Transcriptome response to alkane biofuels in Saccharomyces cerevisiae: identification of efflux pumps involved in alkane tolerance
© Ling et al.; licensee BioMed Central Ltd. 2013
Received: 3 January 2013
Accepted: 19 June 2013
Published: 5 July 2013
Hydrocarbon alkanes have been recently considered as important next-generation biofuels because microbial production of alkane biofuels was demonstrated. However, the toxicity of alkanes to microbial hosts can possibly be a bottleneck for high productivity of alkane biofuels. To tackle this toxicity issue, it is essential to understand molecular mechanisms of interactions between alkanes and microbial hosts, and to harness these mechanisms to develop microbial host strains with improved tolerance against alkanes. In this study, we aimed to improve the tolerance of Saccharomyces cerevisiae, a model eukaryotic host of industrial significance, to alkane biofuels by exploiting cellular mechanisms underlying alkane response.
To this end, we first confirmed that nonane (C9), decane (C10), and undecane (C11) were significantly toxic and accumulated in S. cerevisiae. Transcriptome analyses suggested that C9 and C10 induced a range of cellular mechanisms such as efflux pumps, membrane modification, radical detoxification, and energy supply. Since efflux pumps could possibly aid in alkane secretion, thereby reducing the cytotoxicity, we formed the hypothesis that those induced efflux pumps could contribute to alkane export and tolerance. In support of this hypothesis, we demonstrated the roles of the efflux pumps Snq2p and Pdr5p in reducing intracellular levels of C10 and C11, as well as enhancing tolerance levels against C10 and C11. This result provided the evidence that Snq2p and Pdr5p were associated with alkane export and tolerance in S. cerevisiae.
Here, we investigated the cellular mechanisms of S. cerevisiae response to alkane biofuels at a systems level through transcriptome analyses. Based on these mechanisms, we identified efflux pumps involved in alkane export and tolerance in S. cerevisiae. We believe that the results here provide valuable insights into designing microbial engineering strategies to improve cellular tolerance for highly efficient alkane biofuel production.
KeywordsSaccharomyces cerevisiae Alkanes Biofuels Tolerance Transcriptome Efflux pumps
Next-generation biofuels, such as long-chain alcohols, and fatty-acid- and isoprenoid-derived fuels, offer advantages of high energy density, low freezing point, and compatibility with the existing fuel storage and distribution infrastructure [1–4]. Recently, hydrocarbon alkanes, main components of fossil fuels, have been considered as important next-generation biofuels because their microbial production was demonstrated. Schirmer et al. converted intermediates of fatty acid metabolism to alkanes in Escherichia coli by introducing two enzymes involved in an alkane biosynthesis pathway from cyanobacteria . Bernard et al. identified Arabidopsis alkane synthesis enzymatic components and reconstituted plant alkane biosynthesis in yeast .
Despite these successes in microbial alkane production, the toxicity of alkanes to microbial hosts can eventually be a bottleneck for high productivity. To overcome this toxicity issue, it is imperative to develop engineering strategies to improve microbial tolerance against biofuel alkanes, which requires a clear understanding of molecular mechanisms of interaction between microbial hosts and alkanes. Physiologically, hydrocarbon accumulation in cell membrane causes loss of membrane integrity and function, which ultimately leads to cell death . In response, cells protect themselves against the toxicity of hydrocarbons by ordering the lipid bilayer to modify lipopolysaccharide (LPS) and cell wall/S-layer hydrophobicity, as well as activating the excretion by energy-consuming transport systems . Despite the aforementioned studies, which primarily focused on physiological or cytological effects, there is lack of understanding of the molecular mechanisms of interactions between microbes and alkanes especially at a systems level.
