International Journal of Food Microbiology 194 (2015) 54–61

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International Journal of Food Microbiology journal homepage: www.elsevier.com/locate/ijfoodmicro

Investigation of Geotrichum candidum gene expression during the ripening of Reblochon-type cheese by reverse transcription-quantitative PCR Jessie Castellote a,b, Sébastien Fraud c, Françoise Irlinger a,b, Dominique Swennen d,e, Frédéric Fer d,f, Pascal Bonnarme a,b, Christophe Monnet a,b,⁎ a

INRA, UMR782 Génie et Microbiologie des Procédés Alimentaires, 78850 Thiverval-Grignon, France AgroParisTech, UMR782 Génie et Microbiologie des Procédés Alimentaires, 78850 Thiverval-Grignon, France Actalia Produits Laitiers, 74800 La Roche-sur-Foron, France d INRA, UMR1319 Micalis, 78850 Thiverval-Grignon, France e AgroParisTech, UMR1319 Micalis, 78850 Thiverval-Grignon, France f INRA, UMR518 Mathématiques et Informatique Appliquées, 75005 Paris, France b c

a r t i c l e

i n f o

Article history: Received 15 July 2014 Received in revised form 21 October 2014 Accepted 10 November 2014 Available online 14 November 2014 Keywords: Cheese Reblochon Geotrichum candidum mRNA Gene expression Reverse transcription-quantitative PCR

a b s t r a c t Cheese ripening involves the activity of various bacteria, yeasts or molds, which contribute to the development of the typical color, flavor and texture of the final product. In situ measurements of gene expression are increasingly being used to improve our understanding of the microbial flora activity in cheeses. The objective of the present study was to investigate the physiology and metabolic activity of Geotrichum candidum during the ripening of Reblochon-type cheeses by quantifying mRNA transcripts at various ripening times. The expression of 80 genes involved in various functions could be quantified with a correct level of biological repeatability using a set of three stable reference genes. As ripening progresses, a decrease in expression was observed for genes involved in cell wall organization, translation, vesicular mediated transport, and in cytoskeleton constituents and ribosomal protein genes. There was also a decrease in the expression of mitochondrial F1F0 ATP synthase and plasma membrane H+ ATPase genes. Some genes involved in the catabolism of lactate, acetate and ethanol were expressed to a greater extent at the beginning of ripening. During the second part of ripening, there was an increased expression of genes involved in the transport and catabolism of amino acids, which could be attributed to a change in the energy source. There was also an increase in the expression of genes involved in autophagy and of genes possibly involved in lifespan determination. Quantification of mRNA transcripts may also be used to produce bioindicators relevant for cheesemaking, for example when considering genes encoding enzymes involved in the catabolism of amino acids. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Many species of bacteria, yeasts, and molds are involved in the production of cheeses, where they contribute to the typical sensory properties of the final product. The composition of cheese microbiota is increasingly well-documented, resulting from the development of efficient culture-independent analysis methods (Cocolin et al., 2013; Ercolini, 2013; Jany and Barbier, 2008; Ndoye et al., 2011; Quigley et al., 2011). For a long time, investigation of microbial physiology during cheese ripening was hampered by a lack of methods that could be applied to the solid cheese matrix and in the presence of a more or less complex microbiota. Over the past several years, however, considerable progress has been made, notably for the in situ quantification of ⁎ Corresponding author at: INRA, UMR782 Génie et Microbiologie des Procédés Alimentaires, 78850 Thiverval-Grignon, France. Tel.: +33 1 30 81 54 91; fax: +33 1 30 81 55 97. E-mail address: [email protected] (C. Monnet).

http://dx.doi.org/10.1016/j.ijfoodmicro.2014.11.009 0168-1605/© 2014 Elsevier B.V. All rights reserved.

