Marta Papini

Intawat Nookaew

Gionata Scalcinati

Verena Siewers

Jens ; Nielsen
email: nielsenj@chalmers.se

Phosphoglycerate mutase knock-out mutant Saccharomyces 1 cerevisiae: physiological investigation and transcriptome analysis 2

Keywords: Fermentation glycerophosphomutase, trancriptome analysis, genome scale metabolic model, 26 27 28 29 30

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Introduction 17

The gene GPM1, located on the right arm of chromosome VIII, codes for the enzyme 18 phosphoglycerate mutase (E.C. 5.4.2.1) which catalyzes the interconversion of 3-phosphoglycerate 19 into 2-phosphoglycerate. In the yeast Saccharomyces cerevisiae, this enzyme is a tetramer composed 20 of four identical subunits with a molecular weight of 28,000 Da each [1]. The gene was firstly isolated 21 through functional complementation [2] of mutants classified among glycolytic mutants whose growth 22 was inhibited by glucose [3,4]. GPM1 is a constitutively expressed glycolytic gene and its promoter 23 contains a variety of elements mediating transcriptional control [5]. Significant amounts of the enzyme 24 are present and active also in the cell wall of yeast [6] as it was shown that phosphoglycerate mutase 25 released from yeast cell walls upon alkaline treatment still shows enzymatic activity. Nevertheless, the 26 role of Gpm1 in the cell wall remains largely unknown. Mutation of GPM1 in the yeast genome leads to 27 viable cells only when glycerol and ethanol are provided together as carbon source, whilst growth of 28 the mutant is inhibited in the presence of glucose or ethanol or glycerol alone [4]. It has been

29

suggested that glycerol is needed to feed gluconeogenesis whereas Gibbs free energy is provided 30 through respiration of ethanol.

31

Sequencing of the yeast genome has revealed two genes that have a high sequence homology to 32 GPM1 and these have been named GPM2 and GPM3; GPM2 is positioned on the left arm of 33 chromosome IV, whereas GPM3 is located on the left arm of chromosome XV. Heinisch et.al [7] 34 suggested that GPM2 and GPM3 may represent non-functional homologues formed as a result of 35 gene duplication events. Their work showed that overexpression of GPM2 or GPM3 on a multicopy 36 vector under the control of their own promoters does not complement a gpm1 deletion mutant when 37 grown on complex media containing glucose or a mixture of glycerol/ethanol as carbon sources. Further analysis on the genes promoters indicated that these genes are expressed at too low levels to 1 contribute to a significant glycolytic flux. Overexpression of these two genes under the control of the 2 PFK2 promoter can partially complement the growth defect of gpm1 deletion on synthetic and complex 3 media containing of glycerol and ethanol after prolonged incubation.

4

It is well known that S. cerevisiae is able to adapt its growth to different carbon sources by switching 5 its metabolism. This is possible through expression of different sets of genes according to the carbon 6 source utilized [8,9]. Indeed, in the transition from glucose-based fermentative growth to respiratory 7 growth on glycerol [START_REF] Roberts | Transcriptome profiling of Saccharomyces cerevisiae during a 21 transition from fermentative to glycerol-based respiratory growth reveals extensive metabolic and 22 structural remodeling[END_REF] and during the diauxic shift, the expression levels of many different genes are 8 altered. In both cases the observed physiological response is a decrease in the specific growth rate 9 and as revealed by gene expression analysis, concomitant and similar patterns of gene expression 10 alteration [START_REF] Rodicio | Isolation of the yeast phosphoglyceromutase gene and construction of 2 deletion mutants[END_REF].

11

Growth on non-fermentable carbon sources such as ethanol or glycerol requires the activation of 12 gluconeogenesis and the glyoxylate cycle [12,[START_REF] Dickinson | The metabolism and molecular physiology of Saccharomyces cerevisae[END_REF]. Non-fermentable substrates are used to generate 13 ATP through oxidative phosphorylation and for the generation of sugar phosphates, required by the 14 cell for nucleotide biosynthesis, cell wall generation and for the biosynthesis of storage carbohydrates 15 [START_REF] Dickinson | The metabolism and molecular physiology of Saccharomyces cerevisae[END_REF]. Gluconeogenic genes are regulated at the transcriptional level through the regulator Rds2 [START_REF] Soontorngun | Regulation of 31 gluconeogenesis in Saccharomyces cerevisiae is mediated by activator and repressor functions of 32 Rds2[END_REF]; it 16 is known that the activation of genes of gluconeogenesis depends on upstream activator sequences 17 (UASs) found in their promoters, as well as carbon source response elements (CSREs). In absence of 18 glucose, or at low glucose levels, the Mig1 repressor protein is inactivated through phosphorylation by

19

Snf1. This leads to de-repression of CAT8 [START_REF] Randez-Gil | Glucose derepression of gluconeogenic 34 enzymes in Saccharomyces cerevisiae correlates with phosphorylation of the gene activator Cat8p[END_REF], a target of Mig1. Cat8 promotes the expression of a 20 different set of genes, among which several gluconeogenic genes [16,17]. Key genes that are turned 21 on upon gluconeogenic activation are FBP1 and PCK1 [START_REF] Mercado | The levels of yeast 41 gluconeogenic mRNAs respond to environmental factors[END_REF] encoding fructose-1,6-bisphosphatase [START_REF] Entian | Isolation and primary structure of 43 the gene encoding fructose-1,6-bisphosphatase from Saccharomyces cerevisiae[END_REF] 22 and phosphoenolpyruvate carboxykinase [START_REF] Valdes-Hevia | Isolation and characterization of the gene 46 encoding phosphoenolpyruvate carboxykinase from Saccharomyces cerevisiae[END_REF] respectively, and the glyoxylate cycle genes ICL1,

23

coding for the isocitrate lyase [21], and MLS1 [START_REF] Hartig | Differentially regulated malate 1 synthase genes participate in carbon and nitrogen metabolism of S. cerevisiae[END_REF] coding for malate synthase [23].

24

The aim of this work was to study the physiological response of a strain deleted in the lower part of 25 glycolysis by deleting the gene GPM1. This modification allowed us to study how yeast responds to a 26 non-functional gluconeogenic pathway when growing on a C 2 carbon source. The deletion mutant was 27 characterized under growth on a dual carbon source: glycerol and ethanol, and the cellular 28 adaptations were captured by transcriptome and integrated data analysis. The batch cultures were performed in 1.0 L DasGip stirrer-pro® vessels with a working volume of 0.7 1 L. Agitation was maintained at 600 rpm using a magnetic stirrer integrated in the BioBlock®, which 2 maintained the temperature at 30°C. The aeration was set to 0.5 L min -1

. The pH of the medium was 3 maintained at 5.0 by automatic addition of 2 N KOH. The temperature, agitation, gassing, pH and 4 composition of the offgas were monitored and controlled using the DasGip monitoring and control 5 system. Dissolved oxygen was monitored with an autoclavable polarographic oxygen electrode

6

(Mettler Toledo, Columbus, OH, USA). The effluent gas from the fermentation was analyzed for real-

7

time determination of oxygen and CO 2 concentration by DasGip fedbatch pro® gas analysis systems 8

with the off gas analyzer GA4 based on zirconium dioxide and two-beam infrared sensor. 9 10

24

For microarray analysis a 25 mL sample was withdrawn from the fermentor in the mid-exponential 25 phase (OD 600 = 2.5). The sample was cooled immediately in ice and centrifuged at 2°C and 5000 rpm

26

for 5 min, the supernatant discarded and the biomass immediately frozen in liquid nitrogen. The total

27

RNA was extracted from cells through mechanical disruption with glass beads, digested with DNAse 28 and purified using the RNeasy kit (Qiagen, Hilden, Germany). The quality of the RNA was assayed

33

Biotinylated RNA samples were fragmented and hybridized to Affymetrix Yeast Genome Array 2.0.

