- Research article
- Open Access
Global transcriptional response of Saccharomyces cerevisiae to the deletion of SDH3
© Cimini et al; licensee BioMed Central Ltd. 2009
- Received: 10 September 2008
- Accepted: 06 February 2009
- Published: 06 February 2009
Mitochondrial respiration is an important and widely conserved cellular function in eukaryotic cells. The succinate dehydrogenase complex (Sdhp) plays an important role in respiration as it connects the mitochondrial respiratory chain to the tricarboxylic acid (TCA) cycle where it catalyzes the oxidation of succinate to fumarate. Cellular response to the Sdhp dysfunction (i.e. impaired respiration) thus has important implications not only for biotechnological applications but also for understanding cellular physiology underlying metabolic diseases such as diabetes. We therefore explored the physiological and transcriptional response of Saccharomyces cerevisiae to the deletion of SDH3, that codes for an essential subunit of the Sdhp.
Although the Sdhp has no direct role in transcriptional regulation and the flux through the corresponding reaction under the studied conditions is very low, deletion of SDH3 resulted in significant changes in the expression of several genes involved in various cellular processes ranging from metabolism to the cell-cycle. By using various bioinformatics tools we explored the organization of these transcriptional changes in the metabolic and other cellular functional interaction networks.
Our results show that the transcriptional regulatory response resulting from the impaired respiratory function is linked to several different parts of the metabolism, including fatty acid and sterol metabolism.
- Specific Growth Rate
- Autonomously Replicate Sequence
- Succinate Dehydrogenase Complex
Mitochondrial respiration plays a central role in energy metabolism in eukaryotic cells. It is involved in generating energy, primarily in the form of ATP, upon oxidation of different carbon sources such as ethanol, pyruvate and diverse organic acids. This functionality is conserved across all eukaryotic cells. Consequently, metabolic and regulatory mechanisms governing the mitochondrial energy generation have many implications for the functioning of the cell as a whole. A primary component of mitochondrial metabolism is the TCA cycle which apart from producing NADH needed in the oxidative phosphorylation also supplies precursors for biomass synthesis, e.g. 2-oxoglutarate and oxaloacetate. The coupling between metabolism and oxidative phosphorylation is also reflected in the tight transcriptional regulation of the TCA cycle, for example, as observed in Saccharomyces cerevisiae during the diauxic shift between fermentative and oxidative metabolism. The TCA cycle is also known to be transcriptionally regulated in response to oxygen and carbon substrate concentrations. The succinate dehydrogenase complex (Sdhp) serves as a link between the TCA cycle and electron transport chain. Specifically, FADH2 produced during oxidation of succinate to fumarate acts as an electron donor for ubiquinone. Thus, the flux through the succinate dehydrogenase reaction is directly coupled with the respiratory capacity of the cell. The respiratory chain itself is believed to be transcriptionally regulated by oxygen and heme concentrations, and several other not well characterized mechanisms, e.g. in response to osmotic stress .
Defects in the respiratory chain and related energy metabolism have been shown to be key factors in inducing several human diseases, including diabetes, obesity and certain types of cancers [2–6]. Consequently, respiratory metabolism has been a major focus for studying varying diseases, and there is much interest in using appropriate eukaryotic model systems that can be used for functional studies. The yeast S. cerevisiae has been widely used as a model eukaryotic organism as many cellular processes are conserved to humans, and comparatively large amounts of genomic, metabolomic, and proteomic data is readily available. Furthermore, S. cerevisiae is industrially used for producing several commodity and high added-value compounds, such as bio-ethanol and insulin. Consequently, there is much interest in developing rational design strategies for metabolically engineering yeast to improve the production of desired compounds. Previous studies have suggested the TCA cycle and energy metabolism as primary targets for certain metabolic engineering problems [7, 8].
The Sdhp is a tetramer consisting of two soluble subunits responsible for the dehydrogenase activity, and two hydrophobic subunits that anchor the catalytic subunits to the mitochondrial inner membrane. The SDH3 gene in S. cerevisiae codes for the cytochrome b component of the complex, and as shown previously , disruption of the gene leads to a severe growth defect on non-fermentable carbon sources demonstrating its major role in the complex assembly and function. In light of the importance of Sdh3 for improved understanding of human diseases and metabolic engineering applications, we characterized the physiology of sdh3 Δ mutant and performed genome wide transcription analysis. The cultivation type was chosen to be batch fermentation with glucose as the sole carbon source so as to have glucose-repressed conditions.