Toxicogenomics, which combines genomics and toxicology, is useful for identification of toxicants and their putative mechanisms of action at a systems level. Recently, toxicogenomics has been applied to elucidate mechanisms underlying environment stresses and chemical toxicity to microorganisms [9, 10]. For instance, Chang et al. studied the toxicogenomic response of pathogens to antimicrobials by using microarray-based transcriptome analyses . Carvalho et al. studied transcriptomic response in marine diatom Thalassiosira pseudonana exposed to benzo[a]pyrene . Notably, toxicogenomics has also offered an effective means to study the molecular mechanisms of cell response to organic solvents. For example, based on genome-wide microarray analyses, our group previously identified and reconstituted genetic regulatory networks to improve the tolerance of E. coli against isooctane . In Saccharomyces cerevisiae strain KK-211, genes involved in tolerance to organic solvents were successfully identified based on transcriptome analyses [14, 15]. Note that S. cerevisiae, an established and widely used eukaryotic model for molecular and cellular biology studies, has been used as an experimental model in toxicogenomics [16–18]. In this light, this study aimed to (i) investigate molecular mechanisms underlying cellular response to alkanes at the systems level and (ii) improve cellular tolerance based on these mechanisms in S. cerevisiae, a model eukaryotic host of industrial significance. Briefly, we showed that nonane (C9) and decane (C10) induced cellular mechanisms associated with efflux pumps, membrane modification, radical detoxification, and energy supply. More importantly, based on these cellular mechanisms, we demonstrated that efflux pumps Snq2p and Pdr5p played roles in reducing intracellular levels of C10 and undecane (C11), and enhancing tolerance levels against C10 and C11. Given the aforementioned recent reports on microbial alkane production [5, 19], we envision that the engineering strategy established in this study can readily be extended to develop robust microbial hosts for alkane biofuels production.
Results and discussion
Toxicity and intracellular accumulation of alkanes
Genome-wide gene expression profiles upon exposure to biofuel alkanes
To understand cellular mechanisms underlying S. cerevisiae response to C9-12, transcriptome profiles were generated using whole-genome S. cerevisiae microarrays. Significantly regulated genes (fold changes ≥ 2.0, and p values < 0.05) are summarized in Additional file 3. The data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO Series accession number GSE38653. In addition, quantitative PCR (qPCR) was carried out to validate the microarray data (Additional file 3).
MIPS functional classification of genes differentially regulated by both C9 and C10
fatty acid metabolism
oxidative stress response
tetracyclic and pentacyclic triterpenes
lipid/fatty acid transport
lipid, fatty acid and isoprenoid metabolism
degradation of glutamate
metabolism of amino acid-derived secondary products
C-compound and carbohydrate metabolism
heat shock response
chemical agent resistance
pH stress response
oxidation of fatty acids
biosynthesis of thiamine
degradation of lysine
degradation of glycine
metabolism of thiamine
First of all, notably, four plasma membrane efflux pump genes YOR1, SNQ2, PDR5, and PDR15 were significantly induced by alkane stress. Plasma membrane efflux pumps recognize and extrude a large spectrum of functionally and structurally unrelated drugs such as oligomycin, mycotoxins, and anticancer drugs [25–28]. That is, when cytotoxic compounds cross the plasma membrane to enter cells, a number of efflux pumps help to protect cells from unwanted or damaging xenobiotics. According to Ernst and coworkers , efflux pumps serve as a defense line mediating pleiotropic drug resistance (PDR). Therefore, the result that efflux pump genes were significantly induced here suggested that they might also provide protection to the cells against alkanes.
Other than efflux pump genes, genes involved in stress response for radical detoxification, fatty acid and lipid synthesis, and hexose transport were also induced by alkanes. For example, GRE genes associated with multiple stresses  and heat shock protein gene HSP12 were induced up to 16-fold, and fatty acid synthase genes FAS1 and FAS2 were induced over 4-fold. Acetyl-CoA carboxylase gene ACC1 was up-regulated about 6-fold. Acc1p is a biotin-containing enzyme that participates in fatty acid synthesis by converting acetyl-CoA to malonyl-CoA, which is a rate-limiting step in the fatty acid synthesis pathway . In addition, INO1 gene was induced over 50-fold, which is associated with synthesis of phosphatidylinositol by using glucose-6-P and CDP-DAG as precursors .
