Exploring and dissecting genome-wide gene expression responses of Penicillium chrysogenum to phenylacetic acid consumption and penicillinG production
© Harris et al; licensee BioMed Central Ltd. 2009
Received: 02 September 2008
Accepted: 10 February 2009
Published: 10 February 2009
Since the discovery of the antibacterial activity of penicillin by Fleming 80 years ago, improvements of penicillin titer were essentially achieved by classical strain improvement through mutagenesis and screening. The recent sequencing of Penicillium chrysogenum strain Wisconsin1255-54 and the availability of genomics tools such as DNA-microarray offer new perspective.
In studies on β-lactam production by P. chrysogenum, addition and omission of a side-chain precursor is commonly used to generate producing and non-producing scenarios. To dissect effects of penicillinG production and of its side-chain precursor phenylacetic acid (PAA), a derivative of a penicillinG high-producing strain without a functional penicillin-biosynthesis gene cluster was constructed. In glucose-limited chemostat cultures of the high-producing and cluster-free strains, PAA addition caused a small reduction of the biomass yield, consistent with PAA acting as a weak-organic-acid uncoupler. Microarray-based analysis on chemostat cultures of the high-producing and cluster-free strains, grown in the presence and absence of PAA, showed that: (i) Absence of a penicillin gene cluster resulted in transcriptional upregulation of a gene cluster putatively involved in production of the secondary metabolite aristolochene and its derivatives, (ii) The homogentisate pathway for PAA catabolism is strongly transcriptionally upregulated in PAA-supplemented cultures (iii) Several genes involved in nitrogen and sulfur metabolism were transcriptionally upregulated under penicillinG producing conditions only, suggesting a drain of amino-acid precursor pools. Furthermore, the number of candidate genes for penicillin transporters was strongly reduced, thus enabling a focusing of functional analysis studies.
This study demonstrates the usefulness of combinatorial transcriptome analysis in chemostat cultures to dissect effects of biological and process parameters on gene expression regulation. This study provides for the first time clear-cut target genes for metabolic engineering, beyond the three genes of the β-lactam pathway.
Since the discovery of the production of antibiotics by the filamentous fungus Penicillium chrysogenum by Fleming in 1929 , much effort has been invested in selection and synthesis of strains with improved productivity [2, 3]. This research has made decisive contributions to the successful large-scale production of β-lactam antibiotics after World War II. After isolation of the high-producing wild-type P. chrysogenum strain NRRL 1951 from a cantaloupe , random mutagenesis with irradiation or chemicals, followed by selection for superior production strains, enabled an over 1000-fold increase of penicillin productivity .
The penicillin biosynthesis pathway has been well characterized both genetically and biochemically. Biosynthesis starts with the condensation of the three amino acids cysteine, valine and α-aminoadipic acid to form the tripeptide ACV. This reaction is catalyzed by the non-ribosomal peptide synthase ACVS encoded by pcbAB [6–8]. In the next step, the classic β-lactam ring structure is formed by isopenicillinN synthase (pcbC, IPNS) . IsopenicillinN forms the branch point for the penicillins and cephalosporins. Penicillins can be easily produced from isopenicillinN by the exchange of the α-aminoadipic acid moiety for a CoA activated side-chain, such as phenylacetic acid or phenoxyacetic acid, by acyl-CoA: isopenicillinN acyltransferase (penDE), which results in the production of penicillinG or penicillinV [6, 7]. These three biosynthesis genes were shown to be physically linked in a penicillin biosynthesis gene cluster (pcbAB-pcbC-penDE) [7, 10–13]. Present as a single copy in early strains, this gene cluster was shown to be present as amplified tandem repeats in later, high-producing strains [14, 15]. Although increases in copy number have indeed been shown to result in improved productivity, saturation occurs at very high copy numbers [5, 15], presumably due to limitations elsewhere in metabolism. In P. chrysogenum the biosynthesis pathway is compartmentalised. The enzymes involved in the first steps of biosynthesis, ACVS and IPNS, are localised in the cytosol, whereas the final steps, acyltransferase and the activation of the side-chain precursor by phenylacetyl-CoA ligase, take place in peroxisomes .