34

The Arrays were washed using an Affymetrix GenChip Fluidic station 450 and scanned using a

35

GeneChip® Scanner 3000 7G (Affymetrix Inc., Santa Clara, CA, USA). CEL files were generated 36 using the Comand console software (Affymetrix). All CEL files were submitted to GEO database and

37

have accession number GSE22122. For the integrated analysis, the Q-values for each gene were mapped on the biological graphs 13 described in the gene ontology (GO) annotation [32], genome-scale metabolic model iIN800 [START_REF] Nookaew | The genome-scale 28 metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid 29 metabolism[END_REF] and

14

DNA-protein interaction network [START_REF] Walhout | Unraveling transcription regulatory networks by protein-DNA and protein-protein 31 interaction mapping[END_REF]35]. Successively, the reporter algorithm [36, 37] was applied to 15 indentify features that represented significant responses after comparison of the reference strain and 16 mutant. Only the features that had p-values less than 0.001 were considered to be significant and 17

were selected for construction of heatmaps. strain in batch cultivations in minimal media supplemented with glycerol (3% v/v) and ethanol 2.5%

26

(v/v) as carbon sources. Fig. 1 shows the growth curves of the two strains expressed both in terms of 27 OD 600 (panel A) and of dry cell weight (panel B); in panel C and D, ethanol and glycerol consumption, 28 respectively, are reported. Interestingly, the glycerol concentration was detected by HPLC analysis to 29 be almost constant throughout the fermentation, showing a small decrease only for the reference 30 strain (panel D). For this reason ethanol was considered the only substrate for yields calculations.

31

Table 1 reports the yields on ethanol, calculated in C-moles/C-moles, of carbon dioxide, biomass and 32 acetate, together with specific growth rate and ethanol uptake rate. It is possible to observe that the 33 specific growth rate (µmax) and the specific ethanol uptake rate (rs) are lower in the ∆gpm1 mutant, 34

indicating the heavily impaired growth phenotype of a strain deleted in the glycolytic gene. Biomass 35 yields on substrate (Ysx) are comparable between the two strains, CO 2 yields (YsCO 2 ) report a slightly 36 higher value in the reference strain, whilst acetate yield (YsAc) is higher in the ∆gpm1 mutant. As previously mentioned, the ∆gpm1 mutant did not show glycerol consumption and the reference 3 strain showed poor uptake of glycerol. To gain insight into this behavior and to establish whether the 4 glycerol present in the media was consumed by the cells, we used Flux Balance Analysis (FBA) to 5 calculate the theoretical glycerol requirements and compare the results to the physiological data. On 6 both wild type and mutant, we performed the simulations based on the genome-scale metabolic model 7 ilN800 [START_REF] Nookaew | The genome-scale 28 metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid 29 metabolism[END_REF] using two different approaches. In the first case (reported as "without glycerol" in Table 2),

8

we assumed that glycerol is not consumed and we predicted the in silico growth rate using as 9 constraints the experimental ethanol uptake rate and the experimental production rates of CO 2 , 10 acetate and succinate. In this run of simulations the objective function was maximization of growth.

11

In the second case (reported as "with glycerol" in Table 2) we calculated the minimal theoretical 12 requirements of glycerol consumption. The minimal theoretical glycerol uptake rate was estimated 13 using the same set of constraints of the previous case but differently from above, we constrained the 14 growth rate to the one experimentally measured. In this case the objective function was minimization 15 of glycerol uptake rate. As shown in Table 2, in the first case, when no glycerol consumption is 16 assumed, the in silico specific growth rates are lower than those experimentally determined, both for 17 the reference strain and the deletion mutant. In the second case the theoretical specific glycerol 18 uptake rate was calculated to be 1.298 mmol/gDW/hr for the reference strain and 0.58 mmol/gDW/hr 19 for the ∆gpm1 mutant. These rates would correspond to theoretical glycerol requirement for biomass 20

(glycerol yield in Table 2) of 0.062 C-moles/C-moles for the reference strain and of 0.051 C-moles/C-21 moles for the ∆gpm1 mutant. The trace amount of glycerol requirement for growth supported the 22 finding that no significant glycerol consumption is reported, as shown in Fig. 1D.

24

Transcriptome profile and comparison analysis of the two strains 25

To gain insight into the physiological behavior of the deletion mutant, its transcriptome profile was 26 compared with that of the reference strain using Affymetrix GeneChip Yeast Genome 2.0 [START_REF] Wodicka | Genome-wide expression 39 monitoring in Saccharomyces cerevisiae[END_REF]. To 27 assure the robustness and reproducibility of the data obtained, the starting point of our analysis was 28 hierarchical clustering, shown in Fig. 2a. Here it is possible to observe that the three biological 29 replicates from the reference strain (blue) do cluster separately from the three replicates of the mutant 30 (red). The circular mapping plot of Fig. 2b shows changes in gene expression levels between the 31 reference strain (blue) and the ∆gpm1 mutant (red) where the genes are sorted according to their 32 position on the chromosome. In this figure it can clearly be observed that a considerable number of 33 genes are differentially expressed between the two strains. The 9 most expressed genes in the 34 ∆gpm1 mutant are annotated, as well as GPM1 and the two GPM1 paralogs (GPM2 and GPM3). The 35 gene GPM1, located on chromosome XI, shows the highest differential expression, being the most up-36 regulated gene in the reference strain. Realistically, this likely signifies that its expression is absent in

11

In order to study the effects of GPM1 deletion on gluconeogenesis and on the pentose phosphate 12 pathway (PPP), we first sought to investigate changes in expression levels of genes involved in the 13 central carbon metabolism. Fig. 3a displays a simplified map of the central carbon metabolism. This

17

From a global perspective, we can observe that the greatest number of differentially expressed genes 18 are located in the gluconeogenesis, in the pentose phosphate and, to a less extent, the glyoxylate 19 shunt pathway. The most up-regulated genes in the ∆gpm1 mutant are the gluconeogenic genes 20 FBP1 and TDH1 (fold change above 2.5 and Q-values below 0.01) and the pentose phosphate 21 pathway genes TKL1 (fold change between 2 and 2.5 and Q-value below 0.001) and GND2 (fold 22 change above 1.2). Other gluconeogenic genes which report a significant, but lower, up-regulation are 23 PCK1, coding for the phosphoenolpyruvate carboxykinase and ENO1, coding for the enolase. Apart 24 from the central carbon metabolism, it is possible to observe that the ∆gpm1 mutant has a significant 25 over-expression of genes involved in the peroxisomal fatty acid β-oxidation (FOX2, POX1, SPS13, 26 ECI1, TES1) and, to a less degree, genes involved in glycogen/trehalose catabolism. Glyoxylate cycle 27 genes also report remarkable changes in their expression levels, particularly the malate synthase 28 gene MLS1, isocitrate lyase genes ICL1 and ICL2 and MDH2 gene, coding for the cytoplasmic malate 29 dehydrogenase, both involved in gluconeogenesis and the glyoxylate shunt, which show a fold change 30 of 1.2 and MDH2 having the lowest Q-value.

31

The genes of the lower glycolysis and gluconeogenesis, especially ENO1 [40] and the glyceraldehyde-32 3-phosphate dehydrogenase gene TDH1, are also expressed at a higher level in the ∆gpm1 mutant, 33

suggesting that this mutant attempts to increase its gluconeogenic flux. This is consistent with the 34 observation that the two main gluconeogenic genes FBP1 and PCK1, normally present at low levels 35 during growth on glucose [START_REF] Mercado | The levels of yeast 41 gluconeogenic mRNAs respond to environmental factors[END_REF] but showing increased expression levels upon diauxic shift, are also 36 over-expressed in the ∆gpm1 mutant. The transcript levels of acetyl-CoA synthase gene ACS1, the 37 acetate transporter gene ADY2 and the mitochondrial malic enzyme gene MAE1 are also slightly

18

In the reference strain, the GO analysis revealed up-regulation of genes belonging to just two families,

19

namely ribosome biogenesis and nucleotides biosynthesis families. On the contrary, analysis of the 20 ∆gpm1 mutant displayed a significant upregulation of a considerable number of GO categories. 21

Among the most represented was the stress-response family, a large family including heat shock 22 genes and genes encoding oxidative and membrane stress proteins; with higher p-values we found 23 the β-oxidation of fatty acid family, followed by amino acids biosynthesis and growth on non-optimal 24 carbon sources, respiratory genes, peroxisome induction pathway genes and members of the ATPase 25 family.

