Adaptations in metabolism and protein translation give rise to the Crabtree effect in yeast.

Aerobic fermentation, also referred to as the Crabtree effect in yeast, is a well-studied phenomenon that allows many eukaryal cells to attain higher growth rates at high glucose availability. Not all yeasts exhibit the Crabtree effect, and it is not known why Crabtree-negative yeasts can grow at rates comparable to Crabtree-positive yeasts. Here, we quantitatively compared two Crabtree-positive yeasts, Saccharomyces cerevisiae and Schizosaccharomyces pombe, and two Crabtree-negative yeasts, Kluyveromyces marxianus and Scheffersomyces stipitis, cultivated under glucose excess conditions. Combining physiological and proteome quantification with genome-scale metabolic modeling, we found that the two groups differ in energy metabolism and translation efficiency. In Crabtree-positive yeasts, the central carbon metabolism flux and proteome allocation favor a glucose utilization strategy minimizing proteome cost as proteins translation parameters, including ribosomal content and/or efficiency, are lower. Crabtree-negative yeasts, however, use a strategy of maximizing ATP yield, accompanied by higher protein translation parameters. Our analyses provide insight into the underlying reasons for the Crabtree effect, demonstrating a coupling to adaptations in both metabolism and protein translation.

Proteome reallocation from amino acid biosynthesis to ribosomes enables yeast to grow faster in rich media.

Several recent studies have shown that the concept of proteome constraint, i.e., the need for the cell to balance allocation of its proteome between different cellular processes, is essential for ensuring proper cell function. However, there have been no attempts to elucidate how cells' maximum capacity to grow depends on protein availability for different cellular processes. To experimentally address this, we cultivated Saccharomyces cerevisiae in bioreactors with or without amino acid supplementation and performed quantitative proteomics to analyze global changes in proteome allocation, during both anaerobic and aerobic growth on glucose. Analysis of the proteomic data implies that proteome mass is mainly reallocated from amino acid biosynthetic processes into translation, which enables an increased growth rate during supplementation. Similar findings were obtained from both aerobic and anaerobic cultivations. Our findings show that cells can increase their growth rate through increasing its proteome allocation toward the protein translational machinery.

Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection.

Yeasts are known to have versatile metabolic traits, while how these metabolic traits have evolved has not been elucidated systematically. We performed integrative evolution analysis to investigate how genomic evolution determines trait generation by reconstructing genome-scale metabolic models (GEMs) for 332 yeasts. These GEMs could comprehensively characterize trait diversity and predict enzyme functionality, thereby signifying that sequence-level evolution has shaped reaction networks towards new metabolic functions. Strikingly, using GEMs, we can mechanistically map different evolutionary events, e.g. horizontal gene transfer and gene duplication, onto relevant subpathways to explain metabolic plasticity. This demonstrates that gene family expansion and enzyme promiscuity are prominent mechanisms for metabolic trait gains, while GEM simulations reveal that additional factors, such as gene loss from distant pathways, contribute to trait losses. Furthermore, our analysis could pinpoint to specific genes and pathways that have been under positive selection and relevant for the formulation of complex metabolic traits, i.e. thermotolerance and the Crabtree effect. Our findings illustrate how multidimensional evolution in both metabolic network structure and individual enzymes drives phenotypic variations.

Benchmarking accuracy and precision of intensity-based absolute quantification of protein abundances in Saccharomyces cerevisiae.

Protein quantification via label-free mass spectrometry (MS) has become an increasingly popular method for predicting genome-wide absolute protein abundances. A known caveat of this approach, however, is the poor technical reproducibility, that is, how consistent predictions are when the same sample is measured repeatedly. Here, we measured proteomics data for Saccharomyces cerevisiae with both biological and inter-batch technical triplicates, to analyze both accuracy and precision of protein quantification via MS. Moreover, we analyzed how these metrics vary when applying different methods for converting MS intensities to absolute protein abundances. We demonstrate that our simple normalization and rescaling approach can perform as accurately, yet more precisely, than methods which rely on external standards. Additionally, we show that inter-batch reproducibility is worse than biological reproducibility for all evaluated methods. These results offer a new benchmark for assessing MS data quality for protein quantification, while also underscoring current limitations in this approach.