With some exceptions (e.g. clear increases in the copy numbers of penicillinG biosynthesis genes ), the molecular basis for high-level β-lactam production remains to be elucidated. Several cellular processes have been implicated in improved productivity, including a better utilisation of precursors, higher expression of biosynthesis genes, higher gene dosage and mutations in the regulatory genes controlling gene expression or other steps of the biosynthesis . For rational and successful metabolic engineering, identification of these mutations will be of extreme benefit. With the availability of the P. chrysogenum genome sequence , it is now possible to study penicillinG production at a genome-wide scale.
PenicillinG production requires an exchange of the α-aminoadipic acid side-chain of isopenicillinN for the side-chain precursor phenylacetic acid (PAA) . PAA is a weak acid (pKa = 4.3) and as such is likely to be toxic to cells depending on its concentration and the culture pH. Such toxicity may involve specific inhibitory effects of PAA on key enzymes in biomass or penicillinG production or, alternatively, more general mechanisms such as weak-acid uncoupling [19, 20]. PenicillinG producing P. chrysogenum can metabolise PAA via at least two routes: incorporation in the penicillinG molecule or catabolism via the homogentisate pathway to acetoacetate and fumarate [21–24]. Although this catabolic route has been described in P. chrysogenum [25, 26] little is known about the pleiotropic effects of PAA on P. chrysogenum. Only a few studies dedicated to the uptake of PAA have been reported and these show contrasting results. Hillenga et al.  and Eriksen et al.  both suggest that in production strains, and at moderate concentrations of PAA, this side-chain precursor enters the cell by simple diffusion across the plasma membrane. In contrast, at low external PAA concentrations, a high-affinity transporter has been proposed to contribute to PAA uptake by the Wisconsin54-1255 strain [27, 28].
The specific rate of β-lactam production by P. chrysogenum is strongly dependent on the availability of a suitable side-chain precursor. In many studies on the physiological impact of β-lactam production, a comparison is made between cultures grown in the absence and presence of a side-chain precursor (e.g. phenylacetic acid, phenoxyacetic acid or adipic acid) [19, 29, 30]. While this approach has contributed to our insight in the energetics and kinetics of penicillinG production, it does not allow a clear distinction between effects caused by, on one hand, β-lactam production and, on the other hand, PAA consumption and/or toxicity. A suitable experimental approach would be to compare two strains with similar backgrounds under well-defined culture conditions by using chemostat cultures, in which one strain would only be lacking the penicillin biosynthesis cluster.
Mutants of P. chrysogenum impaired in penicillin biosynthesis have been described [31–33]. Most of these mutants were derived by random mutagenesis from the low-producing strain Wisconsin54-1255, which contains one copy of the penicillin biosynthesis cluster. Although these mutants were very useful for studying gene expression and gene/enzyme relationships, for identification of the factors important for penicillinG production and PAA consumption a strain obtained from a high-producing strain background by targeted deletion is more beneficial. To this end we constructed a strain in which the tandem-repeated penicillin biosynthesis cluster was specifically deleted, whereas the strain background was retained. In such a way it is possible to identify, by a combinatorial approach, those genes affected by phenylacetic acid and those specifically important for penicillinG biosynthesis.
