Combinatorial effects of environmental parameters on transcriptional regulation in Saccharomyces cerevisiae: a quantitative analysis of a compendium of chemostat-based transcriptome data

The fitness trade-off between growth and stress resistance determines the phenotypic landscape

BACKGROUND

A central challenge in biology is to discover a principle that determines individual phenotypic differences within a species. The growth rate is particularly important for a unicellular organism, and the growth rate under a certain condition is negatively associated with that of another condition, termed fitness trade-off. Therefore, there should exist a common molecular mechanism that regulates multiple growth rates under various conditions, but most studies so far have focused on discovering those genes associated with growth rates under a specific condition.

RESULTS

In this study, we found that there exists a recurrent gene expression signature whose expression levels are related to the fitness trade-off between growth preference and stress resistance across various yeast strains and multiple conditions. We further found that the genomic variation of stress-response, ribosomal, and cell cycle regulators are potential causal genes that determine the sensitivity between growth and survival. Intriguingly, we further observed that the same principle holds for human cells using anticancer drug sensitivities across multiple cancer cell lines.

CONCLUSIONS

Together, we suggest that the fitness trade-off is an evolutionary trait that determines individual growth phenotype within a species. By using this trait, we can possibly overcome anticancer drug resistance in cancer cells.

Improving CRISPR-Cas9 mediated genome integration in interspecific hybrid yeasts

Saccharomyces pastorianus is not a classical taxon, it is an interspecific hybrid resulting from the cross of Saccharomyces cerevisiae and Saccharomyces eubayanus. Exhibiting heterosis for phenotypic traits such as wort α-oligosaccharide consumption and fermentation at low temperature, it has been domesticated to become the main workhorse of the brewing industry. Although CRISPR-Cas9 has been shown to be functional in S. pastorianus, repair of CRISPR- induced double strand break is unpredictable and preferentially uses the homoeologous chromosome as template, preventing targeted introduction of the desired repair construct. Here, we demonstrate that lager hybrids can be edited with near 100% efficiency at carefully selected landing sites on the chimeric SeScCHRIII. The landing sites were systematically selected and evaluated for (i) absence of loss of heterozygosity upon CRISPR-editing, (ii) efficiency of the gRNA, and (iii) absence of effect on strain physiology. Successful examples of highly efficient single and double gene integration illustrated that genome editing can be applied in interspecies hybrids, paving the way to a new impulse to lager yeast strain development. DATA AVAILABILITY: Data underlying graphs and figures found in this manuscript are deposited at the 4TU research dat center (https://data.4tu.nl/info/en/) and available through the doi: 10.4121/21648329.

Top-Down, Knowledge-Based Genetic Reduction of Yeast Central Carbon Metabolism

Saccharomyces cerevisiae, whose evolutionary past includes a whole-genome duplication event, is characterized by a mosaic genome configuration with substantial apparent genetic redundancy. This apparent redundancy raises questions about the evolutionary driving force for genomic fixation of “minor” paralogs and complicates modular and combinatorial metabolic engineering strategies. While isoenzymes might be important in specific environments, they could be dispensable in controlled laboratory or industrial contexts. The present study explores the extent to which the genetic complexity of the central carbon metabolism (CCM) in S. cerevisiae, here defined as the combination of glycolysis, the pentose phosphate pathway, the tricarboxylic acid cycle, and a limited number of related pathways and reactions, can be reduced by elimination of (iso)enzymes without major negative impacts on strain physiology. Cas9-mediated, groupwise deletion of 35 of the 111 genes yielded a “minimal CCM” strain which, despite the elimination of 32% of CCM-related proteins, showed only a minimal change in phenotype on glucose-containing synthetic medium in controlled bioreactor cultures relative to a congenic reference strain. Analysis under a wide range of other growth and stress conditions revealed remarkably few phenotypic changes from the reduction of genetic complexity. Still, a well-documented context-dependent role of GPD1 in osmotolerance was confirmed. The minimal CCM strain provides a model system for further research into genetic redundancy of yeast genes and a platform for strategies aimed at large-scale, combinatorial remodeling of yeast CCM.

Analysis of Transcriptomic Response to SO2 by Oenococcus oeni Growing in Continuous Culture

To successfully complete malolactic fermentation (MLF), Oenococcus oeni must overcome wine stress conditions of low pH, high ethanol, and the presence of SO2. Failure to complete MLF may result in detrimental effects to the quality and stability of the resulting wines. Research efforts to date have focused on elucidating the mechanisms and genetic features that confer the ability to withstand low pH and high ethanol concentrations on O. oeni; however, the responses to SO2 stress are less well defined. This study focused on characterizing the transcriptional response of O. oeni to SO2 challenge during cultivation in a continuous system at wine-like pH (3.5). This experimental design allowed the precise discrimination of transcriptional changes linked to SO2 stress from responses associated with growth stage and cultivation parameters. Differential gene expression analysis revealed major transcriptional changes following SO2 exposure and suggested that this compound primarily interacts with intracellular proteins, DNA, and the cell envelope of O. oeni. The molecular chaperone hsp20, which has a demonstrated function in the heat, ethanol, and acid stress response, was highly upregulated, confirming its additional role in the response of this species to SO2 stress. This work also reports the first nanopore-based complete genome assemblies for O. oeni. IMPORTANCE Malolactic fermentation is an indispensable step in the elaboration of most wines and is generally performed by Oenococcus oeni, a Gram-positive heterofermentative lactic acid bacterium species. While O. oeni is tolerant to many of the wine stresses, including low pH and high ethanol concentrations, it has high sensitivity to SO2, an antiseptic and antioxidant compound regularly used in winemaking. Understanding the physiological changes induced in O. oeni by SO2 stress is essential for the development of more robust starter cultures and methods for their use. This study describes the main transcriptional changes induced by SO2 stress in the wine bacterium O. oeni and provides foundational understanding on how this compound interacts with the cellular components and the induced protective mechanisms of this species.

Trans-acting genetic variation affects the expression of adjacent genes

Gene expression differences among individuals are shaped by trans-acting expression quantitative trait loci (eQTLs). Most trans-eQTLs map to hotspot locations that influence many genes. The molecular mechanisms perturbed by hotspots are often assumed to involve "vertical" cascades of effects in pathways that can ultimately affect the expression of thousands of genes. Here, we report that trans-eQTLs can affect the expression of adjacent genes via "horizontal" mechanisms that extend along a chromosome. Genes affected by trans-eQTL hotspots in the yeast Saccharomyces cerevisiae were more likely to be located next to each other than expected by chance. These paired hotspot effects tended to occur at adjacent genes that also show coexpression in response to genetic and environmental perturbations, suggesting shared mechanisms. Physical proximity and shared chromatin state, in addition to regulation of adjacent genes by similar transcription factors, were independently associated with paired hotspot effects among adjacent genes. Paired effects of trans-eQTLs can occur at neighboring genes even when these genes do not share a common function. This phenomenon could result in unexpected connections between regulatory genetic variation and phenotypes.

Vitamin requirements and biosynthesis in Saccharomyces cerevisiae

Chemically defined media for yeast cultivation (CDMY) were developed to support fast growth, experimental reproducibility, and quantitative analysis of growth rates and biomass yields. In addition to mineral salts and a carbon substrate, popular CDMYs contain seven to nine B-group vitamins, which are either enzyme cofactors or precursors for their synthesis. Despite the widespread use of CDMY in fundamental and applied yeast research, the relation of their design and composition to the actual vitamin requirements of yeasts has not been subjected to critical review since their first development in the 1940s. Vitamins are formally defined as essential organic molecules that cannot be synthesized by an organism. In yeast physiology, use of the term "vitamin" is primarily based on essentiality for humans, but the genome of the Saccharomyces cerevisiae reference strain S288C harbours most of the structural genes required for synthesis of the vitamins included in popular CDMY. Here, we review the biochemistry and genetics of the biosynthesis of these compounds by S. cerevisiae and, based on a comparative genomics analysis, assess the diversity within the Saccharomyces genus with respect to vitamin prototrophy.