A single chromosome strain of S. cerevisiae exhibits diminished ethanol metabolism and tolerance.

BACKGROUND: Eukaryotic organisms, like the model yeast S. cerevisiae, have linear chromosomes that facilitate organization and protection of nuclear DNA. A recent work described a stepwise break/repair method that enabled fusion of the 16 chromosomes of S. cerevisiae into a single large chromosome. Construction of this strain resulted in the removal of 30 of 32 telomeres, over 300 kb of subtelomeric DNA, and 107 subtelomeric ORFs. Despite these changes, characterization of the single chromosome strain uncovered modest phenotypes compared to a reference strain. RESULTS: This study further characterized the single chromosome strain and found that it exhibited a longer lag phase, increased doubling time, and lower final biomass concentration compared with a reference strain when grown on YPD. These phenotypes were amplified when ethanol was added to the medium or used as the sole carbon source. RNAseq analysis showed poor induction of genes involved in diauxic shift, ethanol metabolism, and fatty-acid ß-oxidation during growth on ethanol compared to the reference strain. Enzyme-constrained metabolic modeling identified decreased flux through the enzymes that are encoded by these poorly induced genes as a likely cause of diminished biomass accumulation. The diminished growth on ethanol for the single chromosome strain was rescued by nicotinamide, an inhibitor of sirtuin family deacetylases, which have been shown to silence gene expression in heterochromatic regions. CONCLUSIONS: Our results indicate that sirtuin-mediated silencing in the single chromosome strain interferes with growth on non-fermentable carbon sources. We propose that the removal of subtelomeric DNA that would otherwise be bound by sirtuins leads to silencing at other loci in the single chromosome strain. Further, we hypothesize that the poorly induced genes in the single chromosome strain during ethanol growth could be silenced by sirtuins in wildtype S. cerevisiae during growth on glucose.

Big data in yeast systems biology.

Systems biology uses computational and mathematical modeling to study complex interactions in a biological system. The yeast Saccharomyces cerevisiae, which has served as both an important model organism and cell factory, has pioneered both the early development of such models and modeling concepts, and the more recent integration of multi-omics big data in these models to elucidate fundamental principles of biology. Here, we review the advancement of big data technologies to gain biological insight in three aspects of yeast systems biology: gene expression dynamics, cellular metabolism and the regulation network between gene expression and metabolism. The role of big data and complementary modeling approaches, including the expansion of genome-scale metabolic models and machine learning methodologies, are discussed as key drivers in the rapid advancement of yeast systems biology.

Recent applications of engineered animal antioxidant deficiency models in human nutrition and chronic disease.

Dietary antioxidants are essential nutrients that inhibit the oxidation of biologically important molecules and suppress the toxicity of reactive oxygen or nitrogen species. When the total antioxidant capacity is insufficient to quench these reactive species, oxidative damage occurs and contributes to the onset and progression of chronic diseases, such as neurodegenerative diseases, cardiovascular diseases, and cancer. However, epidemiological studies that examine the relationship between antioxidants and disease outcome can only identify correlative associations. Additionally, many antioxidants also have prooxidant effects. Thus, clinically relevant animal models of antioxidant function are essential for improving our understanding of the role of antioxidants in the pathogenesis of complex diseases as well as evaluating the therapeutic potential and risks of their supplementation. Recent progress in gene knockout mice and virus-based gene expression has potentiated these areas of study. Here, we review the current genetically modified animal models of dietary antioxidant function and their clinical relevance in chronic diseases. This review focuses on the 3 major antioxidants in the human body: vitamin C, vitamin E, and uric acid. We examine genetic models of vitamin C synthesis (guinea pig, Osteogenic Disorder Shionogi rat, Gulo(-/-) and SMP30(-/-) mouse mutants) and transport (Slc23a1(-/-) and Slc23a2(-/-) mouse mutants), vitamin E transport (Ttpa(-/-) mouse mutant), and uric acid synthesis (Uox(-/-) mouse mutant). The application of these models to current research goals is also discussed.