mRNA transcripts; RNA can now be extracted from cheeses, either directly or after prior separation of microbial cells. The RNA extracts can be used to quantify mRNA transcripts by reverse transcriptionquantitative PCR. Such analyses have been conducted on various types of experimental cheeses (Ablain et al., 2009; Cretenet et al., 2011; Desfossés-Foucault et al., 2014; Duquenne et al., 2010; Falentin et al., 2010, 2012; Monnet et al., 2008, 2012; Rossi et al., 2011; Taïbi et al., 2011; Trmcic et al., 2011; Ulvé et al., 2008) and also recently on retail cheeses (Monnet et al., 2013). These analyses benefit from the rapidly increasing availability of genome sequences from food microorganisms (Rantsiou et al., 2011), which makes it possible to devise primer pairs for specific targets. In most cases, studies involving mRNA quantification in cheeses concerned only a low number of genes, but reverse transcriptionquantitative PCR can enable several dozen genes to be analyzed. For example, 34 genes common to Lactococcus lactis and Lactobacillus paracasei, plus eight genes specific to either species, were studied during simulated Cheddar cheese manufacturing (Desfossés-Foucault

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et al., 2014). In that study, several groups of genes were shown to be over- or under-expressed in mixed culture in comparison to pure cultures. In another study, 22 Arthrobacter arilaitensis genes involved in iron transport and siderophore production were quantified in model cheeses, and the expression patterns showed that several of these genes were regulated as a function of iron availability in the cheeses (Monnet et al., 2012). Reverse transcription-quantitative PCR therefore has considerable potential for improving our knowledge concerning various aspects of microbial physiology during cheese ripening. Quantification of mRNA transcripts may also be used to produce bioindicators relevant for cheesemaking, for example when considering genes that contribute to desirable biochemical reactions in cheeses, such as the production of aromatic compounds. Reblochon is a French smear-ripened cheese, in which Geotrichum candidum is one of the dominant yeasts (Bärtschi et al., 1994; Cogan et al., 2014). The objective of the present study was to investigate the physiology and activity of G. candidum in Reblochon-type cheeses by quantifying mRNA transcripts at several ripening times. For this, 80 genes involved in different functions were considered. 2. Material and methods 2.1. Strains and growth conditions G. candidum ATCC 204307 was obtained from the American Type Culture Collection (Rockville, MD, USA). For cheese manufacturing, G. candidum Geo2 and Streptococcus thermophilus REBST34 were obtained from SIR (Syndicat Interprofessionnel du Reblochon, Thônes, France), Lactobacillus delbrueckii ssp. bulgaricus TOMLL299 from Savoîcime (Annecy, France), and Debaryomyces hansenii DHLyo and Brevibacterium linens FR22 from Danisco (Dangé Saint Romain, France). Lactic acid bacteria were routinely grown under static conditions at 37 °C in M17 lactose broth (S. thermophilus) or MRS broth (L. delbrueckii ssp. bulgaricus) (Biokar Diagnostics, Beauvais, France). B. linens was grown in aerobic conditions (rotary shaker at 150 rpm) at 25 °C in 50 mL conical flasks containing 10 mL of brain heart infusion broth (Biokar Diagnostics). The yeasts were grown in the same conditions, except that the growth medium was potato dextrose broth (Biokar Diagnostics).

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blender (Ultra-Turrax® model T25; Ika Labortechnik, Staufen, Germany) for 1 min at 14,000 rpm, 10-fold serial dilutions were prepared in isotonic saline and plated in triplicate on suitable agar media. After three days of incubation at 25 °C, aerobic bacteria were counted on brain heart infusion agar supplemented with 50 mg/L amphotericin (Biokar Diagnostics), which inhibits the growth of fungi. S. thermophilus was counted on M17 agar supplemented with 50 mg/L amphotericin after three days of incubation at 37 °C in anaerobic conditions. L. delbrueckii ssp. bulgaricus was counted in the same conditions, except that MRS (pH 5.4) agar was used as the growth medium. The yeasts were counted on yeast extractglucose-chloramphenicol agar (Biokar Diagnostics) after three days of incubation at 25 °C. G. candidum Geo2 and D. hansenii DHLyo could be counted differentially on this medium because of their distinct morphotype.