Results and Discussion
Generation of a penicillin gene cluster-free derivative of a penicillinG high-producing Penicillium chrysogenum strain
GAT TGG CGC TCC TCG TTC ACC
CCA TTA TTT TTC TAG TCG ACA TGG CAT CGA TTC CCA AGG CCA ATG TCC CC
GTT ACA CGC TTT GAT TCT GTG GGT ACC GAT GTT ATA TTC AGC TAC
CCC AAT AGC GGC CGC AGT TGA TAA TAT CAA TAT CTA AAA CTC CC
GGC ATA TAC GAG CAT GGT ACC AGG GAC AGA TGC CCA TCC TTG
GTA TAA AAG GGG AGG GTA CC G GGA AAG ATT TGT GGG CCT G
GTA TGT AGC TGC GGC CGC C TC CGT CTT CAC TTC TTC GCC CGC ACT
CCG CCT TCC TCA CTA ACC GGC CGG CAG GTA CC G ATG GAC TCA GCA TTA TC
CTC TAG AAT GCT ACG GCC GTT CGA GGT ACC TTA TAG GAA AAA GGT AG
CCT TTT CGC TGA GCG GCC GCA ATC ACA GGT ACC GTT TTT GTC GTC
ATG CCT CAA TCC TGG GAA GAA CTG
CTT GAC GTA GAA GAC GGC ACC GGC
CCC GCA GCA CAT ATG CTT CAC ATC CTC TGT CAA GGC
ATG ACA AAC ATC TCA TCA GGG
CAC AGA GAA TGT GCC GTT TCT TTG G
TCA CAT ATC CCC TAC TCC CGA GCC
GAA GAC GTC ATA CTT ATT CTC TG
CGG CAT CGG ATA AAG AGA TCT GG
Physiology of a penicillinG high-producing strain and a cluster-free derivative in chemostat cultures
Physiological and microarray quality parameters for aerobic, glucose-limited chemostat cultures grown at a dilution rate of 0.03 h-1
DS17690 - PAA
DS17690 + PAA
DS50661 - PAA
DS50661 + PAA
Y sx a (g·g -1 )
0.37 ± 0.01
0.35 ± 0.01
0.39 ± 0.01
0.36 ± 0.01
q pen b (μmol·g -1 ·h -1 )
0.00 ± 0.00
19.81 ± 1.47
0.00 ± 0.00
0.00 ± 0.00
q PAA c (μmol·g -1 ·h -1 )
0.00 ± 0.00
24.04 ± 2.38
0.00 ± 0.00
5.60 ± 0.38
q CO2 d (mmol·g -1 ·h -1 )
1.15 ± 0.08
1.42 ± 0.11
1.16 ± 0.05
1.44 ± 0.10
q O2 e (mmol·g -1 ·h -1 )
1.19 ± 0.11
1.42 ± 0.17
1.16 ± 0.11
1.43 ± 0.08
Avg CV f
Pc actA g
4190 ± 170
3560 ± 360
3450 ± 390
3030 ± 680
Pc gdh2 h
1240 ± 120
1140 ± 270
1020 ± 170
1060 ± 180
Although PAA could not be used for penicillinG production in the cluster-free strain, it was still consumed at circa 25% of the rate observed in the DS17690 strain. The PAA consumption rate in the cluster-free strain corresponded quantitatively to the PAA consumption that was not incorporated into penicillinG in the high-producing strain (Table 2). As reported previously for industrial strains of P. chrysogenum, this could be the result of oxidation to 2-hydroxyphenylacetic acid by phenylacetate hydroxylase and subsequent catabolism via the homogentisate pathway [25, 27].
Experimental design of transcriptome analysis and global gene expression responses to penicillinG production and PAA consumption
In total, four pair-wise comparisons between the two strains and the two conditions were performed (Figure 2), yielding a total of 1755 transcripts (representing 13% of the P. chrysogenum genome) that were differentially expressed in at least one of the comparisons based on the statistical criteria applied in this study (|fold difference| ≥ 2; false discovery rate 1%, see Methods section). The majority of the genome (8001 transcripts) did not show significant changes between the four conditions and transcript levels of 30% of the genome (3990 ORFs) was below the detection limit in all four situations (Figure 2).
Enrichment of functional categories in these clusters was assessed according to the MIPS functional categories annotation [17, 39] (Figure 3). To identify possible regulatory networks, a systematic search for possible protein-binding motifs in promoter sequences was performed on the different clusters.