Antagonistic regulation of motility and transcriptome expression by RpoN and RpoS in Escherichia coli.

Bacteria generally possess multiple σ factors that, based on structural and functional similarity, divide into two families: σ(70) and σ(N) . Many studies have revealed σ factor competition within the σ(70) family, while the competition between σ(N) and σ(70) families has yet to be fully explored. Here we report a global antagonistic effect on gene expression between two alternative σ factors, σ(N) (RpoN) and a σ(70) family protein σ(S) (RpoS). Mutations in rpoS and rpoN were found to inversely affect a number of cellular traits, such as the expression of flagellar genes, σ(N) -controlled growth on poor nitrogen sources, and σ(S) -directed expression of acid phosphatase AppA. Transcriptome analysis reveals that about 60% of genes in the RpoN regulon are under reciprocal RpoS control. Furthermore, loss of RpoN led to increased levels of RpoS, while RpoN levels were unaffected by the rpoS mutation. Expression of the flagellar σ(F) factor (FliA), another σ(70) family protein, is controlled positively by RpoN but negatively by RpoS. This positive control by RpoN is likely mediated through the flagellar regulator FlhDC, whose expression is RpoN-dependent. These findings unveil a complex regulatory interaction among σ(N) , σ(S) and σ(F) , which modulates motility, nitrogen utilization, stress response and many other cellular functions.

Quantifying absolute gene expression profiles reveals distinct regulation of central carbon metabolism genes in yeast.

In addition to controlled expression of genes by specific regulatory circuits, the abundance of proteins and transcripts can also be influenced by physiological states of the cell such as growth rate and metabolism. Here we examine the control of gene expression by growth rate and metabolism, by analyzing a multi-omics dataset consisting of absolute-quantitative abundances of the transcriptome, proteome, and amino acids in 22 steady-state yeast cultures. We find that transcription and translation are coordinately controlled by the cell growth rate via RNA polymerase II and ribosome abundance, but they are independently controlled by nitrogen metabolism via amino acid and nucleotide availabilities. Genes in central carbon metabolism, however, are distinctly regulated and do not respond to the cell growth rate or nitrogen metabolism as all other genes. Understanding these effects allows the confounding factors of growth rate and metabolism to be accounted for in gene expression profiling studies.

Mevalonate Pathway Inhibition Slows Breast Cancer Metastasis via Reduced N-glycosylation Abundance and Branching.

Aberrant N-glycan Golgi remodeling and metabolism are associated with epithelial-mesenchymal transition (EMT) and metastasis in patients with breast cancer. Despite this association, the N-glycosylation pathway has not been successfully targeted in cancer. Here, we show that inhibition of the mevalonate pathway with fluvastatin, a clinically approved drug, reduces both N-glycosylation and N-glycan-branching, essential components of the EMT program and tumor metastasis. This indicates novel cross-talk between N-glycosylation at the endoplasmic reticulum (ER) and N-glycan remodeling at the Golgi. Consistent with this cooperative model between the two spatially separated levels of protein N-glycosylation, fluvastatin-induced tumor cell death was enhanced by loss of Golgi-associated N-acetylglucosaminyltransferases MGAT1 or MGAT5. In a mouse model of postsurgical metastatic breast cancer, adjuvant fluvastatin treatment reduced metastatic burden and improved overall survival. Collectively, these data support the immediate repurposing of fluvastatin as an adjuvant therapeutic to combat metastatic recurrence in breast cancer by targeting protein N-glycosylation at both the ER and Golgi. SIGNIFICANCE: These findings show that metastatic breast cancer cells depend on the fluvastatin-sensitive mevalonate pathway to support protein N-glycosylation, warranting immediate clinical testing of fluvastatin as an adjuvant therapy for breast cancer.

Yeast systems biology in understanding principles of physiology underlying complex human diseases.

