Integrated analysis of transcriptome and lipid profiling reveals the co-influences of inositol–choline and Snf1 in controlling lipid biosynthesis in yeast

Deletion of LsSNF1 enhances lipid accumulation in the oleaginous yeast Lipomyces starkeyi

The oleaginous yeast, Lipomyces starkeyi can have diverse industrial applications due to its remarkable capacity to use various carbon sources for the biosynthesis intracellular triacylglycerides (TAGs). In L. starkeyi, TAG synthesis is enhanced through upregulation of genes involved in citrate-mediated acyl-CoA synthesis and Kennedy pathways through the transcriptional regulator LsSpt23p. High expression of LsSPT23 can considerably enhance TAG production. Altering the regulatory factors associated with lipid production can substantially augment lipid productivity. In this study, we identified and examined the L. starkeyi homolog sucrose nonfermenting 1 SNF1 (LsSNF1) of YlSNF1, which encodes a negative regulator of lipid biosynthesis in the oleaginous yeast Yarrowia lipolytica. The deletion of LsSNF1 enhanced TAG productivity in L. starkeyi, suggesting that LsSnf1p is a negative regulator in TAG production. The enhancement of TAG production following deletion of LsSNF1 can primarily be attributed to the upregulation of genes in the citrate-mediated acyl-CoA synthesis and Kennedy pathways, pivotal routes in TAG biosynthesis. The overexpression of LsSPT23 enhanced lipid productivity; strain overexpressing LsSPT23 and without LsSNF1 exhibited increased TAG production capacity per cell. LsSnf1p also has a significant role in the utilization of carbon sources, including xylose or glycerol, in L. starkeyi. Our study results elucidated the role of LsSnf1p in the negative regulation of TAG synthesis in L. starkeyi, which has not previously been reported.

Improving the Production of Punicic Acid in Baker's Yeast by Engineering Genes in Acyl Channeling Processes and Adjusting Precursor Supply

Punicic acid (PuA) is a high-value edible conjugated fatty acid with strong bioactivities and has important potential applications in nutraceutical, pharmaceutical, feeding, and oleochemical industries. Since the production of PuA is severely limited by the fact that its natural source (pomegranate seed oil) is not readily available on a large scale, there is considerable interest in understanding the biosynthesis and accumulation of this plant-based unusual fatty acid in transgenic microorganisms to support the rational design of biotechnological approaches for PuA production via fermentation. Here, we tested the effectiveness of genetic engineering and precursor supply in PuA production in the model yeast strain Saccharomyces cerevisiae. The results revealed that the combination of precursor feeding and co-expression of selected genes in acyl channeling processes created an effective "push-pull" approach to increase PuA content, which could prove valuable in future efforts to produce PuA in industrial yeast and other microorganisms via fermentation.

Genome-Scale Metabolic Modeling from Yeast to Human Cell Models of Complex Diseases: Latest Advances and Challenges

Genome-scale metabolic models (GEMs) are mathematical models that enable systematic analysis of metabolism. This modeling concept has been applied to study the metabolism of many organisms including the eukaryal model organism, the yeast Saccharomyces cerevisiae, that also serves as an important cell factory for production of fuels and chemicals. With the application of yeast GEMs, our knowledge of metabolism is increasing. Therefore, GEMs have also been used for modeling human cells to study metabolic diseases. Here we introduce the concept of GEMs and provide a protocol for reconstructing GEMs. Besides, we show the historic development of yeast GEMs and their applications. Also, we review human GEMs as well as their uses in the studies of complex diseases.

Oleaginicity of the yeast strain Saccharomyces cerevisiae D5A

BACKGROUND

The model yeast, Saccharomyces cerevisiae, is not known to be oleaginous. However, an industrial wild-type strain, D5A, was shown to accumulate over 20% storage lipids from glucose when growth is nitrogen-limited compared to no more than 7% lipid accumulation without nitrogen stress.

METHODS AND RESULTS

To elucidate the mechanisms of S. cerevisiae D5A oleaginicity, we compared physiological and metabolic changes; as well as the transcriptional profiles of the oleaginous industrial strain, D5A, and a non-oleaginous laboratory strain, BY4741, under normal and nitrogen-limited conditions using analytic techniques and next-generation sequencing-based RNA-Seq transcriptomics. Transcriptional levels for genes associated with fatty acid biosynthesis, nitrogen metabolism, amino acid catabolism, as well as the pentose phosphate pathway and ethanol oxidation in central carbon (C) metabolism, were up-regulated in D5A during nitrogen deprivation. Despite increased carbon flux to lipids, most gene-encoding enzymes involved in triacylglycerol (TAG) assembly were expressed at similar levels regardless of the varying nitrogen concentrations in the growth media and strain backgrounds. Phospholipid turnover also contributed to TAG accumulation through increased precursor production with the down-regulation of subsequent phospholipid synthesis steps. Our results also demonstrated that nitrogen assimilation via the glutamate-glutamine pathway and amino acid metabolism, as well as the fluxes of carbon and reductants from central C metabolism, are integral to the general oleaginicity of D5A, which resulted in the enhanced lipid storage during nitrogen deprivation.

