Global transcriptional and physiological responses of Saccharomyces cerevisiae to ammonium, L-alanine, or L-glutamine limitation

Biocatalytic reductive amination with CRISPR-Cas9 engineered yeast

Metabolically engineered baker's yeast can be used to produce chiral amines through whole-cell bioconversion of prochiral ketones. This study investigates the modulation of the alanine-pyruvate metabolic node to enhance reductive amination, using the stereoselective conversion of benzylacetone to (S)-1-methyl-3-phenylpropylamine (MPPA) as a model reaction. Chromosomal integration of multiple copies of the promiscuous omega transaminase from Chromobacterium violaceum (cv-ATA) resulted in an active yeast catalyst. Physiological characterization in bioreactors under aerobic batch cultivation revealed that amine production occurred only under post-diauxic growth on ethanol. To reduce native alanine utilization, the endogenous alanine aminotransferase (ALT1) was knocked out and replaced with cv-ATA. To rapidly employ this strategy in other strains, a simple CRISPR/cas9 method for universal gene replacement was developed. The replacement of ALT1 with cv-ATA improved the reaction by 2.6-fold compared to the control strain with intact ALT1. NMR measurements of metabolites originating from 15N L-alanine and 13C glucose indicated that pyruvate formation during growth on glucose inhibited amine production. Under optimal conditions, the biocatalytic bioconversion of benzylacetone to MPPA reached a yield of 58%.

Deciphering the transcriptomic response of Ilyonectria robusta in relation to ginsenoside Rg1 treatment and the development of Ginseng rusty root rot

Rusty root rot is a severe disease in Ginseng (Panax ginseng C.A.Mey.) production caused by Ilyonectria robusta. The severity of the disease may be related to the residual ginsenosides in soil. In order to elucidate the response mechanism between Rg1 treatment and occurrence of ginseng rust, we performed growth, reproduction and transcriptome analysis on Rg1-treated. The results showed that Rg1 significantly promoted the mycelial growth, and sporulation compared to the control, and aggravated the disease symptoms of Panax ginseng. A total of 6708 transcripts out of 213 131 annotated genes identified from global transcriptomic analysis were differentially expressed in Ilyonectria robusta grown in the Rg1 treatment. These genes were found to be related to the carbon-nitrogen metabolism, transport, and assimilation. Many of these genes were also associated with pathogenicity based on the Phi-base database. Several transcription factors were related to specific biological processes, such as nitrogen utilization. The current results revealed that Rg1 played a major role in the development of rusty root rot by promoting fungal cell growth and affected the expression of genes required for pathogenesis. Rg1 could aggravate the invasion of Ilyonectria robusta on ginseng root, which preliminarily revealed the reason for the aggravation of rusty root rot in ginseng soil-borne.

An updated genome-scale metabolic network reconstruction of Pseudomonas aeruginosa PA14 to characterize mucin-driven shifts in bacterial metabolism

Mucins are present in mucosal membranes throughout the body and play a key role in the microbe clearance and infection prevention. Understanding the metabolic responses of pathogens to mucins will further enable the development of protective approaches against infections. We update the genome-scale metabolic network reconstruction (GENRE) of one such pathogen, Pseudomonas aeruginosa PA14, through metabolic coverage expansion, format update, extensive annotation addition, and literature-based curation to produce iPau21. We then validate iPau21 through MEMOTE, growth rate, carbon source utilization, and gene essentiality testing to demonstrate its improved quality and predictive capabilities. We then integrate the GENRE with transcriptomic data in order to generate context-specific models of P. aeruginosa metabolism. The contextualized models recapitulated known phenotypes of unaltered growth and a differential utilization of fumarate metabolism, while also revealing an increased utilization of propionate metabolism upon MUC5B exposure. This work serves to validate iPau21 and demonstrate its utility for providing biological insights.

Alanine Represses γ-Aminobutyric Acid Utilization and Induces Alanine Transaminase Required for Mitochondrial Function in Saccharomyces cerevisiae

The γ-aminobutyric acid (GABA) shunt constitutes a conserved metabolic route generating nicotinamide adenine dinucleotide phosphate (NADPH) and regulating stress response in most organisms. Here we show that in the presence of GABA, Saccharomyces cerevisiae produces glutamate and alanine through the irreversible action of Uga1 transaminase. Alanine induces expression of alanine transaminase (ALT1) gene. In an alt1Δ mutant grown on GABA, alanine accumulation leads to repression of the GAD1, UGA1, and UGA2 genes, involved in the GABA shunt, which could result in growth impairment. Induced ALT1 expression and negative modulation of the GABA shunt by alanine constitute a novel regulatory circuit controlling both alanine biosynthesis and catabolism. Consistent with this, the GABA shunt and the production of NADPH are repressed in a wild-type strain grown in alanine, as compared to those detected in the wild-type strain grown on GABA. We also show that heat shock induces alanine biosynthesis and ALT1, UGA1, UGA2, and GAD1 gene expression, whereas an uga1Δ mutant shows heat sensitivity and reduced NADPH pools, as compared with those observed in the wild-type strain. Additionally, an alt1Δ mutant shows an unexpected alanine-independent phenotype, displaying null expression of mitochondrial COX2, COX3, and ATP6 genes and a notable decrease in mitochondrial/nuclear DNA ratio, as compared to a wild-type strain, which results in a petite phenotype. Our results uncover a new negative role of alanine in stress defense, repressing the transcription of the GABA shunt genes, and support a novel Alt1 moonlighting function related to the maintenance of mitochondrial DNA integrity and mitochondrial gene expression.

