Embed Size (px)
Citation preview
Yeast 14, 1417–1427 (1998)
Quantitative Analysis of Yeast Gene Function UsingCompetition Experiments in Continuous Culture
FRANK BAGANZ1, ANDREW HAYES1 RONNIE FARQUHAR2, PHILIP R. BUTLER1,DAVID C. J. GARDNER1 AND STEPHEN G. OLIVER1*1Department of Biomolecular Sciences, UMIST, PO Box 88, Manchester M60 1QD, U.K.2Pfizer Central Research, Sandwich, Kent CT13 9NJ, U.K.
One possible route to the evaluation of gene function is a quantitative approach based on the concepts of metaboliccontrol analysis (MCA). An important first step in such an analysis is to determine the effect of deleting individualgenes on the growth rate (or fitness) of S. cerevisiae. Since the specific growth-rate effects of most genes are likely tobe small, we employed competition experiments in chemostat culture to measure the proportion of deletion mutantsrelative to that of a standard strain by using a quantitative PCR method. In this paper, we show that bothdensitometry and GeneScan= analysis can be used with similar accuracy and reproducibility to determine theproportions of (at least) two strains simultaneously, in the range 10–90% of the total cell population. Furthermore,we report on a model competition experiment between two diploid nuclear petite mutants, homozygous for deletionsin the cox5a or pet191 genes, and the standard strain (ho::kanMX4/ho::kanMX4) in chemostat cultures under sixdifferent physiological conditions. The results indicate that competition experiments in continuous culture are asuitable method to distinguish quantitatively between deletion mutants that qualitatively exhibit the samephenotype. ? 1998 John Wiley & Sons, Ltd.
— Saccharomyces cerevisiae; functional genomics; quantitative phenotype; chemostat; competition.
*Correspondence to: S. G. Oliver.
INTRODUCTION
The yeast genome sequence contains a large pro-portion of genes (ca. 38%) whose biological func-tion is completely unknown (Mewes et al., 1997). Itis possible that these genes were not previouslydiscovered by classical or ‘function-first’ (Oliver,1996) molecular genetics because quantitative,rather than qualitative, data are required to revealtheir phenotypic effect. Another reason for consid-ering a quantitative approach to phenotypic analy-sis is the high level of redundancy that is apparentin the yeast genome (Mewes et al., 1997). For boththese reasons, Oliver (1996, 1997) proposed anapproach to the elucidation of gene function thatexploits the concepts of metabolic control analysis(MCA; Kacser and Burns, 1973). An importantfirst step in such an analysis is to determine the
CCC 0749–503X/98/151417–11 $17.50? 1998 John Wiley & Sons, Ltd.
effects of deleting individual genes on the growthrate (or fitness) of S. cerevisiae. Since the specificgrowth-rate effects of most genes are likely to besmall (see Teusink et al., 1998), we need a verysensitive method of measuring any changes ingrowth rate which may result from the specificdeletion of a novel yeast gene. Competition exper-iments between mutant and wild-type yeast havebeen shown to provide such a sensitive way ofmeasuring small growth rate differences (Danhashet al., 1991; Baganz et al., 1997).
In their approach to competition analysis (‘gen-etic footprinting’), Smith et al. (1995) generatedyeast mutants by Ty1 transpositions. These mu-tants were grown in large populations under dif-ferent selections using serial batch transfers toextend the period of competition. The relativeproportions of the different mutant strains in thepopulation were monitored by PCR, using a com-
Received 2 April 1998Accepted 18 July 1998
1418 . .
mon primer complementary to the Ty sequenceand a series of fluorescently-labelled primerscomplementary to the flanking sequences ofthe genes whose quantitative phenotype was tobe assessed. A DNA sequencer equipped withGenScan= software was used to perform theanalysis of the PCR products. Smith et al. (1996)applied this approach to the quantitative pheno-typic analysis of yeast chromosome V. Theywere able to obtain data for 261 (97%) of thepredicted protein-encoding genes which showed,for ca. 60%, a detectable reduction in fitness in oneor more of seven different selection protocols;for the remaining genes, no phenotypic effect wasfound.
In a comparable approach, Thatcher et al.(1998) employed competition experiments to deter-mine the selective fitness of a random collection ofdisruption mutants relative to their wild-typeparental strain. Similar to the ‘genetic footprinting’strategy, the mutants were generated by transpo-son insertion using a mini-Tn3::LEU2 transposoncontaining the lacZ coding sequences (Burns et al.,1994). Again, serial batch transfer was employedto extend the generation times. However, in con-trast to the approach by Smith et al. (1996), theyeliminated all mutants with more than one chro-mosomal insertion from their experiments. Theyfound that ca. 70% of the mutants with no obviousphenotype had a selective disadvantage comparedto the wild-type under routine growth conditions.The authors suggested that this result, which is inagreement with the work by Smith et al. (1996),supports the hypothesis that non-essential genesmay provide a small but significant contribution tothe selective fitness of yeast, even under standardgrowth conditions (‘marginal benefit’; Thatcheret al., 1998), although they cannot completely ruleout that these genes also have a specific functionunder other growth conditions.
