JBC Avanti Polar Lipids

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Originally published In Press as doi:10.1074/jbc.M212308200 on October 8, 2003

J. Biol. Chem., Vol. 279, Issue 6, 4450-4458, February 6, 2004
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Supplemental Data
Right arrow All Versions of this Article:
279/6/4450    most recent
M212308200v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by De Freitas, J. M.
Right arrow Articles by Vulpe, C.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by De Freitas, J. M.
Right arrow Articles by Vulpe, C.
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati  
What's this?

Exploratory and Confirmatory Gene Expression Profiling of mac1{Delta}*

Jeane Maria De Freitas{ddagger}§, Jeung Hyoun Kim{ddagger}§, Helen Poynton{ddagger}, Trent Su{ddagger}, Henri Wintz{ddagger}, Tama Fox{ddagger}, Patty Holman{ddagger}, Alex Loguinov{ddagger}, Sunduz Keles¶, Mark van der Laan¶, and Chris Vulpe{ddagger}||

From the {ddagger}Department of Nutritional Sciences and Toxicology and the Division of Biostatistics, University of California, Berkeley, California 94720

Received for publication, December 3, 2002 , and in revised form, July 29, 2003.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Exploratory outlier identification methods and confirmatory gene expression studies showed induction of the iron regulon in Saccharomyces cerevisiae lacking Mac1p, a copper-responsive transcription factor. The Aft1p/Aft2p binding motif was the most discriminating motif between up- and down-regulated genes, and we identified new genes potentially regulated by Aft1p/Aft2p. In addition, multiple genes encoding proteins containing Fe-S clusters were down-regulated suggesting metabolic reorganization to conserve iron in mac1{Delta}. Null mutants of each of the differentially expressed genes were characterized for copper- or iron-related phenotypes. New or additional support for a role in copper and iron homeostasis is provided in this study for the gene products of AKR1, MRS4, PCA1, SSU1, TIS11, YBR047W, YHL035C, YHR045W, YLR047C, YLR126C, and YTP1.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
In Saccharomyces cerevisiae, copper and iron are cofactors of a large number of enzymes such as components of the mitochondrial respiratory system (1). Yet an excess of free metals can be extremely toxic to cells because they can catalyze formation of oxyradicals (2, 3). To maintain metal homeostasis, complex regulatory mechanisms control import, distribution, storage, and export of metals (4-6).

Copper and iron homeostasis are tightly linked in yeast. Two transcription factors, Mac1p and Aft1p, play key roles in regulating the copper and iron status (for a review, see Ref. 1). In copper deficiency, Mac1p induces transcription of CTR1 and CTR3 encoding copper transporters and FRE1 and FRE7 encoding copper and iron reductases (7-10). In iron deficiency, Aft1p induces transcription of multiple genes involved in copper and iron uptake, intracellular transport, and mobilization (6, 11, 12). In addition to Aft1p, another recently identified transcription factor, Aft2p, activates several genes involved in iron homeostasis (13-15).

Copper is required for high affinity iron transport in yeast. Fet3p, a multicopper protein, oxidizes iron prior to its transport by the iron permease Ftr1p (16-18). Fet5p, another multicopper oxidase, is involved in iron transport in the vacuole (19). Both FET3 and FET5 are induced by iron deficiency mediated through Aft1p. Copper deficiency therefore leads to decreased Fet3p activity, decreased iron transport, and secondary iron deficiency (20).

In this work, we provide a comprehensive view of the transcriptional effects of the absence of Mac1p. The mac1{Delta} represents a unique genetic model of combined copper and iron deficiency in yeast. Previous whole genome studies by Gross et al. (10) used constitutively active MAC1up1 cells (designated as Mac1up1) and identified potential direct transcriptional targets of the Mac1p. We hypothesized that detailed analysis of the compensatory response of yeast to Mac1p absence would complement and extend these studies and provide further insight into the inter-relationship of copper and iron metabolism in yeast. In addition, our reanalysis as described below of the original Mac1up1 data (deposited in the Stanford Microarray Database) indicated caution in the interpretation of this data set. We identified genes differentially expressed in early log phase mac1{Delta} as compared with wild type BY4743 through application of an outlier identification method recently developed by our group (21, 22). We confirmed selected results through miniarray and quantitative PCR methods. We applied a promoter analysis algorithm, LogicMOTIF, developed by us to identify promoter elements that discriminate between up- and down-regulated genes. Finally we individually phenotyped yeast mutants disrupted in differentially expressed genes and revealed novel genes involved in copper and/or iron homeostasis.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Yeast Strains, Media, and Growth Conditions—The S. cerevisiae strains used in this study are derived from BY4741 and BY4743 and were obtained from Research Genetics. For microarray experiments yeast cells were grown in YPD medium (1% yeast extract, 2% peptone, and 2% dextrose). For analysis of metal content, cells were grown on synthetic defined (SD)1 medium (0.67% yeast nitrogen base, 2% dextrose, amino acids) lacking iron and containing 1 µM CuSO4. For phenotype assay, yeast cells were grown overnight in YPD or SD and diluted to A600 = 1 in 1 ml of water. Serial dilutions were made (A600 = 0.1, 0.01, and 0.001), and 5 µl of each dilution were plated on YPD, YPG (yeast extract/peptone containing 3% glycerol), YPG supplemented with 1 mM FeCl4, and YPG supplemented with 1 mM CuSO4, SD, SD + 100 µM bathophenanthrolinedisulfonic acid (Sigma), a Fe(II) chelator, and SD + 100 µM bathocuproinedisulfonic acid (Sigma), a Cu(I)-specific chelator.