SDH3 knockout strain construction
The reference S. cerevisiae strain CENPK 113–5D (Mat a MAL2–8C SUC2 URA3–52) was used for the construction of the sdh3 Δ knockout strain through the cloning-free PCR-based allele replacement method previously described . The upstream SDH3 fragment was amplified by PCR from genomic DNA using the primers SDH3_Up_Fw (sequence 5'-CCGAAATATGGTAAGAGAAAATG-3') and SDH3_Up_Rv (sequence 5'-GCAGGGATGCGGCCGCTGACGACATCGTTTATTATTCTTAGAGC-3'). Similarly, the downstream SDH3 fragment was amplified using the primers SDH3_Dw_Fw (sequence 5'CCGCTGCTAGGCGCGCCGTGCTTTATGATTCTTTAAGGCGACGC-3') and SDH3_Dw_Rv (sequence 5'-GTAATCTGTTATCGATAATCTGCC-3'). Lithium acetate transformation was employed . As described previously , URA3 from Kluyveromyces lactis was used as the selection marker in the transformation process. With this approach, transformants can easily be selected on media lacking uracil, and direct-repeat recombinants can be identified by counter-selecting on media containing 5-Fluoroorotic Acid. The knockout was confirmed by restriction analysis and sequencing (MWG Biotech AG). Yeast extract-peptone-dextrose (YPD) medium, synthetic complete (SC) medium, and SC lacking specific amino acids were prepared as described previously .
All cultures were grown in minimal media prepared according to Verduyn et al.  supplemented with 150 mg/liter of uracil to complement auxotrophy for the deletion mutant strain. Before each experiment, cells from 15% glycerol stock preparations were plated on YPD medium (10 g/liter yeast extract, 20 g/liter of peptone, 20 g/liter glucose) and incubated at 30°C for 24–48 h. A single colony was then used to inoculate pre-cultures.
Batch experiments for physiological characterization of the strains were carried out in a Braun Biostat-B reactor with a working volume of 1.5 liter and 10 g/liter initial glucose concentration. A constant pH of 5 was maintained via automated addition of 2 M NaOH. Aerobic conditions were established by supplying air at the rate of 1.5 liter/min whereas sparging with pure N2 established anaerobic conditions. The concentration of CO2 and O2 in the exhaust gas was determined by the use of a Brüel & Kjær acoustic gas analyzer (Brüel & Kjær, Denmark). For anaerobic cultivation the medium was supplemented with ethanol-dissolved ergosterol and Tween 80 (Sigma-Aldrich) to a final concentration of 0.01 g/liter and 0.42 g/liter, respectively.
Five liter in-house-manufactured bioreactors with 4 liter working volume were used to carry out aerobic batch cultivations for microarray experiments. The initial glucose concentration was increased to 40 g/liter and therefore the concentration of all medium components was accordingly adjusted. The cultivations were carried out in at least triplicate.
C-limited chemostat experiments were also performed in Braun Biostat B with the same process variables as described above for the aerobic batch, at a dilution rate of 0.027 h-1 for the sdh3 Δ strain and 0.05 h-1 for the wild type strain (WT). Steady state samples for extracellular metabolite analysis were collected after 3 volume changes.
Shake flask experiments were also performed to analyse the effects of 3 specific inhibitors of the respiratory chain. The SDH3 mutant and the reference strain were grown in minimal media and treated with antimycin (1 μ g/ml), oligomycin (3 μ g/ml) or carbonyl cyanide m-chlorophenylhydrazone (CCCP) (4.1 μ g/ml) (Sigma-Aldrich) during the early exponential phase.
During the course of all cultivations 5 ml samples were withdrawn from the bioreactor at regular time intervals for the determination of dry cell weight and extracellular metabolites. Glucose, ethanol, pyruvate, succinate, acetate, fumarate, citrate, oxaloacetate and malate were quantified by HPLC using a Biorad HPX-87H column.
RNA isolation and microarray analysis
Duplicate samples for microarray analysis were collected during the aerobic batch experiments with the sdh3 Δ strain when the glucose concentration had reached 20 ± 2 g/liter. Culture aliquots of 20 ml were transferred to falcon tubes already filled with 25 ml of crushed ice and immediately centrifuged at 4000 rpm for 5 minutes. The pellets were rapidly frozen in liquid nitrogen and stored at -80°C until further use. Total RNA was extracted by using the Fast Pro Red Kit (Obiogene, USA) according to the manufacture's instructions with minor modifications. cRNA was synthesized as described in the Affymetrix GeneChip Expression Analysis Manual, after which 15 μ g were hybridized to Yeast Genome S98 oligonucleotide arrays (Affymetrix). Microarrays were scanned in Agilent Gene Scanner (Affymetrix), following which raw data processing was performed with the Microarray Suite software, version 5, with a global scaling factor for target intensity equal to 500. Average difference values, representing the absolute hybridization intensities were then calculated for each probe set. The microarray data was first normalized by using dChip software suit (dChip, version 1.3, Wong Lab, Harvard School of Public Health and Dana-Farber Cancer Institute, Boston MA, USA). Expression levels of all 9335 probes sets were calculated with the Perfect Match model using dChip v1.3. From the 9335 probe sets in the array, the expression level of 6079 annotated unique Open Reading Frames (ORFs) from Saccharomyces Genome Database were extracted. Significance of expression change was calculated in terms of p-values for all the genes by using Student's t-test.