In spite of their successes, both strategies have anumber of drawbacks for an MCA approach tothe elucidation of gene function (see Baganz et al.,1997), which have led us to perform experiments inchemostat cultures with specific deletion mutants,generated by PCR-mediated gene disruption usingthe KanMX marker (Wach et al., 1994), competedagainst an isogenic standard strain carrying aKanMX replacement of the HO gene. This allowsthe accurate quantification of the impact of aspecific single-gene deletion on growth rate and,moreover, the deletion mutant can subsequently beused for more specific phenotypic analyses. HO is
? 1998 John Wiley & Sons, Ltd.
used because it has been demonstrated to be aneutral site for replacement (Baganz et al., 1997).Whenever possible, a diploid strain, homozygousfor the deletion, should be employed because themating of independently derived haploid trans-formants allows any transformation-induced gen-etic lesions (Danhash et al., 1991) to be nullifiedthrough complementation.
In this paper, we evaluate a quantitative PCRanalysis which may be used to determine theproportion of deletion mutants relative to that ofthe standard strain in our competition exper-iments. For quantification of PCR products, eitherdensitometry or the ABI GeneScan= system wasused. The results indicated that both methods weresimilarly accurate and reproducible and may beemployed to determine the proportions of (at least)two strains simultaneously in the range ca. 10–90%of the total cell population (100%). Furthermore,we report on a model competition experimentbetween two diploid nuclear petite mutants,homozygous for deletions in the cox5a or pet191genes, and the standard strain (ho::kanMX4/ho::MX4) in chemostat cultures under six differentphysiological conditions. The test deletants exhib-ited quantitatively, but not qualitatively, differentphenotypes; the cox5a deletant was partlyrespiratory-deficient and the pet191 deletant com-pletely so. The change in the relative proportionsof the three strains was determined by multiplexPCR using densitometry to quantify the PCRproducts. The results showed that, under glucoselimitation (aerobic and anaerobic) and ethanollimitation, the standard strain had a competitiveadvantage relative to the test deletants, whereasunder all other nutrient limitations (i.e. nitrogen,phosphorus and sulphur), the partly respiratory-deficient cox5a deletant exhibited the highestapparent specific growth rate.
MATERIALS AND METHODS
Yeast strains and mediaThe S. cerevisiae strains used in this work are
listed in Table 1. The strains were grown on 1%(w/v) yeast extract, 2% (w/v) peptone, and 2%(w.v) glucose YPD or 3% glycerol (YPG). Further-more, complete synthetic media, containing perlitre: 1·7 g yeast nitrogen base (Difco Laborato-ries), 5 g ammonium sulphate, and 20 g glucose(SC) was used. The medium was supplemented asrequired with 20 mg uracil, adenine and the amino
Yeast 14, 1417–1427 (1998)
1419
acids -histidine, -tryptophan, and 100 mg-leucine. Solid media contained, in addition, 2%(w/v) agar (Difco Laboratories).
Isolation of yeast genomic DNAYeast genomic DNA was prepared as described
by Kaiser et al. (1994).
Table 1. Saccharomyces cerevisiae strains used in this work.
Strain Genotype Source
BMA41-1B MATá; ura3-1; trp1-Ä2; leu2-3,112; his3-11,15;ade2-1;can1-100 A. BaudinBMA41-1B/ÄHO MATá; ura3-1; trp1-Ä2; leu2-3,112; his3-11,15; ade2-1; can1-100; ho::HIS3 F. BaganzFY23/ÄCOX5a MATa; ura3-52; trp1-Ä63; leu2-Ä1; cox5a::kanMX4 A. HutterFY23/ÄPET191 MATa; ura3-52; trp1-Ä63; leu2-Ä1; pet191::kanMX4 A. HutterFY73/ÄCOX5a MATá; ura3-52; trp1-Ä63; leu2-Ä1; cox5a::kanMX4 A. HutterFY73/ÄPET191 MATÄ; ura3-52; trp1-Ä63; leu2-Ä1; pet191::kanMX4 A. HutterFY1679/ÄCOXa MATa/MATá; ura3-52/ura3-52; trp1-Ä63/+; leu2-Ä1/+; his3-Ä200/+;
cox5a::kanMX4/cox5a::kanMX4This work
FY1679/ÄPET191 MATa/MATá; ura3-52/ura3-52; trp1-Ä63/+; leu2-Ä1/+; his3-Ä200/+;pet191::kanMX4/pet191::kanMX4
This work
FY1679/ÄHO MATa/MATá; ura3-52/ura3-52; trp1-Ä63/+; leu2-Ä1/+; his3-Ä200/+;ho::kanMX4/ho::kanMX4
F. Baganz
Table 2. Oligonucleotides used in this work.
Oligo Sequence Description
1 5*-AAATGAGGTTTGCAGAAGCT"3* FP-HO2 5*-GCATTTCTACCACTTTTTTCC-3* RP-HO3 5*-AGCTTGACCGAGAGCAATCCC-3* RP-HIS34 5*-AACGTGAGTCTTTTCCTTACC-3* RP-kanMX5 5*-CGCCTCCCTACGCTTC-3* FP-COX5a6 5*-ACCGCAGTACAGTCTGATTG-3* FP-PET191
The abbreviations FP and RP stand for forward and reverse primer, respectively.