Efficient protein production by yeast requires global tuning of metabolism

The biotech industry relies on cell factories for production of pharmaceutical proteins, of which several are among the top-selling medicines. There is, therefore, considerable interest in improving the efficiency of protein production by cell factories. Protein secretion involves numerous intracellular processes with many underlying mechanisms still remaining unclear. Here, we use RNA-seq to study the genome-wide transcriptional response to protein secretion in mutant yeast strains. We find that many cellular processes have to be attuned to support efficient protein secretion. In particular, altered energy metabolism resulting in reduced respiration and increased fermentation, as well as balancing of amino-acid biosynthesis and reduced thiamine biosynthesis seem to be particularly important. We confirm our findings by inverse engineering and physiological characterization and show that by tuning metabolism cells are able to efficiently secrete recombinant proteins. Our findings provide increased understanding of which cellular regulations and pathways are associated with efficient protein secretion.

The contribution of metabolic pathways to protein secretion is largely unknown. Here, the authors find conserved metabolic patterns in yeast by examining genome-wide transcriptional responses in high protein secretion mutants and reveal critical factors that can be tuned for efficient protein secretion.

Study of the Plasma Membrane Proteome Dynamics Reveals Novel Targets of the Nitrogen Regulation in Yeast

Yeast cells, to be able to grow on a wide variety of nitrogen sources, regulate the set of nitrogen transporters present at their plasma membrane. Such regulation relies on both transcriptional and post-translational events. Although microarray studies have identified most nitrogen-sensitive genes, nitrogen-induced post-translational regulation has only been studied for very few proteins among which the general amino acid permease Gap1. Adding a preferred nitrogen source to proline-grown cells triggers Gap1 endocytosis and vacuolar degradation in an Rsp5-Bul1/2-dependent manner. Here, we used a proteomic approach to follow the dynamics of the plasma membrane proteome after addition of a preferred nitrogen source. We identified new targets of the nitrogen regulation and four transporters of poor nitrogen sources-Put4, Opt2, Dal5, and Ptr2-that rapidly decrease in abundance. Although the kinetics is different for each transporter, we found that three of them-Put4, Dal5, and Ptr2-are endocytosed, like Gap1, in an Rsp5-dependent manner and degraded in the vacuole. Finally, we showed that Gap1 stabilization at the plasma membrane, through deletion of Bul proteins, regulates the abundance of Put4, Dal5 and Ptr2.

Transposable elements and circular DNAs

Circular DNAs are extra-chromosomal fragments that become circularized by genomic recombination events. We have recently shown that yeast LTR elements generate circular DNAs through recombination events between their flanking long terminal repeats (LTRs). Similarly, circular DNAs can be generated by recombination between LTRs residing at different genomic loci, in which case the circular DNA will contain the intervening sequence. In yeast, this can result in gene copy number variations when circles contain genes and origins of replication. Here, I speculate on the potential and implications of circular DNAs generated through recombination between human transposable elements.

Deciphering the transcriptomic response of Fusarium verticillioides in relation to nitrogen availability and the development of sugarcane pokkah boeng disease

Pokkah boeng, caused by Fusarium verticillioides, is a serious disease in sugarcane industry. The disease severity is related to the sugarcane genotype as well as environmental considerations, such as nitrogen application. The impact of the nitrogen source (ammonium sulfate, urea, or sodium nitrate) on sugarcane pokkah boeng disease and its pathogen was investigated in planta and fungal growth and sporulation production was measured in vitro. The results showed that ammonium and nitrate were beneficial to fungal mycelium growth, cell densities, and sporulation, which enhanced the disease symptoms of sugarcane pokkah boeng compared to urea fertilization. A total of 1,779 transcripts out of 13,999 annotated genes identified from global transcriptomic analysis were differentially expressed in F. verticillioides CNO-1 grown in the different sources of nitrogen. These were found to be involved in nitrogen metabolism, transport, and assimilation. Many of these genes were also associated with pathogenicity based on the PHI-base database. Several transcription factors were found to be associated with specific biological processes related to nitrogen utilization. Our results further demonstrated that nitrogen availability might play an important role in disease development by increasing fungal cell growth as well as influencing the expression of genes required for successful pathogenesis.

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.

Steady-state and dynamic gene expression programs in Saccharomyces cerevisiae in response to variation in environmental nitrogen

Steady-state and transiently perturbed nitrogen-limited chemostats show that nitrogen abundance is a primary signal controlling nitrogen-responsive gene expression. When cells experience an increase in nitrogen, some transcripts are rapidly degraded, suggesting that accelerated mRNA degradation contributes to remodeling of gene expression.

Cell growth rate is regulated in response to the abundance and molecular form of essential nutrients. In Saccharomyces cerevisiae (budding yeast), the molecular form of environmental nitrogen is a major determinant of cell growth rate, supporting growth rates that vary at least threefold. Transcriptional control of nitrogen use is mediated in large part by nitrogen catabolite repression (NCR), which results in the repression of specific transcripts in the presence of a preferred nitrogen source that supports a fast growth rate, such as glutamine, that are otherwise expressed in the presence of a nonpreferred nitrogen source, such as proline, which supports a slower growth rate. Differential expression of the NCR regulon and additional nitrogen-responsive genes results in >500 transcripts that are differentially expressed in cells growing in the presence of different nitrogen sources in batch cultures. Here we find that in growth rate–controlled cultures using nitrogen-limited chemostats, gene expression programs are strikingly similar regardless of nitrogen source. NCR expression is derepressed in all nitrogen-limiting chemostat conditions regardless of nitrogen source, and in these conditions, only 34 transcripts exhibit nitrogen source–specific differential gene expression. Addition of either the preferred nitrogen source, glutamine, or the nonpreferred nitrogen source, proline, to cells growing in nitrogen-limited chemostats results in rapid, dose-dependent repression of the NCR regulon. Using a novel means of computational normalization to compare global gene expression programs in steady-state and dynamic conditions, we find evidence that the addition of nitrogen to nitrogen-limited cells results in the transient overproduction of transcripts required for protein translation. Simultaneously, we find that that accelerated mRNA degradation underlies the rapid clearing of a subset of transcripts, which is most pronounced for the highly expressed NCR-regulated permease genes GAP1, MEP2, DAL5, PUT4, and DIP5. Our results reveal novel aspects of nitrogen-regulated gene expression and highlight the need for a quantitative approach to study how the cell coordinates protein translation and nitrogen assimilation to optimize cell growth in different environments.