26

The up-regulation in these families of genes is consistent with the transcription factors found to be 27 over-represented. In particular TF analysis of the ∆gpm1 mutant also revealed increased expression in 28 the genes regulated by transcription factors involved in stress response such as Crz1, Haa1, Hsf1, The transcriptome analysis performed in this study indicates that the severely impaired growth 2 phenotype of the ∆gpm1 strain is associated with a global stress response. As it has been previously 3 shown [3, 4], a ∆gpm1 strain is incapable of growing on glucose as the sole carbon source but growth 4 can be restored in presence of a mixture of glycerol and ethanol (2 to 3% v/v). Previous studies [1] 5 have suggested that ethanol is respired whereas glycerol is needed to feed gluconeogenesis.

6

Physiological data based on HPLC measurements undertaken in this study show that the ∆gpm1 7 mutant does not uptake glycerol whilst a low uptake can be observed in the reference strain. This 8 observation, together with the simulation based on the genome-scale metabolic model, agrees with 9 the hypothesis that ethanol is primarily required to fuel the TCA cycle whereas glycerol is needed, 10 even if only in small amounts (see Table 2), for biomass formation.

11

The respiratory response of the ∆gpm1 strain was also analyzed. Despite CO 2 yields are comparable 12 in the reference and deletion strains (slightly higher in the reference strain), the transcriptome data

13

shows that in the ∆gpm1 mutant there is increased expression of genes under the control of the 14 transcription factors involved in regulation of respiration such as Hap4, main transcriptional activator 15 and global regulator of respiratory genes and Mot3 [START_REF] Sertil | Synergistic repression of 1 anaerobic genes by Mot3 and Rox1 in Saccharomyces cerevisiae[END_REF]. Together with that, up-regulation of genes 16 coding for ATPase and energy metabolism, pentose phosphate pathway and gluconeogenesis, are 17 also observed. These observations could indicate that the mutant attempts to compensate for the 18 energy imbalance by increasing its respiratory capacity. This hypothesis is also suggested by the

26

A further indication of the transcriptome data is the possibility that gluconeogenesis can be fed, at 27 least partially, by the pentose phosphate pathway, a hypothesis supported by the finding that the 28 transketolase gene TKL1 is over-expressed and the NQM1 transladose, whose function is not yet fully 29 elucidated, is also over-expressed.

30

As shown in Fig. 3, the genes involved in glycerol uptake GUT1 and GUT2 do not show increased 31 expression in the ∆gpm1 mutant, neither do ethanol uptake genes even though transcript levels of 32 ALD2 are slightly increased. Interestingly, the genes encoding for the PDC complex genes, particularly

33

PDC6 and PDC1, are up-regulated in the mutant, demonstrating again that there is not a direct 34 correlation between gene transcript levels and fluxes [46,[START_REF] Stelling | Metabolic network structure 12 determines key aspects of functionality and regulation[END_REF].

35

The hypothesis that acetate secretion act as a sink for overflow carbon is supported by the metabolites for biomass synthesis [12] and for the production oxaloacetate that can be converted to 1 phosphoenolpyruvate to fuel gluconeogenesis [START_REF] Chapman | SDH1, the gene encoding the succinate 14 dehydrogenase flavoprotein subunit from Saccharomyces cerevisiae[END_REF]. Our finding that some of the genes of PPP are 2 over-expressed can therefore be linked to the concomitant upregulation of glyoxylate shunt genes.

3

The microarray analysis allowed the linking of the severe growth defect of the ∆gpm1 mutant with 4 expression of some patterns of genes, such as the stress response family, proving to be a useful tool 5 in analyzing biological phenotype. The growth defect of the deletion mutant is partially explained by 6 increased expression of genes involved in ribosome biogenesis, rRNA maturation and elongation and 7 tRNA processing in the reference strain, consistent with its faster growth. In the ∆gpm1 mutant, genes 8 involved in glycogen degradation, such as GDB1, encoding glycogen debranching enzyme required 9 for glycogen degradation [START_REF] Teste | The Saccharomyces cerevisiae YPR184w 16 gene encodes the glycogen debranching enzyme[END_REF], and genes of trehalose metabolism show an increase in transcript 10 levels, with the synthase subunit of the trehalose-6-phosphate synthase/phosphatase complex TPS1 11 [START_REF] Vuorio | Cloning of two related genes encoding the 56-18 kDa and 123-kDa subunits of trehalose synthase from the yeast Saccharomyces cerevisiae[END_REF] reporting a log fold change of 0.524.

12

As it is known that yeast cells growing under stress conditions respond by increasing glycogen 13 catabolism and trehalose metabolism [51], these data appear to supply additional proof of the elevated 14 stress levels in the ∆gpm1 mutant. Increased expression of genes involved in peroxisomal β-fatty acid 15 oxidation can also be observed in the ∆gpm1 mutant, probably because of the need to supply acetyl-16

CoA to the glyoxylate pathway. The physiologically stressed phenotype of the mutant is also in

24

involved as well in the regulation of genes encoding peroxisomal proteins [START_REF] Karpichev | Global regulatory functions of Oaf1p and Pip2p (Oaf2p), 24 transcription factors that regulate genes encoding peroxisomal proteins in Saccharomyces cerevisiae[END_REF]. The mutant also shows 25 derepression of phospholipid biosynthetic genes through Ino2 and up-regulation of sterol biosynthetic 26 genes through Upc2.

27

As mentioned in section 4.4, an interesting outcome from the TF analysis is the finding that Mig1 is 28 among the most significantly regulated transcription factors. Mig1 is an extensively characterized zinc 29 finger transcriptional repressor having target genes that are under carbon catabolite repression [START_REF] Ciriacy | Physiological effects of seven different blocks in glycolysis in 6 Saccharomyces cerevisiae[END_REF].

30

In order to be active, Mig1 needs to be de-phosphorylated by the protein kinase Snf1, a main player in known to be related to Snf1. Our results thus point to the conclusion that even small fluxes through the 1 lower glycolysis may have a robust impact on carbon catabolite repression.

2

This study serves to highligh how transcriptome analysis can serve as valuable tool in physiological 3 studies of mutant strains, as the utilization of this technique can help to identify non-obvious parts of 4 the metabolism that are indirectly involved with the perturbation made on the biological system being

29 using a

 29a BioAnalyzer (Agilent Technologies, Palo Alto, CA, USA). Concentrations were determined 30 using a NanoDrop 2000 Spectrophotometer (Thermo Scientific, Wilmington, DE, USA). 250 ng of the 31 total RNA was used to synthesize cDNA using Affymetrix 3' IVT Express kit and successively aRNA 32 was synthesized (Affymetrix Inc., Santa Clara, CA, USA).

  Physiological characterization of the deletion strain ∆gpm1 in aerobic batch cultivations on a 21 glycerol and ethanol mixture 22 It was previously shown that a S. cerevisiae strain deleted in the glycolytic gene GPM1 (encoding 23 phosphoglycerate mutase) is not able to use glucose as sole carbon source but has the capability to 24 grow on a mixture of ethanol and glycerol [4]. Here we studied the physiological response of a ∆gpm1 25

  of glycerol utilization using in silico simulations based on a yeast genome-scale 1 metabolic model 2

  37the ∆gpm1 mutant, serving as further indication of its successful deletion in the mutant. The paralogs 38 of GPM1, GPM2 and GPM3 have very low expression levels in the ∆gpm1 mutant. Among the most 39 ORF in ∆gpm1 mutant we found NQM1, which codes for a transaldolase of unknown 1 function, induced upon diauxic shift and probably involved in the pentose phosphate pathway [39]. 2 NCA3 and HSP30 are ORFs regulating expression of mitochondrial ATP synthases and HSP30 is 3 known to be induced by ethanol treatment or heat shock. The ORF YKL107W is a putative protein of 4 unknown function (proposed membrane protein). PIR3 is coding for a protein needed in cell wall 5 stability, FMP45 is coding for a mitochondrial protein involved in sphingolipid pathway whereas FMP16 6 is a putative protein of unknown function. SIP18 is a gene involved in osmotic stress and GRE1 is a 7 hydrophilin of unknown function involved in stress response and related to the HOG pathway. From 8 this first view of our microarray data it emerges that the genes most involved in a response upon 9 deletion of GPM1 are those belonging to stress family, cell wall stability and probably the pentose 10 phosphate pathway.