CONCLUSION

This work demonstrated the disequilibrium and rebalance of carbon and nitrogen contribution to the accumulation of lipids in the oleaginous yeast S. cerevisiae D5A. Rather than TAG assembly from acyl groups, the major switches for the enhanced lipid accumulation of D5A (i.e., fatty acid biosynthesis) are the increases of cytosolic pools of acetyl-CoA and NADPH, as well as alternative nitrogen assimilation.

Reconstruction of a Global Transcriptional Regulatory Network for Control of Lipid Metabolism in Yeast by Using Chromatin Immunoprecipitation with Lambda Exonuclease Digestion

To build transcription regulatory networks, transcription factor binding must be analyzed in cells grown under different conditions because their responses and targets differ depending on environmental conditions. We performed whole-genome analysis of the DNA binding of five Saccharomyces cerevisiae transcription factors involved in lipid metabolism, Ino2, Ino4, Hap1, Oaf1, and Pip2, in response to four different environmental conditions in chemostat cultures, which allowed us to keep the specific growth rate constant. Chromatin immunoprecipitation with lambda exonuclease digestion (ChIP-exo) enabled the detection of binding events at a high resolution. We discovered a large number of unidentified targets and thus expanded functions for each transcription factor (e.g., glutamate biosynthesis as a target of Oaf1 and Pip2). Moreover, condition-dependent binding of transcription factors in response to cell metabolic state (e.g., differential binding of Ino2 between fermentative and respiratory metabolic conditions) was clearly suggested. Combining the new binding data with previously published data from transcription factor deletion studies revealed the high complexity of the transcriptional regulatory network for lipid metabolism in yeast, which involves the combinatorial and complementary regulation by multiple transcription factors. We anticipate that our work will provide insights into transcription factor binding dynamics that will prove useful for the understanding of transcription regulatory networks. IMPORTANCE Transcription factors play a crucial role in the regulation of gene expression and adaptation to different environments. To better understand the underlying roles of these adaptations, we performed experiments that give us high-resolution binding of transcription factors to their targets. We investigated five transcription factors involved in lipid metabolism in yeast, and we discovered multiple novel targets and condition-specific responses that allow us to draw a better regulatory map of the lipid metabolism.

Genome scale models of yeast: towards standardized evaluation and consistent omic integration

Genome scale models (GEMs) have enabled remarkable advances in systems biology, acting as functional databases of metabolism, and as scaffolds for the contextualization of high-throughput data. In the case of Saccharomyces cerevisiae (budding yeast), several GEMs have been published and are currently used for metabolic engineering and elucidating biological interactions. Here we review the history of yeast's GEMs, focusing on recent developments. We study how these models are typically evaluated, using both descriptive and predictive metrics. Additionally, we analyze the different ways in which all levels of omics data (from gene expression to flux) have been integrated in yeast GEMs. Relevant conclusions and current challenges for both GEM evaluation and omic integration are highlighted.

Protein kinase Snf1 is involved in the proper regulation of the unfolded protein response in Saccharomyces cerevisiae

Glc7 is the only catalytic subunit of the protein phosphatase type 1 in the yeast S. cerevisiae and, together with its regulatory subunits, is involved in many essential processes. Analysis of the non-essential mutants in the regulatory subunits of Glc7 revealed that the lack of Reg1, and no other subunit, causes hypersensitivity to unfolded protein response (UPR)-inducers, which was concomitant with an augmented UPR element-dependent transcriptional response. The Glc7-Reg1 complex takes part in the regulation of the yeast AMP-activated serine/threonine protein kinase Snf1 in response to glucose. We demonstrate in the present study that the observed phenotypes of reg1 mutant cells are attributable to the inappropriate activation of Snf1. Indeed, growth in the presence of limited concentrations of glucose, where Snf1 is active, or expression of active forms of Snf1 in a wild-type strain increased the sensitivity to the UPR-inducer tunicamycin. Furthermore, reg1 mutant cells showed a sustained HAC1 mRNA splicing and KAR2 mRNA levels during the recovery phase of the UPR, and dysregulation of the Ire1-oligomeric equilibrium. Finally, overexpression of protein phosphatases Ptc2 and Ptc3 alleviated the growth defect of reg1 cells under endoplasmic reticulum (ER) stress conditions. Altogether, our results reveal that Snf1 plays an important role in the attenuation of the UPR, as well as identifying the protein kinase and its effectors as possible pharmacological targets for human diseases that are associated with insufficient UPR activation.