Gene expression responses to removal of the penicillin biosynthesis gene cluster
Gene expression responses to the side-chain precursor phenylacetic acid (PAA)
Both strains showed penicillinG independent metabolism of PAA. In the DS17690 strain, this was evident from the observation that PAA consumption exceeded penicillinG production (Table 2). Indeed, the PAA hydroxylase gene (pahA)  that encodes the first step of the PAA catabolism through the homogentisate pathway was highly induced in cultures grown in the presence of PAA (from +10- to over 100-fold). However, it has been reported, already early in the strain improvement lineage that a mutation (L181F) in this gene results in a dramatic reduction of enzyme activity . Interestingly, a second gene (Pc16g01770), whose predicted protein sequence shared 42% identity with the pahA product also showed strongly elevated transcript levels (≥ + 20-fold) in the presence of PAA. This second gene is 82% identical to Aspergillus nidulans PhacB , which encodes a 3-hydroxyphenylacetate 6-hydroxylase and 3,4-dihydroxyphenylacetate 6-hydroxylase cytochrome P450 monooxygenase capable of converting PAA into 2-hydroxyphenylacetate. This second gene may well be responsible for residual PAA catabolism in industrial strains that carry a loss-of-function mutation in pahA. Based on genome annotation and the transcript profiles of cultures grown in the presence and absence of PAA, the entire homogentisate pathway, which ultimately leads to the formation of fumarate and acetoacetate, could be mapped. Genes encoding a homogentisate dioxygenase (Pc12g09040); a maleylacetoacetate isomerase (Pc12g09020) and a fumarylacetoacetase (Pc12g09030) were tentatively identified, completing the identification of the metabolic pathway. In contrast to the other PAA-utilizing pathway (penicillinG synthesis) the five genes of the homogentisate pathway all showed a strong gene level upregulation in presence of PAA (ranging from +6.6-fold to +90-fold). Apparently, despite the reduction of the pathway's activity, transcriptional regulation of the homogentisate pathway is still functional in high-producing strains of P. chrysogenum. As indicated by their gene identity codes, the ORFs Pc12g09020, Pc12g09030 and Pc12g09040 form a chromosomal cluster. A similar clustering of the homogentisate pathways genes has been observed in Aspergillus nidulans . An additional gene of this cluster, Pc12g09010, that shares 51% of identity with A. nidulans AN1893.3 and displays similarity with a putative transcription factor from Neosartorya fischeri, was also upregulated in presence of PAA in both DS17690 and DS50661 (+2.6-fold and +3.8 respectively). This observation makes it tempting to speculate that Pc12g09010 participates in transcriptional regulation of PAA catabolism (Figure 6). The characterisation of the genes encoding the pathway (a homogentisate dioxygenase (Pc12g09040); a maleylacetoacetate isomerase (Pc12g09020) and a fumarylacetoacetase (Pc12g09030)), the physical clustering of these genes and the presence of a co-clustered putative transcription factor represent interesting targets for metabolic engineering to eliminate residual rates of PAA consumption and to alleviate a potential protein burden  imposed by high-level induction of this pathway.
Transport mechanisms for β-lactam antibiotics and side-chain precursors, both across the fungal plasma membrane and between intracellular compartments, remain incompletely understood. The functional category analysis of genes that showed an increased transcript level in cultures grown in the presence of PAA showed a clear enrichment of the transport-related functions (Figure 3). 42 genes in the functional categories 'cellular transport and transport mechanisms' and 'transport facilitation' showed a significantly increased transcript level in cultures grown with PAA in both strains (Figure 3, group 1). The uptake of undissociated phenylacetic acid in P. chrysogenum has been reported to occur via passive diffusion . However, by analogy to the well-studied non-filamentous yeast Saccharomyces cerevisiae, in which PAA is exported by the ATP-binding cassette transporter Pdr12 , its anion form is likely to be actively exported into the medium as a detoxification mechanism. From the available functional annotation of the Penicillium genome, 2 out the 42 genes (Pc22g14600 and Pc22g20390) display motif signatures of ABC transporters, as well as sequence similarity to the Aspergillus nidulans atrB  and atrD  proteins, respectively. Only Pc22g14600 belongs to the ABC-G transporters cluster  that also includes Pdr12, which makes Pc22g14600 a very attractive candidate for further characterization.
Activation of PAA and the final biosynthetic step in the penicillinG biosynthesis pathway, the exchange of the aminoadipic acid side-chain for PAA, both occur in the peroxisome. This metabolic compartmentation of penicillinG production makes transport of isopenicillinN, PAA and penicillinG across the peroxisomal membrane an integral and important part of penicillinG biosynthesis. Only one of the transporter genes that showed an increased transcript level in cultures grown with PAA (Pc21g09430) showed a clear link with peroxisomes. This gene shows strong similarity to the Saccharomyces cerevisiae ANT1 gene that encodes a peroxisome-localised protein involved in adenine nucleotide transport, medium-chain fatty acid metabolism, and peroxisome proliferation .
The genes that showed a consistently lower transcript level in cultures grown in the presence of PAA (Figure 3 Group 2 (106 genes)), failed to show a clear enrichment of any functional category and, moreover, showed a high incidence of genes with unknown function and/or similarity with a gene of unknown function in another organism.
Dissection of gene expression responses to PAA and to penicillinG production
For future studies into the mechanism, compartmentation and regulation of penicillinG biosynthesis, it would be helpful to dissect transcriptional responses of side-chain precursor availability and penicillinG biosynthesis itself. The cluster-free strain described above synthesizes neither penicillinG nor any of its intermediates, even when grown in presence of PAA. Consequently, genes that show a transcriptional response to PAA that is specific for the high-producing strain DS17690 are likely to be related to penicillinG production rather than to the presence of PAA per se (Groups 5 and 6, Figure 3).