Emergent Gene Expression Responses to Drug Combinations Predict Higher-Order Drug Interactions

Effective design of combination therapies requires understanding the changes in cell physiology that result from drug interactions. Here, we show that the genome-wide transcriptional response to combinations of two drugs, measured at a rigorously controlled growth rate, can predict higher-order antagonism with a third drug in Saccharomyces cerevisiae. Using isogrowth profiling, over 90% of the variation in cellular response can be decomposed into three principal components (PCs) that have clear biological interpretations. We demonstrate that the third PC captures emergent transcriptional programs that are dependent on both drugs and can predict antagonism with a third drug targeting the emergent pathway. We further show that emergent gene expression patterns are most pronounced at a drug ratio where the drug interaction is strongest, providing a guideline for future measurements. Our results provide a readily applicable recipe for uncovering emergent responses in other systems and for higher-order drug combinations. A record of this paper's transparent peer review process is included in the Supplemental Information.

In vivo recombination of Saccharomyces eubayanus maltose-transporter genes yields a chimeric transporter that enables maltotriose fermentation

Saccharomyces eubayanus is the non-S. cerevisiae parent of the lager-brewing hybrid S. pastorianus. In contrast to most S. cerevisiae and Frohberg-type S. pastorianus strains, S. eubayanus cannot utilize the α-tri-glucoside maltotriose, a major carbohydrate in brewer’s wort. In Saccharomyces yeasts, utilization of maltotriose is encoded by the subtelomeric MAL gene family, and requires transporters for maltotriose uptake. While S. eubayanus strain CBS 12357^T harbors four SeMALT genes which enable uptake of the α-di-glucoside maltose, it lacks maltotriose transporter genes. In S. cerevisiae, sequence identity indicates that maltotriose and maltose transporters likely evolved from a shared ancestral gene. To study the evolvability of maltotriose utilization in S. eubayanus CBS 12357^T, maltotriose-assimilating mutants obtained after UV mutagenesis were subjected to laboratory evolution in carbon-limited chemostat cultures on maltotriose-enriched wort. An evolved strain showed improved maltose and maltotriose fermentation in 7 L fermenter experiments on industrial wort. Whole-genome sequencing revealed a novel mosaic SeMALT413 gene, resulting from repeated gene introgressions by non-reciprocal translocation of at least three SeMALT genes. The predicted tertiary structure of SeMalT413 was comparable to the original SeMalT transporters, but overexpression of SeMALT413 sufficed to enable growth on maltotriose, indicating gene neofunctionalization had occurred. The mosaic structure of SeMALT413 resembles the structure of S. pastorianus maltotriose-transporter gene SpMTY1, which has high sequences identity to alternatingly S. cerevisiae MALx1, S. paradoxus MALx1 and S. eubayanus SeMALT3. Evolution of the maltotriose transporter landscape in hybrid S. pastorianus lager-brewing strains is therefore likely to have involved mechanisms similar to those observed in the present study.

Fermentation of the wort sugar maltotriose is critical for the flavor profile obtained during beer brewing. The recently discovered yeast Saccharomyces eubayanus is gaining popularity as an alternative to S. pastorianus and S. cerevisiae for brewing, however it is unable to utilize maltotriose. Here, a combination of non-GMO mutagenesis and laboratory evolution of the S. eubayanus type strain CBS 12357^T was used to enable maltotriose fermentation and improve brewing performance. The improved strain expressed a novel transporter gene, SeMALT413, which was formed by recombination between three different SeMALT maltose-transporter genes. Overexpression of SeMALT413 in CBS 12357^T confirmed its neofunctionalization as a maltotriose transporter. As the S. pastorianus maltotriose transporter SpMty1 has a mosaic structure similar to SeMalT413, maltotriose utilization likely involved similar recombination events during the domestication of current lager brewing strains. Based on a posteriori sequence analysis, the emergence of gene functions has been attributed to gene neofunctionalization in a broad range of organisms. The real-time observation of neofunctionalization during laboratory evolution constitutes an important validation of the relevance and importance of this mechanism for Darwinian evolution.

Multi-study inference of regulatory networks for more accurate models of gene regulation

Gene regulatory networks are composed of sub-networks that are often shared across biological processes, cell-types, and organisms. Leveraging multiple sources of information, such as publicly available gene expression datasets, could therefore be helpful when learning a network of interest. Integrating data across different studies, however, raises numerous technical concerns. Hence, a common approach in network inference, and broadly in genomics research, is to separately learn models from each dataset and combine the results. Individual models, however, often suffer from under-sampling, poor generalization and limited network recovery. In this study, we explore previous integration strategies, such as batch-correction and model ensembles, and introduce a new multitask learning approach for joint network inference across several datasets. Our method initially estimates the activities of transcription factors, and subsequently, infers the relevant network topology. As regulatory interactions are context-dependent, we estimate model coefficients as a combination of both dataset-specific and conserved components. In addition, adaptive penalties may be used to favor models that include interactions derived from multiple sources of prior knowledge including orthogonal genomics experiments. We evaluate generalization and network recovery using examples from Bacillus subtilis and Saccharomyces cerevisiae, and show that sharing information across models improves network reconstruction. Finally, we demonstrate robustness to both false positives in the prior information and heterogeneity among datasets.

Due to increasing availability of biological data, methods to properly integrate data generated across the globe become essential for extracting reproducible insights into relevant research questions. In this work, we developed a framework to reconstruct gene regulatory networks from expression datasets generated in separate studies—and thus, because of technical variation (different dates, handlers, laboratories, protocols etc…), challenging to integrate. Since regulatory mechanisms are often shared across conditions, we hypothesized that drawing conclusions from various data sources would improve performance of gene regulatory network inference. By transferring knowledge among regulatory models, our method is able to detect weaker patterns that are conserved across datasets, while also being able to detect dataset-unique interactions. We also allow incorporation of prior knowledge on network structure to favor models that are somewhat similar to the prior itself. Using two model organisms, we show that joint network inference outperforms inference from a single dataset. We also demonstrate that our method is robust to false edges in the prior and to low condition overlap across datasets, and that it can outperform current data integration strategies.

The Genetic Makeup and Expression of the Glycolytic and Fermentative Pathways Are Highly Conserved Within the Saccharomyces Genus

The ability of the yeast Saccharomyces cerevisiae to convert glucose, even in the presence of oxygen, via glycolysis and the fermentative pathway to ethanol has played an important role in its domestication. Despite the extensive knowledge on these pathways in S. cerevisiae, relatively little is known about their genetic makeup in other industrially relevant Saccharomyces yeast species. In this study we explore the diversity of the glycolytic and fermentative pathways within the Saccharomyces genus using S. cerevisiae, Saccharomyces kudriavzevii, and Saccharomyces eubayanus as paradigms. Sequencing data revealed a highly conserved genetic makeup of the glycolytic and fermentative pathways in the three species in terms of number of paralogous genes. Although promoter regions were less conserved between the three species as compared to coding sequences, binding sites for Rap1, Gcr1 and Abf1, main transcriptional regulators of glycolytic and fermentative genes, were highly conserved. Transcriptome profiling of these three strains grown in aerobic batch cultivation in chemically defined medium with glucose as carbon source, revealed a remarkably similar expression of the glycolytic and fermentative genes across species, and the conserved classification of genes into major and minor paralogs. Furthermore, transplantation of the promoters of major paralogs of S. kudriavzevii and S. eubayanus into S. cerevisiae demonstrated not only the transferability of these promoters, but also the similarity of their strength and response to various environmental stimuli. The relatively low homology of S. kudriavzevii and S. eubayanus promoters to their S. cerevisiae relatives makes them very attractive alternatives for strain construction in S. cerevisiae, thereby expanding the S. cerevisiae molecular toolbox.