Urea enhances cell growth and pyruvate production in Torulopsis glabrata

Torulopsis glabrata is a strain of yeast that is used for the industrial production of pyruvate. Determination of the optimal nutrient environment is vital for obtaining the most efficient production system. In this study, the fermentation parameters, gene transcription levels, activities of key enzymes and metabolites levels were analyzed when either urea or ammonium chloride was used as the sole source of nitrogen. Urea caused an increase in the dry cell weight (18%) and pyruvate productivity was significantly increased (14%). The transcription levels of CAGL0M05533g (DUR1,2), CAGL0J07612g (ZWF1), and CAGL0I02200g (SOL3) were upregulated, but CAGL0G05698g (GDH2) and CAGL0L01089g (GLT1) were down-regulated. The activities of urea amidolyase, NADPH dependent glutamate dehydrogenase and glucose-6-phosphate dehydrogenase were increased by 380, 430, and 140%, respectively. The activities of arginase and glutamate synthase were decreased by 40 and 35%, respectively. The NADPH content was increased by 33%, whilst ATP content was decreased by 37%. This changed the intracellular levels of organic acids and amino acids. The results expand the understanding of the physiological characteristics of yeast species grown with different sources of nitrogen.

Computational approaches for microalgal biofuel optimization: a review

The increased demand and consumption of fossil fuels have raised interest in finding renewable energy sources throughout the globe. Much focus has been placed on optimizing microorganisms and primarily microalgae, to efficiently produce compounds that can substitute for fossil fuels. However, the path to achieving economic feasibility is likely to require strain optimization through using available tools and technologies in the fields of systems and synthetic biology. Such approaches invoke a deep understanding of the metabolic networks of the organisms and their genomic and proteomic profiles. The advent of next generation sequencing and other high throughput methods has led to a major increase in availability of biological data. Integration of such disparate data can help define the emergent metabolic system properties, which is of crucial importance in addressing biofuel production optimization. Herein, we review major computational tools and approaches developed and used in order to potentially identify target genes, pathways, and reactions of particular interest to biofuel production in algae. As the use of these tools and approaches has not been fully implemented in algal biofuel research, the aim of this review is to highlight the potential utility of these resources toward their future implementation in algal research.

Mapping condition-dependent regulation of metabolism in yeast through genome-scale modeling

Background

The genome-scale metabolic model of Saccharomyces cerevisiae, first presented in 2003, was the first genome-scale network reconstruction for a eukaryotic organism. Since then continuous efforts have been made in order to improve and expand the yeast metabolic network.

Results

Here we present iTO977, a comprehensive genome-scale metabolic model that contains more reactions, metabolites and genes than previous models. The model was constructed based on two earlier reconstructions, namely iIN800 and the consensus network, and then improved and expanded using gap-filling methods and by introducing new reactions and pathways based on studies of the literature and databases. The model was shown to perform well both for growth simulations in different media and gene essentiality analysis for single and double knock-outs. Further, the model was used as a scaffold for integrating transcriptomics, and flux data from four different conditions in order to identify transcriptionally controlled reactions, i.e. reactions that change both in flux and transcription between the compared conditions.

Conclusion

We present a new yeast model that represents a comprehensive up-to-date collection of knowledge on yeast metabolism. The model was used for simulating the yeast metabolism under four different growth conditions and experimental data from these four conditions was integrated to the model. The model together with experimental data is a useful tool to identify condition-dependent changes of metabolism between different environmental conditions.

Anaerobic α-amylase production and secretion with fumarate as the final electron acceptor in Saccharomyces cerevisiae

In this study, we focus on production of heterologous α-amylase in the yeast Saccharomyces cerevisiae under anaerobic conditions. We compare the metabolic fluxes and transcriptional regulation under aerobic and anaerobic conditions, with the objective of identifying the final electron acceptor for protein folding under anaerobic conditions. We find that yeast produces more amylase under anaerobic conditions than under aerobic conditions, and we propose a model for electron transfer under anaerobic conditions. According to our model, during protein folding the electrons from the endoplasmic reticulum are transferred to fumarate as the final electron acceptor. This model is supported by findings that the addition of fumarate under anaerobic (but not aerobic) conditions improves cell growth, specifically in the α-amylase-producing strain, in which it is not used as a carbon source. Our results provide a model for the molecular mechanism of anaerobic protein secretion using fumarate as the final electron acceptor, which may allow for further engineering of yeast for improved protein secretion under anaerobic growth conditions.

Amino acid transporter genes are essential for FLO11-dependent and FLO11-independent biofilm formation and invasive growth in Saccharomyces cerevisiae