14 map

 14 displays the genes that were differentially expressed between the reference and the mutant 15 strain. In the figures both fold changes and Q-values are reported. Differences in fold changes are 16 represented with different colors, whereas different thickness of the line represents different Q-values.

38 higher 10 In

 3810 in the ∆gpm1 mutant. The pyruvate decarboxylase genes PDC6 and PDC1 also result over-39 a possible sink of overflow carbon to acetate. Trehalose and glycogen 1 metabolism genes are also found to be up-regulated in the ∆gpm1 mutant. 2Interestingly, the uncharacterized ORF YJL045W is particularly up-regulated in the ∆gpm1 mutant; this 3 ORF codes for the protein Sdh1b [41], a putative minor succinate dehydrogenase. Previous data 4 showed that the same ORF was also overexpressed upon diauxic shift[42]. The increased expression 5 of this ORF might link the anaplerotic production of succinate from the glyoxylate cycle in the cytosol 6 with its transport and subsequent oxidation in the mitochondria by the TCA cycle, suggesting a 7 possible explanation for the increased expression of glyoxylate shunt genes. addition to the investigation the carbon central metabolism, we attempted to detect a possible 11 response to deletion of GPM1 in areas of the metabolism not directly related to glycolysis. To gain 12 greater understanding of the response at different levels of cellular processes, we used the microarray 13 data to perform Gene Ontology (GO) and Transcription Factor (TF) analysis. In GO the families of 14 genes whose changes are most important are reported, whereas TF shows increased or decreased 15 representation of genes regulated by different transcriptor factors. Fig. 4 reports the outcome from the 16 integrated GO (panel a) and TF (panel b) analysis. For both analyses, a p-value of 0.001 was 17 considered to be significant and therefore chosen as cutoff value (see color key for specific p-values).

29 Msn2,

 29 Msn4, Skn7, Sko1, Stb5, as well as the most known key regulator transcription factors of fatty 30 acid β-oxidation Oaf1, Pip2 and Ino2 and transcription factors acting on the respiratory capacity Mot3

31 and

 31 Hap4. Interestingly, also the protein Mig1 emerges among the most over-represneted 32 transcription factors.

33

  It is important to note that the results obtained from GO and TF analyses were consistent with each 34 other and despite the lack of direct link between transcriptome and phenotype, the transcriptome data 35 are in agreement with the physiological data. These findings are also supported by reporter metabolite 36 analysis, GO Cellular compartment and molecular function analysis, for which the results are showed 37 in Supplementary 1.

19 respiratory

 19 quotient RQ (data not shown) which shows that whilst the reference strain has fully 20 respiratory growth, the ∆gpm1 mutant does not respire with the same efficiency. 21It has been shown before[44] that adaptation to growth on ethanol requires the involvement of 22 enzymatic activities from the glyoxylate cycle and gluconeogenesis and our results are in agreement 23 with this. The findings that the pentose phosphate pathway and glyoxylate cycle are up-regulated in 24 the ∆gpm1 mutant, agree with the previous observation that when respiration is not complete the TCA 25 cycle acts as two branches[45].

36

  physiological data showing higher acetate yield on biomass in the ∆gpm1 mutant compared to the 37 reference strain. It has been shown before that S. cerevisiae cells growing on ethanol or acetate have 38 an increased flux through the glyoxylate shunt and malic enzyme. This is in order to supply precursor 39

17

  agreement with Transcription Factor and Gene Ontology analysis. The majority of stress response 18 related gene families are over-represented in the ∆gpm1 mutant and among the most represented 19 transcription factors we find (Fig. 4b) those encoding membrane stress protein Haa1, heat shock 20 transcription factors Hsf1 and Skn7, the more general stress response factors Msn2 and Msn4, the 21 osmotic and oxidative stress transcription factors Sko1 and Stb5, known for its role in the oxidative 22 stress response. Transcription Factor analysis on the ∆gpm1 mutant also showed over-representation 23 of genes encoding transcription factors involved in β-oxidation of fatty acids, i.e. Oaf1 and Pip2,

31 the

 31 glucose repression regulatory cascade[54]. Snf1 plays a relevant role in the cell, controlling 32 biological processes such as glycogen, lipid and steroid metabolism, cell cycle, amino acid and protein 33 synthesis, stress response and redox homeostatis, generation of precursors of energy together with 34 the metabolism of alternative and non fermentable carbon sources. Mig1 can be considered as an35indicator of carbon catabolite repression and the finding that Mig1 is found to be over-represented 36 from the transcription factor analysis could indicate that the ∆gpm1 mutant is less catabolite repressed 37 compared to the reference strain. This observation is consistent with the finding that some of the other 38 transcription factors over-represented such as Msn2, Yap1, Oaf1 and Gcn4, Msn2 and Adr1, are also 39

Fig. 1 Fig. 2 Fig. 3 Fig. 4

 1234 Fig.1Growth curve of the reference strain (black) and ∆gpm1 mutant (grey), reported as OD 600 nm over fermentation time (hrs) (panel A) and growth curve of the reference strain (black) and ∆gpm1 mutant (grey), reported as Dry Cell Weight (DCW) over fermentation time (panel B). For each plot, standard errors are also shown. Both the strains have been cultivated in batch mode in minimal media described in materials and methods using a mixture of glycerol and ethanol as carbon source. In panel C and D respectively, ethanol and glycerol consumption are reported in g L as function of fermentation time (hrs). 205x121mm (400 x 400 DPI)

  The gene GPM1 was deleted using a bipartite method[25]. The regions up-and downstream of the KlURA3 cassette. The GPM1 upstream region and the KlURA 5´part on one hand and the 9 KlURA 3´part and the GPM1 downstream region on the other hand were then fused in a second set of 10 PCRs using the respective outer primers for amplification. This was possible due to complementary