Nutrient sensing and signaling in the yeast Saccharomyces cerevisiae

The yeast Saccharomyces cerevisiae has been a favorite organism for pioneering studies on nutrient-sensing and signaling mechanisms. Many specific nutrient responses have been elucidated in great detail. This has led to important new concepts and insight into nutrient-controlled cellular regulation. Major highlights include the central role of the Snf1 protein kinase in the glucose repression pathway, galactose induction, the discovery of a G-protein-coupled receptor system, and role of Ras in glucose-induced cAMP signaling, the role of the protein synthesis initiation machinery in general control of nitrogen metabolism, the cyclin-controlled protein kinase Pho85 in phosphate regulation, nitrogen catabolite repression and the nitrogen-sensing target of rapamycin pathway, and the discovery of transporter-like proteins acting as nutrient sensors. In addition, a number of cellular targets, like carbohydrate stores, stress tolerance, and ribosomal gene expression, are controlled by the presence of multiple nutrients. The protein kinase A signaling pathway plays a major role in this general nutrient response. It has led to the discovery of nutrient transceptors (transporter receptors) as nutrient sensors. Major shortcomings in our knowledge are the relationship between rapid and steady-state nutrient signaling, the role of metabolic intermediates in intracellular nutrient sensing, and the identity of the nutrient sensors controlling cellular growth.

Integrated analysis, transcriptome-lipidome, reveals the effects of INO-level (INO2 and INO4) on lipid metabolism in yeast

Background

In the yeast Saccharomyces cerevisiae, genes containing UAS_INO sequences are regulated by the Ino2/Ino4 and Opi1 transcription factors, and this regulation controls lipid biosynthesis. The expression level of INO2 and INO4 genes (INO-level) at different nutrient limited conditions might lead to various responses in yeast lipid metabolism.

Methods

In this study, we undertook a global study on how INO-levels (transcription level of INO2 and INO4) affect lipid metabolism in yeast and we also studied the effects of single and double deletions of the two INO-genes (deficient effect). Using 2 types of nutrient limitations (carbon and nitrogen) in chemostat cultures operated at a fixed specific growth rate of 0.1 h-1 and strains having different INO-level, we were able to see the effect on expression level of the genes involved in lipid biosynthesis and the fluxes towards the different lipid components. Through combined measurements of the transcriptome, metabolome, and lipidome it was possible to obtain a large dataset that could be used to identify how the INO-level controls lipid metabolism and also establish correlations between the different components.

Results

In this study, we undertook a global study on how INO-levels (transcription level of INO2 and INO4) affect lipid metabolism in yeast and we also studied the effects of single and double deletions of the two INO-genes (deficient effect). Using 2 types of nutrient limitations (carbon and nitrogen) in chemostat cultures operated at a fixed specific growth rate of 0.1 h-1 and strains having different INO-level, we were able to see the effect on expression level of the genes involved in lipid biosynthesis and the fluxes towards the different lipid components. Through combined measurements of the transcriptome, metabolome, and lipidome it was possible to obtain a large dataset that could be used to identify how the INO-level controls lipid metabolism and also establish correlations between the different components.

Conclusions

Our analysis showed the strength of using a combination of transcriptome and lipidome analysis to illustrate the effect of INO-levels on phospholipid metabolism and based on our analysis we established a global regulatory map.

Reciprocal phosphorylation of yeast glycerol-3-phosphate dehydrogenases in adaptation to distinct types of stress

Eukaryotic cells have evolved mechanisms for ensuring growth and survival in the face of stress caused by a fluctuating environment. Saccharomyces cerevisiae has two homologous glycerol-3-phosphate dehydrogenases, Gpd1 and Gpd2, that are required to endure various stresses, including hyperosmotic shock and hypoxia. These enzymes are only partially redundant, and their unique functions were attributed previously to differential transcriptional regulation and localization. We find that Gpd1 and Gpd2 are negatively regulated through phosphorylation by distinct kinases under reciprocal conditions. Gpd2 is phosphorylated by the AMP-activated protein kinase Snf1 to curtail glycerol production when nutrients are limiting. Gpd1, in contrast, is a target of TORC2-dependent kinases Ypk1 and Ypk2. Inactivation of Ypk1 by hyperosmotic shock results in dephosphorylation and activation of Gpd1, accelerating recovery through increased glycerol production. Gpd1 dephosphorylation acts synergistically with its transcriptional upregulation, enabling long-term growth at high osmolarity. Phosphorylation of Gpd1 and Gpd2 by distinct kinases thereby enables rapid adaptation to specific stress conditions. Introduction of phosphorylation motifs targeted by distinct kinases provides a general mechanism for functional specialization of duplicated genes during evolution.