Microarray Procedures—Total RNA was isolated using the hot phenol procedure.2 Poly(A)+ RNA was prepared by using Oligotex resin from Qiagen (Chatsworth, CA) according to the manufacturer's protocol. cDNA was synthesized from 2 µg of poly(A)+ using RNA Super-Script II RNase H-reverse transcriptase (Invitrogen) and oligo(dT) primer in the presence of aminoallyl-labeled dUTP. cDNA was labeled by incubation with or Cy3 or Cy5 fluorescent dyes (Amersham Biosciences). The Cy3- and Cy5-labeled cDNA were mixed and hybridized to a yeast cDNA microarray containing 6218 yeast open reading frames (Berkeley yeast consortium and College of Natural Resources genomic facility, University of California, Berkeley). Scanning of microarrays and quantification of signals were performed using a laser scanner (GenePix 4000) and GenePix software, Version 3.01 (Axon Instruments, Union City, CA). The ratio of signal intensity from both wild type and mac1{Delta} mutant were normalized by total median intensity, and differentially expressed genes were identified as described below.

Miniarray Procedures—cDNA was synthesized from total yeast RNA and purified using a standard protocol. Selected open reading frames were amplified with primers (Invitrogen) designed to amplify ~300 base pairs unique for each gene. Purified PCR products were quantified using PicoGreen® dye fluorescence. Each open reading frame diluted in 3x SSC was spotted six times onto a lysine-coated slide at four different concentrations (80, 40, 20, and 10 ng/µl) using a custom-built arrayer. Sheared genomic DNA (180 ng/µl), yeast total cDNA, a mixture of 800 randomly chosen yeast open reading frames (70 ng/µl), a mixture of 800 randomly chosen PCR products (70 ng/µl), and poly(A) (1280 ng/µl) were used as normalization controls. {lambda} DNA and 3x SSC were used as negative controls. The probe preparation and hybridization were performed as described above. Four different concentrations (80, 40, 20, and 10 ng/µl) of DNA spots on the miniarray chip were probed,and the concentration that gave the maximal signal intensity for each dye was chosen for the further analysis: 80 ng/µl for AFT1, AKR1, CCC2, NCP1, YFR055W, YHR045W, and YLR047C; 40 ng/µl for ACO1, CCP1, HAP4, MAC1, YOR383C, and YTP1; and 20 ng/µl for DIN7, FET3, LEU1, MRS4, YLR205C, and YRO2.

Statistical Analysis—We have recently developed a novel approach to identify differentially expressed genes between the two mRNA samples based on identification of outliers as candidate differentially expressed genes (see the Supplement for more details) (21, 22). The majority of data points after appropriate normalization lie in the vicinity of the line of equivalence in the scatter plot of log2(Cy5) versus log2(Cy3) intensity values. Other data points, outliers, lie outside the vicinity of the line of equivalence and are considered to be data points of greatest interest since they correspond to genes having noticeably different hybridization intensity. We apply robust scatter plot smoothers to quantify and take into account the distortion of the data set by heteroscedasticity (if any). We consider outliers from this analysis to represent candidate differentially expressed genes. We assign a confidence (p value) for each outlier by calculating simultaneous prediction confidence intervals.

Promoter Analysis—We developed a hybrid method, LogicMOTIF,3 for the purpose of identifying regulatory motifs with the highest discriminative power among the given groups of genes. This method utilizes fast enumerative motif-finding methods that apply to a group of potentially co-regulated genes (24) and the logic regression methodology of Kooperberg et al. (25). The key idea is to first identify potential motifs in each of the up- and down-regulated gene groups separately and then build a classifier based on these potential motifs to discriminate between the two groups. As a classifier, we use boolean expressions of the motif occurrences, and such a classifier can be built by logic regression. The main strengths of our method are that the final model built includes the most discriminative motifs of the up- and down-regulated genes, and this model is able to capture the interactions among the individual motifs. Such interactions can be used to infer synergetic and antagonistic combinations of several motifs.

Quantitative PCR—Primers specific to the genes of interest were selected using the web-based software PRIMER34 and sequence information obtained from Saccharomyces Genome Database. Primers were designed to yield ~300-base pair products for the following genes: AFT1, CCAAGACAAGTCTTCGACCA and CGCCGATGTTATTGTGGTTA; FET3, CACGGACGGTCAATATGAAG and TCCAAAAGTACTCGAAACG; PCA1, ACTCTTTACGGGCAGATGCT and GAACTCCAATCGTTGCTTGA; ERG3, GGAAATCAAGTTGGCAGTCA and TGGCAAGAATCAATGGGTAGA; YTP1, GGATACACGAGGCCAGAAAT and CCAGTTCCTCCAGCTAATCC; and MRS4, GAAGACCGCACTGAGTGGTA and ACTTATCCCACCGCAAAGAC. Primers were designed to yield a 199-base pair product (Invitrogen) for 18 S rRNA, TGATGCCCTTAGACGTTCTG and GTACAAAAGGGCAGGGACGTA. Total RNA isolated from BY4741 and BY4741 mac1{Delta} and BY4742 aft1{Delta} grown to early log phase was reverse-transcribed with StrataScript reverse transcriptase (Stratagene) and random primers (Invitrogen) using the Stratagene ProSTAR First-Strand reverse transcription-PCR system.5 The duplex PCR mixture contained primers for the gene of interest and primers for the control 18 S rRNA. DNA was amplified with an initial 5-min incubation at 95 °C followed by 35 cycles of 15 s at 95 °C, 1 min at 60 °C, and 30 s at 72 °C. 20 µl of the reaction mixture were removed at cycles 28, 30, and 32. A sample of each aliquot was run on a 1.5% agarose gel containing ethidium bromide and visualized under UV light to determine the linear range of DNA amplification for each product. An aliquot in the linear range of the DNA amplification for each product was then run on an 8% acrylamide gel and visualized by staining with ethidium bromide. The intensity of the bands was quantified using ChemiImager 4400 software (Alpha Innotech Corp.). Each target band was normalized by dividing by the intensity of 18 S rRNA band.