Complex human diseases commonly arise from deregulation of cell growth, metabolism, and/or gene expression. Yeast is a eukaryal model organism that is widely used to study these processes. Yeast systems biology benefits from the ability to exert fine experimental control over the cell growth rate and nutrient composition, which allows orthogonal experimental design and generation of multi-omics data at high resolution. This has led to several insights on the principles of cellular physiology, including many cellular processes associated with complex human diseases. Here we review these biological insights together with experimental and modeling approaches developed in yeast to study systems biology. The role of yeast systems biology to further advance systems and personalized therapies for complex diseases is discussed.

Different Routes of Protein Folding Contribute to Improved Protein Production in Saccharomyces cerevisiae.

Protein folding is often considered the flux controlling process in protein synthesis and secretion. Here, two previously isolated Saccharomyces cerevisiae strains with increased α-amylase productivity were analyzed in chemostat cultures at different dilution rates using multi-omics data. Based on the analysis, we identified different routes of the protein folding pathway to improve protein production. In the first strain, the increased abundance of proteins working on the folding process, coordinated with upregulated glycogen metabolism and trehalose metabolism, helped increase α-amylase productivity 1.95-fold compared to the level in the original strain in chemostat culture at a dilution rate of 0.2/h. The second strain further strengthened the folding precision to improve protein production. More precise folding helps the cell improve protein production efficiency and reduce the expenditure of energy on the handling of misfolded proteins. As calculated using an enzyme-constrained genome-scale metabolic model, the second strain had an increased productivity of 2.36-fold with lower energy expenditure than that of the original under the same condition. Further study revealed that the regulation of N-glycans played an important role in the folding precision control and that overexpression of the glucosidase Cwh41p can significantly improve protein production, especially for the strains with improved folding capacity but lower folding precision. Our findings elucidated in detail the mechanisms in two strains having improved protein productivity and thereby provided novel insights for industrial recombinant protein production as well as demonstrating how multi-omics analysis can be used for identification of novel strain-engineering targets.IMPORTANCE Protein folding plays an important role in protein maturation and secretion. In recombinant protein production, many studies have focused on the folding pathway to improve productivity. Here, we identified two different routes for improving protein production by yeast. We found that improving folding precision is a better strategy. Dysfunction of this process is also associated with several aberrant protein-associated human diseases. Here, our findings about the role of glucosidase Cwh41p in the precision control system and the characterization of the strain with a more precise folding process could contribute to the development of novel therapeutic strategies.

Nitrogen limitation reveals large reserves in metabolic and translational capacities of yeast.

Cells maintain reserves in their metabolic and translational capacities as a strategy to quickly respond to changing environments. Here we quantify these reserves by stepwise reducing nitrogen availability in yeast steady-state chemostat cultures, imposing severe restrictions on total cellular protein and transcript content. Combining multi-omics analysis with metabolic modeling, we find that seven metabolic superpathways maintain >50% metabolic capacity in reserve, with glucose metabolism maintaining >80% reserve capacity. Cells maintain >50% reserve in translational capacity for 2490 out of 3361 expressed genes (74%), with a disproportionately large reserve dedicated to translating metabolic proteins. Finally, ribosome reserves contain up to 30% sub-stoichiometric ribosomal proteins, with activation of reserve translational capacity associated with selective upregulation of 17 ribosomal proteins. Together, our dataset provides a quantitative link between yeast physiology and cellular economics, which could be leveraged in future cell engineering through targeted proteome streamlining.

Polymorphism and selection of rpoS in pathogenic Escherichia coli.