Evaluation of a novel cloud-based software platform for structured experiment design and linked data analytics

Open data in science requires precise definition of experimental procedures used in data generation, but traditional practices for sharing protocols and data cannot provide the required data contextualization. Here, we explore implementation, in an academic research setting, of a novel cloud-based software system designed to address this challenge. The software supports systematic definition of experimental procedures as visual processes, acquisition and analysis of primary data, and linking of data and procedures in machine-computable form. The software was tested on a set of quantitative microbial-physiology experiments. Though time-intensive, definition of experimental procedures in the software enabled much more precise, unambiguous definitions of experiments than conventional protocols. Once defined, processes were easily reusable and composable into more complex experimental flows. Automatic coupling of process definitions to experimental data enables immediate identification of correlations between procedural details, intended and unintended experimental perturbations, and experimental outcomes. Software-based experiment descriptions could ultimately replace terse and ambiguous ‘Materials and Methods’ sections in scientific journals, thus promoting reproducibility and reusability of published studies.

Structural, Physiological and Regulatory Analysis of Maltose Transporter Genes in Saccharomyces eubayanus CBS 12357T

Saccharomyces pastorianus lager brewing yeasts are domesticated hybrids of Saccharomyces cerevisiae and cold-tolerant Saccharomyces eubayanus. To understand the contribution of both parental genomes to maltose metabolism in brewing wort, this study focuses on maltose transport in the S. eubayanus type strain CBS 12357^T/FM1318. To obtain complete sequences of the MAL loci of this strain, a near-complete genome assembly was generated using the Oxford Nanopore Technology MinION sequencing platform. Except for CHRXII, all sixteen chromosomes were assembled as single contigs. Four loci harboring putative maltose transporter genes (SeMALT1-4), located in subtelomeric regions of CHRII, CHRV, CHRXIII, and CHRXVI, were completely resolved. The near-identical loci on CHRV and CHRXVI strongly resembled canonical S. cerevisiae MAL loci, while those on CHRII and CHRXIII showed different structures suggestive of gene loss. Overexpression of SeMALT1-4 in a maltose-transport-deficient S. cerevisiae strain restored growth on maltose, but not on maltotriose, indicating maltose-specific transport functionality of all four transporters. Simultaneous CRISPR-Cas9-assisted deletion of only SeMALT2 and SeMALT4, which shared 99.7% sequence identity, eliminated growth of S. eubayanus CBS 12357^T on maltose. Transcriptome analysis of S. eubayanus CBS 12357^T established that SeMALT1 and SeMALT3, are poorly expressed in maltose-grown cultures, while SeMALT2 and SeMALT4 were expressed at much higher levels than SeMALT1 and SeMALT3, indicating that only SeMALT2/4 are responsible for maltose consumption in CBS 12357^T. These results represent a first genomic and physiological characterization of maltose transport in S. eubayanus CBS 12357^T and provides a valuable resource for further industrial exploitation of this yeast.

Optimizing anaerobic growth rate and fermentation kinetics in Saccharomyces cerevisiae strains expressing Calvin-cycle enzymes for improved ethanol yield

BACKGROUND

Reduction or elimination of by-product formation is of immediate economic relevance in fermentation processes for industrial bioethanol production with the yeast Saccharomyces cerevisiae. Anaerobic cultures of wild-type S. cerevisiae require formation of glycerol to maintain the intracellular NADH/NAD+ balance. Previously, functional expression of the Calvin-cycle enzymes ribulose-1,5-bisphosphate carboxylase (RuBisCO) and phosphoribulokinase (PRK) in S. cerevisiae was shown to enable reoxidation of NADH with CO2 as electron acceptor. In slow-growing cultures, this engineering strategy strongly decreased the glycerol yield, while increasing the ethanol yield on sugar. The present study explores engineering strategies to improve rates of growth and alcoholic fermentation in yeast strains that functionally express RuBisCO and PRK, while maximizing the positive impact on the ethanol yield.

RESULTS

Multi-copy integration of a bacterial-RuBisCO expression cassette was combined with expression of the Escherichia coli GroEL/GroES chaperones and expression of PRK from the anaerobically inducible DAN1 promoter. In anaerobic, glucose-grown bioreactor batch cultures, the resulting S. cerevisiae strain showed a 31% lower glycerol yield and a 31% lower specific growth rate than a non-engineered reference strain. Growth of the engineered strain in anaerobic, glucose-limited chemostat cultures revealed a negative correlation between its specific growth rate and the contribution of the Calvin-cycle enzymes to redox homeostasis. Additional deletion of GPD2, which encodes an isoenzyme of NAD+-dependent glycerol-3-phosphate dehydrogenase, combined with overexpression of the structural genes for enzymes of the non-oxidative pentose-phosphate pathway, yielded a CO2-reducing strain that grew at the same rate as a non-engineered reference strain in anaerobic bioreactor batch cultures, while exhibiting a 86% lower glycerol yield and a 15% higher ethanol yield.

CONCLUSIONS

The metabolic engineering strategy presented here enables an almost complete elimination of glycerol production in anaerobic, glucose-grown batch cultures of S. cerevisiae, with an associated increase in ethanol yield, while retaining near wild-type growth rates and a capacity for glycerol formation under osmotic stress. Using current genome-editing techniques, the required genetic modifications can be introduced in one or a few transformations. Evaluation of this concept in industrial strains and conditions is therefore a realistic next step towards its implementation for improving the efficiency of first- and second-generation bioethanol production.

Meta-analytic framework for liquid association

MOTIVATION

Although coexpression analysis via pair-wise expression correlation is popularly used to elucidate gene-gene interactions at the whole-genome scale, many complicated multi-gene regulations require more advanced detection methods. Liquid association (LA) is a powerful tool to detect the dynamic correlation of two gene variables depending on the expression level of a third variable (LA scouting gene). LA detection from single transcriptomic study, however, is often unstable and not generalizable due to cohort bias, biological variation and limited sample size. With the rapid development of microarray and NGS technology, LA analysis combining multiple gene expression studies can provide more accurate and stable results.

RESULTS

In this article, we proposed two meta-analytic approaches for LA analysis (MetaLA and MetaMLA) to combine multiple transcriptomic studies. To compensate demanding computing, we also proposed a two-step fast screening algorithm for more efficient genome-wide screening: bootstrap filtering and sign filtering. We applied the methods to five Saccharomyces cerevisiae datasets related to environmental changes. The fast screening algorithm reduced 98% of running time. When compared with single study analysis, MetaLA and MetaMLA provided stronger detection signal and more consistent and stable results. The top triplets are highly enriched in fundamental biological processes related to environmental changes. Our method can help biologists understand underlying regulatory mechanisms under different environmental exposure or disease states.

AVAILABILITY AND IMPLEMENTATION

A MetaLA R package, data and code for this article are available at http://tsenglab.biostat.pitt.edu/software.htm.

CONTACT

ctseng@pitt.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Chemostat Culture for Yeast Physiology and Experimental Evolution

Continuous culture provides many benefits over the classical batch style of growing yeast cells. Steady-state cultures allow for precise control of growth rate and environment. Cultures can be propagated for weeks or months in these controlled environments, which is important for the study of experimental evolution. Despite these advantages, chemostats have not become a highly used system, in large part because of their historical impracticalities, including low throughput, large footprint, systematic complexity, commercial unavailability, high cost, and insufficient protocol availability. However, we have developed methods for building a relatively simple, low-cost, small footprint array of chemostats that can be run in multiples of 32. This "ministat array" can be applied to problems in yeast physiology and experimental evolution.

Water balance and N-metabolism in broccoli (Brassica oleracea L. var. Italica) plants depending on nitrogen source under salt stress and elevated CO2

Elevated [CO2] and salinity in the soils are considered part of the effects of future environmental conditions in arid and semi-arid areas. While it is known that soil salinization decreases plant growth, an increased atmospheric [CO2] may ameliorate the negative effects of salt stress. However, there is a lack of information about the form in which inorganic nitrogen source may influence plant performance under both conditions. Single factor responses and the interactive effects of two [CO2] (380 and 800ppm), three different NO3(-)/NH4(+) ratios in the nutrient solution (100/0, 50/50 and 0/100, with a total N concentration of 3.5mM) and two NaCl concentrations (0 and 80mM) on growth, leaf gas exchange parameters in relation to root hydraulic conductance and N-assimilating enzymes of broccoli (Brassica oleracea L. var. Italica) plants were determined. The results showed that a reduced NO3(-) or co-provision of NO3(-) and NH4(+) could be an optimal source of inorganic N for broccoli plants. In addition, elevated [CO2] ameliorated the effect of salt exposure on the plant growth through an enhanced rate of photosynthesis, even at low N-concentration. However, NO3(-) or NO3(-)/NH4(+) co-provision display differential plant response to salt stress regarding water balance, which was associated to N metabolism. The results may contribute to our understanding of N-fertilization modes under increasing atmospheric [CO2] to cope with salt stress, where variations in N nutrition significantly influenced plant response.