Transcription factors with activity changes, predicted by Network Component Analysis (NCA)
Overexpression of efflux pumps leads to improved tolerance to biofuel alkanes
Among the aforementioned mechanisms, we were interested in further identifying roles of plasma membrane efflux pumps in alkane tolerance and export because (i) plasma membrane efflux pumps are the first line of defense against drugs or organics, and (ii) microbial efflux pumps reportedly serve as a direct mechanism to improve biofuel tolerance [4, 34–37] as well as productivity . In fungi, efflux pumps, especially ATP-binding cassette (ABC) efflux pumps (e.g. Pdr5p and Snq2p in S. cerevisiae, Cdr1p and Cdr2p in Candida albicans), which contribute to multidrug efflux resistance, have been most extensively studied [25–28, 38]. In our study, all the four efflux pumps induced by alkanes are ABC transporters, and considered as major pleiotropic drug transporters that comprise a PDR network in yeast . Among them, Snq2p, Pdr5p, and Pdr15p belong to a pleiotropic drug resistance (PDR) protein subfamily, and Yor1p belongs to a multidrug resistance-associated protein (MRP) subfamily. Their protein architecture contains multiple transmembrane domains (TMDs) that bind chemical compound and provide a translocation channel, and nucleotide-binding domains (NBDs) that couple ATP hydrolysis to substrate transport. Given that the induced ABC efflux pumps play roles in reducing intracellular alkane accumulation, tolerance of S. cerevisiae cells against alkanes could be improved. Hence, we formed a hypothesis that the significantly induced efflux pumps might also help cells to reduce the intracellular alkane accumulation and thus, provide protection to cells against alkanes.
However, even though the efflux pump genes (YOR1, SNQ2, PDR5, and PDR15) were all induced by both C9 and C10 according to our microarray data (Additional file 3), there was no improvement in tolerance against C9 by complementation of each of the four efflux pumps. In addition, there was no improvement in tolerance against C10 and C11 by complementation of Yor1p or Pdr15p. This outcome was probably due to variance in substrate binding efficacy of various efflux pumps, and/or the involvement of such mechanisms as stress response and membrane modification, in addition to efflux pumps, in cellular tolerance to C9. Engineering approaches such as site-mutation, evolutionary engineering, and gene shuffling can be employed to improve substrate binding efficacy [10, 43–45]. Other than the indigenous efflux pumps in S. cerevisiae, efflux pumps from other microbes can also be introduced to improve tolerance against biofuel alkanes. In addition, future studies could include use of other cellular mechanisms besides efflux pumps as an effort to improve alkane tolerance.
To our knowledge, this is the first report providing the evidence of the direct linkage between alkane tolerance and indigenous efflux pumps, and of being able to develop alkane-tolerant S. cerevisiae by directly harnessing indigenous efflux pumps. Herein, we first confirmed that C9, C10, and C11 were significantly toxic and accumulated in S. cerevisiae. Transcriptome analyses suggested that C9 and C10 induced a range of cellular mechanisms such as efflux pumps, membrane modification, radical detoxification, and energy supply. Since efflux pumps could possibly aid in alkane export, thereby reducing the cytotoxicity, we formed the hypothesis that those induced efflux pumps could contribute to alkane tolerance. In support of this hypothesis, we demonstrated the roles of the efflux pumps Snq2p and Pdr5p in reducing the intracellular levels of C10 and C11, and enhancing the tolerance levels against C10 and C11. This result provided the evidence that Snq2p and Pdr5p were associated with alkane export and tolerance in S. cerevisiae. We believe that the results here provide valuable insights into designing microbial engineering strategies to improve cellular tolerance for highly efficient alkane biofuel production.
Strains, chemicals and plasmids
S. cerevisiae BY4741 (MATa; his3 ∆1; leu2∆0; met15∆0; ura3∆0) was obtained from ATCC (American Type Culture Collection), and gene deletion mutants were constructed in this study (Additional file 3). Yeast extract and peptone were obtained from BD (NJ, USA). Alkanes and other chemicals were obtained from Sigma-Aldrich unless specifically mentioned. pUG6 and pSH47 were used for gene deletion and marker rescue in yeast cells. Plasmid pYES2 (Invitrogen, Grand Island, NY, USA) was used for gene expression in yeast cells. E. coli Top10 was used for gene cloning.