	1	GCAGGGATGCGGCCGCTGACCGGATTGACCGTGTCTAACTAAAACTAAC-3´) and GPM-down-fw
	2	(5´-CCGATGCTAGGCGCGCCGTGCGCTGCTGTTGCCAACCAAGG-3´)	/	GPM-down-rv	(5´-
	3	AAATTCACCAACCTCTCATGGCG), respectively. The 5´ part of the Kluyveromyces lactis KlURA3
	4	cassette	was	amplified	from	vector	pWJ1042	[26]	using	primers	dKL-5´(5'-
	5	GTCAGCGGCCGCATCCCTGCTTCGGCTTCATGGCAATTCCCG-3')			and	Int-3´(5´-
	6	GAGCAATGAACCCAATAACGAAATC3´).	Likewise,	primers		Int-5´	(5´-
	7	CTTGACGTTCGTTCGACTGATGAGC-3´)	and			cKl-3´	(5´-
	8	CACGGCGCGCCTAGCAGCGGTAACGCCAGGGTTTTCCCAGTCAC-3´) were used to amplify the 3´
	part of the 11
	12	primer extensions at the 5´ends of the inner primers. Strain CEN.PK 113-5D was transformed with the
	13 14 15 16	F o r two fusion PCR products, which resulted in integration at the GPM1 locus. Transformants were selected on minimal medium containing 1.7 gL -1 yeast nitrogen base (YNB) without amino acids and (NH 4 ) 2 SO 4 (Alfa Aesar, Karlsruhe, Germany), 5gL -1 (NH 4 ) 2 SO 4 , 0.77 gL -1 complete supplement mixture (CSM) without uracil (MP Biomedicals, Solon, OH, USA), 2.5 % (v/v) glycerol 2.5 % (v/v) ethanol and F o r
	17 18 19	P e -1 agar. Direct repeats flanking the KlURA3 cassette allowed for the loop-out of the marker. 20 gL Strains that had lost the marker gene were selected on minimal medium (see above) containing 50 P mgL -1 uracil and 750 mgL -1 5-fluoroorotic acid (5-FOA). GPM1 deletion was confirmed through PCR e
	20 21	and sequencing of the PCR product.	e r e r
	29 30 22 23 24 25 26 27 28 29 30	R e v i e w The cultivations have been conducted in DasGip fermentors (DasGip, Jülich, Germany) in batch 3.3 Media and growth conditions mode, the two strains (reference strain and deletion mutant) were cultivated in triplicates. For all R cultivations, a previously described [27] mineral salts medium was used. The medium is composed of e (per liter): (NH 4 ) 2 SO 4 , 5 g; KH 2 PO 4 , 3 g; MgSO 4 •7H 2 O, 0.5 g; Antifoam 289 (A-5551, Sigma-Aldrich, St. Louis, MO, USA), 0.05 mL; trace metals, 1 mL and vitamins, 1 mL trace metal solution. The trace metal solution consisted of (per liter): EDTA (sodium salt), 15 g; ZnSO 4 •7H 2 O, 0.45 g; MnCl 2 •2H 2 O, 1 v i e g; CoCl 2 •6H 2 O, 0.3 g; CuSO 4 •5H 2 O, 0.3 g; Na 2 MoO 4 •2H 2 O, 0.4 g; CaCl 2 •2H 2 O, 0.45 g; FeSO 4 •7H 2 O, 0.3 g; H 3 BO 3 , 0.1 g and KI, 0.1 g. The pH of the trace metal solution was adjusted to 4.0 with 2 M w
	31 31	3. Materials and Methods NaOH prior to heat sterilization. The vitamin solution contained (per liter): biotin, 0.05 g; p-amino
	32 32 33 33 34	3.1 Yeast strains benzoic acid, 0.2 g; nicotinic acid, 1 g; Ca-pantothenate, 1 g; pyridoxine-HCl, 1 g; thiamine-HCl, 1 g The S. cerevisiae strains used in this study were CEN.PK 113-5D (MATa SUC2 MAL2-8 C ura3-52; and myo-inositol, 25 g. The pH of the vitamin solution was adjusted to 6.5 with 2 M NaOH. The vitamin solution was filter sterilized and stored at 4°C [27]. The medium was supplemented with 2.5 % (v/v) of
	34 35	kindly provided by P. Kötter, University of Frankfurt, Germany) [24] and CEN.PK 113-5D ∆gpm1. ethanol and 2.5 % (v/v) glycerol as carbon sources. To complement the auxotrophy of the reference
	35 36	strain and of the mutant uracil solution was added to a final concentration of 20 mgL -1 . Cells were
	36 37	3.2 Deletion strain construction grown in 500 mL baffled shake flasks in YNB medium (NH 4 SO 3 , 5gL -1 ; YNB w/o ammonia and w/o
	37 38	amino acids, 1.7 gL	-1 ; CSM w/o uracil, 0.77 gL-1) with 3% (v/v) glycerol and 2.5% (v/v) ethanol as
	38 39	gene carbon sources until they reached mid-exponential phase (OD 600 = 2). The precultures were used to were amplified by PCR using primer pairs GPM-up-fw (5´-
	39 40	GAGGAAGCTCGCTAAATGGGATTCC-3´) inoculate the fermentors to an initial OD 600 = 0.2.	/		GPM-up-rv	(5´-
												4

  Cell dry weight was measured by filtering a known volume of the culture through a pre-dried and pre-12 weighed 0.45 µm pore size nitrocellulose filter (Supor®-450 Membrane Filters, PALL Life Sciences,

	11	3.4 Analytical methods

13

Ann Arbor, MI, USA). The filters with the biomass were washed with water, dried for 15 min in a 14 microwave oven at 150 W and weighed again. The optical density was determined at 600nm using a 15 Genesys 20 Spectrophotometer, Thermo Scientific, Wilmington, DE, USA). 16 Concentrations of glycerol, ethanol, acetate, succinate and pyruvate were analyzed by an isocratic 17 HPLC (UltiMate ® 3000 Nano/Capillary Autosamplers, Dionex , Sunnyvale, CA, USA) with an Aminex 18 HPX-87H ion exchange column (Bio-Rad, Hercules, USA) at 65⁰C using 5 mM H 2 SO 4 as mobile 19 phase at a flow rate of 0.6 mL min -1 . Glucose, glycerol and ethanol were measured with a refraction 20 index detector whereas succinate, acetate and pyruvate with an ultraviolet-visible light absorbance 21 detector. 22 23 3.5 Sampling and total RNA extraction

  The intensity files obtained were normalized using non-linear normalization, qspline methods [28].The 2 expression values were processed by using Probe Logarithmic Intensity Error (PLIER)[START_REF] Therneau | What Does PLIER Really Do?[END_REF] approach

	1	3.6 Data analysis on Microarray
	3
		using	perfect	match	probes	only	(pm-only)
	6
	7	test using linear models together with empirical Bayes was applied [30]. The calculated p-values were
	8	corrected by multiple testing using the method of Benjamini and Hochberg [31]. Changes in gene
	9	expression with a Q-value (adjust-p -value) < 0.05 were considered significant and a cutoff value of
	10	0.05 was applied. The logarithms of Q-values were sorted according to the position of the genes on
	11	the chromosome and plotted in clock-wise direction as a circular plot to illustrate global response of
	12	r o F the gene expression. R suite equipped with bioconductor packages was used for statistical analysis
				P
				e
				e r
				R
				e
					v i e
					w
	38
	39
							6

4

(http://www.affymetrix.com/support/technical/technotes/plier_technote.pdf). To primary evaluate 5 the pattern of the microarray data, hierarchical clustering and statistical analysis based on Student's t-

Table 1 .

 1 Yields on substrate (ethanol) are reported in C-moles/C-moles with relative Standard

	Page 15 of 20		Biotechnology Journal
	33
	34	deviation.
	35
		Yield	Reference strain	∆ ∆ ∆ ∆gpm1
		Ysx	0.442±0.02	0.459±0.02
		YsCO 2	0.178±0.03	0.16±0.008
		YsAc	0.025±0.01	0.067±0.06
		YsSucc	0.005±0.002	0.001±0.001
		µmax (hr -1 )	0.172±0.001	0.097±0.009
	36
	Ysx= biomass yield on substrate; YsCO 2 = CO 2 yield on substrate; YsAc= acetate yield on substrate;
				Wiley-VCH

37

YsSucc= succinate yield on substrate; µmax: maximum growth rate. Pyruvate is not shown due to its 38 low values (trace amounts detected by the HPLC) 39

Table 2 .

 2 Theoretical glycerol uptake rate and growth rate calculated in silico based on a genome-scale 4 metabolic model for reference strain and ∆gpm1 mutant. As explained in the text, 2 different conditions 5

	6	are analyzed.
	7
				Growth	Glycerol uptake	Yxgly*
		Strain	Condition	rate (hr -1 )	(mmol/gDW/hr)
			without glycerol	0.051	0	0
		Reference with glycerol	0.172	1.298	0.062
			without glycerol	0.043	0	0
		∆gpm1	with glycerol	0.093	0.580	0.051

Wiley-VCH Biotechnology Journal


M.D Konstantinos P Economopoulos
email: economopoulos@sni.gr

Theodoros N Sergentanis

Sergentanis Su Lu

Hong Liu
email: liuh@medmail.com.cn

Glutathione S-transferase P1 Ile105Val polymorphism and breast cancer risk: convergence and divergence of the two recent meta-analyses

HAL is

We would like to thank Dr. Economopoulos and Dr. Sergentanis for their thoughtful comments concerning our article [1]. Taking into account the comments, we immediately undertook a thorough check and a full reanalysis of the data. As a result, we found that the correction to the five data points used in the analysis that was originally presented does not change the results or the interpretation of the meta-analysis. Our findings continue to support the existence of a significant association between GSTP1 Ile105Val polymorphism The main results and the heterogeneity tests are presented in Table 1.