Amino acids can induce yeast cell adhesion but how amino acids are sensed and signal the modulation of the FLO adhesion genes is not clear. We discovered that the budding yeast Saccharomyces cerevisiae CEN.PK evolved invasive growth ability under prolonged nitrogen limitation. Such invasive mutants were used to identify amino acid transporters as regulators of FLO11 and invasive growth. One invasive mutant had elevated levels of FLO11 mRNA and a Q320STOP mutation in the SFL1 gene that encodes a protein kinase A pathway regulated repressor of FLO11. Glutamine-transporter genes DIP5 and GNP1 were essential for FLO11 expression, invasive growth and biofilm formation in this mutant. Invasive growth relied on known regulators of FLO11 and the Ssy1-Ptr3-Ssy5 complex that controls DIP5 and GNP1, suggesting that Dip5 and Gnp1 operates downstream of the Ssy1-Ptr3-Ssy5 complex for regulation of FLO11 expression in a protein kinase A dependent manner. The role of Dip5 and Gnp1 appears to be conserved in the S. cerevisiae strain ∑1278b since the dip5 gnp1 ∑1278b mutant showed no invasive phenotype. Secondly, the amino acid transporter gene GAP1 was found to influence invasive growth through FLO11 as well as other FLO genes. Cells carrying a dominant loss-of-function PTR3(647::CWNKNPLSSIN) allele had increased transcription of the adhesion genes FLO1, 5, 9, 10, 11 and the amino acid transporter gene GAP1. Deletion of GAP1 caused loss of FLO11 expression and invasive growth. However, deletions of FLO11 and genes encoding components of the mitogen-activated protein kinase pathway or the protein kinase A pathway were not sufficient to abolish invasive growth, suggesting involvement of other FLO genes and alternative pathways. Increased intracellular amino acid pools in the PTR3(647::CWNKNPLSSIN)-containing strain opens the possibility that Gap1 regulates the FLO genes through alteration of the amino acid pool sizes.

Integrated pathway modules using time-course metabolic profiles and EST data from Milnesium tardigradum

Background

Tardigrades are multicellular organisms, resistant to extreme environmental changes such as heat, drought, radiation and freezing. They outlast these conditions in an inactive form (tun) to escape damage to cellular structures and cell death. Tardigrades are apparently able to prevent or repair such damage and are therefore a crucial model organism for stress tolerance. Cultures of the tardigrade Milnesium tardigradum were dehydrated by removing the surrounding water to induce tun formation. During this process and the subsequent rehydration, metabolites were measured in a time series by GC-MS. Additionally expressed sequence tags are available, especially libraries generated from the active and inactive state. The aim of this integrated analysis is to trace changes in tardigrade metabolism and identify pathways responsible for their extreme resistance against physical stress.

Results

In this study we propose a novel integrative approach for the analysis of metabolic networks to identify modules of joint shifts on the transcriptomic and metabolic levels. We derive a tardigrade-specific metabolic network represented as an undirected graph with 3,658 nodes (metabolites) and 4,378 edges (reactions). Time course metabolite profiles are used to score the network nodes showing a significant change over time. The edges are scored according to information on enzymes from the EST data. Using this combined information, we identify a key subnetwork (functional module) of concerted changes in metabolic pathways, specific for de- and rehydration. The module is enriched in reactions showing significant changes in metabolite levels and enzyme abundance during the transition. It resembles the cessation of a measurable metabolism (e.g. glycolysis and amino acid anabolism) during the tun formation, the production of storage metabolites and bioprotectants, such as DNA stabilizers, and the generation of amino acids and cellular components from monosaccharides as carbon and energy source during rehydration.

Conclusions

The functional module identifies relationships among changed metabolites (e.g. spermidine) and reactions and provides first insights into important altered metabolic pathways. With sparse and diverse data available, the presented integrated metabolite network approach is suitable to integrate all existing data and analyse it in a combined manner.

Identification of metabolic changes in genetically unstable stem cells by using model analysis of gene expression

Stem-cell research seeks to address many different questions related to fundamental stem-cell function with the ultimate goal of being able to control and utilize stem cells for a broad range of therapeutic needs. While a large amount of work is focused on discovering and controlling differentiation mechanisms in stem cells, an equally interesting and important area of work is to understand the basics of stem-cell propagation and self-renewal. With high-throughput genomics and transcriptomic information on hand, it is becoming possible to address some of the detailed mechanistic processes occurring in stem cells, though interpretation of these data is often difficult. In this work, stem cells with genetic abnormalities were compared to genetically normal stem cells using gene-expression array data integrated with a large-scale metabolic model to help interpret changes in metabolism resulting in the identification of several metabolic pathways that were different in the normal and abnormal cells.

Analysis of carbon and nitrogen co-metabolism in yeast by ultrahigh-resolution mass spectrometry applying 13C- and 15N-labeled substrates simultaneously

Alternative metabolic pathways inside a cell can be deduced using stable isotopically labeled substrates. One prerequisite is accurate measurement of the labeling pattern of targeted metabolites. Experiments are generally limited to the use of single-element isotopes, mainly (13)C. Here, we demonstrate the application of direct infusion nanospray, ultrahigh-resolution Fourier transform ion cyclotron resonance-mass spectrometry (FTICR-MS) for metabolic studies using differently labeled elemental isotopes simultaneously--i.e., (13)C and (15)N--in amino acids of a total protein hydrolysate. The optimized strategy for the analysis of metabolism by a hybrid linear ion trap-FTICR-MS comprises the collection of multiple adjacent selected ion monitoring scans. By limiting both the width of the mass range and the number of ions entering the ICR cell with automated gain control, sensitive measurements of isotopologue distribution were possible without compromising mass accuracy and isotope intensity mapping. The required mass-resolving power of more than 60,000 is only achievable on a routine basis by FTICR and Orbitrap mass spectrometers. Evaluation of the method was carried out by comparison of the experimental data to the natural isotope abundances of selected amino acids and by comparison to GC/MS results obtained from a labeling experiment with (13)C-labeled glucose. The developed method was used to shed light on the complexity of the yeast Saccharomyces cerevisiae carbon-nitrogen co-metabolism by administering both (13)C-labeled glucose and (15)N-labeled alanine. The results indicate that not only glutamate but also alanine acts as an amino donor during alanine and valine synthesis. Metabolic studies using FTICR-MS can exploit new possibilities by the use of multiple-labeled elemental isotopes.