S. cerevisiae strains were grown in YPD medium (Yeast extract 10 g/l, Peptone 20 g/l, and D-Glucose 20 g/l), minimum medium (Yeast Nitrogen Base 6.7 g/l, D-Glucose 20 g/l, L-Leucine 0.2 g/l, Uracil 0.1 g/l, 2% n-alkanes; Yeast Nitrogen Base 6.7 g/l, D-Glucose 20 g/l, Yeast Synthetic Drop-out Medium Supplements-Ura- 1.92 g/l), or induction medium (Yeast Nitrogen Base 6.7 g/l, Raffinose 10 g/l, Galactose 20 g/l, and Yeast Synthetic Drop-out Medium Supplements-Ura- 1.92 g/l) at appropriate temperatures. E. coli cells were grown in LB broth at 37°C. Geneticin G418 (PAA Laboratories GmbH, AT) or Ampicillin was used at 200 μg/ml and 100 μg/ml, respectively.
Cell viability, membrane integrity and reactive oxygen species (ROS)
S. cerevisiae BY4741 cells were grown overnight in YPD, diluted into minimal medium with amino acids which contains each of 2% alkanes (C9-C12) with an initial OD600 of 0.2, and followed by shaking incubation at 28°C, and viable cells measurement by CFU (Colony Forming Unit) counting. To investigate whether these alkanes resulted in membrane damage, membrane permeability was analyzed by cell staining. After exposure to alkanes, cells were stained with fluorescent nucleic acid stains SYTO 9 and propidium iodide (PI) (Invitrogen) that only penetrates cells with damaged membrane. Finally, signal intensity in cells was measured using Synergy HT multi-mode microplate reader (Biotek, VT, USA) at wavelength of 485/20 nm (excitation), 528/20 nm (SYTO 9 emission), and 645/40 nm (PI emission). To investigate reactive oxygen species (ROS) generation, alkane-treated cells were collected and stained by CellROX® Green Reagent (Life Technologies). Fluorescence signals were measured by TECAN Infinite 200 microplate reader at wavelength of 485 nm (excitation)/535 nm (emission) and normalized to cell density (OD600).
Alkane extraction and detection
Alkanes were extracted by chloroform-methanol extraction method  with modifications. Firstly, yeast cells were collected and washed with 50 mM Tris.Cl (pH 7.4) then resuspended in 0.4 ml chloroform/methanol mixture (v/v: 2/1). Subsequently, 0.3 g acid-washed glass beads (425-600 μm, Sigma-Aldrich) were added into each sample and cells were lysed using FastPrep-24 (MP Biomedicals, Solon, OH, USA). Then, after the lysate was centrifuged at 4°C at maximum speed for 10 min, an appropriate volume of chloroform and 50 mM Tris.Cl were added to the supernatant for extraction. Finally, the chloroform phase with alkanes was transferred into glass vials for Gas Chromatography (GC) analysis. This was performed using Agilent GC 7890A system (Agilent Technologies, Santa Clara, CA, USA) under the following conditions. With Agilent HP-5 GC column, oven temperature was started at 40°C for 30 sec, and was ramped from 40°C to 220°C at 40°C/min, and FID detector temperature remained at 275°C. A mixture of n-alkanes (10 ppm each) was used as the standard for GC analysis.
RNA preparation, transcriptome analysis, and quantitative PCR
Total RNA was extracted with lyticase for cell wall disruption using RNeasy Mini Kit (Qiagen, Hilden, Germany). Total RNA concentration and integrity were measured using NanoDrop (Thermo Scientific, Wilmington, DE, USA) and Agilent Bioanalyzer 2100, respectively. Subsequently, 200 ng of the qualified total RNA was transcribed and labeled using Agilent RNA Spike-in Kit and Low Input Quick Amp Labeling Kit (two colors). The Cy3- or Cy5-labeled cRNA was hybridized onto Yeast (V2) Gene Expression Microarray (8 × 15 K) at 65°C for 17 h, using Agilent GE Hybridization Kit. Thereafter, the microarray slide was scanned using Agilent Microarray Scanner G2505B. The two-color scanning data were extracted using Agilent Feature Extraction software (v10.5) and analyzed using GeneSpring GX 11. From two biological replicates, differentially expressed genes were selected based on their p values (<0.05, t-test) and fold changes (FC) in n-alkane-treated cells in comparison to control cells (≥2.0).
To validate the microarray data, quantitative PCR (qPCR) was performed with primers targeting the selected genes (Additional file 3) and SsoFast™ EvaGreen® Supermix using iQ5 real-time PCR detection system (Bio-Rad). The qPCR data analysis was conducted with ACT1 as a reference gene using Bio-Rad iQ5 optical system software.