Microarray data were deposited in the Gene Expression Omnibus (GEO) database http://www.ncbi.nlm.nih.gov/geo/ under accession number GSE13924.
In order to identify key proteins involved in the cellular response to SDH3 deletion, we used a systems approach where bio-molecular interaction networks are used as data integration scaffolds [14, 15]. Three different interaction networks were used in this study, viz. metabolic network , regulators/Transcription Factors (TFs)-'regulated genes' network , and 'Gene Ontology' (GO)-'associated genes network' . All three networks can be conceptually seen as a graph where the feature nodes (metabolites, GOs or TFs) are connected with the corresponding functionally linked gene nodes. The p-values for the individual genes were then mapped on each of these networks and the features with significant transcriptional response in their neighbour genes were identified as reporter features, i.e., reporter metabolites, reporter GOs and reporter TFs. The scoring method used for estimating the significance score for the features is summarized in the following three equations :
z gene i = cdf-1 (1- p gene i )
First, p-value for each gene i in the network, p genei , was converted into a z-score by using the inverse normal cumulative distribution function (cdf-1). The resulting z-scores approximately follow a standard normal distribution. The score for each feature j (z featurej ) was then calculated as the average of the z-scores of its neighbour nodes (equation 2). To assess the significance of each z featurej , it was corrected for the background distribution of z scores, by subtracting the mean (μ N ) and then dividing by the standard deviation (σ N ) of 10000 random aggregates of size N selected from the same data; N being the number of neighbours for the feature under consideration. The resulting reporter features can be seen as nodes in the bio-molecular interaction networks around which there are substantial transcriptional changes occurring. These nodes are a metabolite interacting with different enzymes, a transcription factor regulating the transcription of several genes or a common annotation for a group of genes, e.g. GO annotation.
The reporter scores calculated in this study do not require a priori selection of a p-value cut-off at the individual gene level. It may be also of interest to observe the enrichment in the number of significantly changed genes around a particular feature, whereas the cut-off level at the individual gene level is chosen a priori. The biological meaning of such scoring method is discussed by Oliveira et al . The software for calculating such enrichment scores is freely available at: http://www.cmb.dtu.dk/reporters.
A bioinformatics analysis was performed by using Regulatory Sequence Analysis web-tools http://rsat.scmbb.ulb.ac.be/rsat/ in order to identify potential regulatory elements in the promoter region (800 bp region upstream of each start codon) of the genes that showed a significant change in expression following the SDH3 deletion.
In order to study the physiological consequences of SDH3 deletion, a comparison between the reference and mutant strain was performed under well-controlled aerobic and anaerobic batch fermentations in minimal medium with glucose as the sole carbon source. The sdh3 Δ mutant showed a lower maximum specific growth rate (0.25 h-1) in aerobic growth conditions as compared to the reference strain (0.31 h-1). Furthermore, the mutant was incapable of utilizing ethanol as a sole carbon source, as also been reported previously . A similar phenotypic difference was observed in anaerobic batch cultivations, where the maximum specific growth rates were 0.27 h-1 and 0.34 h-1 for the mutant and the reference strain, respectively. Since, under both aerobic and anaerobic conditions the cells are mainly having fermentative metabolism, the lower specific growth rate observed in both conditions are likely to be due to the same mechanisms.
Physiological characterization of the reference and sdh3 Δ strains in aerobic and anaerobic batch cultivations
0.25 ± 0.03
0.083 ± 0.003
0.0027 ± 0.0001
0.35 ± 0.002
0.0062 ± 5.7·10-5
0.108 ± 0.004
0.0061 ± 0.0003
0.31 ± 0.04
0.10 ± 0.01
0.34 ± 0.01
0.0042 ± 0.0008
0.073 ± 0.010
Inhibition of respiration
Shake flask experiments were performed on minimal media to study the effects of three different oxidative phosphorylation inhibitors (antimycin, oligomycin and CCCP) on the SDH3 mutant. Antimycin inhibits the electron transfer and thereby prevents the oxidation of both NADH and succinate. Oligomycin specifically binds to the F0 subunit of the ATP-synthase complex inhibiting ATP synthesis and ATP-synthesis associated respiration. The last inhibitor used, CCCP, is an uncoupling agent that allows free movement of protons across the mitochondrial inner membrane causing a collapse in the H+ gradient that is required for ATP synthesis by the F0F1-ATPase. The addition of oligomycin and antimycin had no effect on the specific growth rate and biomass production in the sdh3 Δ strain while they considerably reduced the final biomass yield in the reference strain and impaired growth on ethanol. The major inhibitory effects in both strains were observed after treatment with CCCP which resulted in reduced specific growth rate and biomass yield in the reference strain, while it completely inhibited growth of sdh3 Δ.