2.4. Biochemical analyses The pH was measured on the homogenized cheese rind. The levels of acetate, ethanol, D-lactate, L-lactate and NH3 contents were assayed using commercially available kits (references 021, 023, 026 and 030; Biosentec, Auzeville Tolosane, France) according to manufacturer's instructions. Total nitrogen was measured with the Kjeldhal method according to standard NF EN ISO 8968 and the non-casein nitrogen content, soluble at pH 4.6, was measured according to NF ISO 27871. Free amino acids were analyzed after fractionation with sulfosalicylic acid and phosphotungstic acid. Two grams of cheese were dispersed in 8 mL of sodium citrate (42 g/L trisodium citrate dihydrate, acidified to pH 2.2 with HCl) with a mechanical blender (Ultra-Turrax®). After centrifugation for 15 min at 5000 ×g and 4 °C, 4 mL of supernatant were recovered and mixed with 200 μL sulfosalicylic acid (600 g/L sulfosalicylic acid dihydrate). After 10 min of incubation on ice, the mixture was centrifuged and 3 mL of supernatant was mixed with 200 μL of phosphotungstic acid (800 g/L). The mixture was incubated for 10 min on ice, centrifuged, and the supernatant was filtered through a 0.2 μm membrane. Free amino acids were then analyzed by HPLC (DelbesPaus et al., 2012).

2.2. Reblochon-type cheesemaking

2.5. Extraction of DNA from liquid cultures

Reblochon-type cheeses were prepared from pasteurized milk inoculated with S. thermophilus REBST34, L. delbrueckii ssp. bulgaricus TOMLL299, G. candidum Geo2, D. hansenii DHLyo and B. linens FR22. The cheesemaking procedure was carried out as previously described (Miszczycha et al., 2013). Twenty-six cheeses (500 ± 20 g) were produced during a single manufacturing run. After an incubation for 4 days at 19.0 ± 0.5 °C and 100% relative humidity (RH), the cheeses were transferred to the ripening room, which was set at 13.0 ± 0.5 °C and 97 ± 2% RH. The cheeses were turned every two days. On day 21, they were wrapped in polypropylene film. Cheeses were sampled on days 1, 5, 14, 19, and 35. At each sampling time, they were cut perpendicular to the surface in order to produce three equivalent parts. One of the three parts was used to measure the concentrations of acetate, ethanol, lactate, lactose, galactose, ammonia, free amino acids and the cheese nitrogen fractions. The upper and lower parts (rinds) of the second part were removed with a knife (thickness of ~ 2–3 mm), pooled, rapidly homogenized with a garlic press, and immediately used for RNA extraction. Cell counts and pH measurements were also conducted on these rind samples. The third part was saved and stored at −30 °C.

Yeast or bacterial liquid cultures (10 mL) were centrifuged for 5 min at 15,000 ×g. DNA was then extracted from the cell pellets as previously described (Monnet et al., 2006).

2.3. Microbiological analyses One gram of cheese rind sampled as described above was mixed with 9 mL of isotonic saline (9 g/L NaCl). After dispersion with a mechanical

2.6. Extraction of RNA from cheeses, DNase treatment, and reverse transcription RNA was extracted from 500 mg rind samples without prior separation of microbial cells as previously described (Monnet et al., 2012), except that the DNase treatment was performed on RNeasy spin columns (Qiagen, Courtaboeuf, France). Three separate extractions were conducted for each cheese sample (technical replicates). Purified RNA was quantified at 260 nm using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). RNA quality was analyzed with an Agilent model 2100 Bioanalyzer (Palo Alto, CA, USA) using RNA chips according to the manufacturer's instructions. Reverse transcription was carried out using the SuperScript® VILO™ cDNA Synthesis kit (Invitrogen, Cergy-Pontoise, France). The reaction mixture contained 500 ng of RNA and 0.3 ng of the exogenous luciferase control mRNA (Promega, Charbonnières, France) added as an exogenous control mRNA, in a final volume of 20 μL. The reverse transcription procedure was run according to the manufacturer's recommendations.