Metal Content Analysis by Inductively Coupled Plasma—Diploid yeast (BY4743 derivatives) cultures were collected by centrifugation and washed twice with 10 mM EDTA and twice with metal-free water. Cell pellets were digested in 1 ml of 3% nitric acid (HNO3) at 98 °C overnight. After digestion, the samples were diluted to 5 ml with ultra-pure water and analyzed using a PerkinElmer Life Sciences inductively coupled plasma-atomic absorption spectrometer to determine the metal content (27). All samples were measured three times, and the experiments were repeated three times. The data are expressed as nanograms of metal/107 cells (A600 = 1).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Reanalysis of MAC1up Microarray Data—We obtained the original hybridization data (experiments 8881 and 8902) from the Stanford Microarray Database for the two hybridizations of MAC1up1 cells described previously by Gross et al. (10). We carried out normalization and data analysis as described under "Materials and Methods" and in the Supplement. In both experiments, an evaluation of a scatter plot of log2(Cy3) versus log2(Cy5) revealed a prominent finger-like pattern in the high intensity areas above the line of equivalence (see Supplemental Fig. 1) calling into question the validity of identified up-regulated genes in these experiments. In addition there are regular patterns in the low intensity areas in these experiments perhaps related to subtraction of "background" hybridization. Consequently there is poor reproducibility of candidate differentially expressed genes between these two experiments (see Supplemental Table 1). As an alternative to the use of the MAC1up strain, we examined gene expression in yeast lacking Mac1p.

Identification of Candidate Differentially Expressed Genes in mac1{Delta}—We used yeast cDNA microarrays to compare the gene expression profiles in early log phase of the haploid mac1{Delta} mutant to the wild type parental strain in three independent experiments. We also reanalyzed a publicly available data set of a similar experiment (26). As shown in Table I, we identified 97 genes using our outlier identification method (see "Materials and Methods" and the Supplement for details) that were differentially expressed at p < 0.1 in at least three of the four experiments.


View this table:
[in this window]
[in a new window]
  		TABLE I
Differentially expressed genes in the mac1{Delta} mutant Presented are the results from the analysis of three independent experiments. Up-regulated genes are indicated in red, and down-regulated genes are indicated in green. Different levels of average differential expression (log2 scale) are indicated by corresponding shades of green or red expression (refer to the color index). Aft1p-regulated genes are in bold. Mac1p-regulated genes are indicated in italic. Transcription factors are shown with a double asterisk (**). Gene functions are described according to the Saccharomyces Genome Data Base, and the cellular role is described according to the Munich Information Center for Protein Sequences (MIPS) Data Base.

 
Confirmation of Differentially Expressed Genes in the mac1{Delta} Mutant—We used multiple replicate miniarray analysis in combination with quantitative PCR to confirm the differential expression of genes (Fig. 1, A and B) We selected 11 up- and eight down-regulated genes showing high (ACO1, LEU1, and YRO2), medium (AKR1, CCP1, FET3, HAP4, MAC1, NCP1, and YHR045W), and low (CCC2, DIN7, MRS4, AFT1, YFR055W, YLR047C, YLR205C, YOR383C, and YTP1) hybridization signal intensity. Custom miniarrays containing 24 replicates of each gene were used for four comparative hybridizations between early log mac1{Delta} and control cells by using independently prepared probes from the same RNA preparation used for microarray experiments (more details on the miniarrays are presented in Supplemental Fig. 3). We confirmed differential expression of each of the 18 genes (Fig. 1A, MAC1 is not shown). AFT1p targets such as FET3, CCC2, and YOR383C (FIT3) were confirmed as up-regulated in mac1{Delta} mutant. MRS4, AKR1, YLR047C, DIN7, NCP1, YTP1, and YLR205C were also confirmed as induced. Representative down-regulated genes including ACO1, CCP1, HAP4, LEU1, YFR055W, YHR045W, and YRO2 were confirmed as down-regulated in the mac1{Delta} strain. These results suggest that our microarray analysis reliably identified differentially expressed genes throughout the range of expression levels.



View larger version (36K):
[in this window]
[in a new window]
  		FIG. 1.
Confirmation of gene expression by miniarray analysis and quantitative PCR. A, four independent hybridizations were done to test the expression of 19 genes using custom gene arrays. Change in gene expression is shown as log2(Cy5/Cy3). B, quantification of AFT1, FET3, MRS4, PCA1, and YTP1 expression levels in mac1{Delta} and aft1{Delta} strains using quantitative PCR. 18 S rRNA was used as a normalization control. WT, wild type.

 
As a further test of our methods, we also assayed expression by quantitative PCR of selected genes in mac1{Delta}, aft1{Delta}, and wild type cells (Fig. 1B). The mac1{Delta} strain was used to confirm the expression of selected genes in response to the absence of Mac1p. 18 S amplification was used to normalize expression levels between samples. The FET3 gene was also used as a control since it is regulated by both Aft1p and its homologue Aft2p in yeast (15). We confirmed increased expression of FET3, AFT1, MRS4, and PCA1 in the mac1{Delta} mutant.

Expression of Genes Encoding Proteins Involved in Copper and Iron Homeostasis—Twenty-five genes encoding proteins previously shown to be involved in copper and iron metabolism in yeast are differentially expressed in the mac1{Delta} mutant (Table I and Fig. 2). A previously identified Mac1p target, YFR055W, encoding a protein of unknown function, was down-regulated in the mac1{Delta} mutant. Similarly, FRE1, a gene regulated both by Mac1p and by Aft1p, is down-regulated in the mac1{Delta} mutant (27, 28) consistent with previous finding that Mac1p affects both basal expression level and iron-dependent induction of FRE1. A number of genes encoding proteins involved in copper metabolism and transport such as CTR2, a low affinity copper transporter, and two P-type ATPases, PCA1 and CCC2, as well as the copper chaperone ATX1 (17, 29-31) are all up-regulated in the mac1{Delta} mutant. One exception is FET4, which is known to play a role in copper, iron, and zinc uptake (32, 33); it is repressed in our data.



