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

Global rewiring of lipid metabolism to produce carotenoid by deleting the transcription factor genes ino2/ino4 in Saccharomyces cerevisiae

The transcription factor complex INO2 and INO4 in Saccharomyces cerevisiae plays a vital role in lipid biosynthesis by activating multiple genes in the biosynthetic pathways of phospholipid, fatty acid, and sterol. Previous studies have reported conflicting results regarding the effects of ino2 and ino4 gene expression levels on target chemicals. Therefore, this study aimed to examine the influence of different ino2 and ino4 expression levels on carotenoid production (e.g., lycopene), which shares a common precursor, acetyl-CoA, with lipid metabolism. Surprisingly, 2.6- and 1.8-fold increase in lycopene yield in the ino2 and ino4 deletion strains were found, respectively. In contrast, ino2 overexpression did not promote lycopene accumulation. Additionally, there was a decrease in intracellular free fatty acids in the ino2 deletion strain. Comparative transcriptome analysis revealed a significant downregulation of genes related to lipid biosynthesis in the ino2 deletion strain. To our knowledge, this is the first report showing that deletion of transcription factor genes ino2 and ino4 can facilitate lycopene accumulation. These findings hold significant implications for the development of metabolically engineered S. cerevisiae with enhanced carotenoid production.

Opi1-mediated transcriptional modulation orchestrates genotoxic stress response in budding yeast

In budding yeast, the transcriptional repressor Opi1 regulates phospholipid biosynthesis by repressing expression of genes containing inositol-sensitive upstream activation sequences. Upon genotoxic stress, cells activate the DNA damage response to coordinate a complex network of signaling pathways aimed at preserving genomic integrity. Here, we reveal that Opi1 is important to modulate transcription in response to genotoxic stress. We find that cells lacking Opi1 exhibit hypersensitivity to genotoxins, along with a delayed G1-to-S-phase transition and decreased gamma-H2A levels. Transcriptome analysis using RNA sequencing reveals that Opi1 plays a central role in modulating essential biological processes during methyl methanesulfonate (MMS)-associated stress, including repression of phospholipid biosynthesis and transduction of mating signaling. Moreover, Opi1 induces sulfate assimilation and amino acid metabolic processes, such as arginine and histidine biosynthesis and glycine catabolism. Furthermore, we observe increased mitochondrial DNA instability in opi1Δ cells upon MMS treatment. Notably, we show that constitutive activation of the transcription factor Ino2-Ino4 is responsible for genotoxin sensitivity in Opi1-deficient cells, and the production of inositol pyrophosphates by Kcs1 counteracts Opi1 function specifically during MMS-induced stress. Overall, our findings highlight Opi1 as a critical sensor of genotoxic stress in budding yeast, orchestrating gene expression to facilitate appropriate stress responses.

Exploration of the Strategy for Improving the Expression of Heterologous Sweet Protein Monellin in Aspergillus niger

Aspergillus niger is a primary cell factory for food-grade protein (enzyme) production due to its strong protein secretion capacity and unique safety characteristics. The bottleneck issue for the current A. niger expression system is the difference in expression yield of heterologous proteins of non-fungal origin compared to those of fungal origin, which is about three orders of magnitude. The sweet protein monellin, derived from West African plants, has the potential to become a food-grade sweetener due to its high sweetness and the benefit of not containing sugar itself, but it is extremely difficult to establish a research model for heterologous expression in A. niger, owing to extremely low expression, a small molecular weight, and being undetectable with conventional protein electrophoresis. HiBiT-Tag was fused with low-expressing monellin in this work to create a research model for heterologous protein expression in A. niger at ultra-low levels. We increased monellin expression by increasing the monellin copy number, fusing monellin with the endogenous highly expressed glycosylase glaA, and eliminating extracellular protease degradation, among other strategies. In addition, we investigated the effects of overexpression of molecular chaperones, inhibiting the ERAD pathway, and enhancing the synthesis of phosphatidylinositol, phosphatidylcholine, and diglycerides in the biomembrane system. Using medium optimization, we finally obtained 0.284 mg/L of monellin in the supernatant of the shake flask. This is the first time recombinant monellin has been expressed in A. niger, with the goal of investigating ways to improve the secretory expression of heterologous proteins at ultra-low levels, which can serve as a model for the expression of other heterologous proteins in A. niger.