Quantitative PCR analysisFor the development of the quantitative PCR
procedure multiplex PCR with three primers perreaction was performed (Table 2), oligonucleotides1–3) and two representative PCR products of496 bp (HO wild-type) and 710 bp (ho::HIS3deletion) amplified. For GeneScan= analysis, thereverse primers (Table 2, oligonucleotides 2 and 3)were 5*-labelled with the fluorescent dyes6-carboxyfluorescein (6-FAM; Applied Biosys-tems, UK). The proportion of the three strains inthe model competition experiment was determined
by multiplex PCR with four primers per reaction? 1998 John Wiley & Sons, Ltd.
(Table 2, oligonucleotides 1, 4, 5 and 6) to amplifythree specific PCR products of 630 bp (ÄHO),770 bp (ÄCOX5a) and 920 bp (ÄPET191). Thereaction mix consisted of 2·5 units of Taq DNApolymerase (Boehringer-Mannheim Ltd, UK),50 m KCl, 10 m Tris-HCl (pH 8·3), 1·5 mMgCl2, 0·1 m each dNTP, 0·2 ì each primer,and ca. 20–100 ng of template DNA in a finalvolume of 50 ìl. All reactions were performedusing a programmable thermal cycler (Techne,UK); using the following conditions: 1 cycle of 2min at 94)C; followed by 15–30 cycles of 0·5 mindenaturation of 94)C; 0·5 min annealing at 55)C;and 1 min elongation at 72)C. A final elongationstep for 2 min at 72)C completed the reaction.
For densitometric measurements, PCR products(10 ìl) were separated on 1·2% agarose gels. Thegel was photographed and the negative scanned onan HP scanner (Scanjet 4c/T, Hewlett Packard,UK) and saved as a tagged image file. This filewas imported into the image analysis software
=
(Molecular Analyst , Bio-Rad) and the ratio ofYeast 14, 1417–1427 (1998)
1420 . .
PCR products determined by profile analysis ofthe bands with peak integration.
For GenScan analysis, 1 ìl of fluorescently-labelled PCR products were mixed with 3 ìl ofloading dye containing 0·5 ìl of loading dye con-taining 0·5 ìl of internal size standard GS-2500ROX (Applied Biosystems, UK) and separatedby on a 5% non-denaturing polyacrylamide gel(Flowgen Instruments Ltd, UK). Fragments weredetected using an automated ABI 363 DNAsequencer in GeneScan mode (Genescan 672=
software, Applied Biosystems, UK). The programestimated both the sizes of the PCR products, viathe internal lane standard, and the integratedfluorescence emissions of individual bands.
Competition experiments in chemostat cultureFor the model competition experiment, yeast
strains were cultivated as described by Baganzet al. (1997). In order to establish the chemostatconditions for competition experiments, determi-nation of four nutrient limitations (carbon, nitro-gen, phosphorus and sulphur) was carried out inbatch culture using shake flasks. The concen-tration of the limiting nutrient was varied while theconcentrations of all other media componentswere kept constant. In each case, the nutrientconcentrations chosen for the competitions waswithin the portion of the curve where biomassconcentration was directly proportional to nutri-ent concentration. The validity of the chosen con-centration to exert nutrient limitation in chemostatculture was then verified by nutrient-pulse exper-iments in continuous culture. Full details andresults of these control experiments are provided inBaganz (1997).
Equal volumes of the three strains, grown tosimilar cell density, were mixed and used as inocu-lum. A batch competition was carried out beforecontinuous culture conditions were established.During the batch competition, and in continuousculture, samples were taken once a day for deter-mination of cell density (see Baganz et al., 1997)and PCR analysis.
Determination of the proportion of deletionmutants by replica-plating
For mixing experiment with two strains, theproportion of the ho::HIS3 deletants wasmeasured, as described by Baganz et al. (1997). Inorder to determine the proportion of the threestrains in the competition experiment (i.e. ho,
? 1998 John Wiley & Sons, Ltd.
cox5a, and pet191 deletants), the appropriatelydiluted cells were spread onto YPD plates. After 1day of incubation, the plates were scored and thecolonies replica-plated onto YPG medium. Thereplica-plates were scored for the ho deletant after1 day of incubation and, following incubation foranother 1–2 days, also for the slowly growingcox5a deletant. The proportion of the pet191deletant was calculated as 100% minus the totalnumber of colonies on YPG plates.
Calculation of growth rate differential and dataanalysis
The specific growth rate differential (ó) wascalculated as described by Baganz et al. (1997).For statistical analysis of data and preparation ofgraphs, the software Microcal Origin= (MicrocalSoftware Inc., USA) was used.