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2.7. Real-time PCR Oligonucleotide primers targeting exons of G. candidum genes were designed using the primer3 online interface (http://bioinfo.ut.ee/ primer3/) with a common set of optimal parameters (size: 20 bp; Tm: 62 °C; G/C content: 50%), and synthesized by Eurogentec (Seraing, Belgium). Sequences of the G. candidum ATCC 204307 target genes (NCBI BioProject accession: PRJEB5752) were kindly provided by Serge Casaregola (INRA, UMR1319 Micalis, Thiverval-Grignon, France). PCR amplifications and analyses were done using a Biorad CFX Connect™ real-time PCR detection system (Bio-Rad, Marnes-la-Coquette, France) and a SYBR® Green real-time PCR kit (SsoAdvanced Universal SYBR® Green Supermix, Bio-Rad). The thermocycling program consisted of initial denaturation at 95 °C for 5 min, followed by 40 cycles of denaturation (95 °C/5 s) and annealing/extension (60 °C/20 s). Fluorescence acquisition was done at the end of each extension. After real-time PCR, a melting curve analysis was performed by measuring fluorescence during heating from 65 to 95 °C with 0.2 °C increments. Quantification cycle (Cq) values were determined with CFX manager software (version 3.1), using the regression determination mode. Standard curves were generated by plotting Cq values vs. log10 (1/F), where F is the dilution factor of the reverse transcribed RNA from one cheese sample (dilutions of cDNAs were performed after reverse transcription). PCR efficiency (E) was calculated for each primer pair from the slopes of the standard curves using the following formula: E = 10−1/slope − 1. At least six dilutions were tested for each primer pair, and linearity was assessed until a Cq value of about 30. The G. candidum genes investigated in the present study and the corresponding primer pairs are presented in Supplementary Table 1. Real-time PCR measurements of the cheese RNA samples were made on 1/10 dilutions of the reverse-transcribed RNA. The absence of PCR inhibition was verified by also analyzing 1/20 and 1/50 dilutions. The absence of DNA contamination in the RNA samples was confirmed with non-reverse transcribed samples (minus-RT controls). Specificity of the primer pairs was assessed by performing real-time PCR experiments with purified genomic DNA of the five strains used for cheese manufacturing (listed in Section 2.1), at the concentration of 2000 pg/μL. Only primers for which amplification occurred uniquely for G. candidum DNA were selected. In addition, standard curves were produced with DNA from G. candidum Geo2 (strain used for cheese manufacturing) and from G. candidum ATCC 204307 (strain whose sequence was used to design the primers). In all cases, the amplification efficiency and the Y intercept value of the Cq vs. log10 of DNA concentration curves were similar, showing that there was no difference in target sequences or that these differences had no impact on amplification. 2.8. Real-time PCR data analysis The quantities of RNA targets were normalized to the quantities of internal reference genes. The stability of potential reference genes was evaluated by using the geNorm VBA applet for Microsoft Excel (Vandesompele et al., 2002). This program calculates the gene expression stability measure (M) for a potential reference gene as the average pairwise variation for that gene with all other reference genes tested. After selecting reference genes, the Cq values for each gene of interest were transformed into relative quantities (Q) with a calibrator (cal) sample (cheese on day 5) and using the gene-specific PCR efficiency (E), calculated as follows: Q = (1 + E)(calCq − sampleCq) (Bustin and Nolan, 2009). Normalization was then applied by dividing the relative quantities of genes of interest by the geometric mean of the relative quantities of selected reference genes (normalization factor). The abundances of RNA transcripts were also normalized with reference to the quantity of RNA and the surface of cheese rind. For normalization against the quantity of RNA, the Cq value for each gene of interest (GOI) was transformed into relative quantities with a calibrator (cal) sample, taking differences of reverse transcription efficiency between the sample and the calibrator into account; this was determined