The transporter for penicillinG in P. chrysogenum is still unknown. Although it cannot be excluded that the penicillin transporter is among the 700 constitutively transcribed transporters, the 36 transporters in group 5 form an interesting group for initial analysis. Out of the 36 transporter-encoding genes found in group 5, 18 were assigned to transport of a wide range of nitrogen sources, including urea (2 genes), allantoate (7 genes), various amino acids including lysine and methionine (8 genes) and oligopeptides (1 gene). Whereas lysine and methionine transport might be related to the sulfur status of the cells (see above) the role of the other genes is more elusive. However, two observations on transport-related genes provide interesting leads for follow-up studies. Firstly, Pc22g11250, whose transcript profile correlated perfectly with the production of penicillin G, shows strong similarity with the A. niger gene An15g07460, which encodes an oligopeptide transporter. Interestingly transport of β-lactams through human intestinal epithelium involves an oligopeptide (di- and tripeptide) transporter . We are currently investigating the possibility that this transporter is involved in penicillinG export.
The second example a priori has no relationship with the penicillin synthesis; a group of 8 genes, whose products all show similarities with the yeast allantoate transporter Dal5, exhibited a significant upregulation under penicillinG producing conditions. These genes belong to a larger genome-wide family of 30 members. Although these 30 transporters share the same description "strong similarity to Dal5", they display very different expression profiles. Without functional analysis studies on these genes, any biological interpretation of this observation would remain speculative.
Along with the penicillinG synthesis, 18 genes that could be involved in secondary metabolism were also expressed to a higher level under penicillinG producing conditions. This group harboured two genes Pc21g23730 and Pc21g20650 that exhibit strong similarities with a feruoyl-CoA synthetase from A niger and a 4-coumarate-CoA ligase from Arabidopsis thaliana, respectively. While the transcript levels of these genes, remained lower than those of the two PAA-inducible putative aryl-CoA ligases mentioned above, this does not rule out a possible contribution of their gene products to in vivo PAA activation, which is further supported by the putative peroxisomal targeting signal that both harbour.
Analysis of upstream regulatory sequences
80 years after Fleming's discovery of the antibacterial activity of penicillin, research on Penicillium chrysogenum has now become accessible to genomics approaches . In the present study, we have integrated microarray-based transcriptome analysis with chemostat cultivation. This approach, which has already shown to be fruitful in other organisms such as Saccharomyces cerevisiae [54, 55, 58, 59], Trichoderma reesei  and Escherichia coli [61, 62], enables an investigation of the effect of individual culture parameters on genome-wide gene expression regulation. Reproducibility of transcript data is often cited as an additional advantage of chemostat-based microarray analysis . Although steady-state chemostat cultivation of filamentous fungi is experimentally more challenging than chemostat cultivation of non-filamentous microorganisms, the excellent reproducibility of the transcript data obtained with P. chrysogenum indicates that this does not preclude accurate and reproducible chemostat-based transcriptome analysis.
In aerobic, glucose-limited chemostat cultures of S. cerevisiae, ca. 86 % of its 6400 genes  showed a detectable transcript level. Of the much larger genome of P. chrysogenum, cultivation under similar conditions yielded a detectable transcript for only 67 % of the genes. Furthermore, of the 1755 genes that showed a differential transcript level under at least one of the conditions, 53% has an unknown function. This percentage is similar to the percentage of unclassified proteins throughout the whole genome sequence . These observations illustrate the formidable challenges that remain to be addressed in the functional analysis of the genomes of filamentous fungi. The identification of gene function in P. chrysogenum is likely to benefit tremendously from the rapid sequencing, annotation and analysis of the genomes of other filamentous fungi, such as those of N. crassa , A. fumigatus , A. nidulans , A. oryzae , A. niger [67, 68], T. reesei . For example, the recent characterisation of a new sulfate transporter gene, astA, in A. nidulans, homologous to the S. cerevisiae Dal5 transporter  enabled us to tentatively interpret the involvement of a similar gene in P. chrysogenum as being part of a broader sulfur-related response.