RESULTS AND DISCUSSION
Quantitative PCR evaluationFor the optimization of the PCR conditions,
mixing experiments with two DNA templates wereperformed using the HIS3 replacement in theBMA41-1B genetic background (see Baganz et al.,1997) as templates, since the HIS3-mediateddeletants (Rieger et al., 1997) of chromosome IIIwere the only significant collection of mutantsavailable at the time we began our study. GenomicDNA of the HO wild-type strain BMA41-1B andthe ho::HIS3 deletant (Baganz et al., 1997) weremixed in various proportions (see Table 3) and tworepresentative PCR products of 496 bp (HO wild-type) and 710 bp (ho::HIS3 deletion) amplified bymultiplex PCR. Quantification of these PCR prod-ucts was achieved by DNA fragment analysis usingboth densitometry and GeneScan=. The twoanalysis methods yielded similar results; the bestcorrelation of the PCR product ratios [HO/(HO+HIS3)] with the template ratios was ob-tained by using ca. 100 ng of template DNA in50 ìl reaction volume and the lowest cycle number,i.e. 15 or 16 cycles for GeneScan and densitometricanalysis, respectively. For PCR amplifications with§20 cycles, larger deviations of the product ratioscompared to the template ratios were found, par-ticularly if the proportion of HO DNA in thetemplate mixture was small (see Figure 1).
In order to determine the reproducibility of thePCR method, three independent reactions wereperformed under the optimized conditions and the
Yeast 14, 1417–1427 (1998)
1421
Table 3. Comparison of PCR product ratios [HO/(HO+HIS3)] from densitometric andGeneScan analysis with the template ratios [HO/(HO+his3)]. ‘Average’ mean values andstandard errors from three independent PCR amplifications with 15 and 16 cycles fordensitometric and GeneScan analysis, respectively, are shown.
PCR mixture Template ratio*
PCR product ratio
Densitometry GeneScan
1 0·104&0·005 0·101&0·006 0·097&0·0082 0·303&0·012 0·296&0·009 0·316&0·0143 0·499&0·014 0·506&0·007 0·526&0·0324 0·701&0·012 0·681&0·007 0·709&0·0045 0·899&0·006 0·891&0·014 0·893&0·003
*Assuming a pipetting error of &5%.
Figure 1. Effect of cycle number on the PCR product/template ratio obtained from GeneScan=
analysis. Results of PCRs with 15 (.), 20 (,), 25 (0), and 30 (1) cycles are shown. The dottedline indicates the directly proportional relationship between template and product ratios,assuming that the amplification efficiencies of both templates (HO and HIS3) are the same.
products analysed in triplicate using densitometryand GeneScan. The standard error of triplicateloadings was in all cases &5%, indicating a similarhigh precision for both methods. Since this errorwas, for all mixtures, smaller than the variationbetween the mean values of the PCR productratios of three independent PCRs (i.e. ca. &10%),the ‘average’ mean values of the product ratioswere calculated and compared to the templateratios. The results (Table 3) show a good corre-
? 1998 John Wiley & Sons, Ltd.
lation between the template ratios and the ‘aver-age’ PCR product ratios. In most cases, thedifference was within the error margin of &4% fordensitometric and &6% for GeneScan analysis.Although a direct comparison of these twomethods is not possible because of the PCRconditions employed (i.e. cycle number andoligonucleotide primers), the results suggestedthat both methods could be used with similaraccuracy for the quantification of PCR products.
Yeast 14, 1417–1427 (1998)
1422 . .
However, due to the fact that the GeneScan analy-sis is more expensive and time-consuming, thedensitometric method alone was used for furtherexperiments.
In order to mimic the situation in competitionexperiments, two strains (i.e. the parental HOwild-type BMA41-1B and the ho::HIS3 deletant)were grown to a similar cell density in YPD(OD600=1·4) and mixed in various proportions.Replica-plating was used to determine the ratio ofthe two strains in these mixtures. The results inTable 4 indicate that the standard error in theestimation of the ratio (obtained from 5–6 replicas)is dependent on the proportion of the ho::HIS3deletant in the mixture. The largest error wasobtained with mixture 1 (ca.&10%), which con-tained the smallest proportion of the ho::HIS3deletant and it decreased with higher proportionsof this strain in the mixture (ca.&1% in mixture5). This is probably due to the smaller number ofho deletant colonies on the replica-plates in mix-ture 1, compared to mixture 5 using the samedilution for the preparation. Genomic DNA iso-lated from these mixtures was used as a templatefor PCR, using 18 instead of 16 amplificationcycles due to the lower template concentration(data not shown). Three independent reactionswere carried out and triplicate loadings of the twoPCR products were quantified by densitometry.The difference of the means from three indepen-dent PCRs was in all cases larger than the standarderror of triplicate loadings of one PCR analysis.Thus, the ‘average’ mean values were calculatedand compared to the template ratios. The resultslisted in Table 4 indicate that the correlationbetween the PCR product ratios and the template
? 1998 John Wiley & Sons, Ltd.
was comparable to the first mixing experiment.The difference was, in all cases, within the errormargin of &7%. This result suggested that theadditional mixing step, and the template prep-aration, had no significant effect on the accuracyof the quantitative PCR analysis. However, com-pared to the first mixing experiment, the error inthe estimation of the template ratio is probablylarger, due to the serial dilutions required forreplica-plating and the relatively small number ofcolonies on the replica plates.