Network component analysis (NCA)
The Network Component Analysis is an algorithm that calculates relative activities of transcription factors from gene expression data . In brief, a matrix of gene expression data ([E]), which contain genes in rows and experimental conditions in columns, are prepared for NCA analysis. Subsequently, the [E] matrix is decomposed into two matrices, a connectivity strength (CS) matrix ([A]) with transcription factors (columns) and genes (rows) and a transcription factor activity (TFA) matrix ([P]) with transcription factors (rows) an experimental conditions (columns). In our study, to perform NCA analysis, first, genes in the gene expression data matrix ([E]) were built from 147 genes (fold changes ≥ 2.0, and p values < 0.05), up- or down-regulated under the three conditions (C10 vs. control at 24 h and 48 h, and C9 vs. control at 48 h); second, the connectivity strength (CS) matrix [A] from Lee and coworkers  was used to primarily define connectivity relations between the genes and the transcription factors; third, the final TFA matrix [P] was deduced by NCA.
Yeast gene deletion
Gene deletion was carried out as previously described . Briefly, loxP-Kan-loxP gene disruption cassettes were amplified using PCR with primers as shown in Additional file 3 and pUG6 as the template, which were then purified using QIAquick Gel Extraction Kit (Qiagen). The purified cassettes were used for yeast transformation as previously described. The colonies were screened on YPD plates containing 200 μg/ml G418 and confirmed by PCR. To perform marker rescue, pSH47 was transformed into mutants, selected on minimal medium (Ura-) plates, and followed by Cre/loxP-mediated marker removal in induction medium. Finally, selection for the loss of marker was performed in YPD containing 1 mg/ml 5-fluoro-orotic acid (Thermo Scientific).
Plasmid construction and analyses of yeast strains overexpressing efflux pump genes
The gene specific primers (Additional file 3) and iProof High-Fidelity DNA Polymerase (Bio-Rad) were employed to amplify target genes by PCR with S. cerevisiae BY4741 genomic DNA as a template. DNA fragments were purified and cloned into an expression vector pYES2. The recombinant plasmids were prepared using QIAprep Miniprep Kit (Qiagen), transformed into S. cerevisiae mutants by electroporation , and followed by selection on Geneticin-containing minimal medium plates. To investigate effects of mutants expressing efflux pumps on cell growth and alkane accumulation, cells harboring a recombinant plasmid were grown in an induction medium plus alkanes. Subsequently, optical density of cells at 600 nm (OD600) and intracellular alkanes were measured.
Network component analysis
This study was financially supported by the Competitive Research Program of the National Research Foundation of Singapore (NRF-CRP5-2009-03).
- Fischer CR, Klein-Marcuschamer D, Stephanopoulos G: Selection and optimization of microbial hosts for biofuels production. Metab Eng 2008,10(6):295-304. 10.1016/j.ymben.2008.06.009View ArticleGoogle Scholar
- Fortman JL, Chhabra S, Mukhopadhyay A, Chou H, Lee TS, Steen E, Keasling JD: Biofuel alternatives to ethanol: pumping the microbial well. Trends Biotechnol 2008,26(7):375-381. 10.1016/j.tibtech.2008.03.008View ArticleGoogle Scholar
- Lee SK, Chou H, Ham TS, Lee TS, Keasling JD: Metabolic engineering of microorganisms for biofuels production: from bugs to synthetic biology to fuels. Curr Opin Biotechnol 2008,19(6):556-563. 10.1016/j.copbio.2008.10.014View ArticleGoogle Scholar