Requirements for Carnitine Shuttle-Mediated Translocation of Mitochondrial Acetyl Moieties to the Yeast Cytosol

In many eukaryotes, the carnitine shuttle plays a key role in intracellular transport of acyl moieties. Fatty acid-grown Saccharomyces cerevisiae cells employ this shuttle to translocate acetyl units into their mitochondria. Mechanistically, the carnitine shuttle should be reversible, but previous studies indicate that carnitine shuttle-mediated export of mitochondrial acetyl units to the yeast cytosol does not occur in vivo. This apparent unidirectionality was investigated by constitutively expressing genes encoding carnitine shuttle-related proteins in an engineered S. cerevisiae strain, in which cytosolic acetyl coenzyme A (acetyl-CoA) synthesis could be switched off by omitting lipoic acid from growth media. Laboratory evolution of this strain yielded mutants whose growth on glucose, in the absence of lipoic acid, was l-carnitine dependent, indicating that in vivo export of mitochondrial acetyl units to the cytosol occurred via the carnitine shuttle. The mitochondrial pyruvate dehydrogenase complex was identified as the predominant source of acetyl-CoA in the evolved strains. Whole-genome sequencing revealed mutations in genes involved in mitochondrial fatty acid synthesis (MCT1), nuclear-mitochondrial communication (RTG2), and encoding a carnitine acetyltransferase (YAT2). Introduction of these mutations into the nonevolved parental strain enabled l-carnitine-dependent growth on glucose. This study indicates intramitochondrial acetyl-CoA concentration and constitutive expression of carnitine shuttle genes as key factors in enabling in vivo export of mitochondrial acetyl units via the carnitine shuttle.

This study demonstrates, for the first time, that Saccharomyces cerevisiae can be engineered to employ the carnitine shuttle for export of acetyl moieties from the mitochondria and, thereby, to act as the sole source of cytosolic acetyl-CoA. Further optimization of this ATP-independent mechanism for cytosolic acetyl-CoA provision can contribute to efficient, yeast-based production of industrially relevant compounds derived from this precursor. The strains constructed in this study, whose growth on glucose depends on a functional carnitine shuttle, provide valuable models for further functional analysis and engineering of this shuttle in yeast and other eukaryotes.

Alternative reactions at the interface of glycolysis and citric acid cycle in Saccharomyces cerevisiae

Pyruvate and acetyl-coenzyme A, located at the interface between glycolysis and TCA cycle, are important intermediates in yeast metabolism and key precursors for industrially relevant products. Rational engineering of their supply requires knowledge of compensatory reactions that replace predominant pathways when these are inactivated. This study investigates effects of individual and combined mutations that inactivate the mitochondrial pyruvate-dehydrogenase (PDH) complex, extramitochondrial citrate synthase (Cit2) and mitochondrial CoA-transferase (Ach1) in Saccharomyces cerevisiae. Additionally, strains with a constitutively expressed carnitine shuttle were constructed and analyzed. A predominant role of the PDH complex in linking glycolysis and TCA cycle in glucose-grown batch cultures could be functionally replaced by the combined activity of the cytosolic PDH bypass and Cit2. Strongly impaired growth and a high incidence of respiratory deficiency in pda1Δ ach1Δ strains showed that synthesis of intramitochondrial acetyl-CoA as a metabolic precursor requires activity of either the PDH complex or Ach1. Constitutive overexpression of AGP2, HNM1, YAT2, YAT1, CRC1 and CAT2 enabled the carnitine shuttle to efficiently link glycolysis and TCA cycle in l-carnitine-supplemented, glucose-grown batch cultures. Strains in which all known reactions at the glycolysis-TCA cycle interface were inactivated still grew slowly on glucose, indicating additional flexibility at this key metabolic junction.

Excessive by-product formation: A key contributor to low isobutanol yields of engineered Saccharomyces cerevisiae strains

It is theoretically possible to engineer Saccharomyces cerevisiae strains in which isobutanol is the predominant catabolic product and high-yielding isobutanol-producing strains are already reported by industry. Conversely, isobutanol yields of engineered S. cerevisiae strains reported in the scientific literature typically remain far below 10% of the theoretical maximum. This study explores possible reasons for these suboptimal yields by a mass-balancing approach. A cytosolically located, cofactor-balanced isobutanol pathway, consisting of a mosaic of bacterial enzymes whose in vivo functionality was confirmed by complementation of null mutations in branched-chain amino acid metabolism, was expressed in S. cerevisiae. Product formation by the engineered strain was analysed in shake flasks and bioreactors. In aerobic cultures, the pathway intermediate isobutyraldehyde was oxidized to isobutyrate rather than reduced to isobutanol. Moreover, significant concentrations of the pathway intermediates 2,3-dihydroxyisovalerate and α-ketoisovalerate, as well as diacetyl and acetoin, accumulated extracellularly. While the engineered strain could not grow anaerobically, micro-aerobic cultivation resulted in isobutanol formation at a yield of 0.018±0.003 mol/mol glucose. Simultaneously, 2,3-butanediol was produced at a yield of 0.649±0.067 mol/mol glucose. These results identify massive accumulation of pathway intermediates, as well as overflow metabolites derived from acetolactate, as an important, previously underestimated contributor to the suboptimal yields of 'academic' isobutanol strains. The observed patterns of by-product formation is consistent with the notion that in vivo activity of the iron-sulphur-cluster-requiring enzyme dihydroxyacid dehydratase is a key bottleneck in the present and previously described 'academic' isobutanol-producing yeast strains.

Comparative assessment of native and heterologous 2-oxo acid decarboxylases for application in isobutanol production by Saccharomyces cerevisiae

BACKGROUND

Decarboxylation of α-ketoisovalerate to isobutyraldehyde is a key reaction in metabolic engineering of Saccharomyces cerevisiae for isobutanol production with published studies relying on overexpression of either the native ARO10 gene or of the Lactococcus lactis kivD decarboxylase gene resulting in low enzymatic activities. Here, we compare relevant properties for isobutanol production of Aro10, KivD and an additional, less studied, L. lactis decarboxylase KdcA.

RESULTS

To eliminate interference by native decarboxylases, each 2-oxo acid decarboxylase was overexpressed in a 'decarboxylase-negative' (pdc1Δ pdc5Δ pdc6Δ aro10Δ) S. cerevisiae background. Kinetic analyses in cell extracts revealed a superior V max/K m ratio of KdcA for α-ketoisovalerate and a wide range of linear and branched-chain 2-oxo acids. However, KdcA also showed the highest activity with pyruvate which, in engineered strains, can contribute to formation of ethanol as a by-product. Removal of native decarboxylase genes eliminated growth on valine as sole nitrogen source and subsequent complementation of this growth impairment by expression of each decarboxylase indicated that based on the increased growth rate, the in vivo activity of KdcA with α-ketoisovalerate was higher than that of KivD and Aro10. Moreover, during oxygen-limited incubation in the presence of glucose, strains expressing kdcA or kivD showed a ca. twofold higher in vivo rate of conversion of α-ketoisovalerate into isobutanol than an ARO10-expressing strain. Finally, cell extracts from cultures grown on different nitrogen sources revealed increased activity of constitutively expressed KdcA after growth on both valine and phenylalanine, while KivD and Aro10 activity was only increased after growth on phenylalanine suggesting a difference in the regulation of these enzymes.

CONCLUSIONS

This study illustrates important differences in substrate specificity, enzyme kinetics and functional expression between different decarboxylases in the context of isobutanol production and identifies KdcA as a promising alternative decarboxylase not only for isobutanol production but also for other branched-chain and linear alcohols.