View larger version (36K):
[in this window]
[in a new window]
  		FIG. 2.
Copper and iron metabolism pathway representation of protein products of differentially expressed genes in mac1{Delta} Protein products of up-regulated genes are shown in red, and down-regulated genes are shown in green. Copper and iron ions are reduced by cell surface reductases Fre1p/Fre2p prior to their uptake by Ctr1p (and Ctr3p) and Fet3p/Ftr1p complex, respectively. Within the cell, copper is scavenged by the cytosolic copper chaperone Atx1p for delivery to Ccc2p. In the secretory pathway, Ccc2p, a membrane copper ATPase, transports copper ions into Golgi-related vesicles where copper is incorporated into Fet3p. Fet3p forms a complex with Ftr1p and relocates to the plasma membrane to function in high affinity iron uptake. In the mitochondria, iron may be transported by Mrs4p. Fet4p transports iron and is possibly located in the plasma membrane. Several members of the Frep family whose location is not known are shown in the cytosol, i.e. Fre3p, Fre4p, Fre5p, Fre6p, and Fre7p. Pca1p is a potential P-type metal ATPase of unknown function. In the vacuole, Fet5p/Fth1p transporters participate in iron efflux, whereas Ccc1p may participate in iron uptake. Siderophore-iron transport is made by Arn1p, Arn2p, Arn3p, and Arn4p with the help of cell wall proteins Fit1p, Fit2p, and Fit3p. Mac1p and Aft1p, the copper- and iron-responsive transcriptional factors, are shown at the nucleus.

 
Aft1p, the major iron-responsive transcriptional activator controlling iron homeostasis, as well as all its known targets were induced in the mac1{Delta} mutant. Up-regulated genes included ones encoding siderophore transporters and transport facilitators (ARN1, ARN2, ARN3, ARN4, FIT1, FIT2, and FIT3) (34-37); ionic iron uptake and transport (FRE2, FET3, FTR1, and FET5) (18, 19); an intracellular copper chaperone (ATX1); a P-type copper ATPase (CCC2) (17, 29); ferric reductase homologues such as FRE3, FRE4, and FRE5; and TIS11 of unknown function (9, 36). Of the members of the iron regulon, only the vacuolar iron transporter FTH1 and FRE6 were not up-regulated.

Down-regulation of Genes Encoding Iron- and Copper-containing Proteins—While genes encoding iron-containing proteins involved in iron metabolism (mainly the FRE genes) were up-regulated in mac1{Delta} mutant, multiple genes encoding Fe-S cluster-containing proteins were down-regulated in mac1{Delta} mutant (see Supplemental Fig. 5). These include LYS4, LEU1, and ACO1 encoding homoaconitase, 3-isopropylmalate dehydratase, and aconitase, respectively, and YJL200C of unknown function. In addition, copper- and/or iron-containing components of the mitochondrial respiratory chain such as CYC1 (cytochrome c isoform), SDH3 (succinate dehydrogenase 3), SDH4 (succinate dehydrogenase 4), and CCP1 (cytochrome c peroxidase). CTT1 (cytosolic catalase) and HEM15 (ferroche-latase) were down-regulated.

Aft1p Regulatory Sequence Is a Discriminatory Motif of the Up-regulated Genes in mac1{Delta}—We looked for motifs in the upstream region of differentially expressed genes that discriminate between the genes that are up-regulated and the genes that are down-regulated using the LogicMOTIF algorithm (described under "Materials and Methods"). Using this approach, we determined that the sequence TGCACCSW corresponding to the Aft1p consensus sequence (Fig. 3A) was the most discriminating motif between the up- and down-regulated genes. In addition to known Aft1p targets (see Supplemental Fig. 4), PCA1 YTP1, YHL035C, YLR126C and YBR047W (Fig. 3B) contained one or more Aft1p binding motifs in the promoter region of these genes between nucleotide positions -233 and -85 relative to the start codon. Interestingly the eight most highly regulated genes in our data set all contain the Aft1p regulatory motif (Table I and Fig. 3, and see Supplemental Fig. 4).



View larger version (19K):
[in this window]
[in a new window]
  		FIG. 3.
Aft1p motif in genes differentially expressed in mac1{Delta} A search for discriminating motifs between the up- and down-regulated genes using LogicMOTIF algorithm identified the sequences TGCACCSW which correspond to the Aft1p consensus sequence. In A, we show a sequence logo of discriminating motif generated by using a computer code available at www.bio.cam.ac.uk/seqlogo/logo.cgi and computed using Alpro and Makelogo. In B, we list five genes identified in this study not previously shown to contain this motif in the upstream region. A complete list of motifs and locations is presented in the Supplement.

 
Phenotypic Analysis of Genes Regulated in mac1{Delta} Mutant—To confirm the role in metal metabolism of genes that were differentially expressed in the mac1{Delta} mutant, we tested the ability of 35 null mutants disrupted in selected genes (see Supplemental Fig. 6 for the complete list) to grow in different copper and iron conditions. Growth of the diploid mutants was tested in copper deficiency, iron deficiency, copper overload, and iron overload, and on a non-fermentable carbon source. No mutant displayed increased sensitivity to copper or iron overload (data not shown), but as shown in Fig. 4A, four mutants, akr1{Delta}, mrs4{Delta}, yhr045w{Delta}, and ylr047c{Delta} were unable to grow in iron deficiency. In addition, mrs4{Delta}, yhr045w{Delta}, and ylr047c{Delta} were respiratory deficient, but their phenotype was not rescued by the addition of 1 mM copper or iron into the growth medium (data not shown). Measurement of total intracellular copper and iron content in these four mutants revealed that three of them, namely akr1{Delta}, mrs4{Delta}, and yhr045w{Delta} accumulate iron at twice the level of wild type cells (Fig. 4B). The copper content was not altered in these same mutants except for akr1{Delta}, which showed a slight increase in its intracellular copper concentration. Two of the mutants (mrs4{Delta} and yhr045w{Delta}) showed double the zinc content of wild type, and the akr1{Delta} mutant showed a 4-fold increase in zinc content (see Supplemental Fig. 8). Phenotype assays have revealed that 22 of the 35 mutants screened were unable to grow on non-fermentable carbon sources such as glycerol (see Supplemental Fig. 6). To test whether the lack of growth was directly linked to disturbances in iron and copper homeostasis, we tested whether addition of copper and iron in the YPG medium would rescue the petite phenotype in these strains. Only one mutant, ssu1{Delta}, was rescued by addition of iron and copper (Fig. 4A).