Global overview of transcriptional response to SDH3 deletion
Affected genes sorted by MIPS categories
MIPS functional category
Number of genes
MIPS functional category
Number of genes
124 (26, 98)
15 (4, 11)
41 (7, 34)
15 (4, 11)
41 (11, 30)
15 (7, 8)
27 (4, 23)
14 (4, 10)
C-compound and carbohydrate metabolism
25 (10, 15)
Protein targeting, sorting and translocation
13 (1, 12)
25 (3, 22)
12 (3, 9)
Fungal/microrganismic cell type differentiation
23 (2, 21)
12 (1, 11)
Cellular sensing and response
20 (1, 19)
Lipid, fatty acid and isoprenoid metabolism
11 (4, 7)
Transported compounds (substrates)
17 (8, 9)
11 (3, 8)
16 (7, 9)
9 (4, 5)
9 (4, 5)
9 (3, 6)
Reporter-Gene Ontology terms for the sdh3 Δ mutant
Number of associated genes
late endosome to vacuole transport (P)
processing of 20S pre-rRNA (P)
small nucleolar ribonucleoprotein complex (C)
35S primary transcript processing (P)
rRNA processing (P)
spliceosome complex (C)
ubiquitin-dependent protein catabolism (P)
transcription regulator activity (F)
barrier septum formation (P)
asymmetric protein localization (P)
regulation of meiosis (P)
rRNA modification (P)
diaminohydroxyphosphoribosylaminopyrimidine deaminase activity (F)
DNA replication initiation (P)
septin ring (C)
nuclear mRNA splicing, via spliceosome (P)
lagging strand elongation (P)
glucosamine 6-phosphate N-acetyltransferase activity (F)
Around 20 genes sharing the GO term "transcriptional regulator activity" also scored high. These genes may provide insight into the regulatory pathways that might be responsible for observed transcriptional response in this variety of processes. This set involves not only global transcriptional regulators such as Ume6p, but also specific regulators, especially related to cell cycle and DNA repair.
Reporter TFs analysis
Reporter-Transcription Factors for the sdh3 Δ mutant
Number of associated genes
Expression fold change
We searched for conserved sequence motifs in the upstream region of the genes with significant transcriptional response. With all the 434 genes queried together, no significantly over represented motif was found. However, when up and down regulated genes were queried separately we identified significantly over-represented sequence motifs. About 40% of up-regulated genes contain a 6-mer "TCGTCA" in their promoter region, while this motif is characteristic for only 20% of the genes in the whole genome. 29% of the significantly down-regulated genes also contain this motif in their promoter region. The list of all the significant genes with this motif is provided as supplementary information (additional file 3). The sequence TCGTCA corresponds to the known DNA binding site for the Autonomously Replicating Sequence (ARS) binding transcription factor Abf1p, a multifunctional global regulator . Binding motifs for certain other transcriptional regulators such as Hsf1p (Heat shock transcription factor) and Ino4p (Inositol requiring) were also identified in the upstream regions of significantly down-regulated genes. Expression levels of ABF1 and INO4 were altered significantly in the SDH3 deletion mutant.
Reporter Metabolite and subnetwork analysis
List of the top scoring reporter metabolites
Number of neighbours
Transcriptional readjustments in respiratory pathways
Since Sdhp provides reducing power to the respiratory chain, we expected that the SDH3 deletion would also lead to changes in genes involved in the respiratory pathways. Indeed, we observed that several genes involved in the oxidative phosphorylation, including, cytochrome c oxidase subunits/assembly (COX 5B/6/7/8/10/12/13/14/15/18/20), heme biosynthesis (HEM3/14), mitochondrial F1F0 ATP-synthase (ATP18), ubiquinone biosynthesis (COQ3/5, CBP4), and bc1p complex (CYT1, RIP1, ABC1, COR1, QCR6/7) were affected following the deletion. The majority of these genes were down regulated except for COX14/15 and COQ3, possibly due to different regulatory mechanisms governing their expression.