The results from these mixing experimentsshowed that the PCR procedure with 16 or 18cycles, depending on the template concentrations(ca. 20–100 ng of total genomic DNA in 50 ìlreaction volume), yielded exponential amplifica-tion and that the generated PCR products could beaccurately quantified by densitometric or Gene-Scan analysis. The error margin of this methodwas ca. &5% using triplicate loadings of threeindependent PCRs. Overall, it can be concludedthat the quantitative PCR procedure is an accuratemethod to measure the proportions of (at least)two strains simultaneously in the range ca. 10–90%of the total cell population (100%). Although all ofthese reconstitution experiments were carried outby using the HIS3 replacement in BAM41-1B, itcan be assumed that similar results could beobtained using the KanMX4 marker in theFY1679 strain, as the data shown below suggest.
Table 4. Comparison of PCR product ratios [HIS3/(HO+HIS3)] from densitometry with the templateratios from mixing experiment with two strains. ‘Aver-age’ mean values from three independent PCR amplifi-cations with 18 cycles and standard errors are shown.The template ratios were obtained from replica-plating(5–6 replicas).
Mixture Template ratio PCR product ratio
1 0·126&0·012 0·117&0·0082 0·286&0·016 0·308&0·0033 0·508&0·020 0·502&0·0124 0·682&0·013 0·726&0·0175 0·835&0·008 0·852&0·010
Model competition experimentIn order to demonstrate the feasibility of our
quantitative approach to the analysis of gene func-tion, a model competition experiment between theFY1679/ÄHO standard strain (i.e. ho::kanMX4/ho::kanMX4 deletant, see Baganz et al., 1997) andtwo test deletants, in which genes with knownfunction (COX5a and PET191 have been dis-rupted with the same marker, was carried out inchemostat culture.
Both test deletants exhibited a respiratory-deficient phenotype (nuclear petite), since thenuclear genes COX5a and PET191 are requiredfor respiratory functions. COX5a codes forsubunit Va of cytochrome c oxidase (Cumskyet al., 1985) and the gene product of PET191is a cytochrome c oxidase assembly factor(McEwen et al., 1993). The pet191 deletant iscompletely respiratory deficient (McEwen et al.,1993), whereas the cox5a deletant is still able torespire at 10–15% of the wild-type rate due to the
Yeast 14, 1417–1427 (1998)
1423
presence of a second gene, COX5b, encoding anisoform of the cytochrome c oxidase subunit Vpolypeptide, called Vb (Trueblood and Poyton,1987). Homozygous cox5a::kanMX4/cox5a::kan-MX4 and pet191::kanMX4/pet191::kanMX4FY1679 deletion mutants were generated by cross-ing two independently derived FY23 and FY73cox5a::kanMX4 or pet191::kan MX4 deletionmutants.
The change in the relative proportions of thethree strains in the chemostat was determined bymultiplex PCR using densitometry to quantify thePCR products. The ratio of the three strains in theinoculum was determined by three independentPCRs and the ‘average’ mean values calculated,since the standard error of triplicate loadings wasin all cases similar to the differences of the means(&3%) which is in agreement with the results ofthe reconstitution experiments. The results showeda good correlation between the PCR analysis andthe expected proportions from mixing equal vol-umes of these three strains with the same celldensity (ca. 33% each).
The competition experiments between the stan-dard strain and the test deletants were carriedout in chemostats under six different physio-logical conditions (see Baganz et al., 1997); i.e.glucose-limited/anaerobic, glucose-limited/aerobic,ethanol-limited, N-limited, P-limited, andS-limited (Figure 2). The initial ratio of these threestrains, at the start of the batch phase of growth,was approximately 1 : 1·3 : 1 (ho:coxa:pet191deletant) and the dilution rate was fixed atD=0·1 h"1. It should be noted that the batchphase kinetics may provide additional informationabout the phenotypes of the competing strains. Incontrast to chemostat culture, growth conditionsare not constant in batch, moreover the cells aregrowing at (or very close to) their maximumspecific growth rate. For our MCA approach tofunctional analysis, we are interested in whatoccurs at steady state and thus our methodprimarily relies on monitoring the relative changein the concentration of the three competing strainsin the chemostat population.
Under glucose limitation, similar results wereobtained with either aerobic or anaerobic condi-tions. In both cases, the proportion of the standardstrain increased by 25–30% before a ‘quasi steady-state’, i.e. a constant cell density, was obtained.This was mirrored by a similar large decrease inthe proportion of the pet191 deletant, whichdropped to non-detectable levels after a ‘quasi
? 1998 John Wiley & Sons, Ltd.
steady-state’ was reached. In contrast, the pro-portion of the cox5a deletant decreased only by10–15% over ca. 24 generations in continuousculture (see Figure 2A, B).