- Dunlop MJ: Engineering microbes for tolerance to next-generation biofuels. Biotechnol Biofuels 2011, 4: 32. 10.1186/1754-6834-4-32View ArticleGoogle Scholar
- Schirmer A, Rude MA, Li X, Popova E, del Cardayre SB: Microbial biosynthesis of alkanes. Science 2010,329(5991):559-562. 10.1126/science.1187936View ArticleGoogle Scholar
- Bernard A, Domergue F, Pascal S, Jetter R, Renne C, Faure JD, Haslam RP, Napier JA, Lessire R, Joubes J: Reconstitution of plant alkane biosynthesis in yeast demonstrates that Arabidopsis ECERIFERUM1 and ECERIFERUM3 are core components of a very-long-chain alkane synthesis complex. Plant Cell 2012,24(7):3106-3118. 10.1105/tpc.112.099796View ArticleGoogle Scholar
- Pinkart HC, White DC: Phospholipid biosynthesis and solvent tolerance in Pseudomonas putida strains. J Bacteriol 1997,179(13):4219-4226.Google Scholar
- Sikkema J, de Bont JA, Poolman B: Mechanisms of membrane toxicity of hydrocarbons. Microbiol Rev 1995,59(2):201-222.Google Scholar
- Nuwaysir EF, Bittner M, Trent J, Barrett JC, Afshari CA: Microarrays and toxicology: the advent of toxicogenomics. Mol Carcinog 1999,24(3):153-159. 10.1002/(SICI)1098-2744(199903)24:3<153::AID-MC1>3.0.CO;2-PView ArticleGoogle Scholar
- Afshari CA, Nuwaysir EF, Barrett JC: Application of complementary DNA microarray technology to carcinogen identification, toxicology, and drug safety evaluation. Cancer Res 1999,59(19):4759-4760.Google Scholar
- Chang W, Toghrol F, Bentley WE: Toxicogenomic response of Staphylococcus aureus to peracetic acid. Environ Sci Technol 2006,40(16):5124-5131. 10.1021/es060354bView ArticleGoogle Scholar
- Carvalho RN, Bopp SK, Lettieri T: Transcriptomics responses in marine diatom Thalassiosira pseudonana exposed to the polycyclic aromatic hydrocarbon benzo[a]pyrene. PLoS One 2011,6(11):e26985. 10.1371/journal.pone.0026985View ArticleGoogle Scholar
- Kang A, Chang MW: Identification and reconstitution of genetic regulatory networks for improved microbial tolerance to isooctane. Mol Biosyst 2012,8(4):1350-1358. 10.1039/c2mb05441hView ArticleGoogle Scholar
- Matsui K, Hirayama T, Kuroda K, Shirahige K, Ashikari T, Ueda M: Screening for candidate genes involved in tolerance to organic solvents in yeast. Appl Microbiol Biotechnol 2006,71(1):75-79. 10.1007/s00253-006-0328-3View ArticleGoogle Scholar
- Ghosh AK, Ramakrishnan G, Rajasekharan R: YLR099C (ICT1) encodes a soluble Acyl-CoA-dependent lysophosphatidic acid acyltransferase responsible for enhanced phospholipid synthesis on organic solvent stress in Saccharomyces cerevisiae. J Biol Chem 2008,283(15):9768-9775. 10.1074/jbc.M708418200View ArticleGoogle Scholar
- Dos Santos SC, Teixeira MC, Cabrito TR, Sa-Correia I: Yeast toxicogenomics: genome-wide responses to chemical stresses with impact in environmental health, pharmacology, and biotechnology. Front Genet 2012, 3: 63.View ArticleGoogle Scholar
- Yasokawa D, Murata S, Kitagawa E, Iwahashi Y, Nakagawa R, Hashido T, Iwahashi H: Mechanisms of copper toxicity in Saccharomyces cerevisiae determined by microarray analysis. Environ Toxicol 2008,23(5):599-606. 10.1002/tox.20406View ArticleGoogle Scholar
- Yasokawa D, Iwahashi H: Toxicogenomics using yeast DNA microarrays. J Biosci Bioeng 2010,110(5):511-522. 10.1016/j.jbiosc.2010.06.003View ArticleGoogle Scholar
- Harger M, Zheng L, Moon A, Ager C, An JH, Choe C, Lai Y-L, Mo B, Zong D, Smith MD, et al.: Expanding the product profile of a microbial alkane biosynthetic pathway. ACS Synth Biol 2012,2(1):59-62.View ArticleGoogle Scholar