Results from the physiological characterization indicate that the SDH3 deletion mutant shows a growth defect under both aerobic and anaerobic conditions. This observation was partly expected for aerobic conditions and could be attributed to a major reduction in the respiratory capacity of the mutant strain, which is also supported by the carbon limited chemostat experiments. However, as the flux through the TCA cycle of the reference strain at the studied growth conditions is very low  due to glucose repression of respiration, it clearly points out that Sdh3 plays an additional role than being involved in respiration. This hypothesis is confirmed by our finding that there is a similar reduction in specific growth rate under anaerobic conditions. This additional role of Sdh3 may be different at the two different growth conditions. Thus, at anaerobic growth conditions it is likely that Sdh3 simply plays a role in redox balancing, whereas at aerobic growth conditions where there is predominantly fermentative metabolism, it may still be important to have functional respiration in order to prevent oxidative damage in the cells. Treatment with respiratory inhibitors antimycin and oligomycin had no effect on growth and final biomass yield of the sdh3 Δ mutant while it severely affected biomass production of the reference strain. These additional experiments with mitochondrial inhibitors demonstrated that SDH3 deletion is sufficient to impair the electron transport system preventing the oxidation of NADH and succinate. However, the general down regulation of respiratory genes clearly implies that the cells are not attempting to restore their respiratory capacity. This is in line with the results obtained for respiratory deficient petite cells grown on non-repressive raffinose .
Off gas profiles during the batch experiments showed that the oxygen uptake rate in sdh3 Δ is ~80% lower compared to the reference strain. Since oxygen is not used as final electron acceptor in the respiratory chain, as inferred from the experiments with mitochondrial inhibitors, the oxygen consumed is used in other cellular pathways. In yeast species non respiratory oxygen consumption is favoured when respiratory functions are impaired or absent, however, little is known about the non-respiratory oxygen uptake of aerobically growing cells.
During anaerobiosis, S. cerevisiae strongly increases glycerol production to provide for non-respiratory oxidation of NADH to NAD+, and consistent with the hypothesis of an altered redox metabolism originating from the deletion of SDH3 and consequent impairment of oxidative phosphorylation, a higher glycerol production was observed in aerobic cultivations of the mutant strain (similar yield as obtained in anaerobic cultivation). Thus, the deletion strain seems to readjust its metabolic fluxes mimicking growth under anaerobic conditions.
In an attempt to uncover the molecular mechanisms behind the observed global transcriptional response, we searched for significantly over-represented sequence motifs in the promoter regions of genes with significantly changed expression in the SDH3 mutant. This analysis revealed a binding site for Abf1p in 40% of the significantly up-regulated genes. Several different chromatin-related events such as DNA replication , gene silencing , chromatin remodeling , nucleotide excision repair , and gene activation and repression  involve direct binding of Abf1p. The genes that are reported to be transcriptionally regulated by Abf1p are involved in a multitude of cellular processes including carbon source regulation [32, 33] nitrogen utilization [34, 35] sporulation , meiosis , and ribosomal biogenesis [30, 38]. Since these processes are also significantly affected in the SDH3 mutant, Abf1p appears to be one of the major mediators of the transcriptional response following the SDH3 disruption and consequent respiratory impairment. This is further supported by the fact that ABF1 transcription is significantly down regulated by more than four-fold in the SDH3 mutant, whereas under usual conditions ABF1 is found to be present in abundance [28, 39]. Down regulation of ABF1 under anaerobic conditions also supports its role as a mediator of transcriptional changes in response to the absence of respiration. The binding motif recognized by Ino4p was also identified in the promoter analysis. Ino4p is a known regulator of genes involved in phospholipid synthesis [40, 41], some of which also showed altered expression in the SDH3 mutant (ACC1, CHO1, EKI1 and DPP1).
The reporter metabolites indicate specific parts of metabolism where significant transcriptional regulation is exerted, either to maintain homeostasis and/or to adjust the metabolite levels to altered demands. Two of the top scoring reporter metabolites suggested an interesting connection between mitochondrial impairment and lipid metabolism. Zymosterol is the immediate precursor of ergosterol, the major yeast sterol, which has a functional similarity to cholesterol in higher eukaryotic membranes, and is only synthesised in yeast under aerobic conditions. Our data show up-regulation of AUS1, an important sterol uptake gene normally expressed under anaerobic conditions, and down regulation of several key genes in the ergosterol biosynthesis pathway (ERG11, ERG25, ERG27, ERG5, ERG9) due to the deletion of SDH3. This information indicates the presence of a link that connects mitochondrial respiration and/or TCA cycle activity to the sterol biosynthetic pathways. We postulate that one of the mediators of this functional link is Hap1p which is known to positively regulate the AUS1 expression that is up-regulated in the SDH3 deletion mutant. Since the effect of mitochondrial impairment on lipid metabolism has been a focus of several studies related to metabolic diseases such as diabetes, our results may help in identifying similar regulatory mechanisms in human cells.