View larger version (52K):
[in this window]
[in a new window]
  		FIG. 4.
Phenotype analysis. Dilutions of yeast cultures were plated on SD, SD + 100 µM bathophenanthroline (BPS), and SD + 100 µM bathocuproine (BCS) (A) or on YPD, YPG, YPG + 1 mM FeCl4 (Fe), and YPG + 1 mM CuSO4 (Cu) (C) followed by incubation at 30 °C for 3 days. B shows intracellular copper and iron content in the different mutants as measured by inductively coupled plasma. The averages of three independent experiments are shown with errors bars indicating the S.D. WT, wild type.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
mac1{Delta}, a Model to Study Copper and Iron Deficiency in Yeast—We used whole genome microarray analysis and selected representative confirmatory gene expression studies to identify differentially expressed genes in the mac1{Delta} mutant compared with the wild type. Gene expression provided unexpected insights into the complex regulatory and metabolic rearrangements resulting from copper and iron deficiency. Our results show that a large proportion of the genes induced in mac1 null are involved in iron and copper metabolism. As expected, the absence of the Mac1 protein results in down-regulation of all the Mac1p targets including the high affinity copper transporter CTR1. Similarly we noted up-regulation of an array of genes involved in copper and iron uptake and transport indicative of copper and secondary iron deficiency. ARN1, ARN2, ARN3, ARN4, FIT1, FIT2, FIT3, and FRE2 are the top eight of the differentially expressed genes, and they all have the Aft1p/Aft2p consensus sequences in their upstream regions (see Supplemental Fig. 4B). Neither the number nor the location of the Aft1p/Aft2p sites distinguishes these genes from other members of the iron regulon. We combined the expression studies with systematic phenotype analysis of null mutants of each of the differentially expressed genes and identified previously unsuspected components of copper and iron metabolism.

Potential Novel Iron and Copper Homeostasis Genes—New or additional support for a role in copper and iron homeostasis is provided in this study for the gene products of AKR1, MRS4, PCA1, SSU1, TIS11, YBR047W, YHL035C, YHR045W, YLR047C, YLR126C, and YTP1. AKR1 is up-regulated in mac1{Delta}, and akr1{Delta} is sensitive to iron deficiency. Akr1p contains three copies of a 33-amino acid helix-loop-helix ankyrin motif (pfam00023) involved in protein-protein interactions in a large number of functionally diverse proteins mainly from eukaryotes including 53BP2 (p53-binding protein) and NF-{kappa}B. In addition, Akr1p contains a predicted DHHC zinc finger domain (pfam01529) to the C terminus of the ankyrin repeats possibly also involved in protein-protein or protein-DNA interactions. Akr1p may be involved in endocytosis (38, 39), and a recent report demonstrated palmitoyltransferase activity, suggesting a role in tethering proteins to membranes (40). Several yeast mutants involved in vacuole function and secretory pathways have shown impairment of iron homeostasis (41, 42). We suggest that ankyrin may play an indirect role in copper and iron metabolism through its role in palmitoylation and endocytosis.

We have also found that MRS4 is highly induced in the mac1{Delta} mutant. This result and the fact that MRS4 is down-regulated in the aft1{Delta} mutant suggest that MRS4 expression is Aft1p/Aft2p-dependent. We also demonstrated that the MRS4 gene is essential for growth in limiting iron. While this work was in progress, Mrs4p was proposed to be involved in mitochondrial iron transport in yeast (43). A mutant for MRS4 shows decreased iron transport into the mitochondria, suggesting that Mrs4p is likely to be an iron transporter in the mitochondria. We have also observed increased iron content in the mrs4{Delta} consistent with a role in iron metabolism.

Pca1p is a member of the CPX-type metal-transporting ATPase family that includes CCC2. The up-regulation of PCA1 in the mac1{Delta} mutant, the presence of a putative Aft1p consensus, the decrease in expression in the aft1{Delta} strain, and the respiratory deficient phenotype suggests that Pca1p plays a role in iron metabolism.

The addition of copper or iron rescued the ability of ssu1{Delta} to grow on non-fermentable carbon sources. Ssu1p is a predicted sulfite transporter, and we speculate that excess intracellular sulfur compounds results in depletion of cellular copper and/or iron. Excess copper or iron provides additional sulfite ligands to free up sufficient copper and/or iron for respiration.

TIS11/CTH2 encodes a zinc finger transcription factor with two putative zinc fingers (CX8CX5CX3H type). Although the function of Tis11p is not known, its expression has been linked to iron metabolism previously, and TIS11 is considered as part of the iron regulon (13, 36, 43). It is induced in AFT2-1up-expressing cells and in the mrs3mrs4 deletion mutant and decreased in aft1{Delta} mutants. Our results further confirm that TIS11 is part of the iron regulon.

Both YBR047W and YHL035C are up-regulated in mac1{Delta} and contain a consensus Aft1p binding site in the promoter region. Yhl035cp is member of the multidrug resistance family/ATP-binding cassette transporter family and is 64% identical to the yeast bile acid transporter Ybt1p, while the predicted Ybr047wp shows no appreciable similarity with other proteins. yhl035c{Delta} mutant was unable to grow on glycerol but showed no discernible growth phenotype on low copper or iron YPD media, although previous studies showed slow growth (44) in multiple conditions.

YHR045W is highly repressed in the mac1{Delta} null mutant. Moreover yeast cells containing a deletion in this gene are extremely sensitive to low iron conditions and show increased intracellular iron. yhr045w{Delta} mutant also shows a respiratory deficient phenotype that is not rescued by 1 mM copper or iron. Protein prediction analysis suggests that Yhr045wp contains a single potential transmembrane helix domain. In addition, sequence analysis by PSORT shows that Yhr045wp has a peroxisomal targeting signal, RITPSTEQL, suggesting that this protein may be localized in the peroxisome. Additionally BLAST analysis detects weak homology of Yhr045wp to a Corynebacterium metalloendopeptidase (NP599406.1).