BACKGROUND: Though RpoS is important for survival of pathogenic Escherichia coli in natural environments, polymorphism in the rpoS gene is common. However, the causes of this polymorphism and consequential physiological effects on gene expression in pathogenic strains are not fully understood. RESULTS: In this study, we found that growth on non-preferred carbon sources can efficiently select for loss of RpoS in seven of ten representative verocytotoxin-producing E. coli (VTEC) strains. Mutants (Suc++) forming large colonies on succinate were isolated at a frequency of 10-8 mutants per cell plated. Strain O157:H7 EDL933 yielded mainly mutants (about 90%) that were impaired in catalase expression, suggesting the loss of RpoS function. As expected, inactivating mutations in rpoS sequence were identified in these mutants. Expression of two pathogenicity-related phenotypes, cell adherence and RDAR (red dry and rough) morphotype, were also attenuated, indicating positive control by RpoS. For the other Suc++ mutants (10%) that were catalase positive, no mutation in rpoS was detected. CONCLUSION: The selection for loss of RpoS on poor carbon sources is also operant in most pathogenic strains, and thus is likely responsible for the occurrence of rpoS polymorphisms among E. coli isolates.

An actionable sterol-regulated feedback loop modulates statin sensitivity in prostate cancer.

OBJECTIVE: The statin family of cholesterol-lowering drugs has been shown to induce tumor-specific apoptosis by inhibiting the rate-limiting enzyme of the mevalonate (MVA) pathway, HMG-CoA reductase (HMGCR). Accumulating evidence suggests that statin use may delay prostate cancer (PCa) progression in a subset of patients; however, the determinants of statin drug sensitivity in PCa remain unclear. Our goal was to identify molecular features of statin-sensitive PCa and opportunities to potentiate statin-induced PCa cell death. METHODS: Deregulation of HMGCR expression in PCa was evaluated by immunohistochemistry. The response of PCa cell lines to fluvastatin-mediated HMGCR inhibition was assessed using cell viability and apoptosis assays. Activation of the sterol-regulated feedback loop of the MVA pathway, which was hypothesized to modulate statin sensitivity in PCa, was also evaluated. Inhibition of this statin-induced feedback loop was performed using RNA interference or small molecule inhibitors. The achievable levels of fluvastatin in mouse prostate tissue were measured using liquid chromatography-mass spectrometry. RESULTS: High HMGCR expression in PCa was associated with poor prognosis; however, not all PCa cell lines underwent apoptosis in response to treatment with physiologically-achievable concentrations of fluvastatin. Rather, most cell lines initiated a feedback response mediated by sterol regulatory element-binding protein 2 (SREBP2), which led to the further upregulation of HMGCR and other lipid metabolism genes. Overcoming this feedback mechanism by knocking down or inhibiting SREBP2 potentiated fluvastatin-induced PCa cell death. Notably, we demonstrated that this feedback loop is pharmacologically-actionable, as the drug dipyridamole can be used to block fluvastatin-induced SREBP activation and augment apoptosis in statin-insensitive PCa cells. CONCLUSION: Our study implicates statin-induced SREBP2 activation as a PCa vulnerability that can be exploited for therapeutic purposes using clinically-approved agents.

Statin-Induced Cancer Cell Death Can Be Mechanistically Uncoupled from Prenylation of RAS Family Proteins.

The statin family of drugs preferentially triggers tumor cell apoptosis by depleting mevalonate pathway metabolites farnesyl pyrophosphate (FPP) and geranylgeranyl pyrophosphate (GGPP), which are used for protein prenylation, including the oncoproteins of the RAS superfamily. However, accumulating data indicate that activation of the RAS superfamily are poor biomarkers of statin sensitivity, and the mechanism of statin-induced tumor-specific apoptosis remains unclear. Here we demonstrate that cancer cell death triggered by statins can be uncoupled from prenylation of the RAS superfamily of oncoproteins. Ectopic expression of different members of the RAS superfamily did not uniformly sensitize cells to fluvastatin, indicating that increased cellular demand for protein prenylation cannot explain increased statin sensitivity. Although ectopic expression of HRAS increased statin sensitivity, expression of myristoylated HRAS did not rescue this effect. HRAS-induced epithelial-to-mesenchymal transition (EMT) through activation of zinc finger E-box binding homeobox 1 (ZEB1) sensitized tumor cells to the antiproliferative activity of statins, and induction of EMT by ZEB1 was sufficient to phenocopy the increase in fluvastatin sensitivity; knocking out ZEB1 reversed this effect. Publicly available gene expression and statin sensitivity data indicated that enrichment of EMT features was associated with increased sensitivity to statins in a large panel of cancer cell lines across multiple cancer types. These results indicate that the anticancer effect of statins is independent from prenylation of RAS family proteins and is associated with a cancer cell EMT phenotype.Significance: The use of statins to target cancer cell EMT may be useful as a therapy to block cancer progression. Cancer Res; 78(5); 1347-57. ©2017 AACR.