by analysis of the exogenous RNA (luciferase mRNA, PCR primers Luc_1495-1514 and Luc_1636-1618) added to the cheese RNA samples before the reverse transcription reaction (Monnet et al., 2013). The resulting relative abundance value was designated ARNA and was calculated as follows: h i h i ðcalCqðGOIÞ–sampCqðGOIÞÞ ðcalCqðLUCÞ−sampCqðLUCÞÞ = ðELUC þ 1Þ ARNA ¼ ðEGOI þ 1Þ

where EGOI and ELUC are the PCR efficiencies for the GOI and luciferase (LUC) targets, and Cq (GOI) and Cq (LUC) are the corresponding Cq values for the sample (samp) or the calibrator (cal). This corresponds to normalization against total RNA since the quantity of RNA being reverse-transcribed is the same for all the samples. For normalization against the quantity of cheese, the relative abundance value was designated A cheese and calculated as follows: Acheese ¼ ARNA  Ysamp =Ycal where Ysamp and Ycal are the RNA extraction yields (μg of RNA per cm2 of cheese) for the sample and the calibrator, respectively. The Y samp /Y cal ratio is a correction factor that takes into account the fact that different quantities of cheese rinds had to be processed in order to produce the same quantity of RNA used in the reverse transcription reactions of the sample and the calibrator (500 ng of RNA were used in each reverse transcription). Differences of abundance levels between days 5 and 35 were compared using Student's test. 3. Results 3.1. Microbial growth, substrate consumption and proteolysis during ripening Reblochon-type cheeses were manufactured at the pilot-scale as described in Material and methods. The density of S. thermophilus was close to 109 CFU/g on day 1 and remained constant throughout ripening, whereas L. delbrueckii ssp. bulgaricus, whose concentration on day 1 was 2 × 108 CFU/g, decreased during ripening (Fig. 1). D. hansenii and G. candidum reached their maximum concentration after about 10 days of ripening. The pH increased and reached a value of 6.8 on the surface on day 35 (Fig. 2). Lactose was exhausted at the beginning or ripening, whereas galactose, whose concentration on day 1 was about 10 g/kg, was exhausted after approximately 20 days of ripening. D- and L-lactate were also consumed by the cheese microorganisms. Ethanol and acetate were detected only on day 5 (N0.1 g/kg), when their concentrations were 0.39 and 0.26 g/kg (results not shown). Proteolysis occurred primarily after 14 days of ripening, in parallel to an increase in the concentration of ammonia, a compound resulting primarily from amino acid catabolism in cheese (Fig. 3). Extensive proteolysis after 20 days of ripening was confirmed by the assay of free amino acids (Supplementary Table 2). The most abundant free amino acid on day 35 was glutamate, whose concentration was 2.3 g/kg. 3.2. Selection of reference genes Total RNA was extracted from the cheese rinds on days 5, 14, 19 and 35. At each sampling point, three cheeses were sampled (considered as biological replicates), and three RNA extractions were run on each cheese (technical replicates). Capillary electrophoresis analyses showed that fungal rRNA (18S and 26S) was more abundant than bacterial rRNA (16S and 23S) from day 5 to day 19, whereas the opposite was observed on day 35 (Supplementary Fig. 1). After reverse transcription of the RNA samples, nine G. candidum genes involved in different cellular functions

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Fig. 3. Concentrations of ammonia and of non-casein nitrogen and non-protein nitrogen fractions during the ripening of Reblochon-type cheeses. The upper error bars represent the standard deviations (three biological replicates). The ammonia concentration from days 1 to 14 was below the level of detection (b20 mg/100 g).

(between 0.68 and 1.13), which is the default limit defined by Vandesompele et al. (2002). A combination of the three genes RPN2, TFC1 and TAF10 was selected, since it satisfied the criteria for reliable data normalization, with an average M value of 0.45 (Supplementary Fig. 2).