YLR047C was up-regulated in the mac1{Delta} mutant, and its absence resulted in a growth defect in iron deficiency and iron accumulation. Ylr047cp is a likely member of the copper/ferric reductase protein family, which includes Fre1-7p and Ygl160wp (45, 46). FRE1-FRE6 are regulated by Aft1p (9). FRE1 and FRE7 are also regulated through Mac1p (9). Fre1p and Fre2p are plasma membrane proteins involved in copper and iron uptake, while Fre3p can facilitate iron uptake from ferrioxamine B and ferrichrome in yeast, presumably by acting as a ferric-siderophore reductase in the plasma membrane. Preliminary experiments have shown that Fre4p may also be a siderophore-iron reductase (47). Our results support a role of Ylr047cp in iron homeostasis, and we suggest the name FRE8.

YLR126C encodes a protein with a glutamine amidotransferase domain (COG0518.1, GuaA), which is present in multiple proteins involved in nucleotide transport or metabolism as well as anthranilate synthases. Interestingly a relationship between anthranilate excretion and iron metabolism has been noted previously in yeast (48) and Rhizobium leguminosarum (49, 50).

YTP1 is induced in mac1{Delta}, contains an Aft1p consensus binding site, and encodes a predicted 11-transmembrane domain protein with four regions reported to have similarity to mitochondrial electron transport proteins (51). Interestingly there are 10 cysteine residues within the predicted transmembrane domain consistent with a potential role in metal transport. We found no appreciable growth defects of ytp1{Delta} similar to previous reports (52).

Regulation of Zinc-, Cadmium-, and Manganese-related Genes in mac1{Delta} Mutant Suggests Additional Roles in Copper and Iron Metabolism—Homeostasis of transition metals is maintained by a complex network of transporters that work coordinately. Alteration of steady state levels of one metal may lead to disturbances in other metals. We observed that two key genes in zinc metabolism, COT1 and ZRT3 (53) encoding proteins responsible for storage and mobilization of zinc from the vacuole, were induced. The induction of ZRT3 is unlikely to be Zap1p-mediated (54) since none of the known Zap1p targets are down-regulated in mac1{Delta} mutant except FET4.

The CAD1 (also known as YAP2) gene, overexpressed in the mac1{Delta} mutant, was first identified as playing a role in cadmium metabolism in yeast (55). It encodes a member of the c-Jun family of transcriptional activators (56). Overexpression of CAD1/YAP2 and its homologue YAP1 leads to increased resistance to a variety of drugs and metals and to the iron chelator 1,10-phenanthroline (57), suggesting an involvement in metal homeostasis or an interaction with metals. The expression of YAP2 in the mac1{Delta} mutant is consistent with these observations.

The CCC1 gene, recently identified as encoding a vacuolar protein, is repressed in the mac1{Delta} mutant. Although the role of CCC1 in metal metabolism is not clear, a recent report identified Ccc1p as an iron/manganese transporter in the vacuole (23). In our study, no other gene involved in manganese metabolism is induced besides CCC1, suggesting that CCC1 may have an additional role in iron metabolism that is not shared with other manganese transporters. Moreover Ccc1p is repressed when another vacuolar iron transport system, Fet5p/Fth1p, is induced in mac1{Delta} mutant.

Disordered Respiratory Metabolism in Copper and Iron Deficiency—Iron and copper are major components of enzymes involved in oxidoreduction and thus play a predominant role in energy metabolism pathways. The tricarboxylic acid cycle and the respiratory chain utilize multiple iron- and copper-containing proteins making these pathways vulnerable to disturbances in iron and copper homeostasis (1). For example, aconitase and succinate dehydrogenase in the tricarboxylic acid cycle require iron-sulfur or heme prosthetic groups. Similarly many of the known proteins involved in respiratory functions including subunits of succinate dehydrogenase, complex III, and cytochromes contain iron, and in the case of cytochrome c oxidase contain copper as well. Genes encoding copper- or iron-containing enzymes with a role in respiratory metabolism and various amino acid biosynthetic pathways were indeed down-regulated in the mac1{Delta} mutant. A predicted result of such changes would be an increased carbon flow from glucose to ethanol (end product from fermentation), conservation of glycolytic intermediates and pyruvate for fermentation, and decreased respiration resulting in a shift in energy metabolism away from the tricarboxylic acid cycle and respiratory metabolism toward fermentation (see Supplemental Fig. 7 for an illustration of the pathways). The pattern in mac1{Delta} mutant is distinct from glucose-mediated repression of the tricarboxylic acid cycle. Glucose repression results in down-regulation of expression of KGD2 ({alpha}-ketoglutarate dehydrogenase) and FUM1 (fumarase), whereas repression occurs at ACO1, SDH3, and SDH4 in mac1{Delta} mutant. The enzymes in glycolysis do not require metals (1), therefore it is possible that cells under low copper and iron condition rely on glycolysis. As cells require both carbon substrates and oxidative catalysts (metals) to carry out respiration, it is perhaps not surprising that distinct mechanisms provide regulatory control to ensure appropriate use of energy sources.


   FOOTNOTES
 
* This work was supported by grants from the International Copper Association and the University of California Life Sciences Informatics Program (to C. V.). The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Back

The on-line version of this article (available at http://www.jbc.org) contains supplemental data. Back

§ Both authors contributed equally to this work. Back

|| To whom correspondence should be addressed. Tel.: 510-642-1834; Fax: 510-642-0535; E-mail: vulpe{at}uclink4.berkeley.edu.

1 The abbreviation used is: SD, synthetic defined. Back

2 See www.microarrays.org. Back

3 S. Keles, M. van der Laan, and C. Vulpe, manuscript submitted. Back

4 Available at www.genome.wi.mit.edu/cgi-bin/primer/primer3_www.cgi Back

5 See www.stratagene.com/manual/200420.pdf. Back


   ACKNOWLEDGMENTS
 
We acknowledge Caroline Kane for providing us the RESGEN yeast strains for phenotype experiments, Chris Seidel and Hugh Lam for key roles in manufacturing yeast arrays and for helpful discussions, Dr. Joseph DeRisi for helpful advice, Angela Armendariz for invaluable help, Dr. Barry Shane for the generous use of equipment, and Dr. David Eide for helpful discussion and for the metal analysis data of several yeast mutants.