Ethanolamine is a precursor for the synthesis of phosphatydilethanolamine (PE), one of the major phospholipids components in cellular membranes of S. cerevisiae, and its role is crucial for growth when mitochondrial function is required . In humans impairment or alterations in the phospholipids metabolism are suggested to represent important aspects of certain diseases . Expression of several genes involved in PE biosynthesis such as EKI1 (ethanolamine kinase/choline kinase), EPT1 ( dyacil-glycerol-ethanolamine-phosphotrasferase), and CHO1 (phosphatidylserine synthase) is known to be transcriptionally regulated by Ino4p, and they are all down-regulated in the sdh3 Δ strain. As found in the promoter analysis, the binding motif of Ino4p was significantly over-represented in the promoter region of some of the significantly down-regulated genes, making it another potential mediator of the signal between respiratory chain and lipid metabolism.
Overall, we present a genome-wide transcription analysis of SDH3 deleted S. cerevisiae mutant grown under glucose-repressed conditions. By using an integrative systems biology approach we reconstructed a qualitative regulatory map that links mitochondrial respiration with fatty acid and sterol metabolism. Several bioinformatics and systems biology algorithms were employed to elucidate the transcriptional hot-spots in different cellular networks, including metabolic networks and transcriptional regulatory networks that significantly respond to the deletion of SDH3. Several regulatory proteins including the Hap-complex, Hap1, Ino4, Abf1 and Sln1 appear to play an important role in mediating the cellular response following respiratory defect at the succinate dehydrogenase node.
We thank Gaëlle Lettier and Uffe Mortensen for the help in strain construction. We also thank Manny Otero for fruitful comments. Gerald Hofmann is acknowledged for helpful discussions.
- Krantz M, Hohmann S: Investigation of the link between the osmotic stress response and control of energy metabolism in baker's yeast, Saccharomyces cerevisiae. Yeast. 2003, 20: S170-Google Scholar
- Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, et al.: PGC-1 alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nature Genetics. 2003, 34: 267-273.View ArticlePubMedGoogle Scholar
- Baysal BE, Ferrell RE, Willett-Brozick JE, Lawrence EC, Myssiorek D, Bosch A, et al.: Mutations in SDHD, a mitochondrial complex II gene, in hereditary paraganglioma. Science. 2000, 287: 848-851.View ArticlePubMedGoogle Scholar
- Rotig A, Cormier V, Chatelain P, Francois R, Saudubray JM, Rustin P, et al.: Deletion of Mitochondrial-Dna in A Case of Early-Onset Diabetes-Mellitus, Optic Atrophy, and Deafness (Wolfram Syndrome, Mim 222300). Journal of Clinical Investigation. 1993, 91: 1095-1098.PubMed CentralView ArticlePubMedGoogle Scholar
- Boudina S, Sena S, O'Neill BT, Tathireddy P, Young ME, Abel ED: Reduced mitochondrial oxidative capacity and increased mitochondrial uncoupling impair myocardial energetics in obesity. Circulation. 2005, 112: 2686-2695.View ArticlePubMedGoogle Scholar
- Leone TC, Lehman JJ, Finck BN, Schaeffer PJ, Wende AR, Boudina S, et al.: PGC-1 alpha deficiency causes multi-system energy metabolic derangements: Muscle dysfunction, abnormal weight control and hepatic steatosis. PLos Biol. 2005, 3 (4): e101-PubMed CentralView ArticlePubMedGoogle Scholar
- Arikawa Y, Kuroyanagi T, Shimosaka M, Muratsubaki H, Enomoto K, Kodaira R, et al.: Effect of gene disruptions of the TCA cycle on production of succinic acid in Saccharomyces cerevisiae. J Biosci Bioeng. 1999, 87 (1): 28-36.View ArticlePubMedGoogle Scholar
- Patil KR, Rocha I, Forster J, Nielsen J: Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics. 2005, 6: 308-PubMed CentralView ArticlePubMedGoogle Scholar
- Daignanfornier B, Valens M, Lemire BD, Bolotinfukuhara M: Structure and Regulation of Sdh3, the Yeast Gene Encoding the Cytochrome-B(560) Subunit of Respiratory Complex-II. J Biol Chem. 1994, 269: 15469-15472.Google Scholar
- Erdeniz N, Mortensen UH, Rothstein R: Cloning-free PCR-based allele replacement methods. Genome Res. 1997, 7: 1174-1183.PubMed CentralPubMedGoogle Scholar