3.3. Gene expression during ripening

Fig. 1. Bacterial (A) and yeast (B) cell counts on the surface of Reblochon-type cheeses during ripening. The upper error bars represent the standard deviations (three biological replicates).

were evaluated for their potential as internal standard controls for gene expression measurements. The nine selected genes (ACT1, FBA1, RPL5, UCB6, TDH3, GCN4, RPN2, TFC1 and TAF10) were orthologs of Saccharomyces cerevisiae genes which are frequently used as reference genes (Cankorur-Cetinkaya et al., 2012; Teste et al., 2009). For the 36 samples (4 sampling times, 3 biological replicates and 3 technical replicates), the stability of expression (M) of the candidate reference genes was measured using the geNorm VBA applet. All genes had M values below 1.5

Fig. 2. pH of cheese rind and concentrations of L-lactate, D-lactate, galactose and lactose during the ripening of Reblochon-type cheeses. The upper error bars represent the standard deviations (three biological replicates).

Genes of interest were selected, taking account their known involvement in major cellular functions, and also by considering genes for which expression data were available (Cholet et al., 2007; Mansour et al., 2008, 2009). Gene expression levels were expressed as the -fold change relative to the calibrator, which was the mean of the cheese samples on day 5. The distribution of the standard errors corresponding to the normalized expression levels is shown in Supplementary Fig. 3. The median value was 13.6% for the technical replicates. This shows that the entire technical procedure of gene expression measurement (RNA extraction, DNase treatment, reverse transcription and real-time PCR) had a correct level of repeatability. The median value of the standard errors of the biological replicates (that represent variations between the cheeses in the manufacturing batch) was 16.3% (Supplementary Fig. 3). The correct level of biological repeatability can also be seen on the gene expression heatmap (Fig. 4). In addition, double factor hierarchical clustering of the data showed that the biological replicates formed four separate branches corresponding to the four days of sampling. The cheeses on days 14 and 19 were very similar but could be distinguished by the clustering procedure. The expression of G. candidum genes as a function of the ripening time is shown in Supplementary Table 3. The expression of the following genes decreased during ripening: five ribosomal protein genes (RPS0A, RPL5, RPS4B, RPL6A and RPS3), genes involved in translation initiation (TIF2) and elongation (HEF3), lactate dehydrogenase genes involved in lactate catabolism (CYB2, DLD1 and DLD2), the ACS1 acetyl-CoA synthetase gene involved in ethanol and acetate catabolism, oligopeptide transporter gene OPT2, genes involved in the TCA cycle (MDH1, FUM1), four genes encoding subunits of mitochondrial F1F0 ATP synthase (ATP1, ATP2, ATP5 and ATP7), plasma membrane H+ATPase PMA1, genes involved in cell wall organization (SED1_1, SED1_2, FLO11, FKS1_1, FKS1_2 and BGL2), cytoskeleton constituents (TUB1, TUB2_1, TUB2_2 and ACT1) and vesicular mediated transport (SSA4 and CHC1), mitochondrial metalloendopeptidase PRD1, branched-chain amino acid aminotransferases BAT1 and BAT2, alanine transaminase ALT1, and alpha aminoadipate reductase LYS2.

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Fig. 4. Double-factor hierarchical clustering of Geotrichum candidum gene expression during the ripening of Reblochon-type cheeses. Expressions were measured at four ripening times and normalized with reference to a set of three reference genes. The replicates correspond to separate cheeses (biological replicates). The log10 values of -fold changes in gene expression relative to the mean expression on day 5 are color-coded, with red indicating up-regulation and green down-regulation. The heat-map and double-factor hierarchical clusterings were constructed with the Gitools open-source tool (Perez-Llamas and Lopez-Bigas, 2011). With this tool, lines of different colors show proximity between the factors. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