In order to determine the competitive fitness ofthe cox5a deletant relative to the standard strain,the growth rate differentials were calculated. Theresults (Table 5) indicated that the cox5a deletanthad a similar disadvantage in specific growth raterelative to the standard strain, under both glucose-limited conditions, which is equivalent to 3–4% pergeneration (however, the standard error of theslope (ó) was relatively large; &25% and &32%under anaerobic and aerobic conditions, respect-ively). The competitive disadvantage of the testdeletants seen under aerobic conditions is prob-ably due to the respiratory deficiency of thesestrains, since S. cerevisiae exhibits a strictly respir-atory glucose metabolism at low dilution ratesunder the conditions employed (Postma et al.,1989). The difference in respiratory capabilitybetween the test deletants could explain why thepet191 deletant was totally outcompeted, whereasthe cox5a deletant exhibited a much smallergrowth rate penalty relative to the standard strain.On the other hand, the similar competitive advan-tage of the standard strain found under anaerobicconditions was not expected, since only fermenta-tion of glucose should occur (Verduyn et al., 1990).We cannot provide a ready explanation for thisresult, but would note that Smith et al. (1996) alsofound unanticipated phenotypes for mutants ofknown genes.
Under ethanol limitation, both mutants werevirtually outcompeted by the standard strain bythe end of batch growth (Figure 2C). This resultconfirmed that the totally respiratory-deficientpet191 deletant is unable to grow on non-fermentable carbon sources (McEwen et al., 1986).The apparently similar large growth rate penaltyfor the cox5a deletant indicates that the low levelof respiration exhibited by this strain is not suffi-cient to sustain growth on a non-fermentablecarbon source, which is in agreement with thework of Trueblood and Poyton (1987). This resultsuggests that the maximum specific growth rate ofthe cox5a deletant was much smaller than that ofthe standard strain under these conditions.
Under N-limitation, the proportion of the cox5adeletant increased by ca. 30% over 25 generationsin continuous culture, concomitant with a similarlarge decrease in the standard strain’s proportion.Simultaneously, the proportion of the pet191
Yeast 14, 1417–1427 (1998)
1424 . .
Fig
ure
2.C
ompe
titi
onex
peri
men
tsbe
twee
nth
est
anda
rdst
rain
(ho
dele
tant
)an
dtw
ote
stde
leta
nts
inch
emos
tat
cult
ure
unde
rsi
xdi
ffer
ent
phys
iolo
gica
lcon
diti
ons:
gluc
ose-
limit
ed/a
erob
ic(A
),gl
ucos
e-lim
ited
/ana
erob
ic(B
),et
hano
l-lim
ited
(C),
N-l
imit
ed(D
),P
-lim
ited
(E)a
ndS-
limit
ed(F
).T
hepr
opor
tion
ofth
eth
ree
hom
ozyg
ous
dele
tant
s,de
term
ined
byqu
anti
tati
veP
CR
,is
plot
ted
agai
nst
the
num
ber
ofge
nera
tion
s:ho
(.);
cox5
a(-
);an
dpe
t191
(0)
dele
tant
.T
hein
itia
lra
tio
ofth
eth
ree
stra
ins
was
ca.
1:1
·3:1
(ho:
cox5
a:pe
t191
)an
dth
edi
luti
onra
tese
tat
D=
0·1
h"
1.
The
erro
rba
rssh
owst
anda
rder
rors
from
the
mea
nof
trip
licat
em
easu
rem
ents
.T
hefir
star
row
indi
cate
sth
est
art
ofth
ech
emos
tat
com
peti
tion
and
the
seco
ndar
row
whe
na
‘qua
sist
eady
-sta
te’
was
obta
ined
.
? 1998 John Wiley & Sons, Ltd. Yeast 14, 1417–1427 (1998)
1425
deletant decreased from ca. 30% to ca. 15% over 10generations in continuous culture, followed by a10% increase after a ‘quasi steady-state’ wasobtained (see Figure 2D). These data suggest thatthe cox5a deletant has a considerable advantage inspecific growth rate over the other two strains. Inorder to determine the competitive fitness ofthe test deletants relative to the standard strain, thegrowth rate differentials were calculated. Theresults (Table 5) indicate that both test deletantshad a competitive advantage relative to thestandard strain. The large advantage in specificgrowth rate for the cox5a deletant, which isequivalent to 8–9% per generation, underlined theobserved competition kinetics, whereas the slightlysmaller growth advantage of the pet191 deletantover the standard strain (5–6% per generation),was not so obvious from Figure 2D.
In comparison to the experiment underN-limitation, similar competition kinetics wereobserved under P- and S-limitation (Figure 2E, F)although these were run for a shorter period oftime. From these data, growth rate differentials ofca. 2·0% h"1 and ca. 0·6% h"1 for the cox5a andpet191 deletants, respectively, were estimated fromthe competition experiment under S-limitation (seeTable 5). This result indicated that the cox5adeletant had a large advantage in specific growthrate compared to the standard strain, which isequivalent to ca. 14% per generation. In contrast,the pet191 deletant had a much smaller competi-tive advantage (ca. 4% per generation), which is inagreement with the observed kinetics (see Figure2F). On the other hand, the growth rate differen-tials calculated from the data of the P-limitedcompetition, indicated that both test deletants hada similar large advantage in specific growth rate
? 1998 John Wiley & Sons, Ltd.
relative to the standard strain, which is equivalentto 12–15% per generation.