Carefully designed transcriptomics experiments can help to prioritize targets for functional analysis based on at least three criteria: (i) the absence of a detectable transcript level rules out that the gene product contributes to either fitness or industrial performance under the experimental conditions, (ii) gene expression regulation can provide insight into the possible role of gene products in an experimental context, although relations between transcript profiles and contribution to fitness are not necessarily straightforward [71–73], and (iii) the availability of possible sequence-derived information on the putative catalytic, structural or regulatory role(s) of gene products that suggest a role in fitness and/or industrial performance . Based on these criteria, several priority targets for future functional analysis studies have been identified in the present study (see Results and Discussion section).
The present study demonstrates how a simple combinatorial design of chemostat experiments, involving two P. chrysogenum strains, can be applied to dissect effects of side-chain-precursor availability and β-lactam production. While similar approaches have previously been applied to dissect effects of oxygen availability and nutrient limitation in S. cerevisiae [54, 55, 74], this is to our knowledge the first application of such an approach to a product-forming system. Our experimental design required a strain of P. chrysogenum that lacked a functional penicillin gene cluster. A cluster-free strain previously described , was derived from the low-producing Wisconsin54-1255 strain. As we sought to maximize the difference between producing and non-producing scenarios, a new cluster-free strain (DS50661) was derived from a penicillinG-high-producing strain background (DS17690). The approach presented in this paper should be applicable to the production of other secondary metabolites in systems where production can be controlled by the addition of a precursor molecule. In P. chrysogenum, this might for example include the production, by engineered strains, of the cephalosporin precursors 7-amino-deacetoxycephalosporanic acid (7-ADCA) , adipoyl-7-amino-3-carbamoyloxymethyl-3-cephem-4-carboxylic acid (ad7-ACCCA)  and deacetylcephalosporin C .
Penicillium chrysogenum strains DS17690 is a high producing strain and derived from the strain improvement program of DSM [29, 35]. DS50661 lacks the penicillin biosynthesis cluster and was constructed from DS17690 as described in this paper.
Preparation of protoplasts
Preparation of P. chrysogenum protoplasts was performed as described by Cantoral et al. , using Glucanex (Novo Nordisk, Bagsvaerd, Denmark) instead of Novozyme as the cell wall degrading enzyme. Protoplasts were separated from the mycelium, washed and plated on mineral medium agar , without phenylacetic acid, but supplemented with 15 g.l-1 agar to solidify and 1 M sucrose for osmotic stabilization. Regenerating colonies were transferred to plates without sucrose to induce sporulation. Spores were collected, washed with 0.85 % NaCl, diluted and plated out on YEPD agar plates (10 g l-1 Yeast Extract, 10 g l-1 Peptone, 20 g l-1 glucose and 15 g l-1 agar). Isolated colonies were transferred to mineral medium agar plates serving as stock culture plates.
Genomic DNA isolation
To isolate genomic DNA, P. chrysogenum strains were grown in mineral-medium shake-flask cultures for 48 h at 25°C and 280 rpm. Cells were harvested, washed with 0.85 % NaCl and the pellet was frozen in liquid N2. Frozen cells were grinded using a pestle and mortar, transferred to a plastic tube and an equal volume of phenol:CHCl3:isoamylalcohol (25:24:1) was added. This mixture was vortexed vigorously, centrifuged and the aqueous phase was transferred to a fresh tube. This procedure was repeated twice, each time using a fresh volume of phenol:CHCl3:isoamylalcohol (25:24:1). Finally, DNA was isolated from the aqueous phase by ethanol precipitation according to standard procedures.
Estimation of penicillin biosynthetic gene cluster copy numbers
Genomic DNA (3 μg) was digested with Eco RI, separated on a 0.6% agarose gel and transferred to a nylon membrane by vacuum Southern Blotting. Fragments of the pcbC and niaA genes were used as probes. The former probe gives an indication of the copy number of penicillin biosynthetic genes and the latter probe is a single copy gene (encoding nitrite reductase), in P. chrysogenum DS17690. The probe sequences were amplified using gene specific primers 1–4 (Table 1) and labelled with the ECL non-radioactive hybridisation kit (Amersham, Little Chalfont, UK) according to the supplier's instructions. The ratio between the intensity of both signals (pcbC /niaA) was used to estimate the relative gene copy number of the penicillin-gene cluster. The parent strain DS17690 and the single-copy lab strain Wisconsin54-1255 were used as controls.