Systematic Optimization of HPO-CPR to Boost (+)-Nootkatone Synthesis in Engineered Saccharomyces cerevisiae

As an important and expensive natural sesquiterpene compound in grapefruit, the interest in (+)-nootkatone is stimulated by its strong grapefruit-like odor and physiological activities, which induce efforts for its microbial production. However, the low catalytic efficiency of the cytochrome P450-P450 reductase (HPO-CPR) system is the main challenge. We developed a high-throughput screening (HTS) method using the principle of the color reaction between carbonyl compounds and 2,4-dinitrophenylhydrazine (DNPH), which could rapidly screen the activity of candidate HPO mutants. After optimizing the pairing of HPO and CPR and through semirational design, the optimal mutant HPO_M18 with catalytic performance 2.54 times that of the initial was obtained. An encouraging (+)-nootkatone titer of 2.39 g/L was achieved through two-stage fed-batch fermentation after metabolic engineering and endoplasmic reticulum engineering, representing the highest titer reported to date. Our findings lay the foundation for the development of an economically viable bioprocess for (+)-nootkatone.

Structural Analysis of Ino2p/Ino4p Mutual Interactions and Their Binding Interface with Promoter DNA

Gene expression is mediated by a series of regulatory proteins, i.e., transcription factors. Under different growth conditions, the transcriptional regulation of structural genes is associated with the recognition of specific regulatory elements (REs) in promoter DNA. The manner by which transcription factors recognize distinctive REs is a key question in structural biology. Previous research has demonstrated that Ino2p/Ino4p heterodimer is associated with the transcriptional regulation of phospholipid biosynthetic genes. Mechanistically, Ino2p/Ino4p could specifically recognize the inositol/choline-responsive element (ICRE), followed by the transcription activation of the phospholipid biosynthetic gene. While the promoter DNA sequence for Ino2p has already been characterized, the structural basis for the mutual interaction between Ino2p/Ino4p and their binding interface with promoter DNA remain relatively unexplored. Here, we have determined the crystalline structure of the Ino2p^DBD/Ino4p^DBD/DNA ternary complex, which highlights some residues (Ino2p^{His12/Glu16/Arg20/Arg44} and Ino4p^{His12/Glu16/Arg19/Arg20}) associated with the sequence-specific recognition of promoter DNA. Our biochemical analysis showed that mutating these residues could completely abolish protein–DNA interaction. Despite the requirement of Ino2p and Ino4p for interprotein–DNA interaction, both proteins can still interact—even in the absence of DNA. Combined with the structural analysis, our in vitro binding analysis demonstrated that residues (Arg35, Asn65, and Gln69 of Ino2p^DBD and Leu59 of Ino4p^DBD) are critical for interprotein interactions. Together, these results have led to the conclusion that these residues are critical to establishing interprotein–DNA and protein–DNA mutual interactions.

Transcription factors and ABC transporters: from pleiotropic drug resistance to cellular signaling in yeast

Budding yeast Saccharomyces cerevisiae survives in microenvironments utilizing networks of regulators and ATP-binding cassette (ABC) transporters to circumvent toxins and a variety of drugs. Our understanding of transcriptional regulation of ABC transporters in yeast is mainly derived from the study of multidrug resistance protein networks. Over the past two decades, this research has not only expanded the role of transcriptional regulators in pleiotropic drug resistance (PDR) but evolved to include the role that regulators play in cellular signaling and environmental adaptation. Inspection of the gene networks of the transcriptional regulators and characterization of the ABC transporters has clarified that they also contribute to environmental adaptation by controlling plasma membrane composition, toxic-metal sequestration, and oxidative stress adaptation. Additionally, ABC transporters and their regulators appear to be involved in cellular signaling for adaptation of S. cerevisiae populations to nutrient availability. In this review, we summarize the current understanding of the S. cerevisiae transcriptional regulatory networks and highlight recent work in other notable fungal organisms, underlining the expansion of the study of these gene networks across the kingdom fungi.