Overall, the results of the competition exper-iments under N-, P- and S-limitation suggestedthat the cox5a deletant had a significant advantagein specific growth rate compared to the other twostrains. The reason for this is unclear. However,under these conditions where glucose should be inexcess, it can be assumed that the cells exhibit arespiro-fermentative metabolism (e.g. see Larssonet al., 1993). It is conceivable that the cox5adeletant may be better able to adapt its cellularfunctions to these conditions than the standardstrain due to the deletant’s already limited respir-atory capacity. On the other hand, its remaininglimited degree of respiration could explain theadvantage in specific growth rate of the cox5adeletant compared to that of the pet191 deletant.
Table 5. Specific growth rate differential (ó) for testdeletants (x1) relative to the standard strain (x1). ó isdefined as slope of 1n (x1/x2) against time.
Limitation Test deletant ó (% h"1)
Glucose/aerobic cox5a "0·397&0·127Glucose/anaerobic cox5a "0·554&0·140Nitrogen cox5a 1·242&0·231
pet191 0·815&0·147Phosphorus cox5a 2·198&0·830
pet191 1·698&0·849Sulphur cox5a 2·022&0·433
pet191 0·602&0·041
CONCLUSIONS
Our results indicate that competition experimentsin continuous culture can be used, in a quantitativeapproach to functional analysis based on ‘top-down’ MCA, for the initial assignment of noveldeletion mutants to a metabolic unit. Bothmutants showed similar competitive behaviour,relative to the standard strain, under all conditionstested. Moreover, we were able to distinguishquantitatively between deletion mutants whichexhibit a qualitatively similar phenotype, thusunderlining the sensitivity of the approach.Although these conclusions should be verified byfurther competition experiments involving mutantscarrying deletions in known genes, it seemsreasonable to pursue this general approach.
Our results have also shown that quantitativePCR analysis can be used to determine the pro-portion of at least three strains in the culturesimultaneously. The next step will be to increasethe throughput of the analysis. The densitometricmethod employed for the quantification of PCRproducts is limited in the extent to which it can bemultiplexed. This means that repeated PCR reac-tions must be performed on a single DNA extractfrom a culture where several test strains are incompetition with the standard. The use of differentfluorescently-labelled primers in combination withGeneScan= analysis permits further improvementsin throughput. However, this method also has onlya limited multiplexing capability. Therefore, therecent development of a method to generate
Yeast 14, 1417–1427 (1998)
1426 . .
deletion mutants carrying specific oligonucleotidetags (so-called ‘molecular bar-codes’; Shoemakeret al., 1996) offers the best prospect of more ef-ficient quantification by employing hybridization-array technology (Schena et al., 1996). This wouldallow a larger number of mutants to be investi-gated in competition experiments under a widerrange of selective conditions.
Another likely advantage of the ‘molecularbar-coding’ approach relates to possible inter-actions between competing strains in the culture.Especially when a large number of mutants arecompeted against the standard strain, there is apossibility that the estimated growth rate differen-tial for any one mutant (relative to the standardstrain) is affected by the presence of the othermutants. One way to avoid this problem is toperform competition experiments with a largeexcess of the standard strain (e.g. §99%) and verysmall proportions of the deletion mutants. Thisshould minimize any possible interactions betweenthe mutants other than competition for thegrowth-limiting nutrient and, thus, allow us toincrease the number of mutants in our competitionexperiments. Our data show that the quantitativePCR procedure must be further improved if it is tohave the sensitivity to deal with such large tem-plate ratios. The hybridization-array analysis ofpopulations of bar-coded mutants may not sufferfrom the same drawback. In the next stage of thisproject, these methods will be applied to the analy-sis of novel deletion mutants to verify their suit-ability and reliability for the MCA approach to thequantitative analysis of gene function. It is not theaim of this analysis to provide a detailed physi-ological explanation of the effect of a givendeletion on yeast’s phenotype. Rather, this methodis designed to group novel genes into primaryfunctional categories by the similarity of theirdeletant’s competition kinetics, either to oneanother or to those of deletants of known genes(Teusink et al., 1998).
ACKNOWLEDGEMENTS
We are grateful to Anton Hutter, Achim Wachand Christoph Cullin for gifts of strains and plas-mids, and to Paul Lane for helpful discussions.Work on the functional analysis of the yeastgenome, in our laboratory, is supported by theChemicals and Pharmaceuticals Directorate ofthe BBSRC, the EUROFAN Project of the EC,
the Wellcome Trust, Pfizer Central Research,? 1998 John Wiley & Sons, Ltd.
Applied Biosystems, and Amersham International.FB would like to thank Pfizer Central Research fortheir generous provision of a Studentship.
REFERENCES
Baganz, F. (1997). A quantitative approach to theanalysis of gene function in Saccharomyces cerevisiae.Phd thesis, UMIST.
Baganz, F., Hayes, A., Marren, D., Gardner, D. C. J.and Oliver, S. G. (1997). Suitability of replacementmarkers for functional analysis studies in Saccharo-myces cerevisiae. Yeast 13, 1563–1573.