The interplay between cell signalling and the mevalonate pathway in cancer.

The mevalonate (MVA) pathway is an essential metabolic pathway that uses acetyl-CoA to produce sterols and isoprenoids that are integral to tumour growth and progression. In recent years, many oncogenic signalling pathways have been shown to increase the activity and/or the expression of MVA pathway enzymes. This Review summarizes recent advances and discusses unique opportunities for immediately targeting this metabolic vulnerability in cancer with agents that have been approved for other therapeutic uses, such as the statin family of drugs, to improve outcomes for cancer patients.

Genome-wide RNAi analysis reveals that simultaneous inhibition of specific mevalonate pathway genes potentiates tumor cell death.

The mevalonate (MVA) pathway is often dysregulated or overexpressed in many cancers suggesting tumor dependency on this classic metabolic pathway. Statins, which target the rate-limiting enzyme of this pathway, 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), are promising agents currently being evaluated in clinical trials for anti-cancer efficacy. To uncover novel targets that potentiate statin-induced apoptosis when knocked down, we carried out a pooled genome-wide short hairpin RNA (shRNA) screen. Genes of the MVA pathway were amongst the top-scoring targets, including sterol regulatory element binding transcription factor 2 (SREBP2), 3-hydroxy-3-methylglutaryl-coenzyme A synthase 1 (HMGCS1) and geranylgeranyl diphosphate synthase 1 (GGPS1). Each gene was independently validated and shown to significantly sensitize A549 cells to statin-induced apoptosis when knocked down. SREBP2 knockdown in lung and breast cancer cells completely abrogated the fluvastatin-induced upregulation of sterol-responsive genes HMGCR and HMGCS1. Knockdown of SREBP2 alone did not affect three-dimensional growth of lung and breast cancer cells, yet in combination with fluvastatin cell growth was disrupted. Taken together, these results show that directly targeting multiple levels of the MVA pathway, including blocking the sterol-feedback loop initiated by statin treatment, is an effective and targetable anti-tumor strategy.

Immediate utility of two approved agents to target both the metabolic mevalonate pathway and its restorative feedback loop.

New therapies are urgently needed for hematologic malignancies, especially in patients with relapsed acute myelogenous leukemia (AML) and multiple myeloma. We and others have previously shown that FDA-approved statins, which are used to control hypercholesterolemia and target the mevalonate pathway (MVA), can trigger tumor-selective apoptosis. Our goal was to identify other FDA-approved drugs that synergize with statins to further enhance the anticancer activity of statins in vivo. Using a screen composed of other FDA approved drugs, we identified dipyridamole, used for the prevention of cerebral ischemia, as a potentiator of statin anticancer activity. The statin-dipyridamole combination was synergistic and induced apoptosis in multiple myeloma and AML cell lines and primary patient samples, whereas normal peripheral blood mononuclear cells were not affected. This novel combination also decreased tumor growth in vivo. Statins block HMG-CoA reductase (HMGCR), the rate-limiting enzyme of the MVA pathway. Dipyridamole blunted the feedback response, which upregulates HMGCR and HMG-CoA synthase 1 (HMGCS1) following statin treatment. We further show that dipyridamole inhibited the cleavage of the transcription factor required for this feedback regulation, sterol regulatory element-binding transcription factor 2 (SREBF2, SREBP2). Simultaneously targeting the MVA pathway and its restorative feedback loop is preclinically effective against hematologic malignancies. This work provides strong evidence for the immediate evaluation of this novel combination of FDA-approved drugs in clinical trials.