Genome-scale metabolic reconstructions of Pichia stipitis and Pichia pastoris and in silico evaluation of their potentials

Background

Pichia stipitis and Pichia pastoris have long been investigated due to their native abilities to metabolize every sugar from lignocellulose and to modulate methanol consumption, respectively. The latter has been driving the production of several recombinant proteins. As a result, significant advances in their biochemical knowledge, as well as in genetic engineering and fermentation methods have been generated. The release of their genome sequences has allowed systems level research.

Results

In this work, genome-scale metabolic models (GEMs) of P. stipitis (iSS884) and P. pastoris (iLC915) were reconstructed. iSS884 includes 1332 reactions, 922 metabolites, and 4 compartments. iLC915 contains 1423 reactions, 899 metabolites, and 7 compartments. Compared with the previous GEMs of P. pastoris, PpaMBEL1254 and iPP668, iLC915 contains more genes and metabolic functions, as well as improved predictive capabilities. Simulations of physiological responses for the growth of both yeasts on selected carbon sources using iSS884 and iLC915 closely reproduced the experimental data. Additionally, the iSS884 model was used to predict ethanol production from xylose at different oxygen uptake rates. Simulations with iLC915 closely reproduced the effect of oxygen uptake rate on physiological states of P. pastoris expressing a recombinant protein. The potential of P. stipitis for the conversion of xylose and glucose into ethanol using reactors in series, and of P. pastoris to produce recombinant proteins using mixtures of methanol and glycerol or sorbitol are also discussed.

Conclusions

In conclusion the first GEM of P. stipitis (iSS884) was reconstructed and validated. The expanded version of the P. pastoris GEM, iLC915, is more complete and has improved capabilities over the existing models. Both GEMs are useful frameworks to explore the versatility of these yeasts and to capitalize on their biotechnological potentials.

Imbalance of heterologous protein folding and disulfide bond formation rates yields runaway oxidative stress

Background

The protein secretory pathway must process a wide assortment of native proteins for eukaryotic cells to function. As well, recombinant protein secretion is used extensively to produce many biologics and industrial enzymes. Therefore, secretory pathway dysfunction can be highly detrimental to the cell and can drastically inhibit product titers in biochemical production. Because the secretory pathway is a highly-integrated, multi-organelle system, dysfunction can happen at many levels and dissecting the root cause can be challenging. In this study, we apply a systems biology approach to analyze secretory pathway dysfunctions resulting from heterologous production of a small protein (insulin precursor) or a larger protein (α-amylase).

Results

HAC1-dependent and independent dysfunctions and cellular responses were apparent across multiple datasets. In particular, processes involving (a) degradation of protein/recycling amino acids, (b) overall transcription/translation repression, and (c) oxidative stress were broadly associated with secretory stress.

Conclusions

Apparent runaway oxidative stress due to radical production observed here and elsewhere can be explained by a futile cycle of disulfide formation and breaking that consumes reduced glutathione and produces reactive oxygen species. The futile cycle is dominating when protein folding rates are low relative to disulfide bond formation rates. While not strictly conclusive with the present data, this insight does provide a molecular interpretation to an, until now, largely empirical understanding of optimizing heterologous protein secretion. This molecular insight has direct implications on engineering a broad range of recombinant proteins for secretion and provides potential hypotheses for the root causes of several secretory-associated diseases.

Fifteen years of large scale metabolic modeling of yeast: developments and impacts

Since the first large-scale reconstruction of the Saccharomyces cerevisiae metabolic network 15 years ago the development of yeast metabolic models has progressed rapidly, resulting in no less than nine different yeast genome-scale metabolic models. Here we review the historical development of large-scale mathematical modeling of yeast metabolism and the growing scope and impact of applications of these models in four different areas: as guide for metabolic engineering and strain improvement, as a tool for biological interpretation and discovery, applications of novel computational framework and for evolutionary studies.

iAB-RBC-283: A proteomically derived knowledge-base of erythrocyte metabolism that can be used to simulate its physiological and patho-physiological states

BACKGROUND

The development of high-throughput technologies capable of whole cell measurements of genes, proteins, and metabolites has led to the emergence of systems biology. Integrated analysis of the resulting omic data sets has proved to be hard to achieve. Metabolic network reconstructions enable complex relationships amongst molecular components to be represented formally in a biologically relevant manner while respecting physical constraints. In silico models derived from such reconstructions can then be queried or interrogated through mathematical simulations. Proteomic profiling studies of the mature human erythrocyte have shown more proteins present related to metabolic function than previously thought; however the significance and the causal consequences of these findings have not been explored.

RESULTS

Erythrocyte proteomic data was used to reconstruct the most expansive description of erythrocyte metabolism to date, following extensive manual curation, assessment of the literature, and functional testing. The reconstruction contains 281 enzymes representing functions from glycolysis to cofactor and amino acid metabolism. Such a comprehensive view of erythrocyte metabolism implicates the erythrocyte as a potential biomarker for different diseases as well as a 'cell-based' drug-screening tool. The analysis shows that 94 erythrocyte enzymes are implicated in morbid single nucleotide polymorphisms, representing 142 pathologies. In addition, over 230 FDA-approved and experimental pharmaceuticals have enzymatic targets in the erythrocyte.

CONCLUSION

The advancement of proteomic technologies and increased generation of high-throughput proteomic data have created the need for a means to analyze these data in a coherent manner. Network reconstructions provide a systematic means to integrate and analyze proteomic data in a biologically meaning manner. Analysis of the red cell proteome has revealed an unexpected level of complexity in the functional capabilities of human erythrocyte metabolism.

Genome-scale metabolic models of Saccharomyces cerevisiae

Systematic analysis of Saccharomyces cerevisiae metabolic functions and pathways has been the subject of extensive studies and established in many aspects. With the reconstruction of the yeast genome-scale metabolic (GSM) network and in silico simulation of the GSM model, the nature of the underlying cellular processes can be tested and validated with the increasing metabolic knowledge. GSM models are also being exploited in fundamental research studies and industrial applications. In this chapter, the principle concepts for construction, simulation and validation of GSM models, progressive applications of the yeast GSM models, and future perspectives are described. This will support and encourage researchers who are interested in systemic analysis of yeast metabolism and systems biology.

