Cassava Root Growth in Different Seasons

<div><p>Cassava is a crop of hope for the 21^st century. Great advantages of cassava over other crops are not only the capacity of carbohydrates, but it is also an easily grown crop with fast development. As a plant which is highly tolerant to a poor environment, cassava has been believed to own an effective acclimation process, an intelligent mechanism behind its survival and sustainability in a wide range of climates. Herein, we aimed to investigate the transcriptional regulation underlying the adaptive development of a cassava root to different seasonal cultivation climates. Gene co-expression analysis suggests that <i>AP2-EREBP transcription factor</i> (<i>ERF1</i>) orthologue (D142) played a pivotal role in regulating the cellular response to exposing to wet and dry seasons. The <i>ERF</i> shows crosstalk with gibberellin, via <i>ent</i>-Kaurene synthase (D106), in the transcriptional regulatory network that was proposed to modulate the downstream regulatory system through a distinct signaling mechanism. While sulfur assimilation is likely to be a signaling regulation for dry crop growth response, calmodulin-binding protein is responsible for regulation in the wet crop. With our initiative study, we hope that our findings will pave the way towards sustainability of cassava production under various kinds of stress considering the future global climate change.</p></div

Transcription regulatory networks (TF-target network) of cassava root development in wet and dry growing seasons.

<p>(<b>a</b>) C-Dry: cortex-dry, (<b>b</b>) P-Dry: parenchyma-dry, (<b>c</b>) C-Wet: cortex-wet, (<b>d</b>) P-Wet: parenchyma-wet. The highlighted nodes (diamond: transcription factor genes and circle: target genes) and edges (blue: positive relationship and red: negative relationship) represent the active gene and gene association, presumably functioning under the condition. The numbers aligned with the highlighted edges denote the PCC of the expression profile for the associated gene pair. (<b>e</b>) demonstrates the expression profiles of D142 and D106 which are the key regulating factors in the transcription regulatory networks</p

Expression profiles of key genes in the cortex transcriptional regulatory network.

<p>The gene expression (red bar) was superimposed onto the precipitation curve (blue line) for comparison: (<b>a-b</b>) D82, (<b>c-d</b>) D106 and (<b>e-f</b>) D142.</p

<i>Cis</i>-regulatory element analysis in the upstream region of the D142-target genes.

<p>The highlighted nodes and edges marked the associated TF-target pair whose transcriptional regulatory relationship could be supported by the finding of TF-binding site (TFBS).</p

Scheme of the proposed model.

<p>Scheme of the proposed model.</p

Overall methodology.

<p>Overall methodology.</p

Expression profiles of key genes in the parenchyma transcriptional regulatory network.

<p>The gene expression (red bar) was superimposed onto the precipitation curve (blue line) for comparison: (<b>a-b</b>) D82, (<b>c-d</b>) D106, (<b>e-f</b>) D142, (<b>g-h</b>) W17 and (<b>i-j</b>) W20.</p