Dominance from the perspective of gene-gene and gene-chemical interactions

In this study, we used genetic interaction (GI) and gene-chemical interaction (GCI) data to compare mutations with different dominance phenotypes. Our analysis focused primarily on Saccharomyces cerevisiae, where haploinsufficient genes (HI; genes with dominant loss-of-function mutations) were found to be participating in gene expression processes, namely, the translation and regulation of gene transcription. Non-ribosomal HI genes (mainly regulators of gene transcription) were found to have more GIs and GCIs than haplosufficient (HS) genes. Several properties seem to lead to the enrichment of interactions, most notably, the following: importance, pleiotropy, gene expression level and gene expression variation. Importantly, after these properties were appropriately considered in the analysis, the correlation between dominance and GI/GCI degrees was still observed. Strikingly, for the GCIs of heterozygous strains, haploinsufficiency was the only property significantly correlated with the number of GCIs. We found ribosomal HI genes to be depleted in GIs/GCIs. This finding can be explained by their high variation in gene expression under different genetic backgrounds and environmental conditions. We observed the same distributions of GIs among non-ribosomal HI, ribosomal HI and HS genes in three other species: Schizosaccharomyces pombe, Drosophila melanogaster and Homo sapiens. One potentially interesting exception was the lack of significant differences in the degree of GIs between non-ribosomal HI and HS genes in Schizosaccharomyces pombe.

Environmental Interactions and Epistasis Are Revealed in the Proteomic Responses to Complex Stimuli

Ultimately, the genotype of a cell and its interaction with the environment determine the cell’s biochemical state. While the cell’s response to a single stimulus has been studied extensively, a conceptual framework to model the effect of multiple environmental stimuli applied concurrently is not as well developed. In this study, we developed the concepts of environmental interactions and epistasis to explain the responses of the S. cerevisiae proteome to simultaneous environmental stimuli. We hypothesize that, as an abstraction, environmental stimuli can be treated as analogous to genetic elements. This would allow modeling of the effects of multiple stimuli using the concepts and tools developed for studying gene interactions. Mirroring gene interactions, our results show that environmental interactions play a critical role in determining the state of the proteome. We show that individual and complex environmental stimuli behave similarly to genetic elements in regulating the cellular responses to stimuli, including the phenomena of dominance and suppression. Interestingly, we observed that the effect of a stimulus on a protein is dominant over other stimuli if the response to the stimulus involves the protein. Using publicly available transcriptomic data, we find that environmental interactions and epistasis regulate transcriptomic responses as well.

A Minimal Set of Glycolytic Genes Reveals Strong Redundancies in Saccharomyces cerevisiae Central Metabolism

As a result of ancestral whole-genome and small-scale duplication events, the genomes of Saccharomyces cerevisiae and many eukaryotes still contain a substantial fraction of duplicated genes. In all investigated organisms, metabolic pathways, and more particularly glycolysis, are specifically enriched for functionally redundant paralogs. In ancestors of the Saccharomyces lineage, the duplication of glycolytic genes is purported to have played an important role leading to S. cerevisiae's current lifestyle favoring fermentative metabolism even in the presence of oxygen and characterized by a high glycolytic capacity. In modern S. cerevisiae strains, the 12 glycolytic reactions leading to the biochemical conversion from glucose to ethanol are encoded by 27 paralogs. In order to experimentally explore the physiological role of this genetic redundancy, a yeast strain with a minimal set of 14 paralogs was constructed (the "minimal glycolysis" [MG] strain). Remarkably, a combination of a quantitative systems approach and semiquantitative analysis in a wide array of growth environments revealed the absence of a phenotypic response to the cumulative deletion of 13 glycolytic paralogs. This observation indicates that duplication of glycolytic genes is not a prerequisite for achieving the high glycolytic fluxes and fermentative capacities that are characteristic of S. cerevisiae and essential for many of its industrial applications and argues against gene dosage effects as a means of fixing minor glycolytic paralogs in the yeast genome. The MG strain was carefully designed and constructed to provide a robust prototrophic platform for quantitative studies and has been made available to the scientific community.

Functional expression of a heterologous nickel-dependent, ATP-independent urease in Saccharomyces cerevisiae

In microbial processes for production of proteins, biomass and nitrogen-containing commodity chemicals, ATP requirements for nitrogen assimilation affect product yields on the energy producing substrate. In Saccharomyces cerevisiae, a current host for heterologous protein production and potential platform for production of nitrogen-containing chemicals, uptake and assimilation of ammonium requires 1 ATP per incorporated NH3. Urea assimilation by this yeast is more energy efficient but still requires 0.5 ATP per NH3 produced. To decrease ATP costs for nitrogen assimilation, the S. cerevisiae gene encoding ATP-dependent urease (DUR1,2) was replaced by a Schizosaccharomyces pombe gene encoding ATP-independent urease (ure2), along with its accessory genes ureD, ureF and ureG. Since S. pombe ure2 is a Ni(2+)-dependent enzyme and Saccharomyces cerevisiae does not express native Ni(2+)-dependent enzymes, the S. pombe high-affinity nickel-transporter gene (nic1) was also expressed. Expression of the S. pombe genes into dur1,2Δ S. cerevisiae yielded an in vitro ATP-independent urease activity of 0.44±0.01 µmol min(-1) mg protein(-1) and restored growth on urea as sole nitrogen source. Functional expression of the Nic1 transporter was essential for growth on urea at low Ni(2+) concentrations. The maximum specific growth rates of the engineered strain on urea and ammonium were lower than those of a DUR1,2 reference strain. In glucose-limited chemostat cultures with urea as nitrogen source, the engineered strain exhibited an increased release of ammonia and reduced nitrogen content of the biomass. Our results indicate a new strategy for improving yeast-based production of nitrogen-containing chemicals and demonstrate that Ni(2+)-dependent enzymes can be functionally expressed in S. cerevisiae.

Controllability analysis of transcriptional regulatory networks reveals circular control patterns among transcription factors

Transcriptional regulation is the most committed type of regulation in living cells where transcription factors (TFs) control the expression of their target genes and TF expression is controlled by other TFs forming complex transcriptional regulatory networks that can be highly interconnected. Here we analyze the topology and organization of nine transcriptional regulatory networks for E. coli, yeast, mouse and human, and we evaluate how the structure of these networks influences two of their key properties, namely controllability and stability. We calculate the controllability for each network as a measure of the organization and interconnectivity of the network. We find that the number of driver nodes nD needed to control the whole network is 64% of the TFs in the E. coli transcriptional regulatory network in contrast to only 17% for the yeast network, 4% for the mouse network and 8% for the human network. The high controllability (low number of drivers needed to control the system) in yeast, mouse and human is due to the presence of internal loops in their regulatory networks where the TFs regulate each other in a circular fashion. We refer to these internal loops as circular control motifs (CCM). The E. coli transcriptional regulatory network, which does not have any CCMs, shows a hierarchical structure of the transcriptional regulatory network in contrast to the eukaryal networks. The presence of CCMs also has influence on the stability of these networks, as the presence of cycles can be associated with potential unstable steady-states where even small changes in binding affinities can cause dramatic rearrangements of the state of the network.

An alternative, arginase-independent pathway for arginine metabolism in Kluyveromyces lactis involves guanidinobutyrase as a key enzyme

Most available knowledge on fungal arginine metabolism is derived from studies on Saccharomyces cerevisiae, in which arginine catabolism is initiated by releasing urea via the arginase reaction. Orthologues of the S. cerevisiae genes encoding the first three enzymes in the arginase pathway were cloned from Kluyveromyces lactis and shown to functionally complement the corresponding deletion in S. cerevisiae. Surprisingly, deletion of the single K. lactis arginase gene KlCAR1 did not completely abolish growth on arginine as nitrogen source. Growth rate of the deletion mutant strongly increased during serial transfer in shake-flask cultures. A combination of RNAseq-based transcriptome analysis and (13)C-(15)N-based flux analysis was used to elucidate the arginase-independent pathway. Isotopic (13)C(15)N-enrichment in γ-aminobutyrate revealed succinate as the entry point in the TCA cycle of the alternative pathway. Transcript analysis combined with enzyme activity measurements indicated increased expression in the Klcar1Δ mutant of a guanidinobutyrase (EC.3.5.3.7), a key enzyme in a new pathway for arginine degradation. Expression of the K. lactis KLLA0F27995g (renamed KlGBU1) encoding guanidinobutyrase enabled S. cerevisiae to use guanidinobutyrate as sole nitrogen source and its deletion in K. lactis almost completely abolish growth on this nitrogen source. Phylogenetic analysis suggests that this enzyme activity is widespread in fungi.