The expression of the following genes involved in the catabolism of amino acids increased during ripening: NAD+-dependent glutamate dehydrogenase GDH2, cytoplasmic aspartate aminotransferase AAT2, aromatic aminotransferase ARO8 and isopropylmalate dehydrogenase LEU1. An increase was also observed for the ribosomal protein gene RPL10, the general amino acid permease GAP1, glycogen catabolism genes (GDB1 and SGA1), RIM101 transcriptional repressor involved in the response to pH and in cell wall construction, MTH1, the negative regulator of the glucose-sensing signal transduction pathway, GCN4, the transcriptional activator of amino acid biosynthetic genes, cyclin PCL5, PPZ2 serine/threonine protein phosphatase involved in osmoregulation, an NAD+ biosynthesis gene (BNA2), genes involved in autophagy (ATG2, ATG8, ATG15), vacuole proteolysis (PRC1 and PEP4) and endoplasmic reticulum-associated degradation of unfolded secreted proteins (UBC6 and LHS1), and urea amidolyase DUR1,2. 3.4. Normalization of transcript abundances against the quantity of cheese Transcript abundances may also be normalized with reference to the quantity of cheese, rather than to reference genes. This may be useful when the objective is not to investigate the physiological status of the cells, but to estimate the abundance of transcripts corresponding to key biochemical reactions that occur during ripening. We used the method described by Monnet et al. (2013), in which differences of reverse transcription efficiencies from one sample to another are taken into account by the analysis of exogenous control mRNA. The reverse transcription efficiencies of exogenous luciferase control mRNA added to the cheese RNA samples before reverse transcription were between 46.1 and 104.2% of the efficiency obtained in pure water (mean efficiency for the 36 cheese RNA samples was 72.5% with a standard deviation of 13.3%). The transcript abundances of the housekeeping genes TAF10, TFC1 and RPN2 (previously used as reference genes) decreased during ripening (Fig. 5), and were about 2–3 times lower on day 35 than on day 5. On the contrary, there was a large increase of GDH2 transcripts, which are involved in the catabolism of amino acids.

RNA and rapidly stops changes in mRNA composition after sampling, and the devising of specific real-time PCR primers. Classical gene expression measurements, in which abundances are normalized with reference to a set of stable reference genes, provide data on transcription regulation mechanisms such as inductions or repressions. It should be emphasized that several other regulations that do not affect the abundance of transcripts exist in microbial cells, e.g. translational regulations, proteolytic cleavages, allosteric regulations. Some gene expression measurement results provided better understanding of how G. candidum cells adapt to environment changes during cheese ripening. On the basis of abundance levels normalized with reference to the three reference genes RPN2, TFC1 and TAF10, we observed a decrease in the expression of several genes involved in cell wall organization (SED1_1, SED1_2, FLO11, FKS1_1, FKS1_2 and BGL2), in vesicular-mediated transport (SSA4 and CHC1), translation initiation and elongation (TIF2 and HEF3), in cytoskeleton constituents (TUB1, TUB2_1, TUB2_2 and ACT1), and ribosomal proteins (except RPL10). As most of the production of G. candidum biomass occurs at the beginning of ripening, this decrease is probably an energy-sparing mechanism. For example, genes encoding ribosomal proteins are among the most transcriptionally active in the genome, and cells have transcription regulations that adapt ribosome synthesis to requirements (Li et al., 1999). The large increase in the expression of the ribosomal protein gene RPL10 on day 35 may be related to its role as regulator involved in replicative life span (Chiocchetti et al., 2007), but to our knowledge the transcriptional regulation of this gene has not been described. The

4. Discussion In the present study, we conducted gene expression measurements of G. candidum during the ripening of Reblochon-type cheeses and showed how these data can be used to assess microbial activity. Consistent and repeatable gene expression data during cheese ripening could be obtained by a classical normalization approach with reference genes. In our opinion, several elements are critical for these analyses, such as the use of an RNA extraction procedure that yields high quantities of

Fig. 5. Geotrichum candidum transcript abundances normalized with reference to the quantity of cheese (Acheese) vs. the ripening day. Values are relative to the abundances on day 5. The upper error bars represent the standard deviations (three biological replicates).