Dr. Economopoulos pointed out that our meta-analysis missed three sizeable eligible studies [2][3][4]. Actually, these three articles were not overlooked in our analysis. In the articles by McCarty et al.

[3] and Chang et al. [2], authors provided the genotype frequencies of AG+GG instead of the frequency of each single genotype, which failed to meet our inclusion criteria previously described in our article [1].

In Steck et al.'s article [4], we found that the frequencies of genotypes in controls given in the results part of the text seem not in accordance to those listed in this article's Table 1. For the above reasons, we excluded the three articles from our meta-analysis, though they do have a relatively large number of subjects.

There is another issue raised by Dr. Economopoulos that the genotype frequencies for Ile/Ile, Ile/Val, Val/Val in cases and controls should be 54-35-13-77-22-3 for the study by Torresan et al. [8] However, the HWE in controls calculated based on Dr.

Economopoulos' data (χ 2 = 0.813, P = 0.367) seems not in line with that in the original publication (χ2 = 1.0; P > 0.30). Meanwhile, the HWE from our analysis confirms the validity of our data (χ 2 = 1.043, P = 0.307).

After taking the suggestion of Dr. Economopoulos and Dr.

Sergentanis, we reached a more elaborating conclusion which remains to provide strong support for an association between GSTP1 Ile105Val polymorphism and elevated breast cancer risk.

  and increased breast cancer susceptibility in Asian population (for recessive model Val/Val vs. Ile/Ile + Ile/Val: OR = 1.36, 95% CI = 1.13-1.63, Fig.1) and hospital-based studies (for Val/Val vs. Ile/Ile: OR = 1.32, 95% CI = 1.07-1.63; for recessive model Val/Val vs. Ile/Ile + Ile/Val: OR = 1.24, 95% CI = 1.01-1.51; for dominant model: Val/Val + Ile/Val vs. Ile/Ile: OR = 1.14, 95% CI = 1.01-1.27, Fig.2).

Fig. 2 OR

 2 Fig.2 OR of breast cancer associated with GSTP1 Ile105Val polymorphism for the Val/Val genotype compared with the

Table 1

 1 Summary ORs and 95% CIs of GSTP1 Ile105Val polymorphism and breast cancer risk

		Val/Val vs. Ile/Ile		Ile/Val vs. Ile/Ile		Val/Val vs.Ile/Ile+Ile/Val		Val/Val+Ile/Val vs.Ile/Ile
		OR (95% CI)	P*	OR (95% CI)	P*	OR (95% CI)	P*	OR (95% CI)	P*
	Total	0.99(0.79-1.24)	0.00 †	0.99(0.88-1.12)	0.00 †	0.99(0.83-1.19) 0.00 †	1.00(0.88-1.14)	0.00 †
	Ethnicity
	Asian	1.28(0.92-1.78)	0.03 †	1.07(0.94-1.23)	0.08	1.36(1.13-1.63) 0.11	1.10(0.94-1.28)	0.02 †
	European	0.93(0.68-1.28)	0.00 †	0.98(0.81-1.17)	0.00 †	0.94(0.73-1.20)	0.00 †	0.98(0.80-1.20)	0.00 †
	Source of control
	Populaiton based	0.85(0.61-1.17)	0.00 †	0.92(0.78-1.10)	0.00 †	0.89(0.69-1.15) 0.00 †	0.92(0.76-1.11)	0.00 †
	Hospital based	1.32(1.07-1.63)	0.43	1.11(0.98-1.25)	0.42	1.24(1.01-1.51) 0.64	1.14(1.01-1.27)	0.19
	Menopausal status


Pekka Ruohonen
email: pekka.ruohonen@tkk.fi

Pekka Ahtila

Analysis of a mechanical pulp and paper mill using advanced composite curves

Keywords: Energy efficiency, heat recovery, integrated pulp and paper mill

In this study a mechanical pulp and paper mill is analysed using advanced composite curves. It is a graphical pinch-based approach that takes into account the existing heat exchanger network and the utilities actually used at the mill. The possibilities of making a cost-effective heat exchanger retrofit in an operating mechanical pulp and paper mill are discussed. The study also shows that the advanced composite curves can be used to describe the amount of nonisothermal mixing taking place in the process.

Introduction

In a world of rising energy prices and tighter limits on carbon dioxide (CO 2 ) emissions, it is becoming more and more important to consider energy efficiency in industry. Many retrofit projects have been carried out in recent years, but there is still a large potential to reduce primary energy consumption. One important aspect is the possible use of available secondary heat sources.

Pinch-based process integration methods are used in this paper. Pinch analysis [START_REF] Linnhoff | A user guide on process integration for the efficient use of energy[END_REF][START_REF] Linnhoff | A user guide on process integration for the efficient use of energy[END_REF][START_REF] Kemp | Pinch Analysis and Process Integration: A User Guide on Process Integration for the Efficient Use of Energy[END_REF] is a well-known and widely used process integration method. The idea is to convert information on all the heating and cooling needs of the process into targets for hot and cold utility consumption. Originally, pinch analysis was used to find targets for hot and cold utilities in greenfield cases, but several methodologies for retrofit situations have been developed, e. g. [START_REF] Tjoe | Pinch technology retrofit: setting targets for an existing plant[END_REF][START_REF] Asante | An automated approach for heat exchanger network retrofit featuring minimal topology modifications[END_REF][START_REF] Asante | An automated and interactive approach for heat exchanger network retrofit[END_REF][START_REF] Varbanov | Rules for paths construction for HENs debottlenecking[END_REF].

The use of advanced composite curves is a fairly new method developed at Chalmers University of Technology by Nordman and Berntsson [START_REF] Nordman | Use of advanced composite curves for assessing cost-effective HEN retrofit 1: Theory and concepts[END_REF] based on earlier work by Nordman and Berntsson [START_REF] Nordman | New Pinch Technology Based HEN Analysis Methodologies for Cost-Efficient Retrofitting[END_REF] and Wallin [START_REF] Wallin | Process Integration of Industrial Heat Pumps in Grass-root and Retrofit Situations[END_REF]. It is a graphical pinch-based approach that takes into account the existing heat exchanger network and the utilities actually used at the mill. With this information, it is possible to predict the retrofit-related costs of a certain part of the heat exchanger network. The method also reveals the possibilities to integrate heat pumps into the system when considering different amounts of heat exchanger network retrofit. The method has been tested previously in two case studies concerning chemical pulping [START_REF] Nordman | Use of advanced composite curves for assessing cost-effective HEN retrofit 1: Case studies[END_REF].

Advanced composite curves have not previously been used to analyse a mechanical pulp and paper mill, where the temperature levels differ significantly from chemical pulping.

In this study, the possibilities of making a cost-effective heat exchanger retrofit in an operating mechanical pulp and paper mill are discussed. The study also shows that the advanced composite curves can be used to describe the amount of nonisothermal mixing taking place in the process. Two cases are presented to illustrate this phenomenon.

Methodology

Advanced composite curves [START_REF] Nordman | Use of advanced composite curves for assessing cost-effective HEN retrofit 1: Theory and concepts[END_REF] are a pinch-based method developed especially for retrofit situations. The advanced curves take into account the existing heat exchanger network at the mill. Therefore, it is possible to estimate with good precision how costly it will be to improve the heat exchanger network.

There are four curves above the pinch and four curves below. The curves above are:

• The Hot Utility Curve (HUC), which shows the hot utilities currently used at the mill.

• The Actual Heat Load Curve (AHLC), which shows what the hot utilities are used for.

• The Extreme Heat Load Curve (EHLC), which shows how the hot utilities would be used if the current amount of hot utilities was used to heat streams at temperature levels as high as possible. This would be the solution that traditional pinch gives using a ∆T min that corresponds to the current amount of hot utilities being used.

• The Theoretical Heat Load Curve (THLC), which shows the thermodynamic possibilities to use the hot utilities to heat streams at temperature levels as low as possible, when a minimum temperature difference for the heat exchangers (∆T min, HX ) is used. The minimum temperature difference for the heat exchangers should be as small as the smallest reasonable value for a new heat exchanger. A temperature difference of 5 K is usually used.