- Gietz RD, Woods RA: Transformation of yeast by lithium acetate/single-stranded carrier DNA/polyethylene glycol method. Methods Enzymol. 2002, 350: 87-96.View ArticlePubMedGoogle Scholar
- Sherman F: Getting started with yeast. Methods Enzymol. 1991, 194: 3-21.View ArticlePubMedGoogle Scholar
- Verduyn C, Postma E, Scheffers WA, Vandijken JP: Physiology of Saccharomyces cerevisiae in Anaerobic Glucose-Limited Chemostat Cultures. Journal of General Microbiology. 1990, 136: 395-403.View ArticlePubMedGoogle Scholar
- Patil KR, Nielsen J: Uncovering transcriptional regulation of metabolism by using metabolic network topology. PNAS. 2005, 102: 2685-2689.PubMed CentralView ArticlePubMedGoogle Scholar
- Oliveira AP, Patil KR, Nielsen J: Architecture of transcriptional regulatory circuits is knitted over the topology of bio-molecular interaction networks. BMC Systems Biology. 2008, 2: 17-PubMed CentralView ArticlePubMedGoogle Scholar
- Forster J, Famili I, Fu P, Palsson BO, Nielsen J: Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res. 2003, 13: 244-253.PubMed CentralView ArticlePubMedGoogle Scholar
- van Helden J, Gilbert D, Wernisch L, Schroeder M, Wodak S: Application of Regulatory Sequence Analysis and Metabolic Network Analysis to the Interpretation of Gene Expression Data. Computational Biology. 2001, 147-163. Berlin/Heidelberg: SpringerView ArticleGoogle Scholar
- Westergaard SL, Bro C, Olsson L, Nielsen J: Elucidation of the role of Grr1p in glucose sensing by Saccharomyces cerevisiae through genome-wide transcription analysis. FEMS Yeast Res. 2004, 5: 193-204.View ArticlePubMedGoogle Scholar
- Storey JD, Tibshirani R: Statistical significance for genomewide studies. PNAS. 2003, 100: 9440-9445.PubMed CentralView ArticlePubMedGoogle Scholar
- Breitling R, Armengaud P, Amtmann A, Herzyk P: Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Letters. 2004, 573: 83-92.View ArticlePubMedGoogle Scholar
- Regenberg B, Grotkjaer T, Winther O, Fausboll A, Akesson M, Bro C, et al.: Growth-rate regulated genes have profound impact on interpretation of transcriptome profiling in Saccharomyces cerevisiae. Genome Biology. 2006, 7:Google Scholar
- Brauer MJ, Huttenhower C, Airoldi EM, Rosenstein R, Matese JC, Gresham D, et al.: Coordination of growth rate, cell cycle, stress response, and metabolic activity in yeast. Mol Biol Cell. 2008, 19: 352-367.PubMed CentralView ArticlePubMedGoogle Scholar
- Chen Z, Odstrcil EA, Tu BP, McKnight SL: Restriction of DNA replication to the reductive phase of the metabolic cycle protects genome integrity. Science. 2007, 316: 1916-1919.View ArticlePubMedGoogle Scholar
- Raghevendran V, Patil KR, Olsson L, Nielsen J: Hap4 Is Not Essential for Activation of Respiration at Low Specific Growth Rates in Saccharomyces cerevisiae. J Biol Chem. 2006, 281: 12308-12314.View ArticlePubMedGoogle Scholar
- Miyake T, Reese J, Loch CM, Auble DT, Li R: Genome-wide analysis of ARS (autonomously replicating sequence) binding factor 1 (Abf1p)-mediated transcriptional regulation in Saccharomyces cerevisiae. J Biol Chem. 2004, 279: 34865-34872.View ArticlePubMedGoogle Scholar
- Gombert AK, dos Santos MM, Christensen B, Nielsen J: Network identification and flux quantification in the central metabolism of Saccharomyces cerevisiae under different conditions of glucose repression. Journal of Bacteriology. 2001, 183: 1441-1451.PubMed CentralView ArticlePubMedGoogle Scholar
- Epstein CB, Waddle JA, Hale W, Dave V, Thornton J, Macatee TL, et al.: Genome-wide responses to mitochondrial dysfunction. Mol Biol Cell. 2001, 12: 297-308.PubMed CentralView ArticlePubMedGoogle Scholar
- Buchman AR, Kimmerly WJ, Rine J, Kornberg RD: 2 Dna-Binding Factors Recognize Specific Sequences at Silencers, Upstream Activating Sequences, Autonomously Replicating Sequences, and Telomeres in Saccharomyces cerevisiae. Mol Cell Biol. 1988, 8: 210-225.PubMed CentralView ArticlePubMedGoogle Scholar
- Miyake T, Loch CM, Li R: Identification of a multifunctional domain in autonomously replicating sequence-binding factor 1 required for transcriptional activation, DNA replication, and gene silencing. Mol Cell Biol. 2002, 22: 505-516.PubMed CentralView ArticlePubMedGoogle Scholar