Burns, N., Grimwade, B., Ross-Macdonald, P. B., Choi,E. Y., Finberg, K., Roeder, G. S. and Snyder, M.(1994). Large-scale analysis of gene expression, pro-tein localization, and gene disruption in Saccharomy-ces cerevisiae. Genes Dev. 8, 1087–1105.
Cumsky, M. G., Ko, C., Trueblood, C. E. and Poyton,R. O. (1985). Two nonidentical forms of subunit V arefunctional in yeast cytochrome c oxidase. Proc. Natl.Acad. Sci. USA 82, 2235–2239.
Danhash, N., Gardner, D. C. J. and Oliver, S. G. (1991).Heritable damage to yeast caused by transformation.Bio/Technology 9, 179–182.
Kacser, H. and Burns, J. A. (1973). The control of flux.Symp. Soc. Exp. Biol. 27, 65–104.
Kaiser, C., Michaelis, S. and Mitchell, A. (1994). Meth-ods in Yeast Genetics: A Laboratory Course Manual.Cold Spring Harbor Laboratory Press, New York.
Larsson, C., von Stockar, O, Marison, I. and Gustafsson,L. (1993). Growth and metabolism of Saccharomycescerevisiae in chemostat cultures under carbon-,nitrogen-, or carbon- and nitrogen-limiting condi-tions. J. Bacteriol. 175, 4809–4816.
McEwen, J. E., Ko, C., Kloeckener-Gruisem, B. andPoyton, R. O. (1986). Nuclear functions required forcytochrome c oxidase biogenesis in Saccharomycescerevisiae—characterization of mutants in 34 comp-lementation groups. J. Biol. Chem. 261, 1872–1879.
McEwen, J. E., Hong, K. H., Park, S. and Preciado, G.T. (1993). Sequence and chromosomal localization oftwo PET genes required for cytochrome c oxidaseassembly in Saccharomyces cerevisiae. Curr. Genet. 23,9–14.
Mewes, H. W., Albermann, K., Bahr, M., Frishman, D.,Gleissner, A., Hani, J., Heumann, K., Kleine, K.,Maierl, A., Oliver, S. G., Pfeiffer, F. and Zollner, A.(1997). Overview of the yeast genome. Nature 387(Suppl.), 7–65.
Oliver, S. G. (1996). From DNA sequence to biologicalfunction. Nature 379, 597–600.
Oliver, S. G. (1997). From gene to screen with yeast.Curr. Opinion Genet. Dev. 7, 405–409.
Postma, E., Verduyn, C., Scheffers, W. A. and van
Dijken, J. P. (1989). Enzymatic analysis of theYeast 14, 1417–1427 (1998)
1427
Crabtree effect in glucose-limited chemostat culturesof Saccharomyces cerevisiae. Appl. Environ. Microbiol.55, 468–477.
Rieger, K.-J., Kaniak, A., Copee, J.-Y., et al. (1997).Large-scale phenotypic analysis—the pilot project onchromosome III. Yeast 13, 1547–1562.
Schena, M., Shalon, D., Heller, R., Chai, A., Brown,P. O. and Davis, R. W. (1996). Parallel humangenome analysis—microarray-based expression moni-toring of 1000 genes. Proc. Natl Acad. Sci. USA 93,10614–10619.
Shoemaker, D. D., Lashkari, D. A., Morris, D.,Mittmann, M. and Davis, R. W. (1996). Quantitativephenotypic analysis of yeast deletion mutants using ahighly parallel molecular bar-coding strategy. NatureGenet. 14, 450–456.
Smith, V., Botstein, D. and Brown, P. O. (1995). Geneticfootprinting—a genomic strategy for determining agene’s function given its sequence. Proc. Natl. Acad.Sci. USA 92, 6479–6483.
Smith, V., Chou, K. N., Lashkari, D., Botstein, D. andBrown, P. O. (1996). Functional analysis of the genes
? 1998 John Wiley & Sons, Ltd.
of yeast chromosome V by genetic footprinting.Science 274, 2069–2074.
Teusink, B., Baganz, F., Westerhoff, H. V. and Oliver,S. G. (1998). Metabolic control analysis as a toolin the elucidation of the function of novel genes. InTuite, M. F. and Brown, A. J. (Eds), Methods inMicrobiology 26, 297–336.
Thatcher, J. W., Shaw, J. M. and Dickinson, W. J.(1998). Marginal fitness contributions of nonessentialgenes in yeast.Proc. Natl. Acad. Sci. USA 95, 253–257.
Trueblood, C. E.and Poyton, R. O. (1987). Differentialeffectiveness of yeast cytochrome c oxidase subunit Vgenes results from differences in expression, not func-tion. Mol. Cell. Biol. 7, 3520–3526.
Verduyn, C., Postma, E., Scheffers, W. A. and VanDijken, J. P. (1990). Physiology of Saccharomycescerevisiae in anaerobic glucose-limited chemostat cul-tures. Gen. Microbiol. 136,395–403.
Wach, A., Brachat, A., Pohlmann, R. and Philippsen, P.(1994). New heterologous modules for classicalor PCR-based gene disruptions in Saccharomycescerevisiae. Yeast 10, 1793–1808.
Yeast 14, 1417–1427 (1998)