Adaptation to diverse nitrogen-limited environments by deletion or extrachromosomal element formation of the GAP1 locus

To study adaptive evolution in defined environments, we performed evolution experiments with Saccharomyces cerevisiae (yeast) in nitrogen-limited chemostat cultures. We used DNA microarrays to identify copy-number variation associated with adaptation and observed frequent amplifications and deletions at the GAP1 locus. GAP1 encodes the general amino acid permease, which transports amino acids across the plasma membrane. We identified a self-propagating extrachromosomal circular DNA molecule that results from intrachromosomal recombination between long terminal repeats (LTRs) flanking GAP1. Extrachromosomal DNA circles (GAP1(circle)) contain GAP1, the replication origin ARS1116, and a single hybrid LTR derived from recombination between the two flanking LTRs. Formation of the GAP1(circle) is associated with deletion of chromosomal GAP1 (gap1Δ) and production of a single hybrid LTR at the GAP1 chromosomal locus. The GAP1(circle) is selected following prolonged culturing in L-glutamine-limited chemostats in a manner analogous to the selection of oncogenes present on double minutes in human cancers. Clones carrying only the gap1Δ allele were selected under various non-amino acid nitrogen limitations including ammonium, urea, and allantoin limitation. Previous studies have shown that the rate of intrachromosomal recombination between tandem repeats is stimulated by transcription of the intervening sequence. The high level of GAP1 expression in nitrogen-limited chemostats suggests that the frequency of GAP1(circle) and gap1Δ generation may be increased under nitrogen-limiting conditions. We propose that this genomic architecture facilitates evolvability of S. cerevisiae populations exposed to variation in levels and sources of environmental nitrogen.

Use of genome-scale metabolic models for understanding microbial physiology

The exploitation of microorganisms in industrial, medical, food and environmental biotechnology requires a comprehensive understanding of their physiology. The availability of genome sequences and accumulation of high-throughput data allows gaining understanding of microbial physiology at the systems level, and genome-scale metabolic models represent a valuable framework for integrative analysis of metabolism of microorganisms. Genome-scale metabolic models are reconstructed based on a combination of genome sequence information and detailed biochemical information, and these reconstructed models can be used for analyzing and simulating the operation of metabolism in response to different stimuli. Here we discuss the requirement for having detailed physiological insight in order to exploit microorganisms for production of fuels, chemicals and pharmaceuticals. We further describe the reconstruction process of genome-scale metabolic models and different algorithms that can be used to apply these models to gain improved insight into microbial physiology.

Systems biology of industrial microorganisms

The field of industrial biotechnology is expanding rapidly as the chemical industry is looking towards more sustainable production of chemicals that can be used as fuels or building blocks for production of solvents and materials. In connection with the development of sustainable bioprocesses, it is a major challenge to design and develop efficient cell factories that can ensure cost efficient conversion of the raw material into the chemical of interest. This is achieved through metabolic engineering, where the metabolism of the cell factory is engineered such that there is an efficient conversion of sugars, the typical raw materials in the fermentation industry, into the desired product. However, engineering of cellular metabolism is often challenging due to the complex regulation that has evolved in connection with adaptation of the different microorganisms to their ecological niches. In order to map these regulatory structures and further de-regulate them, as well as identify ingenious metabolic engineering strategies that full-fill mass balance constraints, tools from systems biology can be applied. This involves both high-throughput analysis tools like transcriptome, proteome and metabolome analysis, as well as the use of mathematical modeling to simulate the phenotypes resulting from the different metabolic engineering strategies. It is in fact expected that systems biology may substantially improve the process of cell factory development, and we therefore propose the term Industrial Systems Biology for how systems biology will enhance the development of industrial biotechnology for sustainable chemical production.

Reconstruction of the yeast Snf1 kinase regulatory network reveals its role as a global energy regulator

Highly conserved among eukaryotic cells, the AMP-activated kinase (AMPK) is a central regulator of carbon metabolism. To map the complete network of interactions around AMPK in yeast (Snf1) and to evaluate the role of its regulatory subunit Snf4, we measured global mRNA, protein and metabolite levels in wild type, Δsnf1, Δsnf4, and Δsnf1Δsnf4 knockout strains. Using four newly developed computational tools, including novel DOGMA sub-network analysis, we showed the benefits of three-level ome-data integration to uncover the global Snf1 kinase role in yeast. We for the first time identified Snf1's global regulation on gene and protein expression levels, and showed that yeast Snf1 has a far more extensive function in controlling energy metabolism than reported earlier. Additionally, we identified complementary roles of Snf1 and Snf4. Similar to the function of AMPK in humans, our findings showed that Snf1 is a low-energy checkpoint and that yeast can be used more extensively as a model system for studying the molecular mechanisms underlying the global regulation of AMPK in mammals, failure of which leads to metabolic diseases.

Applications of genome-scale metabolic reconstructions

The availability and utility of genome-scale metabolic reconstructions have exploded since the first genome-scale reconstruction was published a decade ago. Reconstructions have now been built for a wide variety of organisms, and have been used toward five major ends: (1) contextualization of high-throughput data, (2) guidance of metabolic engineering, (3) directing hypothesis-driven discovery, (4) interrogation of multi-species relationships, and (5) network property discovery. In this review, we examine the many uses and future directions of genome-scale metabolic reconstructions, and we highlight trends and opportunities in the field that will make the greatest impact on many fields of biology.