Functional characterization of a Penicillium chrysogenum mutanase gene induced upon co-cultivation with Bacillus subtilis

BACKGROUND

Microbial gene expression is strongly influenced by environmental growth conditions. Comparison of gene expression under different conditions is frequently used for functional analysis and to unravel regulatory networks, however, gene expression responses to co-cultivation with other microorganisms, a common occurrence in nature, is rarely studied under laboratory conditions. To explore cellular responses of the antibiotic-producing fungus Penicillium chrysogenum to prokaryotes, the present study investigates its transcriptional responses during co-cultivation with Bacillus subtilis.

RESULTS

Steady-state glucose-limited chemostats of P. chrysogenum grown under penillicin-non-producing conditions were inoculated with B. subtilis. Physiological and transcriptional responses of P. chrysogenum in the resulting mixed culture were monitored over 72 h. Under these conditions, B. subtilis outcompeted P. chrysogenum, as reflected by a three-fold increase of the B. subtilis population size and a two-fold reduction of the P. chrysogenum biomass concentration. Genes involved in the penicillin pathway and in synthesis of the penicillin precursors and side-chain were unresponsive to the presence of B. subtilis. Moreover, Penicillium polyketide synthase and nonribosomal peptide synthase genes were either not expressed or down-regulated. Among the highly responsive genes, two putative α-1,3 endoglucanase (mutanase) genes viz Pc12g07500 and Pc12g13330 were upregulated by more than 15-fold and 8-fold, respectively. Measurement of enzyme activity in the supernatant of mixed culture confirmed that the co-cultivation with B. subtilis induced mutanase production. Mutanase activity was neither observed in pure cultures of P. chrysogenum or B. subtilis, nor during exposure of P. chrysogenum to B. subtilis culture supernatants or heat-inactivated B. subtilis cells. However, mutanase production was observed in cultures of P. chrysogenum exposed to filter-sterilized supernatants of mixed cultures of P. chrysogenum and B. subtilis. Heterologous expression of Pc12g07500 and Pc12g13330 genes in Saccharomyces cerevisiae confirmed that Pc12g07500 encoded an active α-1,3 endoglucanase.

CONCLUSION

Time-course transcriptional profiling of P. chrysogenum revealed differentially expressed genes during co-cultivation with B. subtilis. Penicillin production was not induced under these conditions. However, induction of a newly characterized P. chrysogenum gene encoding α-1,3 endoglucanase may enhance the efficacy of fungal antibiotics by degrading bacterial exopolysaccharides.

The antifungal eugenol perturbs dual aromatic and branched-chain amino acid permeases in the cytoplasmic membrane of yeast

Eugenol is an aromatic component of clove oil that has therapeutic potential as an antifungal drug, although its mode of action and precise cellular target(s) remain ambiguous. To address this knowledge gap, a chemical-genetic profile analysis of eugenol was done using ∼4700 haploid Saccharomyces cerevisiae gene deletion mutants to reveal 21 deletion mutants with the greatest degree of susceptibility. Cellular roles of deleted genes in the most susceptible mutants indicate that the main targets for eugenol include pathways involved in biosynthesis and transport of aromatic and branched-chain amino acids. Follow-up analyses showed inhibitory effects of eugenol on amino acid permeases in the yeast cytoplasmic membrane. Furthermore, phenotypic suppression analysis revealed that eugenol interferes with two permeases, Tat1p and Gap1p, which are both involved in dual transport of aromatic and branched-chain amino acids through the yeast cytoplasmic membrane. Perturbation of cytoplasmic permeases represents a novel antifungal target and may explain previous observations that exposure to eugenol results in leakage of cell contents. Eugenol exposure may also contribute to amino acid starvation and thus holds promise as an anticancer therapeutic drug. Finally, this study provides further evidence of the usefulness of the yeast Gene Deletion Array approach in uncovering the mode of action of natural health products.

Genome-scale analyses of butanol tolerance in Saccharomyces cerevisiae reveal an essential role of protein degradation

BACKGROUND

n-Butanol and isobutanol produced from biomass-derived sugars are promising renewable transport fuels and solvents. Saccharomyces cerevisiae has been engineered for butanol production, but its high butanol sensitivity poses an upper limit to product titers that can be reached by further pathway engineering. A better understanding of the molecular basis of butanol stress and tolerance of S. cerevisiae is important for achieving improved tolerance.

RESULTS

By combining a screening of the haploid S. cerevisiae knock-out library, gene overexpression, and genome analysis of evolutionary engineered n-butanol-tolerant strains, we established that protein degradation plays an essential role in tolerance. Strains deleted in genes involved in the ubiquitin-proteasome system and in vacuolar degradation of damaged proteins showed hypersensitivity to n-butanol. Overexpression of YLR224W, encoding the subunit responsible for the recognition of damaged proteins of an ubiquitin ligase complex, resulted in a strain with a higher n-butanol tolerance. Two independently evolved n-butanol-tolerant strains carried different mutations in both RPN4 and RTG1, which encode transcription factors involved in the expression of proteasome and peroxisomal genes, respectively. Introduction of these mutated alleles in the reference strain increased butanol tolerance, confirming their relevance in the higher tolerance phenotype. The evolved strains, in addition to n-butanol, were also more tolerant to 2-butanol, isobutanol and 1-propanol, indicating a common molecular basis for sensitivity and tolerance to C3 and C4 alcohols.

CONCLUSIONS

This study shows that maintenance of protein integrity plays an essential role in butanol tolerance and demonstrates new promising targets to engineer S. cerevisiae for improved tolerance.

New insights into sulfur metabolism in yeasts as revealed by studies of Yarrowia lipolytica

Yarrowia lipolytica, located at the frontier of hemiascomycetous yeasts and fungi, is an excellent candidate for studies of metabolism evolution. This yeast, widely recognized for its technological applications, in particular produces volatile sulfur compounds (VSCs) that fully contribute to the flavor of smear cheese. We report here a relevant global vision of sulfur metabolism in Y. lipolytica based on a comparison between high- and low-sulfur source supplies (sulfate, methionine, or cystine) by combined approaches (transcriptomics, metabolite profiling, and VSC analysis). The strongest repression of the sulfate assimilation pathway was observed in the case of high methionine supply, together with a large accumulation of sulfur intermediates. A high sulfate supply seems to provoke considerable cellular stress via sulfite production, resulting in a decrease of the availability of the glutathione pathway's sulfur intermediates. The most limited effect was observed for the cystine supply, suggesting that the intracellular cysteine level is more controlled than that of methionine and sulfate. Using a combination of metabolomic profiling and genetic experiments, we revealed taurine and hypotaurine metabolism in yeast for the first time. On the basis of a phylogenetic study, we then demonstrated that this pathway was lost by some of the hemiascomycetous yeasts during evolution.

Computational codon optimization of synthetic gene for protein expression

Background

The construction of customized nucleic acid sequences allows us to have greater flexibility in gene design for recombinant protein expression. Among the various parameters considered for such DNA sequence design, individual codon usage (ICU) has been implicated as one of the most crucial factors affecting mRNA translational efficiency. However, previous works have also reported the significant influence of codon pair usage, also known as codon context (CC), on the level of protein expression.

Results

In this study, we have developed novel computational procedures for evaluating the relative importance of optimizing ICU and CC for enhancing protein expression. By formulating appropriate mathematical expressions to quantify the ICU and CC fitness of a coding sequence, optimization procedures based on genetic algorithm were employed to maximize its ICU and/or CC fitness. Surprisingly, the in silico validation of the resultant optimized DNA sequences for Escherichia coli, Lactococcus lactis, Pichia pastoris and Saccharomyces cerevisiae suggests that CC is a more relevant design criterion than the commonly considered ICU.