Identification and Characterization of a Novel Basic Helix-Loop-Helix Transcription Factor of Phospholipid Synthesis Regulation in Aspergillus niger

The synthesis of phospholipids relies on a sort of genes, whose promoter regions contain inositol-sensitive upstream activation sequence (UAS_INO) and are regulated by the basic helix-loop-helix (bHLH)-type ino2/ino4 transcription factor (TF) pair. Ten putative bHLH TFs have been found through whole genome sequencing of Aspergillus niger, but none of these TFs have been characterized. In this study, we identified and characterized the bHLH-type TF ino2(An02g04350) in A. niger. Electrophoretic mobility shift assay (EMSA) and yeast two-hybrid assay demonstrated that ino2 functions as a homodimer in UAS_INO genes (e.g., ino1 and cho1) and binds to opi1(An1g02370) in vitro. Real-time quantitative PCR of ino1 and quantification of total phospholipid indicated that the ino2 disruptant downregulated the transcription of ino1 and the amount of total cellular phosphatidylinositol. In addition, phenotype analyses showed that a loss of ino2 led to resistance to cell wall interference and DNA damage. Comparative transcriptome analyses showed that more than 1000 genes and GO terms associated with UAS_INO, endoplasmic reticulum-associated protein degradation, phosphatidylinositol synthesis, chitin synthesis, and fatty acid synthesis were differentially expressed in Δino2 compared to the wild type (WT). Taken together, these observations indicate that the bHLH TF ino2 functions as a homodimer that regulates the synthesis of phosphatidylinositol, fatty acid, and chitin and influences the homeostasis of the endoplasmic reticulum membrane.

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.

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.

Lycopene overproduction in Saccharomyces cerevisiae through combining pathway engineering with host engineering

BACKGROUND

Microbial production of lycopene, a commercially and medically important compound, has received increasing concern in recent years. Saccharomyces cerevisiae is regarded as a safer host for lycopene production than Escherichia coli. However, to date, the lycopene yield (mg/g DCW) in S. cerevisiae was lower than that in E. coli and did not facilitate downstream extraction process, which might be attributed to the incompatibility between host cell and heterologous pathway. Therefore, to achieve lycopene overproduction in S. cerevisiae, both host cell and heterologous pathway should be delicately engineered.

RESULTS

In this study, lycopene biosynthesis pathway was constructed by integration of CrtE, CrtB and CrtI in S. cerevisiae CEN.PK2. When YPL062W, a distant genetic locus, was deleted, little acetate was accumulated and approximately 100 % increase in cytosolic acetyl-CoA pool was achieved relative to that in parental strain. Through screening CrtE, CrtB and CrtI from diverse species, an optimal carotenogenic enzyme combination was obtained, and CrtI from Blakeslea trispora (BtCrtI) was found to have excellent performance on lycopene production as well as lycopene proportion in carotenoid. Then, the expression level of BtCrtI was fine-tuned and the effect of cell mating types was also evaluated. Finally, potential distant genetic targets (YJL064W, ROX1, and DOS2) were deleted and a stress-responsive transcription factor INO2 was also up-regulated. Through the above modifications between host cell and carotenogenic pathway, lycopene yield was increased by approximately 22-fold (from 2.43 to 54.63 mg/g DCW). Eventually, in fed-batch fermentation, lycopene production reached 55.56 mg/g DCW, which is the highest reported yield in yeasts.

CONCLUSIONS

Saccharomyces cerevisiae was engineered to produce lycopene in this study. Through combining host engineering (distant genetic loci and cell mating types) with pathway engineering (enzyme screening and gene fine-tuning), lycopene yield was stepwise improved by 22-fold as compared to the starting strain. The highest lycopene yield (55.56 mg/g DCW) in yeasts was achieved in 5-L bioreactors. This study provides a good reference of combinatorial engineering of host cell and heterologous pathway for microbial overproduction of pharmaceutical and chemical products.

Enhancing antibody folding and secretion by tailoring the Saccharomyces cerevisiae endoplasmic reticulum

Background

The yeast Saccharomyces cerevisiae provides intriguing possibilities for synthetic biology and bioprocess applications, but its use is still constrained by cellular characteristics that limit the product yields. Considering the production of advanced biopharmaceuticals, a major hindrance lies in the yeast endoplasmic reticulum (ER), as it is not equipped for efficient and large scale folding of complex proteins, such as human antibodies.

Results

Following the example of professional secretory cells, we show that inducing an ER expansion in yeast by deleting the lipid-regulator gene OPI1 can improve the secretion capacity of full-length antibodies up to fourfold. Based on wild-type and ER-enlarged yeast strains, we conducted a screening of a folding factor overexpression library to identify proteins and their expression levels that enhance the secretion of antibodies. Out of six genes tested, addition of the peptidyl-prolyl isomerase CPR5 provided the most beneficial effect on specific product yield while PDI1, ERO1, KAR2, LHS1 and SIL1 had a mild or even negative effect to antibody secretion efficiency. Combining genes for ER enhancement did not induce any significant additional effect compared to addition of just one element. By combining the Δopi1 strain, with the enlarged ER, with CPR5 overexpression, we were able to boost the specific antibody product yield by a factor of 10 relative to the non-engineered strain.