The corresponding curves below the pinch are:

• The Cold Utility Curve (CUC)

• The Actual Cooling Load Curve (ACLC)

• The Extreme Cooling Load Curve (ECLC)

• The Theoretical Cooling Load Curve (TCLC) EHLC and THLC show the limits for possible heat exchange temperature levels in the heat exchanger network. Therefore AHLC should always be between them. If AHLC is close to THLC, it means that the design is originally poor, the heaters are located at low-temperature levels, and thus more heat exchanger area is installed than would be necessary. In a retrofit case this installed available area might be useful when located somewhere else, and replacing heaters situated at low temperature levels usually requires a smaller number of matches to be changed. Therefore AHLC lying close to THLC is an indication of a relatively cost-effective retrofit and AHLC lying close to EHLC is an indication of a relatively expensive retrofit. A more comprehensive description of the method can be found in Reference [START_REF] Nordman | Use of advanced composite curves for assessing cost-effective HEN retrofit 1: Theory and concepts[END_REF].

The ProPi ® software package [12] is used in the pinch analysis. ProPi ® was developed by Chalmers Industry Energy Analysis, Sweden. It includes composite curves and grand composite curves, and calculates utility targets. A simulation model of the mill has been constructed using the Balas ® [13] simulation software. The simulation model provides the data for the pinch analysis and is used to examine the mill-wide effects of the proposed changes on the heat exchanger network.

Description of the case

The mill

The considered mill produces mechanical publication papers on three paper machines and one coating machine. The products are supercalendered paper and medium-weight coated paper. In addition to the paper machines, the mill has a debarking plant, four mechanical pulp lines, a bleaching plant, a water supply plant, a wastewater treatment plant, maintenance facilities and material and product storages. On the mill site there is a hydropower plant and a combined heat and power (CHP) plant. Of the pulp lines, two are ground-wood pulp (GW) and two are pressurized ground-wood pulp (PGW). A simplified flow sheet of the mill is shown in Figure 1.

The simulation model

The required data has been gathered from the mill. The simulation model has been simplified by using only two components, water and wood, and chemical reactions have been considered only as a source or sink of heat. The model presents the paper machines and grinderies in detail and the steam production and water treatment in a more simplified manner.

The year has been divided into summer and winter periods. The temperatures of the inlet air and water are different for these two periods, but the operation of the mill remains unchanged.

The existing heat exchanger network

The grid of the original heat exchanger network is presented in Figure 2 As shown in Figure 2, one of the hot streams (hot stream 5) is splitted in three and different amounts of heat is recovered from each branch. Afterwards, these three streams, now on different temperature levels, are mixed in the same pipe line. Therefore part of the quality of the heat content in the hottest stream is lost. The mixed stream is further splitted in three branches, of which two are cooled using river water coolers and one is used to recover heat. Afterwards, the three streams are mixed again, causing the second nonisothermal mixing in the process.

A pinch analysis of the mill has been carried out in Reference [START_REF] Ruohonen | Analysis of alternative secondary heat uses to improve energy efficiency -Case: a Finnish mechanical pulp and paper mill[END_REF]. The results show energy efficiency improvement of 7.9 MW steam and 21.6 MW of external cooling in winter. The respective results for summer are 4.4 MW steam and 25.4 MW cooling.

The restrictions and assumptions of the analysis

The paper machine heat recovery has been treated as a hot utility because there is no need to cool the air down. Whenever there is a surplus of heat, the air is simply exhausted into the environment. This is, of course, a more cost effective way to get rid of surplus heat than using cooling towers that consume electricity.

In the model, a relative humidity of 100 % for outlet air is assumed after each heat exchanger, and heat capacity flows are treated as constants inside each step. This can cause inaccuracies in the results with regard to paper machine heat recovery as the heat exchange is strongly non-linear in reality [START_REF] Sivill | Thermodynamic of dryer section heat recovery in paper machines[END_REF].

Results and discussion

Two cases are presented in this paper. The first case focuses on the mill's water systems and is shown to demonstrate the use of the advanced composite curves on a PGW mill.

The second case, including more streams, shows that the advanced composite curves can be used to investigate the amount of nonisothermal mixing occurring in the system.

Case 1: The mill's water systems

This part of the study focuses on the water systems of the mill. The heat recovery of the paper machines is assumed to remain on the present level. The steam needed in the drying sections is not included in the analysis. The data used in this study differs slightly from the analysis in Reference [START_REF] Ruohonen | Analysis of alternative secondary heat uses to improve energy efficiency -Case: a Finnish mechanical pulp and paper mill[END_REF] as the waste water treatment is arranged differently.

The study is divided into two periods: winter and summer.

Winter

The stream data of the winter situation is shown in Table 1. The analysis covers all the steams shown in Figure 2, except the heat recovery of the paper machines. The numbering of the streams is the same as in Figure 2. The composite curves and grand composite curve of the process are shown in Figures 3 and4. The ∆T min is 10 K.

The analysis gives a minimum hot utility demand of 2.4 MW and minimum cold utility demand of 16.9 MW. The current steam consumption, 7.1 MW, corresponds to a ∆T min of 18 K. Therefore, this value is chosen when constructing the THLC. The ∆T min, HX of cold utility targets between ∆T min values of 5 and 10 K. The advanced composite curves on the hot side, HUC, EHLC, AHLC and THLC, are shown in Fig. 5.

Figure 5 shows that AHLC is relatively close to EHLC for the first 4.7 MW, and for the remaining 2.4 MW the curves are identical. For the last 2.4 MW the curves are also identical to the THLC. This predicts a relatively costly retrofit for the first 4.7 MW and no possibilities for the remaining 2.4 MW. A detailed analysis of the process shows that the savings of 4.7 MW are possible by investing in three new heat exchangers and piping. The remaining 2.4 MW would require water-air heat exchangers with ∆T min below 5 K. As Figure 5 suggests, this is not a valid option.

Figure 6 shows the respective curves on the cold side: TCLC, ACLC, ECLC and CUC.

Figure 6 shows that when using the paper machine heat recovery as before, there is still a large cooling demand after the heat exchanger network retrofit. Currently, as the CUC shows, the heat is discharged into the river at temperature levels between 10 and 40 °C.

Theoretically, the heat could be taken from the process at temperature levels between 65 and 80 °C. This could lead to an opportunity to use this heat for heat pumping. These possibilities are shown in Figure 7 presenting the THLC and TCLC.

Figure 7 shows that the remaining hot utility demand is much smaller than the remaining cold utility demand. This suggests that heat transformers might be a feasible option to utilize this heat.

The improved heat exchanger network that satisfies the saving target of 4.7 MW is shown in Figure 8.

Summer

The stream data for summer is shown in Table 2. The analysis covers the same streams as the winter analysis, except there is not need to heat the ventilation fluid (cold stream 9). The cooling needs of the hot stream (hot stream 5) are then covered by using more river water. The numbering of the streams is the same as in Figure 2. As expected, in summer time the hot utility consumption decreases and cold utility consumption increases. The main reasons for this are the incoming temperatures of water and air, of which water has a much stronger influence. The summer time advanced composite curves on the hot side, HUC, EHLC, AHLC and THLC, are shown in Fig. 9.

Figure 10 shows the respective curves on the cold side: TCLC, ACLC, ECLC and CUC From Figure 10 it can be seen that the ECLC and ACLC are almost identical for a while. This is due to a nonisothermal mixing occurring at the grinderies when a stream is split, cooled down with different loads, and then mixed. To demonstrate this phenomenon in a clearer way, another analysis has been made using more streams in the analysis.

Case 2: The mill and its surroundings

In addition to the data shown in the previous analysis, the steam used in the drying sections and in the supercalander has been included. Paper machine heat recovery is treated as a utility. The study also takes into account the district heating requirements of the nearby village. The study is divided into two periods: winter and summer.

Winter

The data used in the analysis is shown in Table 3. Three streams (H4, C4 and C13) have been left out of the analysis because of changes in the waste water treatment. Three new cold stream have been included; C15 representing district heating, C16 representing steam needed at the drying section, and C17 representing steam used at the super calander. The numbering of streams H5 to H9 and C1 to C14 is the same as in Figure2.