Conclusions

The proposed CC optimization framework can complement and enhance the capabilities of current gene design tools, with potential applications to heterologous protein production and even vaccine development in synthetic biotechnology.

Substrate specificity of thiamine pyrophosphate-dependent 2-oxo-acid decarboxylases in Saccharomyces cerevisiae

Fusel alcohols are precursors and contributors to flavor and aroma compounds in fermented beverages, and some are under investigation as biofuels. The decarboxylation of 2-oxo acids is a key step in the Ehrlich pathway for fusel alcohol production. In Saccharomyces cerevisiae, five genes share sequence similarity with genes encoding thiamine pyrophosphate-dependent 2-oxo-acid decarboxylases (2ODCs). PDC1, PDC5, and PDC6 encode differentially regulated pyruvate decarboxylase isoenzymes; ARO10 encodes a 2-oxo-acid decarboxylase with broad substrate specificity, and THI3 has not yet been shown to encode an active decarboxylase. Despite the importance of fusel alcohol production in S. cerevisiae, the substrate specificities of these five 2ODCs have not been systematically compared. When the five 2ODCs were individually overexpressed in a pdc1Δ pdc5Δ pdc6Δ aro10Δ thi3Δ strain, only Pdc1, Pdc5, and Pdc6 catalyzed the decarboxylation of the linear-chain 2-oxo acids pyruvate, 2-oxo-butanoate, and 2-oxo-pentanoate in cell extracts. The presence of a Pdc isoenzyme was also required for the production of n-propanol and n-butanol in cultures grown on threonine and norvaline, respectively, as nitrogen sources. These results demonstrate the importance of pyruvate decarboxylases in the natural production of n-propanol and n-butanol by S. cerevisiae. No decarboxylation activity was found for Thi3 with any of the substrates tested. Only Aro10 and Pdc5 catalyzed the decarboxylation of the aromatic substrate phenylpyruvate, with Aro10 showing superior kinetic properties. Aro10, Pdc1, Pdc5, and Pdc6 exhibited activity with all branched-chain and sulfur-containing 2-oxo acids tested but with markedly different decarboxylation kinetics. The high affinity of Aro10 identified it as a key contributor to the production of branched-chain and sulfur-containing fusel alcohols.

Impact of velvet complex on transcriptome and penicillin G production in glucose-limited chemostat cultures of a β-lactam high-producing Penicillium chrysogenum strain

The multicomponent global regulator Velvet complex has been identified as a key regulator of secondary metabolite production in Aspergillus and Penicillium species. Previous work indicated a massive impact of PcvelA and PclaeA deletions on penicillin production in prolonged batch cultures of P. chrysogenum, as well as substantial changes in transcriptome. The present study investigated the impact of these mutations on product formation and genome-wide transcript profiles under glucose-limited aerobic conditions, relevant for industrial production of β-lactams. Predicted amino acid sequences of PcVelA and PcLaeA in this strain were identical to those in its ancestor Wisconsin54-1255. Controls were performed to rule out transformation-associated loss of penicillin-biosynthesis clusters. The correct PcvelA and PclaeA deletion strains revealed a small reduction of penicillin G productivity relative to the reference strain, which is a much smaller reduction than previously reported for prolonged batch cultures of similar P. chrysogenum mutants. Chemostat-based transcriptome analysis yielded only 23 genes with a consistent differential response in the PcvelAΔ and PclaeAΔ mutants when grown in the absence of the penicillin G side-chain precursor phenylacetic acid. Eleven of these genes belonged to two small gene clusters, one of which contained a gene with high homology to the aristolochene synthase. These results provide a clear caveat that the impact of the Velvet complex on secondary metabolism in filamentous fungi is strongly context dependent.

Correlation of gene expression and protein production rate - a system wide study

BACKGROUND

Growth rate is a major determinant of intracellular function. However its effects can only be properly dissected with technically demanding chemostat cultivations in which it can be controlled. Recent work on Saccharomyces cerevisiae chemostat cultivations provided the first analysis on genome wide effects of growth rate. In this work we study the filamentous fungus Trichoderma reesei (Hypocrea jecorina) that is an industrial protein production host known for its exceptional protein secretion capability. Interestingly, it exhibits a low growth rate protein production phenotype.

RESULTS

We have used transcriptomics and proteomics to study the effect of growth rate and cell density on protein production in chemostat cultivations of T. reesei. Use of chemostat allowed control of growth rate and exact estimation of the extracellular specific protein production rate (SPPR). We find that major biosynthetic activities are all negatively correlated with SPPR. We also find that expression of many genes of secreted proteins and secondary metabolism, as well as various lineage specific, mostly unknown genes are positively correlated with SPPR. Finally, we enumerate possible regulators and regulatory mechanisms, arising from the data, for this response.

CONCLUSIONS

Based on these results it appears that in low growth rate protein production energy is very efficiently used primarly for protein production. Also, we propose that flux through early glycolysis or the TCA cycle is a more fundamental determining factor than growth rate for low growth rate protein production and we propose a novel eukaryotic response to this i.e. the lineage specific response (LSR).

Cellular responses of Saccharomyces cerevisiae at near-zero growth rates: transcriptome analysis of anaerobic retentostat cultures

Extremely low specific growth rates (below 0.01 h(-1) ) represent a largely unexplored area of microbial physiology. In this study, anaerobic, glucose-limited retentostats were used to analyse physiological and genome-wide transcriptional responses of Saccharomyces cerevisiae to cultivation at near-zero specific growth rates. While quiescence is typically investigated as a result of carbon starvation, cells in retentostat are fed by small, but continuous carbon and energy supply. Yeast cells cultivated near-zero specific growth rates, while metabolically active, exhibited characteristics previously associated with quiescence, including accumulation of storage polymers and an increased expression of genes involved in exit from the cell cycle into G(0) . Unexpectedly, analysis of transcriptome data from retentostat and chemostat cultures showed, as specific growth rate was decreased, that quiescence-related transcriptional responses were already set in at specific growth rates above 0.025 h(-1) . These observations stress the need for systematic dissection of physiological responses to slow growth, quiescence, ageing and starvation and indicate that controlled cultivation systems such as retentostats can contribute to this goal. Furthermore, cells in retentostat do not (or hardly) divide while remaining metabolically active, which emulates the physiological status of metazoan post-mitotic cells. We propose retentostat as a powerful cultivation tool to investigate chronological ageing-related processes.

Recovering key biological constituents through sparse representation of gene expression

MOTIVATION

Large-scale RNA expression measurements are generating enormous quantities of data. During the last two decades, many methods were developed for extracting insights regarding the interrelationships between genes from such data. The mathematical and computational perspectives that underlie these methods are usually algebraic or probabilistic.

RESULTS

Here, we introduce an unexplored geometric view point where expression levels of genes in multiple experiments are interpreted as vectors in a high-dimensional space. Specifically, we find, for the expression profile of each particular gene, its approximation as a linear combination of profiles of a few other genes. This method is inspired by recent developments in the realm of compressed sensing in the machine learning domain. To demonstrate the power of our approach in extracting valuable information from the expression data, we independently applied it to large-scale experiments carried out on the yeast and malaria parasite whole transcriptomes. The parameters extracted from the sparse reconstruction of the expression profiles, when fed to a supervised learning platform, were used to successfully predict the relationships between genes throughout the Gene Ontology hierarchy and protein-protein interaction map. Extensive assessment of the biological results shows high accuracy in both recovering known predictions and in yielding accurate predictions missing from the current databases. We suggest that the geometrical approach presented here is suitable for a broad range of high-dimensional experimental data.