Conclusions

Engineering protein folding in vivo is a major task for biopharmaceuticals production in yeast and needs to be optimized at several levels. By rational strain design and high-throughput screening applications we were able to increase the specific secreted antibody yields of S. cerevisiae up to 10-fold, providing a promising strain for further process optimization and platform development for antibody production.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-016-0488-5) contains supplementary material, which is available to authorized users.

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.

Regulation of amino-acid metabolism controls flux to lipid accumulation in Yarrowia lipolytica

Yarrowia lipolytica is a promising microbial cell factory for the production of lipids to be used as fuels and chemicals, but there are few studies on regulation of its metabolism. Here we performed the first integrated data analysis of Y. lipolytica grown in carbon and nitrogen limited chemostat cultures. We first reconstructed a genome-scale metabolic model and used this for integrative analysis of multilevel omics data. Metabolite profiling and lipidomics was used to quantify the cellular physiology, while regulatory changes were measured using RNAseq. Analysis of the data showed that lipid accumulation in Y. lipolytica does not involve transcriptional regulation of lipid metabolism but is associated with regulation of amino-acid biosynthesis, resulting in redirection of carbon flux during nitrogen limitation from amino acids to lipids. Lipid accumulation in Y. lipolytica at nitrogen limitation is similar to the overflow metabolism observed in many other microorganisms, e.g. ethanol production by Sacchromyces cerevisiae at nitrogen limitation.

Comprehensive analysis of forty yeast microarray datasets reveals a novel subset of genes (APha-RiB) consistently negatively associated with ribosome biogenesis

BACKGROUND

The scale and complexity of genomic data lend themselves to analysis using sophisticated mathematical techniques to yield information that can generate new hypotheses and so guide further experimental investigations. An ensemble clustering method has the ability to perform consensus clustering over the same set of genes from different microarray datasets by combining results from different clustering methods into a single consensus result.

RESULTS

In this paper we have performed comprehensive analysis of forty yeast microarray datasets. One recently described Bi-CoPaM method can analyse expressions of the same set of genes from various microarray datasets while using different clustering methods, and then combine these results into a single consensus result whose clusters' tightness is tunable from tight, specific clusters to wide, overlapping clusters. This has been adopted in a novel way over genome-wide data from forty yeast microarray datasets to discover two clusters of genes that are consistently co-expressed over all of these datasets from different biological contexts and various experimental conditions. Most strikingly, average expression profiles of those clusters are consistently negatively correlated in all of the forty datasets while neither profile leads or lags the other.

CONCLUSIONS

The first cluster is enriched with ribosomal biogenesis genes. The biological processes of most of the genes in the second cluster are either unknown or apparently unrelated although they show high connectivity in protein-protein and genetic interaction networks. Therefore, it is possible that this mostly uncharacterised cluster and the ribosomal biogenesis cluster are transcriptionally oppositely regulated by some common machinery. Moreover, we anticipate that the genes included in this previously unknown cluster participate in generic, in contrast to specific, stress response processes. These novel findings illuminate coordinated gene expression in yeast and suggest several hypotheses for future experimental functional work. Additionally, we have demonstrated the usefulness of the Bi-CoPaM-based approach, which may be helpful for the analysis of other groups of (microarray) datasets from other species and systems for the exploration of global genetic co-expression.

InCoB2013 introduces Systems Biology as a major conference theme

The Asia-Pacific Bioinformatics Network (APBioNet) held the first International Conference on Bioinformatics (InCoB) in Bangkok in 2002 to promote North-South networking. Commencing as a forum for Asia-Pacific researchers to interact with and learn from with scientists of developed countries, InCoB has become a major regional bioinformatics conference, with participants from the region as well as North America and Europe. Since 2006, InCoB has selected the best submissions for publication in BMC Bioinformatics. In response to the growth and maturation of data-driven approaches, InCoB added BMC Genomics in 2009 and with the introduction of this conference supplement, BMC Systems Biology to its journal choices for submitting authors. Co-hosting InCoB2013 with the second International Conference for Translational Bioinformatics (ICTBI) is in line with InCoB's support for the current trend in taking bioinformatics to the bedside, along with a systems approach to solving biological problems.