Comparison of next generation sequencing technologies for transcriptome characterization

Background

We have developed a simulation approach to help determine the optimal mixture of sequencing methods for most complete and cost effective transcriptome sequencing. We compared simulation results for traditional capillary sequencing with "Next Generation" (NG) ultra high-throughput technologies. The simulation model was parameterized using mappings of 130,000 cDNA sequence reads to the Arabidopsis genome (NCBI Accession SRA008180.19). We also generated 454-GS20 sequences and de novo assemblies for the basal eudicot California poppy (Eschscholzia californica) and the magnoliid avocado (Persea americana) using a variety of methods for cDNA synthesis.

Results

The Arabidopsis reads tagged more than 15,000 genes, including new splice variants and extended UTR regions. Of the total 134,791 reads (13.8 MB), 119,518 (88.7%) mapped exactly to known exons, while 1,117 (0.8%) mapped to introns, 11,524 (8.6%) spanned annotated intron/exon boundaries, and 3,066 (2.3%) extended beyond the end of annotated UTRs. Sequence-based inference of relative gene expression levels correlated significantly with microarray data. As expected, NG sequencing of normalized libraries tagged more genes than non-normalized libraries, although non-normalized libraries yielded more full-length cDNA sequences. The Arabidopsis data were used to simulate additional rounds of NG and traditional EST sequencing, and various combinations of each. Our simulations suggest a combination of FLX and Solexa sequencing for optimal transcriptome coverage at modest cost. We have also developed ESTcalc http://fgp.huck.psu.edu/NG_Sims/ngsim.pl, an online webtool, which allows users to explore the results of this study by specifying individualized costs and sequencing characteristics.

Conclusion

NG sequencing technologies are a highly flexible set of platforms that can be scaled to suit different project goals. In terms of sequence coverage alone, the NG sequencing is a dramatic advance over capillary-based sequencing, but NG sequencing also presents significant challenges in assembly and sequence accuracy due to short read lengths, method-specific sequencing errors, and the absence of physical clones. These problems may be overcome by hybrid sequencing strategies using a mixture of sequencing methodologies, by new assemblers, and by sequencing more deeply. Sequencing and microarray outcomes from multiple experiments suggest that our simulator will be useful for guiding NG transcriptome sequencing projects in a wide range of organisms.

Chemostat-based micro-array analysis in baker's yeast

Chemostat cultivation of micro-organisms offers unique opportunities for experimental manipulation of individual environmental parameters at a fixed, controllable specific growth rate. Chemostat cultivation was originally developed as a tool to study quantitative aspects of microbial growth and metabolism. Renewed interest in this cultivation method is stimulated by the availability of high-information-density techniques for systemic analysis of microbial cultures, which require high reproducibility and careful experimental design. Genome-wide analysis of transcript levels with DNA micro-arrays is currently the most commonly applied of these high-information-density analysis tools for microbial gene expression. Based on published studies on the yeast Saccharomyces cerevisiae, a critical overview is presented of the possibilities and pitfalls associated with the combination of chemostat cultivation and transcriptome analysis with DNA micro-arrays. After a brief introduction to chemostat cultivation and micro-array analysis, key aspects of experimental design of chemostat-based micro-array experiments are discussed. The main focus of this review is on key biological concepts that can be accessed by chemostat-based micro-array analysis. These include effects of specific growth rate on transcriptional regulation, context-dependency of transcriptional responses, correlations between transcript profiles and contribution of the corresponding proteins to cellular function and fitness, and the analysis and application of evolutionary adaptation during prolonged chemostat cultivation. It is concluded that, notwithstanding the incompatibility of chemostat cultivation with high-throughput analysis, integration of chemostat cultivation with micro-array analysis and other high-information-density analytical approaches (e.g. proteomics and metabolomics techniques) offers unique advantages in terms of reproducibility and experimental design in comparison with standard batch cultivation systems. Therefore, chemostat cultivation and derived methods for controlled cultivation of micro-organisms are anticipated to become increasingly important in microbial physiology and systems biology.

Transcription factor control of growth rate dependent genes in Saccharomyces cerevisiae: a three factor design

Background

Characterization of cellular growth is central to understanding living systems. Here, we applied a three-factor design to study the relationship between specific growth rate and genome-wide gene expression in 36 steady-state chemostat cultures of Saccharomyces cerevisiae. The three factors we considered were specific growth rate, nutrient limitation, and oxygen availability.

Results

We identified 268 growth rate dependent genes, independent of nutrient limitation and oxygen availability. The transcriptional response was used to identify key areas in metabolism around which mRNA expression changes are significantly associated. Among key metabolic pathways, this analysis revealed de novo synthesis of pyrimidine ribonucleotides and ATP producing and consuming reactions at fast cellular growth. By scoring the significance of overlap between growth rate dependent genes and known transcription factor target sets, transcription factors that coordinate balanced growth were also identified. Our analysis shows that Fhl1, Rap1, and Sfp1, regulating protein biosynthesis, have significantly enriched target sets for genes up-regulated with increasing growth rate. Cell cycle regulators, such as Ace2 and Swi6, and stress response regulators, such as Yap1, were also shown to have significantly enriched target sets.

Conclusion

Our work, which is the first genome-wide gene expression study to investigate specific growth rate and consider the impact of oxygen availability, provides a more conservative estimate of growth rate dependent genes than previously reported. We also provide a global view of how a small set of transcription factors, 13 in total, contribute to control of cellular growth rate. We anticipate that multi-factorial designs will play an increasing role in elucidating cellular regulation.