- de Smet MJ, Kingma J, Witholt B: The effect of toluene on the structure and permeability of the outer and cytoplasmic membranes of Escherichia coli. Biochim Biophys Acta 1978,506(1):64-80. 10.1016/0005-2736(78)90435-2View ArticleGoogle Scholar
- Inoue A, Horikoshi K: A pseudomonas thrives in high-concentrations of toluene. Nature 1989,338(6212):264-266. 10.1038/338264a0View ArticleGoogle Scholar
- Kawamoto T, Kanda T, Tanaka A: Preparation of an organic solvent-tolerant strain from baker’s yeast. Appl Microbiol Biotechnol 2001,55(4):476-479. 10.1007/s002530000536View ArticleGoogle Scholar
- Gill CO, Ratledge C: Toxicity of n-Alkanes, n-Alk-1-enes, n-Alkan-1-ols and n-Alkyl-1-bromides towards Yeasts. Microbiology 1972,72(1):165-172.Google Scholar
- Mewes HW, Frishman D, Guldener U, Mannhaupt G, Mayer K, Mokrejs M, Morgenstern B, Munsterkotter M, Rudd S, Weil B: MIPS: a database for genomes and protein sequences. Nucleic Acids Res 2002,30(1):31-34. 10.1093/nar/30.1.31View ArticleGoogle Scholar
- Bissinger PH, Kuchler K: Molecular cloning and expression of the Saccharomyces cerevisiae STS1 gene product. A yeast ABC transporter conferring mycotoxin resistance. J Biol Chem 1994,269(6):4180-4186.Google Scholar
- Grigoras I, Lazard M, Plateau P, Blanquet S: Functional characterization of the Saccharomyces cerevisiae ABC-transporter Yor1p overexpressed in plasma membranes. Biochim Biophys Acta 2008,1778(1):68-78. 10.1016/j.bbamem.2007.08.035View ArticleGoogle Scholar
- Kolaczkowski M, Kolaczowska A, Luczynski J, Witek S, Goffeau A: In vivo characterization of the drug resistance profile of the major ABC transporters and other components of the yeast pleiotropic drug resistance network. Microb Drug Resist 1998,4(3):143-158. 10.1089/mdr.1998.4.143View ArticleGoogle Scholar
- Mahe Y, Parle-McDermott A, Nourani A, Delahodde A, Lamprecht A, Kuchler K: The ATP-binding cassette multidrug transporter Snq2 of Saccharomyces cerevisiae: a novel target for the transcription factors Pdr1 and Pdr3. Mol Microbiol 1996,20(1):109-117. 10.1111/j.1365-2958.1996.tb02493.xView ArticleGoogle Scholar
- Ernst R, Klemm R, Schmitt L, Kuchler K: Yeast ATP-binding cassette transporters: cellular cleaning pumps. Methods Enzymol 2005, 400: 460-484.View ArticleGoogle Scholar
- Garay-Arroyo A, Covarrubias AA: Three genes whose expression is induced by stress in Saccharomyces cerevisiae. Yeast 1999,15(10A):879-892. 10.1002/(SICI)1097-0061(199907)15:10A<879::AID-YEA428>3.0.CO;2-QView ArticleGoogle Scholar
- Schneiter R, Guerra CE, Lampl M, Tatzer V, Zellnig G, Klein HL, Kohlwein SD: A novel cold-sensitive allele of the rate-limiting enzyme of fatty acid synthesis, acetyl coenzyme A carboxylase, affects the morphology of the yeast vacuole through acylation of Vac8p. Mol Cell Biol 2000,20(9):2984-2995. 10.1128/MCB.20.9.2984-2995.2000View ArticleGoogle Scholar
- Hong ME, Lee KS, Yu BJ, Sung YJ, Park SM, Koo HM, Kweon DH, Park JC, Jin YS: Identification of gene targets eliciting improved alcohol tolerance in Saccharomyces cerevisiae through inverse metabolic engineering. J Biotechnol 2010,149(1–2):52-59.View ArticleGoogle Scholar
- Monteiro PT, Mendes ND, Teixeira MC, d’Orey S, Tenreiro S, Mira NP, Pais H, Francisco AP, Carvalho AM, Lourenco AB, et al.: YEASTRACT-DISCOVERER: new tools to improve the analysis of transcriptional regulatory associations in Saccharomyces cerevisiae. Nucleic Acids Res 2008,36(Database issue):D132-D136.Google Scholar
- Li XZ, Zhang L, Poole K: Role of the multidrug efflux systems of Pseudomonas aeruginosa in organic solvent tolerance. J Bacteriol 1998,180(11):2987-2991.Google Scholar