Deletion of a single-copy penicillin biosynthetic gene cluster
Primer sets used for construction of double homologous crossover cassettes
Introduced restriction site
Introduced restriction site
Eco 52I, Acc 65I
Eco 52I, Acc 65I
Media and media composition
The mineral medium was prepared as described  and contained per litre of demineralised water 7.5 g glucose, 3.5 g (NH4)2SO4, 0.8 g KH2PO4, 0.5 g MgSO4·7H2O, 10 mL of a trace element solution. The trace element solution contained 15 g·L-1 Na2EDTA·2H2O, 0.5 g·L-1 Cu2SO4·5H2O, 2 g·L-1 ZnSO4·7H2O, 2 g·L-1 MnSO4·H2O, 4 g·L-1 FeSO4·7H2O, and 0.5 g·L-1 CaCl2·2H2O. Production of penicillinG was induced by adding 0.58 g·L-1 phenylacetic acid (PAA) to the medium. To ensure similar residual concentrations of PAA, 0.30 g·L-1 was added to the medium of the cluster-free strain DS50661.
Aerobic glucose-limited chemostat cultivation was performed at 25°C in 3-litre turbine-stirred bioreactors (Applikon, Schiedam, The Netherlands) with a working volume of 1.8 L. The pH was maintained at 6.5 via automated addition of 2 M NaOH (ADI 1030 biocontroller, Applikon, Schiedam, The Netherlands). The fermentor was sparged with air at a flow rate of 0.9 L·min-1 using a Brooks mass-flow controller (Brooks Instruments, Hatfield, USA) and stirred at 750 rpm. The dissolved-oxygen concentration was continuously monitored with an oxygen electrode (Applisens, Schiedam, The Netherlands). Continuous cultivation was initiated after 50–60 h of batch cultivation. The feed medium was supplied continuously by a peristaltic pump (Masterflex, Cole Parmer, USA) and the dilution rate was set at 0.03 h-1 for all chemostat experiments. Effluent was removed discontinuously by means of a special overflow device, which has been described previously . The time interval between effluent removals was fixed in such a way that each time approximately 1 % of the culture volume was removed. To prevent excessive foaming, silicone antifoam (10 % vol/vol, BDH Chemicals Ltd, Poole, UK) was discontinuously added at timed intervals. The offgas was cooled by a condensor at 4°C after drying with a Perma Pure dryer (type MD-110-48P-4, Perma Pure, Toms River, USA) oxygen and carbon dioxide concentrations were determined with a NGA 2000 analyser (Rosemount Analytical, Orville, USA). Off-gas flow rates were determined from an average of 10 measurements using a SAGA digital flow meter (Ion Science, Cambridge, UK). Specific rates of carbon dioxide and oxygen consumption were calculated as described previously .
Determination of culture dry weight
Culture samples (10 mL) were filtered over preweighed glass fiber filters (Type A/E, Pall Life Sciences, East Hills, USA). The filters were washed with demineralised water and dried for 20 min at 600 W in a microwave oven and were subsequently weighed.
Substrate and metabolite analysis
Glucose concentrations in the medium were determined by HPLC using an Aminex HPX-87H column (Biorad, Hercules, USA) at 60°C with 5 mM H2SO4 as the mobile phase. Phenylacetic acid and penicillinG concentrations were determined by isocratic HPLC using a Platinum EPS C18 column (Alltech, Deerfield, USA) at 30°C. The mobile phase consisted of 5 M acetonitrile with 5 mM KH2PO4 and 6 mM H3PO4.
Sampling and RNA extraction procedures
60 mL of culture broth was sampled and rapidly filtered over a glass fiber filter (Type A/E, Pall Life Sciences, East Hills, USA). The filter with mycelium was wrapped in aluminium foil, quenched in liquid nitrogen and stored at -80°C until further use. For total RNA extraction, half of the pellet was grounded by mortar and pestle under constant cooling with liquid nitrogen. The powder was dissolved in 5 mL of Trizol reagent (Invitrogen) and 1 mL chloroform (Sigma) and mixed well. The two phases were separated by centrifugation (4600 g, 15 min). Total RNA was isolated using a phenol-chloroform extraction method, which consisted of two extraction steps in acid-phenol/chloroform/isoamyl alcohol (5:1, pH 4.8, Ambion, Foster City, USA), followed by a chloroform extraction step. Each time the phases were separated by centrifugation (4600 g, 15 min). Total RNA was precipitated for 30 min at -20°C in 96% ethanol and 0.3 M sodium acetate. The RNA was collected by centrifugation at 23000 g for 15 min and dissolved in RNAse free H2O.