Characterization of global yeast quantitative proteome data generated from the wild-type and glucose repression saccharomyces cerevisiae strains: the comparison of two quantitative methods

The quantitative proteomic analysis of complex protein mixtures is emerging as a technically challenging but viable systems-level approach for studying cellular function. This study presents a large-scale comparative analysis of protein abundances from yeast protein lysates derived from both wild-type yeast and yeast strains lacking key components of the Snf1 kinase complex. Four different strains were grown under well-controlled chemostat conditions. Multidimensional protein identification technology followed by quantitation using either spectral counting or stable isotope labeling approaches was used to identify relative changes in the protein expression levels between the strains. A total of 2388 proteins were relatively quantified, and more than 350 proteins were found to have significantly different expression levels between the two strains of comparison when using the stable isotope labeling strategy. The stable isotope labeling based quantitative approach was found to be highly reproducible among biological replicates when complex protein mixtures containing small expression changes were analyzed. Where poor correlation between stable isotope labeling and spectral counting was found, the major reason behind the discrepancy was the lack of reproducible sampling for proteins with low spectral counts. The functional categorization of the relative protein expression differences that occur in Snf1-deficient strains uncovers a wide range of biological processes regulated by this important cellular kinase.

Transcriptional responses ofSaccharomyces cerevisiaeto preferred and nonpreferred nitrogen sources in glucose-limited chemostat cultures

Aerobic, glucose-limited chemostat cultures of Saccharomyces cerevisiae grown with six different nitrogen sources were subjected to transcriptome analysis. The use of chemostats enabled an analysis of nitrogen-source-dependent transcriptional regulation at a fixed specific growth rate. A selection of preferred (ammonium and asparagine) and nonpreferred (leucine, phenylalanine, methionine and proline) nitrogen sources was investigated. For each nitrogen source, distinct sets of genes were induced or repressed relative to the other five nitrogen sources. In total, 131 such 'signature transcripts' were identified in this study. In addition to signature transcripts, genes were identified that showed a transcriptional coresponse to two or more of the six nitrogen sources. For example, 33 genes were transcriptionally upregulated in leucine-grown, phenylalanine-grown and methionine-grown cultures; this was partly attributed to the involvement of common enzymes in the dissimilation of these amino acids. In addition to specific transcriptional responses elicited by individual nitrogen sources, their impact on global regulatory mechanisms such as nitrogen catabolite repression (NCR) were monitored. NCR-sensitive gene expression in the chemostat cultures showed that ammonium and asparagine were 'rich' nitrogen sources. By this criterion, leucine, proline and methionine were 'poor' nitrogen sources, and phenylalanine showed an 'intermediate' NCR response.

Relative gene expression quantification in a fungal gas-phase biofilter

Monitoring of gas-phase biofilter performance generally relies on macroscale measurements that neglect the molecular level phenomena that can control the biodegradation process. The present study was undertaken to determine whether or not quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) could detect changes in relative gene expression resulting from feed variations typically encountered in the field. Specifically, homogentisate-1,2-dioxygenase, ElHDO, expression was quantified as a function of short-term chemical feed variations and shutdown period in a biofilter seeded with a pure culture of the fungus Exophiala lecanii-corni. ElHDO was previously shown to be involved in ethylbenzene degradation in E. lecanii-corni. Overall, relative gene target expression numbers (T(N)) were consistent with gas-phase biofilter performance during each short-term experiment although no direct mathematical correlation was found between T(N) and ethylbenzene removal rate. During the chemical feed experiments, no effect on T(N) was measured in the presence of o-xylene which does not affect ElHDO expression. In the presence of phenylacetate, an inducer of ElHDO, T(N) increased once a threshold substrate concentration was exceeded. When methyl propyl ketone, a repressor of ElHDO, was introduced, T(N) decreased rapidly and acted as a leading indicator of bioreactor failure. In the transient loading experiments, ElHDO expression slowly decreased over a 24-h time period when the ethylbenzene feed was discontinued, but rapidly recovered upon its re-introduction. These results indicate that qRT-PCR reflects microbial activity changes that occur in gas-phase biofilters in response to short-term changes in feed conditions and provides a useful complement to the macroscale measurements typically collected.

Hierarchical amino acid utilization and its influence on fermentation dynamics: rifamycin B fermentation using Amycolatopsis mediterranei S699, a case study

Background

Industrial fermentation typically uses complex nitrogen substrates which consist of mixture of amino acids. The uptake of amino acids is known to be mediated by several amino acid transporters with certain preferences. However, models to predict this preferential uptake are not available. We present the stoichiometry for the utilization of amino acids as a sole carbon and nitrogen substrate or along with glucose as an additional carbon source. In the former case, the excess nitrogen provided by the amino acids is excreted by the organism in the form of ammonia. We have developed a cybernetic model to predict the sequence and kinetics of uptake of amino acids. The model is based on the assumption that the growth on a specific substrate is dependent on key enzyme(s) responsible for the uptake and assimilation of the substrates. These enzymes may be regulated by mechanisms of nitrogen catabolite repression. The model hypothesizes that the organism is an optimal strategist and invests resources for the uptake of a substrate that are proportional to the returns.

Results

Stoichiometric coefficients and kinetic parameters of the model were estimated experimentally for Amycolatopsis mediterranei S699, a rifamycin B overproducer. The model was then used to predict the uptake kinetics in a medium containing cas amino acids. In contrast to the other amino acids, the uptake of proline was not affected by the carbon or nitrogen catabolite repression in this strain. The model accurately predicted simultaneous uptake of amino acids at low cas concentrations and sequential uptake at high cas concentrations. The simulated profile of the key enzymes implies the presence of specific transporters for small groups of amino acids.

Conclusion

The work demonstrates utility of the cybernetic model in predicting the sequence and kinetics of amino acid uptake in a case study involving Amycolatopsis mediterranei, an industrially important organism. This work also throws some light on amino acid transporters and their regulation in A. mediterranei .Further, cybernetic model based experimental strategy unravels formation and utilization of ammonia as well as its inhibitory role during amino acid uptake. Our results have implications for model based optimization and monitoring of other industrial fermentation processes involving complex nitrogen substrate.