- Lascaris RF, de Groot E, Hoen PB, Mager WH, Planta RJ: Different roles for Abf1p and a T-rich promoter element in nucleosome organization of the yeast RPS28A gene. Nucl Acids Res. 2000, 28: 1390-1396.PubMed CentralView ArticlePubMedGoogle Scholar
- Yu SR, Owen-Hughes T, Friedberg EC, Waters R, Reed SH: The yeast Rad7/Rad16/Abf1 complex generates superhelical torsion in DNA that is required for nucleotide excision repair. Dna Repair. 2004, 3: 277-287.View ArticlePubMedGoogle Scholar
- Yoo HY, Jung SY, Kim YH, Kim JY, Jung GH, Rho HM: Transcriptional Control of the Saccharomyces-Cerevisiae Adh1 Gene by Autonomously Replicating Sequence Binding-Factor-1. Current Microbiology. 1995, 31: 163-168.View ArticlePubMedGoogle Scholar
- Chambers A, Stanway C, Tsang JSH, Henry Y, Kingsman AJ, Kingsman SM: Ars Binding Factor-I Binds Adjacent to Rap1 at the Uass of the Yeast Glycolytic Genes Pgk and Pyk1. Nucl Acids Res. 1990, 18: 5393-5399.PubMed CentralView ArticlePubMedGoogle Scholar
- Kovari LZ, Cooper TG: Participation of Abf-1 Protein in Expression of the Saccharomyces cerevisiae Car1 Gene. Journal of Bacteriology. 1991, 173: 6332-6338.PubMed CentralPubMedGoogle Scholar
- Park HD, Scott S, Rai R, Dorrington R, Cooper TG: Synergistic operation of the CAR2 (Ornithine transaminase) promoter elements in Saccharomyces cerevisiae. J Bacteriol. 1999, 181: 7052-7064.PubMed CentralPubMedGoogle Scholar
- Ozsarac N, Straffon MJ, Dalton HE, Dawes IW: Regulation of gene expression during meiosis in Saccharomyces cerevisiae: SPR3 is controlled by both ABFI and a new sporulation control element. Mol Cell Biol. 1997, 17: 1152-1159.PubMed CentralView ArticlePubMedGoogle Scholar
- Gailus-Durner V, Xie J, Chintamaneni C, Vershon AK: Participation of the yeast activator Abf1 in meiosis-specific expression of the HOP1 gene. Mol Cell Biol. 1996, 16: 2777-2786.PubMed CentralView ArticlePubMedGoogle Scholar
- Della SF, Ciafre SA, Marck C, Santoro B, Presutti C, Sentenac A, et al.: The ABF1 factor is the transcriptional activator of the L2 ribosomal protein genes in Saccharomyces cerevisiae. Mol Cell Biol. 1990, 10: 2437-2441.View ArticleGoogle Scholar
- Kraakman LS, Griffioen G, Zerp S, Groeneveld P, Thevelein JM, Mager WH, et al.: Growth-related expression of ribosomal protein genes in Saccharomyces cerevisiae. Mol Gen Genet. 1993, 239: 196-204.PubMedGoogle Scholar
- Schwank S, Ebbert R, Rautenstrauss K, Schweizer E, Schuller HJ: Yeast transcriptional activator INO2 interacts as an Ino2p/Ino4p basic helix-loop-helix heteromeric complex with the inositol/choline-responsive element necessary for expression of phospholipid biosynthetic genes in Saccharomyces cerevisiae. Nucleic Acids Res. 1995, 23: 230-237.PubMed CentralView ArticlePubMedGoogle Scholar
- Santiago TC, Mamoun CB: Genome expression analysis in yeast reveals novel transcriptional regulation by inositol and choline and new regulatory functions for Opi1p, Ino2p, and Ino4p. J Biol Chem. 2003, 278: 38723-38730.View ArticlePubMedGoogle Scholar
- Storey MK, Clay KL, Kutateladze T, Murphy RC, Overduin M, Voelker DR: Phosphatidylethanolamine has an essential role in Saccharomyces cerevisiae that is independent of its ability to form hexagonal phase structures. J Biol Chem. 2001, 276: 48539-48548.PubMedGoogle Scholar
- Ellison DW, Beal MF, Martin JB: Phosphoethanolamine and ethanolamine are decreased in Alzheimer's disease and Huntington's disease. Brain Res. 1987, 417: 389-392.View ArticlePubMedGoogle Scholar
- McCammon MT, Epstein CB, Przybyla-Zawislak B, Alister-Henn L, Butow RA: Global Transcription Analysis of Krebs Tricarboxylic Acid Cycle Mutants Reveals an Alternating Pattern of Gene Expression and Effects on Hypoxic and Oxidative Genes. Mol Biol Cell. 2003, 14: 958-972.PubMed CentralView ArticlePubMedGoogle Scholar
- Tai SL, Boer VM, ran-Lapujade P, Walsh MC, de Winde JH, Daran JM, et al.: Two-dimensional transcriptome analysis in chemostat cultures: combinatorial effects of oxygen availability and macronutrient limitation in Saccharomyces cerevisiae. J Biol Chem. 2005, 280: 437-447.View ArticlePubMedGoogle Scholar
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