   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. De Freitas, J., Wintz, H., Kim, J. H., Poynton, H., Fox, T., and Vulpe, C. (2003) Biometals 16, 185-197[CrossRef][Medline] [Order article via Infotrieve]
  2. Koppenol, W. H. (2001) Redox Rep. 6, 229-234[CrossRef][Medline] [Order article via Infotrieve]
  3. Liochev, S. I., and Fridovich, I. (1999) IUBMB Life 48, 157-161[Medline] [Order article via Infotrieve]
  4. Valentine, J. S., and Gralla, E. B. (1997) Science 278, 817-818[Free Full Text]
  5. Pena, M. M., Lee, J., and Thiele, D. J. (1999) J. Nutr. 129, 1251-1260[Abstract/Free Full Text]
  6. Eide, D. J. (2000) Adv. Microb. Physiol. 43, 1-38[Medline] [Order article via Infotrieve]
  7. Labbe, S., Zhu, Z., and Thiele, D. J. (1997) J. Biol. Chem. 272, 15951-15958[Abstract/Free Full Text]
  8. Yamaguchi-Iwai, Y., Serpe, M., Haile, D., Yang, W., Kosman, D. J., Klausner, R. D., and Dancis, A. (1997) J. Biol. Chem. 272, 17711-17718[Abstract/Free Full Text]
  9. Martins, L. J., Jensen, L. T., Simon, J. R., Keller, G. L., and Winge, D. R. (1998) J. Biol. Chem. 273, 23716-23721[Abstract/Free Full Text]
  10. Gross, C., Kelleher, M., Iyer, V. R., Brown, P. O., and Winge, D. R. (2000) J. Biol. Chem. 275, 32310-32316[Abstract/Free Full Text]
  11. Yamaguchi-Iwai, Y., Dancis, A., and Klausner, R. D. (1995) EMBO J. 14, 1231-1239[Medline] [Order article via Infotrieve]
  12. Yamaguchi-Iwai, Y., Stearman, R., Dancis, A., and Klausner, R. D. (1996) EMBO J. 15, 3377-3384[Medline] [Order article via Infotrieve]
  13. Rutherford, J. C., Jaron, S., Ray, E., Brown, P. O., and Winge, D. R. (2001) Proc. Natl. Acad. Sci. U. S. A. 98, 14322-14327[Abstract/Free Full Text]
  14. Blaiseau, P. L., Lesuisse, E., and Camadro, J. M. (2001) J. Biol. Chem. 276, 34221-34226[Abstract/Free Full Text]
  15. Rutherford, J. C., Jaron, S., and Winge, D. R. (2003) J. Biol. Chem. 278, 27636-27643[Abstract/Free Full Text]
  16. Askwith, C., Eide, D., Van Ho, A., Bernard, P. S., Li, L., Davis-Kaplan, S., Sipe, D. M., and Kaplan, J. (1994) Cell 76, 403-410[CrossRef][Medline] [Order article via Infotrieve]
  17. Yuan, D. S., Stearman, R., Dancis, A., Dunn, T., Beeler, T., and Klausner, R. D. (1995) Proc. Natl. Acad. Sci. U. S. A. 92, 2632-2636[Abstract/Free Full Text]
  18. Stearman, R., Yuan, D. S., Yamaguchi-Iwai, Y., Klausner, R. D., and Dancis, A. (1996) Science 271, 1552-1557[Abstract]
  19. Spizzo, T., Byersdorfer, C., Duesterhoeft, S., and Eide, D. (1997) Mol. Gen. Genet. 256, 547-556[Medline] [Order article via Infotrieve]
  20. Askwith, C. C., de Silva, D., and Kaplan, J. (1996) Mol. Microbiol. 20, 27-34[CrossRef][Medline] [Order article via Infotrieve]
  21. Deleted in proof
  22. Loguinov, A. V., Mian, I. S., and Vulpe, C. D. (2003) Genome Biol., in press
  23. Li, L., Chen, O. S., McVey Ward, D., and Kaplan, J. (2001) J. Biol. Chem. 276, 29515-29519[Abstract/Free Full Text]
  24. van Helden, J., Andre, B., and Collado-Vides, J. (1998) J. Mol. Biol. 281, 827-842[CrossRef][Medline] [Order article via Infotrieve]
  25. Kooperberg, C., Ruczinski, I., LeBlanc, M. L., and Hsu, L. (2001) Genet. Epidemiol. 21, Suppl. 1, S626-S631[Medline] [Order article via Infotrieve]
  26. Hughes, T. R., Marton, M. J., Jones, A. R., Roberts, C. J., Stoughton, R., Armour, C. D., Bennett, H. A., Coffey, E., Dai, H., He, Y. D., Kidd, M. J., King, A. M., Meyer, M. R., Slade, D., Lum, P. Y., Stepaniants, S. B., Shoemaker, D. D., Gachotte, D., Chakraburtty, K., Simon, J., Bard, M., and Friend, S. H. (2000) Cell 102, 109-126[CrossRef][Medline] [Order article via Infotrieve]
  27. Georgatsou, E., Mavrogiannis, L. A., Fragiadakis, G. S., and Alexandraki, D. (1997) J. Biol. Chem. 272, 13786-13792[Abstract/Free Full Text]
  28. Casas, C., Aldea, M., Espinet, C., Gallego, C., Gil, R., and Herrero, E. (1997) Yeast 13, 621-637[CrossRef][Medline] [Order article via Infotrieve]
  29. Lin, S. J., Pufahl, R. A., Dancis, A., O'Halloran, T. V., and Culotta, V. C. (1997) J. Biol. Chem. 272, 9215-9220[Abstract/Free Full Text]
  30. Kampfenkel, K., Kushnir, S., Babiychuk, E., Inze, D., and Van Montagu, M. (1995) J. Biol. Chem. 270, 28479-28486[Abstract/Free Full Text]
  31. Rad, M. R., Kirchrath, L., and Hollenberg, C. P. (1994) Yeast 10, 1217-1225[CrossRef][Medline] [Order article via Infotrieve]
  32. Hassett, R., Dix, D. R., Eide, D. J., and Kosman, D. J. (2000) Biochem. J. 351, 477-484[Medline] [Order article via Infotrieve]
  33. Waters, B. M., and Eide, D. J. (2002) J. Biol. Chem. 277, 33749-33757[Abstract/Free Full Text]
  34. Yun, C. W., Tiedeman, J. S., Moore, R. E., and Philpott, C. C. (2000) J. Biol. Chem. 275, 16354-16359[Abstract/Free Full Text]
  35. Yun, C. W., Ferea, T., Rashford, J., Ardon, O., Brown, P. O., Botstein, D., Kaplan, J., and Philpott, C. C. (2000) J. Biol. Chem. 275, 10709-10715[Abstract/Free Full Text]
  36. Foury, F., and Talibi, D. (2001) J. Biol. Chem. 276, 7762-7768[Abstract/Free Full Text]
  37. Protchenko, O., Ferea, T., Rashford, J., Tiedeman, J., Brown, P. O., Botstein, D., and Philpott, C. C. (2001) J. Biol. Chem. 276, 49244-49250[Abstract/Free Full Text]
  38. Givan, S. A., and Sprague, G. F., Jr. (1997) Mol. Biol. Cell 8, 1317-1327[Abstract]
  39. Feng, Y., and Davis, N. G. (2000) Mol. Cell. Biol. 20, 5350-5359[Abstract/Free Full Text]
  40. Roth, A. F., Feng, Y., Chen, L., and Davis, N. G. (2002) J. Cell Biol. 159, 23-28[Abstract/Free Full Text]
  41. Yuan, D. S., Dancis, A., and Klausner, R. D. (1997) J. Biol. Chem. 272, 25787-25793[Abstract/Free Full Text]
  42. Szczypka, M. S., Zhu, Z., Silar, P., and Thiele, D. J. (1997) Yeast 13, 1423-1435[CrossRef][Medline] [Order article via Infotrieve]
  43. Foury, F., and Roganti, T. (2002) J. Biol. Chem. 277, 24475-24483[Abstract/Free Full Text]
  44. Burns, N., Grimwade, B., Ross-Macdonald, P. B., Choi, E. Y., Finberg, K., Roeder, G. S., and Snyder, M. (1994) Genes Dev. 8, 1087-1105[Abstract/Free Full Text]
  45. Finegold, A. A., Shatwell, K. P., Segal, A. W., Klausner, R. D., and Dancis, A. (1996) J. Biol. Chem. 271, 31021-31024[Abstract/Free Full Text]
  46. Georgatsou, E., and Alexandraki, D. (1999) Yeast 15, 573-584[CrossRef][Medline] [Order article via Infotrieve]
  47. Yun, C. W., Bauler, M., Moore, R. E., Klebba, P. E., and Philpott, C. C. (2001) J. Biol. Chem. 276, 10218-10223[Abstract/Free Full Text]
  48. Lesuisse, E., Simon, M., Klein, R., and Labbe, P. (1992) J. Gen. Microbiol. 138 (Pt 1), 85-89[Medline] [Order article via Infotrieve]
  49. Rioux, C. R., Jordan, D. C., and Rattray, J. B. (1986) Arch. Biochem. Biophys. 248, 183-189[CrossRef][Medline] [Order article via Infotrieve]
  50. Rioux, C. R., Jordan, D. C., and Rattray, J. B. (1986) Arch. Biochem. Biophys. 248, 175-182[CrossRef][Medline] [Order article via Infotrieve]
  51. West, R. W., Jr., Crivellone, M. D., Ma, J., and Thomas, S. (1996) Gene (Amst.) 169, 119-124[CrossRef][Medline] [Order article via Infotrieve]
  52. Pandolfo, D., De Antoni, A., Lanfranchi, G., and Valle, G. (1996) Yeast 12, 1071-1076[CrossRef][Medline] [Order article via Infotrieve]
  53. MacDiarmid, C. W., Gaither, L. A., and Eide, D. (2000) EMBO J. 19, 2845-2855[CrossRef][Medline] [Order article via Infotrieve]
  54. Lyons, T. J., Gasch, A. P., Gaither, L. A., Botstein, D., Brown, P. O., and Eide, D. J. (2000) Proc. Natl. Acad. Sci. U. S. A. 97, 7957-7962[Abstract/Free Full Text]
  55. Lesuisse, E., and Labbe, P. (1995) Microbiology 141, 2937-2943[Abstract]
  56. Hirata, D., Yano, K., and Miyakawa, T. (1994) Mol. Gen. Genet. 242, 250-256[CrossRef][Medline] [Order article via Infotrieve]
  57. Bossier, P., Fernandes, L., Rocha, D., and Rodrigues-Pousada, C. (1993) J. Biol. Chem. 268, 23640-23645[Abstract/Free Full Text]

Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati    What's this?


This article has been cited by other articles:


Home page
 MicrobiologyHome page
A. Woodacre, R. P. Mason, R. E. Jeeves, and A. M. Cashmore
Copper-dependent transcriptional regulation by Candida albicans Mac1p
Microbiology, May 1, 2008; 154(5): 1502 - 1512.
[Abstract] [Full Text] [PDF]

Home page
 J. Biol. Chem.Home page
A. Singh, N. Kaur, and D. J. Kosman
The Metalloreductase Fre6p in Fe-Efflux from the Yeast Vacuole
J. Biol. Chem., September 28, 2007; 282(39): 28619 - 28626.
[Abstract] [Full Text] [PDF]

Home page
 J. Biol. Chem.Home page
D. J. Adle, D. Sinani, H. Kim, and J. Lee
A Cadmium-transporting P1B-type ATPase in Yeast Saccharomyces cerevisiae
J. Biol. Chem., January 12, 2007; 282(2): 947 - 955.
[Abstract] [Full Text] [PDF]

Home page
 Physiol. GenomicsHome page
H. van Bakel, E. Strengman, C. Wijmenga, and F. C. P. Holstege
Gene expression profiling and phenotype analyses of S. cerevisiae in response to changing copper reveals six genes with new roles in copper and iron metabolism
Physiol Genomics, August 11, 2005; 22(3): 356 - 367.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Supplemental Data
Right arrow All Versions of this Article:
279/6/4450    most recent
M212308200v1
Right arrow Alert me when this article is cited
Right arrow