Background: Up to now, there have been three published versions of a yeast genome-scale metabolic model:iFF708,iND750andiLL672. 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 theSaccharomyces cerevisiaegenome-scale model,iIN800that includes a more rigorous and detailed description 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 scalein silicosingle gene deletions byiIN800were consistent with experimental data. In addition,¹³C-labeling experiments validated the new biomass equations and calculated intracellular fluxes. To demonstrate the applicability ofiIN800, 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.Conclusion: Performing integrated analyses usingiIN800as 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.

Background: Starch serves as a temporal storage of carbohydrates in plant leaves during day/night cycles. To study transcriptional regulatory modules of this dynamic metabolic process, we conducted gene regulation network analysis based on small-sample inference of graphical Gaussian model (GGM).Results: Time-series significant analysis was applied forArabidopsisleaf transcriptome data to obtain a set of genes that are highly regulated under a diurnal cycle. A total of 1,480 diurnally regulated genes included 21 starch metabolic enzymes, 6 clock-associated genes, and 106 transcription factors (TF). A starch-clock-TF gene regulation network comprising 117 nodes and 266 edges was constructed by GGM from these 133 significant genes that are potentially related to the diurnal control of starch metabolism. From this network, we found that β-amylase 3 (b-amy3: At4g17090), which participates in starch degradation in chloroplast, is the most frequently connected gene (a hub gene). The robustness of gene-to-gene regulatory network was further analyzed by TF binding site prediction and by evaluating global co-expression of TFs and target starch metabolic enzymes. As a result, two TFs, indeterminate domain 5 (AtIDD5: At2g02070) and constans-like (COL: At2g21320), were identified as positive regulators of starch synthase 4 (SS4: At4g18240). The inference model of AtIDD5-dependent positive regulation ofSS4gene expression was experimentally supported by decreasedSS4mRNA accumulation inAtidd5mutant plants during the light period of both short and long day conditions. COL was also shown to positively controlSS4mRNA accumulation. Furthermore, the knockout of AtIDD5 and COL led to deformation of chloroplast and its contained starch granules. This deformity also affected the number of starch granules per chloroplast, which increased significantly in both knockout mutant lines.Conclusions: In this study, we utilized a systematic approach of microarray analysis to discover the transcriptional regulatory network of starch metabolism inArabidopsisleaves. With this inference method, the starch regulatory network ofArabidopsiswas found to be strongly associated with clock genes and TFs, of which AtIDD5 and COL were evidenced to controlSS4gene expression and starch granule formation in chloroplasts.

In cyanobacteria, the CO₂-concentrating mechanism (CCM) is a vital biological process that provides effective photosynthetic CO₂ fixation by elevating the CO₂ level near the active site of Rubisco. This process enables the adaptation of cyanobacteria to various habitats, particularly in CO₂-limited environments. Although CCM of freshwater and marine cyanobacteria are well studied, there is limited information on the CCM of cyanobacteria living under alkaline environments. Here, we aimed to explore the molecular components of CCM in 12 alkaliphilic cyanobacteria through genome-based analysis. These cyanobacteria included 6 moderate alkaliphiles; Pleurocapsa sp. PCC 7327, Synechococcus spp., Cyanobacterium spp., Spirulina subsalsa PCC 9445, and 6 strong alkaliphiles (i.e. Arthrospira spp.). The results showed that both groups belong to β-cyanobacteria based on β-carboxysome shell proteins with form 1B of Rubisco. They also contained standard genes, ccmKLMNO cluster, which is essential for β-carboxysome formation. Most strains did not have the high-affinity Na⁺/HCO₃^- symporter SbtA and the medium-affinity ATP-dependent HCO₃^- transporter BCT1. Specifically, all strong alkaliphiles appeared to lack BCT1. Beside the transport systems, carboxysomal β-CA, CcaA, was absent in all alkaliphiles, except for three moderate alkaliphiles: Pleurocapsa sp. PCC 7327, Cyanobacteriumstranieri PCC 7202, and Spirulina subsalsa PCC 9445. Furthermore, comparative analysis of the CCM components among freshwater, marine, and alkaliphilic β-cyanobacteria revealed that the basic molecular components of the CCM in the alkaliphilic cyanobacteria seemed to share more degrees of similarity with freshwater than marine cyanobacteria. These findings provide a relationship between the CCM components of cyanobacteria and their habitats.

Background: Spirulina (Arthrospira) platensis is a well-known filamentous cyanobacterium used in the production of many industrial products, including high value compounds, healthy food supplements, animal feeds, pharmaceuticals and cosmetics, for example. It has been increasingly studied around the world for scientific purposes, especially for its genome, biology, physiology, and also for the analysis of its small-scale metabolic network. However, the overall description of the metabolic and biotechnological capabilities of S. platensis requires the development of a whole cellular metabolism model. Recently, the S. platensis C1 (Arthrospira sp. PCC9438) genome sequence has become available, allowing systems-level studies of this commercial cyanobacterium. Results: In this work, we present the genome-scale metabolic network analysis of S. platensis C1, i AK692, its topological properties, and its metabolic capabilities and functions. The network was reconstructed from the S. platensis C1 annotated genomic sequence using Pathway Tools software to generate a preliminary network. Then, manual curation was performed based on a collective knowledge base and a combination of genomic, biochemical, and physiological information. The genome-scale metabolic model consists of 692 genes, 837 metabolites, and 875 reactions. We validated i AK692 by conducting fermentation experiments and simulating the model under autotrophic, heterotrophic, and mixotrophic growth conditions using COBRA toolbox. The model predictions under these growth conditions were consistent with the experimental results. The i AK692 model was further used to predict the unique active reactions and essential genes for each growth condition. Additionally, the metabolic states of i AK692 during autotrophic and mixotrophic growths were described by phenotypic phase plane (PhPP) analysis. Conclusions: This study proposes the first genome-scale model of S. platensis C1, i AK692, which is a predictive metabolic platform for a global understanding of physiological behaviors and metabolic engineering. This platform could accelerate the integrative analysis of various “-omics” data, leading to strain improvement towards a diverse range of desired industrial products from Spirulina.

Cassava is a well-known starchy root crop utilized for food, feed and biofuel production. However, the comprehension underlying the process of starch production in cassava is not yet available.