- Ramos JL, Duque E, Gallegos MT, Godoy P, Ramos-Gonzalez MI, Rojas A, Teran W, Segura A: Mechanisms of solvent tolerance in gram-negative bacteria. Annu Rev Microbiol 2002, 56: 743-768. 10.1146/annurev.micro.56.012302.161038View ArticleGoogle Scholar
- Rojas A, Duque E, Mosqueda G, Golden G, Hurtado A, Ramos JL, Segura A: Three efflux pumps are required to provide efficient tolerance to toluene in Pseudomonas putida DOT-T1E. J Bacteriol 2001,183(13):3967-3973. 10.1128/JB.183.13.3967-3973.2001View ArticleGoogle Scholar
- Tsukagoshi N, Aono R: Entry into and release of solvents by Escherichia coli in an organic-aqueous two-liquid-phase system and substrate specificity of the AcrAB-TolC solvent-extruding pump. J Bacteriol 2000,182(17):4803-4810. 10.1128/JB.182.17.4803-4810.2000View ArticleGoogle Scholar
- Coleman JJ, Mylonakis E: Efflux in fungi: la piece de resistance. PLoS Pathog 2009,5(6):e1000486. 10.1371/journal.ppat.1000486View ArticleGoogle Scholar
- Paumi CM, Chuk M, Snider J, Stagljar I, Michaelis S: ABC transporters in Saccharomyces cerevisiae and their interactors: new technology advances the biology of the ABCC (MRP) subfamily. Microbiol Mol Biol Rev 2009,73(4):577-593. 10.1128/MMBR.00020-09View ArticleGoogle Scholar
- Dunlop MJ, Dossani ZY, Szmidt HL, Chu HC, Lee TS, Keasling JD, Hadi MZ, Mukhopadhyay A: Engineering microbial biofuel tolerance and export using efflux pumps. Mol Syst Biol 2011, 7: 487.View ArticleGoogle Scholar
- Koronakis V, Sharff A, Koronakis E, Luisi B, Hughes C: Crystal structure of the bacterial membrane protein TolC central to multidrug efflux and protein export. Nature 2000,405(6789):914-919. 10.1038/35016007View ArticleGoogle Scholar
- Li XZ, Nikaido H: Efflux-mediated drug resistance in bacteria: an update. Drugs 2009,69(12):1555-1623. 10.2165/11317030-000000000-00000View ArticleGoogle Scholar
- Bokma E, Koronakis E, Lobedanz S, Hughes C, Koronakis V: Directed evolution of a bacterial efflux pump: adaptation of the E. coli TolC exit duct to the Pseudomonas MexAB translocase. FEBS Lett 2006,580(22):5339-5343. 10.1016/j.febslet.2006.09.005View ArticleGoogle Scholar
- Huffer S, Roche CM, Blanch HW, Clark DS: Escherichia coli for biofuel production: bridging the gap from promise to practice. Trends Biotechnol 2012,30(10):538-545. 10.1016/j.tibtech.2012.07.002View ArticleGoogle Scholar
- Lo T-M, Teo WS, Ling H, Chen B, Kang A, Chang MW: Microbial engineering strategies to improve cell viability for biochemical production. Biotechnol Adv 2013. in pressGoogle Scholar
- Maeng JH, Sakai Y, Tani Y, Kato N: Isolation and characterization of a novel oxygenase that catalyzes the first step of n-alkane oxidation in Acinetobacter sp. strain M-1. J Bacteriol 1996,178(13):3695-3700.Google Scholar
- Lee TI, Rinaldi NJ, Robert F, Odom DT, Bar-Joseph Z, Gerber GK, Hannett NM, Harbison CT, Thompson CM, Simon I, et al.: Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 2002,298(5594):799-804. 10.1126/science.1075090View ArticleGoogle Scholar
- Gueldener U, Heinisch J, Koehler GJ, Voss D, Hegemann JH: A second set of loxP marker cassettes for Cre-mediated multiple gene knockouts in budding yeast. Nucleic Acids Res 2002,30(6):e23. 10.1093/nar/30.6.e23View ArticleGoogle Scholar
- Manivasakam P, Schiestl RH: High efficiency transformation of Saccharomyces cerevisiae by electroporation. Nucleic Acids Res 1993,21(18):4414-4415. 10.1093/nar/21.18.4414View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.