The grand composite curve of the process is shown in Figure 11. The curve shows a very low pinch point and a zero demand for cold utility.

The advanced composite curves on the hot side, HUC, EHLC, AHLC and THLC, are shown in Fig. 12.

The process itself requires the same 2.4 MW hot utility as in the previous analysis. The rest of the minimum hot utility consumption comes from the steam uses and district heating, both of which must be covered by steam.

Figure 13 shows the respective curves on the cold side: TCLC, ACLC, ECLC and CUC.

The identical part of ACLC and ECLC has changed into an actual crossing of the curves. This is due to the increased proportion of mixing caused by the added streams.

Theoretically, there is no cooling need so the TCLC becomes a straight line.

Summer

The differences between winter and summer are similar as in the previous case. The data used in the analysis is shown in Table 4. The streams are the same as in the winter case, except the ventilation fluid (cold stream 9), which does not need heat during to summer time. The advanced composite curves on the hot side are shown in Figure 14 and on the cold side in Figure 15.

The crossing of ACLC and ECLC, already shown in the winter case, is emphasized in summer time. This shows that the advanced composite curves can be used to investigate the amount of mixing occurring in the system.

Conclusions

A mechanical pulp and paper mill has been analysed using advanced composite curves.

The two cases presented show that the advanced composite curves can be used to investigate the amount of nonisothermal mixing occurring in the system. This means that the curves give more information than previously reported in the literature. The consequences of this observation require further studies.

The analysis of the water system, which is comparable to previous studies, shows that the advanced composite curves give a lot more information on the studied system than traditional pinch-based methods. The real temperature levels shown in the curves give guidance to the engineer as to where to look for improvement potential.

In this study, the relatively cost-effective part of the improvement potential was identified and the amount given by the analysis can be seen as technically feasible.

The advanced composite curves have so far been used only in the pulp and paper industry. There are also numerous other branches of energy-intensive industry where promising results would be expected, e.g. steel, metallurgy, chemical and food industries.

  . The temperature levels and amounts of transferred heat represent winter data. The conventional notation of pinch analysis has been used. The upper streams from left to right are hot streams that need to be cooled, and the lower streams from right to left are cold streams that need to be heated. Inlet and outlet temperatures are presented for each stream. The unbroken lines represent water and the dotted lines air. The hot streams have been numbered H 1 to 9 and the cold streams C 1 to 14. The first three hot streams (hot streams 1-3) represent humid air from the drying sections of the paper machines. The next five streams (hot streams 4-8) are all filtrates in the grinderies. The last hot stream (hot stream 9) represents the cooling needs of all three paper machines. All heating demands are in the paper machines. The first three cold streams (cold streams 1-3) are paper machine inlet air, the next five (cold streams 4-8) are chemically treated process water, the next stream (cold stream 9) is a fluid used in grindery ventilation, and the last five (cold streams 10-14) are circulating waters used in the paper machines. Cold utilities are covered by river water coolers and hot utilities by steam from the power plant.

Figure 1 :

 1 Figure 1: Main material and energy flows in the mill and between the mill and the power plant.

Figure 2 .

 2 Figure 2. The original heat exchanger network. Case1. Winter data.

Figure 3 .

 3 Figure 3. Composite curves of the process with ∆T min 10 K. Case 1. Winter data.

Figure 4 .

 4 Figure 4. Grand composite curve of the process with ∆T min 10 K. Case 1. Winter data.

Figure 5 .

 5 Figure 5. Hot utility curve (HUC), Extreme heat load curve (EHLC), Actual heat load curve (AHLC) and Theoretical heat load curve (THLC). Case 1. Winter data.

Figure 6 .

 6 Figure 6. Theoretical cooling load curve (TCLC), Actual cooling load curve (ACLC), Extreme cooling load curve (ECLC) and Cold utility curve (CUC). Case 1. Winter data.

Figure 7 .

 7 Figure 7. THLC and TCLC. Case 1. Winter data.

Figure 8 .

 8 Figure 8. The improved heat exchanger network. Case 1. Winter data.

Figure 9 .

 9 Figure 9. Hot utility curve (HUC), Extreme heat load curve (EHLC), Actual heat load curve (AHLC) and Theoretical heat load curve (THLC). Case 1. Summer data.

Figure 10 .

 10 Figure 10. Theoretical cooling load curve (TCLC), Actual cooling load curve (ACLC), Extreme cooling load curve (ECLC) and Cold utility curve (CUC). Case 1. Summer data.

Figure 11 .

 11 Figure 11. Grand composite curve. Case 2. Winter data.

Figure 12 .

 12 Figure 12. Hot utility curve (HUC), Extreme heat load curve (EHLC), Actual heat load curve (AHLC) and Theoretical heat load curve (THLC). Case 2. Winter data.

Figure 13 .

 13 Figure 13. Theoretical cooling load curve (TCLC), Actual cooling load curve (ACLC), Extreme cooling load curve (ECLC) and Cold utility curve (CUC). Case 2. Winter data.

Figure 14 .

 14 Figure 14. Hot utility curve (HUC), Extreme heat load curve (EHLC), Actual heat load curve (AHLC) and Theoretical heat load curve (THLC). Case 2. Summer data.

Figure

  Figure 15. Theoretical cooling load curve (TCLC), Actual cooling load curve (ACLC), Extreme cooling load curve (ECLC) and Cold utility curve (CUC). Case 2. Summer data.

  [START_REF] Sivill | Thermodynamic of dryer section heat recovery in paper machines[END_REF]. Theoretical cooling load curve (TCLC), Actual cooling load curve (ACLC), Extreme cooling load curve (ECLC) and Cold utility curve (CUC). Case 2. Summer data.

					Electricity										Wood
	Natural gas,			CHP											Wood treatment		Chips				Mechanical pulping				Cooling
	Peat, Bio fuels	55 °C	48 °C		51 °C		38 °C		56 °C	Bark 58 °C	56 °C	54 °C	Steam 30 °C	Effluent 0 °C 0 °C Cold 1	Cold 2	0 °C	Cold 3	12 °C	Cold 4	18 °C	Pulp Cold 5 18 Heat Cold 6 °C	23 °C	Cold 7	23 °C	Cold 8	40 °C	Cold 9	49 °C	Cold 10	49 °C	Cold 11	49 °C	Cold 12	49 °C	Cold 13	51 °C	Cold 14
														De-
					Sludge						watering			Effluent													Paper
																		treatment						Paper machines
																				Effluent
									5162 kW	5240 kW		7261 kW					kW									Cooling	1707 kW
																											1672 kW
																																		4306 kW
					53 °C																								9573 kW		3337 kW
		59 °C	55 °C																					4214 kW
							63 °C	°C 66		°C 71							75 °C	2336 kW	75 °C	75 2789 kW °C	43 °C					49 °C	45 °C			7271 kW	2208 kW	2557 kW
																											1197 kW					643 kW		2847 kW	854 kW	1978 kW	3206 kW
																																			6474 kW
																				464 kW			773 kW		1167 kW
			85 °C	Hot 2	85 °C	Hot 3	80 °C	Hot 4	°C	Hot 5	75 °C	Hot 6	72 °C	Hot 7	66 °C	Hot 8	31 °C	Hot 9	90 °C	96 °C		96 °C	65 °C		24 °C		55 °C		60 °C		48 °C	52 °C	54 °C	62 °C	67 °C	60 °C

Acknowledgements

This study is part of a research project called Optimization of the cooling systems in industry in CHP production -TeJo. The project is part of the Tekes (Finnish Funding Agency for Technology and Innovation) technology programme Climbus. The study has been financed by Tekes, VTT, the Finnish Forest Industries Federation, Finnish Energy Industries, Rautaruukki Oyj, Harjavalta Industrial Park, Outotec Oyj and HK-Scan Oyj.

ACCEPTED MANUSCRIPT

All the research partners in the TeJo project are acknowledged, especially Juha Hakala's part in constructing the simulation model. The part played by the mill personnel in providing the data for the analysis is gratefully acknowledged.