Predicting metabolic fluxes using gene expression differences as constraints

A standard approach to estimate intracellular fluxes on a genome-wide scale is flux-balance analysis (FBA), which optimizes an objective function subject to constraints on (relations between) fluxes. The performance of FBA models heavily depends on the relevance of the formulated objective function and the completeness of the defined constraints. Previous studies indicated that FBA predictions can be improved by adding regulatory on/off constraints. These constraints were imposed based on either absolute or relative gene expression values. We provide a new algorithm that directly uses regulatory up/down constraints based on gene expression data in FBA optimization (tFBA). Our assumption is that if the activity of a gene drastically changes from one condition to the other, the flux through the reaction controlled by that gene will change accordingly. We allow these constraints to be violated, to account for posttranscriptional control and noise in the data. These up/down constraints are less stringent than the on/off constraints as previously proposed. Nevertheless, we obtain promising predictions, since many up/down constraints can be enforced. The potential of the proposed method, tFBA, is demonstrated through the analysis of fluxes in yeast under nine different cultivation conditions, between which approximately 5,000 regulatory up/down constraints can be defined. We show that changes in gene expression are predictive for changes in fluxes. Additionally, we illustrate that flux distributions obtained with tFBA better fit transcriptomics data than previous methods. Finally, we compare tFBA and FBA predictions to show that our approach yields more biologically relevant results.

Individual vs. combinatorial effect of elevated CO2 conditions and salinity stress on Arabidopsis thaliana liquid cultures: comparing the early molecular response using time-series transcriptomic and metabolomic analyses

BACKGROUND

In this study, we investigated the individual and combinatorial effect of elevated CO2 conditions and salinity stress on the dynamics of both the transcriptional and metabolic physiology of Arabidopsis thaliana liquid hydroponic cultures over the first 30 hours of continuous treatment. Both perturbations are of particular interest in plant and agro-biotechnological applications. Moreover, within the timeframe of this experiment, they are expected to affect plant growth to opposite directions. Thus, a major objective was to investigate whether this expected "divergence" was valid for the individual perturbations and to study how it is manifested under the combined stress at two molecular levels of cellular function, using high-throughput analyses.

RESULTS

We observed that a) high salinity has stronger effect than elevated CO2 at both the transcriptional and metabolic levels, b) the transcriptional responses to the salinity and combined stresses exhibit strong similarity, implying a robust transcriptional machinery acting to the salinity stress independent of the co-occurrence of elevated CO2, c) the combinatorial effect of the two perturbations on the metabolic physiology is milder than of the salinity stress alone. Metabolomic analysis suggested that the beneficial role of elevated CO2 on salt-stressed plants within the timeframe of this study should be attributed to the provided additional resources; these allow the plants to respond to high salinity without having to forfeit other major metabolic functions, and d) 9 h-12 h and 24 h of treatment coincide with significant changes in the metabolic physiology under any of the investigated stresses. Significant differences between the acute and longer term responses were observed at both molecular levels.

CONCLUSIONS

This study contributes large-scale dynamic omic data from two levels of cellular function for a plant system under various stresses. It provides an additional example of the power of integrated omic analyses for the comprehensive study of the molecular physiology of complex biological systems. Moreover, taking into consideration the particular interest of the two investigated perturbations in plant biotechnology, enhanced understanding of the molecular physiology of the plants under these conditions could lead to the design of novel metabolic engineering strategies to increase the resistance of commercial crops to salinity stress.

Karyotype and identification of all homoeologous chromosomes of allopolyploid Brassica napus and its diploid progenitors

Investigating recombination of homoeologous chromosomes in allopolyploid species is central to understanding plant breeding and evolution. However, examining chromosome pairing in the allotetraploid Brassica napus has been hampered by the lack of chromosome-specific molecular probes. In this study, we establish the identification of all homoeologous chromosomes of allopolyploid B. napus by using robust molecular cytogenetic karyotypes developed for the progenitor species Brassica rapa (A genome) and Brassica oleracea (C genome). The identification of every chromosome among these three Brassica species utilized genetically mapped bacterial artificial chromosomes (BACs) from B. rapa as probes for fluorescent in situ hybridization (FISH). With this BAC-FISH data, a second karyotype was developed using two BACs that contained repetitive DNA sequences and the ubiquitous ribosomal and pericentromere repeats. Using this diagnostic probe mix and a BAC that contained a C-genome repeat in two successive hybridizations allowed for routine identification of the corresponding homoeologous chromosomes between the A and C genomes of B. napus. When applied to the B. napus cultivar Stellar, we detected one chromosomal rearrangement relative to the parental karyotypes. This robust novel chromosomal painting technique will have biological applications for the understanding of chromosome pairing, homoeologous recombination, and genome evolution in the genus Brassica and will facilitate new applied breeding technologies that rely upon identification of chromosomes.

A multi-level study of recombinant Pichia pastoris in different oxygen conditions

Background

Yeasts are attractive expression platforms for many recombinant proteins, and there is evidence for an important interrelation between the protein secretion machinery and environmental stresses. While adaptive responses to such stresses are extensively studied in Saccharomyces cerevisiae, little is known about their impact on the physiology of Pichia pastoris. We have recently reported a beneficial effect of hypoxia on recombinant Fab secretion in P. pastoris chemostat cultivations. As a consequence, a systems biology approach was used to comprehensively identify cellular adaptations to low oxygen availability and the additional burden of protein production. Gene expression profiling was combined with proteomic analyses and the ¹³C isotope labelling based experimental determination of metabolic fluxes in the central carbon metabolism.

Results

The physiological adaptation of P. pastoris to hypoxia showed distinct traits in relation to the model yeast S. cerevisiae. There was a positive correlation between the transcriptomic, proteomic and metabolic fluxes adaptation of P. pastoris core metabolism to hypoxia, yielding clear evidence of a strong transcriptional regulation component of key pathways such as glycolysis, pentose phosphate pathway and TCA cycle. In addition, the adaptation to reduced oxygen revealed important changes in lipid metabolism, stress responses, as well as protein folding and trafficking.

Conclusions

This systems level study helped to understand the physiological adaptations of cellular mechanisms to low oxygen availability in a recombinant P. pastoris strain. Remarkably, the integration of data from three different levels allowed for the identification of differences in the regulation of the core metabolism between P. pastoris and S. cerevisiae. Detailed comparative analysis of the transcriptomic data also led to new insights into the gene expression profiles of several cellular processes that are not only susceptible to low oxygen concentrations, but might also contribute to enhanced protein secretion.

Nutrient control of eukaryote cell growth: a systems biology study in yeast

Background

To elucidate the biological processes affected by changes in growth rate and nutrient availability, we have performed a comprehensive analysis of the transcriptome, proteome and metabolome responses of chemostat cultures of the yeast, Saccharomyces cerevisiae, growing at a range of growth rates and in four different nutrient-limiting conditions.

Results

We find significant changes in expression for many genes in each of the four nutrient-limited conditions tested. We also observe several processes that respond differently to changes in growth rate and are specific to each nutrient-limiting condition. These include carbohydrate storage, mitochondrial function, ribosome synthesis, and phosphate transport. Integrating transcriptome data with proteome measurements allows us to identify previously unrecognized examples of post-transcriptional regulation in response to both nutrient and growth-rate signals.

Conclusions

Our results emphasize the unique properties of carbon metabolism and the carbon substrate, the limitation of which induces significant changes in gene regulation at the transcriptional and post-transcriptional level, as well as altering how many genes respond to growth rate. By comparison, the responses to growth limitation by other nutrients involve a smaller set of genes that participate in specific pathways.

See associated commentary http://www.biomedcentral.com/1741-7007/8/62

Fewer permutations, more accurate P-values

Motivation: Permutation tests have become a standard tool to assess the statistical significance of an event under investigation. The statistical significance, as expressed in a P-value, is calculated as the fraction of permutation values that are at least as extreme as the original statistic, which was derived from non-permuted data. This empirical method directly couples both the minimal obtainable P-value and the resolution of the P-value to the number of permutations. Thereby, it imposes upon itself the need for a very large number of permutations when small P-values are to be accurately estimated. This is computationally expensive and often infeasible.

Results: A method of computing P-values based on tail approximation is presented. The tail of the distribution of permutation values is approximated by a generalized Pareto distribution. A good fit and thus accurate P-value estimates can be obtained with a drastically reduced number of permutations when compared with the standard empirical way of computing P-values.

Availability: The Matlab code can be obtained from the corresponding author on request.

Contact:tknijnenburg@systemsbiology.org

Supplementary information:Supplementary data are available at Bioinformatics online.