Microarray analysis: probe preparation and target hybridisation
Double stranded cDNA synthesis was carried out using 10 μg of total RNA and the components of the One Cycle cDNA Synthesis Kit (Affymetrix, Santa Clara, USA). The double-stranded cDNA was purified with the GeneChip Sample Cleanup Module (Affymetrix/Qiagen) followed by in vitro transcription and labelling using the GeneChip IVT labeling Kit (Affymetrix). Finally, labelled cRNA was purified (GeneChip Sample Cleanup Module, Affymetrix/Qiagen) prior to fragmentation. 15 μg of fragmented, biotinylated cRNA was hybridised to Affymetrix custom-made Penicillium chrysogenum GeneChip® microarrays (array code DSM_PENa520255F) at 45°C for 16 h as described in the Affymetrix users' manual. Washing and staining of arrays were performed using the GeneChip® Fluidics Station 400 and scanning with the Affymetrix GeneArray Scanner 3000.
Acquisition and quantification of array images were performed using Affymetrix GeneChip Operating Software (GCOS version 1.2). Before comparison, all arrays were globally scaled to a target value of 100 using the average signal from all gene features. To the 15,531 transcript features on the arrays, a filter was applied to extract 13,746 open reading frames of which there were 13,485 different genes. This discrepancy was due to several genes being represented more than once. To represent the variation in triplicate measurements, the coefficient of variation (S.D. divided by the mean) was calculated. When the genes were ranked according to increasing average intensity, the average coefficient of variation showed a sharp increase for the genes with the lowest expression. Therefore, all genes in which the average expression in all conditions was below 12 were removed from the dataset. Subsequently all remaining values below 12 were set to a value of 12. To assess differential expression, the Significance Analysis of Microarrays (SAM version 1.21) add-in to Microsoft™ Excel was used for comparisons of replicate array experiments [37, 84]. SAM identifies genes with statistically significant changes in expression by assimilating a set of gene-specific t tests. Each gene is assigned a score on the basis of its change in gene expression relative to the standard deviation of repeated measurements for that gene. Genes with scores greater than a threshold are deemed potentially significant. The percentage of such genes identified by chance is the false discovery rate (FDR). To estimate the FDR, nonsense genes are identified by analyzing permutations of the measurements. The threshold can be adjusted to identify smaller or larger sets of genes, and FDRs are calculated for each set . Here internal SAM parameters for fold-change threshold and the false discovery rate values were set at 2 and 1% respectively.
The genes with significantly changed expression in one of the comparisons were arranged in groups via overlapping them in Microsoft™ Excel.
Enrichment of MIPS categories (version 1.3) was assessed by Fisher's Exact test employing hypergeometric distribution with a p-value cut-off of 10-4 (with a Bonferroni correction). The probability was calculated as follows: the p-value of observing z genes, belonging to the same functional category is:
, where N is the total number of genes in a category, M is the total number of differentially expressed genes in the cluster and G is the total number of P. chrysogenum genes.
Promoter analysis was performed using the web-based software Multiple Em for Motif Elucidation (MEME)  incorporated in the software package Genedata Phylosopher (Genedata, Basel, Switzerland). The promoters (from -800, 0) of each set of co-regulated genes were analysed for overrepresented decanucleotides. Promoters with an E-value < 10-5 and without long stretches of A and T (> 40% GC content) were included in the analyses. Consensus sequences were depicted using the web based application WebLogo, version 2.8.2 [86, 87]. The transcriptome data analysed in this study have deposited at the Genome Expression Omnibus database http://www.ncbi.nlm.nih.gov/geo/ under the accession number GSE12632.
D.M.H, J-M. D. and J.T.P. acknowledge the financial support from the Netherlands Organisation for Scientific Research (NWO) via the IBOS Programme (Integration of Biosynthesis and Organic Synthesis) of Advanced Chemical Technologies for Sustainability (ACTS) and from the Netherlands Genomics Initiative. We thank Marcel van den Broek, Rintze Zelle and Theo Knijnenburg for their help in the bioinformatics work.
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