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expression of RIM101, a transcriptional repressor involved in response to pH, increased throughout ripening, probably due to the increase of pH. It has in fact been shown that homologs of this gene in Metarhizium robertsii (Huang et al., in press) and Gaeumannomyces graminis (Daval et al., 2013) are induced in alkaline conditions. The expression of PMA1, a gene encoding plasma membrane H+-ATPase, the major regulator of cytoplasmic pH, decreased considerably during ripening. This gene is known to be regulated in order to cope with requirements (Schmitt et al., 2006) and its decreased activity can be explained by the increase of cheese pH during ripening. The expression of the three lactate dehydrogenases, especially DLD1, decreased during ripening. Interestingly, metatranscriptome analyses of Camembert-type cheeses also revealed a decrease of G. candidum lactate dehydrogenase gene transcripts when the ripening time increased (Lessard et al., 2014). In the context of cheese ripening, the decrease of DLD1 expression may be related to the decrease of lactate, which is an energy substrate consumed by G. candidum (Boutrou and Gueguen, 2005). The same reasoning may be applied for ACS1 which encodes an acetyl-coA synthetase required for the catabolism of ethanol and acetate, and which is induced by acetate in S. cerevisiae (de Jong-Gubbels et al., 1997). In our work, acetate and ethanol were detected only on day 5, and ACS1 expression decreased after this time point. S. cerevisiae OPT2 encodes a tetra- and pentapeptide transporter (Aouida et al., 2009); the G. candidum ortholog was strongly repressed after day 5, while expression of GAP1, a general amino acid permease that directs the uptake of all naturally occurring L-amino acids, increased throughout ripening. This may indicate a higher abundance of free amino acids in the cheese curd when the ripening time increases and more extensive catabolism of amino acids. Glutamate is the most abundant amino acid in caseins (27 to 38 glutamate and glutamine residues per molecule, depending on the casein type) and is frequently the most abundant free amino acid in cheeses (Rosenberg and Altemueller, 2001). Interestingly, we observed a large increase of glutamate levels in the Reblochon-type cheeses produced in the present study, especially after day 19. In S. cerevisiae, GDH2 encodes an NAD-dependent glutamate dehydrogenase, the main pathway for glutamate degradation (Miller and Magasanik, 1990), yielding NADH, ammonia, and alpha-ketoglutarate which can enter into the TCA cycle. The large increase of G. candidum GDH2 expression during ripening may thus reveal an increase of glutamate utilization as an energy source, at the expense of other substrates, such as galactose or lactate. Interestingly, in S. cerevisiae, GDH2 is regulated by a carbon control pathway, and transcriptional activation has been established when quantities of glucose are limiting (Coschigano et al., 1991). Even if it is not possible to attribute the totality of ammonia production during cheese ripening to G. candidum, the increase of this compound after day 14 may also indicate the glutamate catabolism by the NAD-dependent glutamate dehydrogenase Gdh2. NAD-glutamate dehydrogenase transcripts attributed to G. candidum were also detected in the metatranscriptome analyses of Camembert-type cheeses (Lessard et al., 2014), but in contrast to the present study, their abundance decreased during ripening (maximum abundance observed after nine days of ripening). This may reveal differences of G. candidum metabolism in the two types of cheeses. Alpha-ketoglutarate produced from glutamate may also serve as substrate for transamination reactions, and favor the catabolism of other amino acids. Interestingly, there was a large increase of AAT2 expression at the end of ripening. This gene encodes an aspartate transaminase, which can be used to catabolize aspartate by formation of oxaloacetate, a constituent of the TCA cycle. The combined activity of GDH2 and AAT2 may thus promote the catabolism of aspartate during ripening. The expression of another transaminase gene, ARO8 which encodes an aromatic aminotransferase active towards phenylalanine, tyrosine and tryptophan (Iraqui et al., 1998), also increased during ripening. A hypothesis may be advanced according to which the formation of glutamate from alpha-ketoglutarate by Aro8p, followed by the regeneration of alpha-ketoglutarate by Gdh2p, which results in a net production of the electron donor NADH, is a pathway used by G. candidum to produce

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energy from aromatic amino acids. GCN4 encodes a transcriptional activator of amino acid biosynthesis genes and of several other genes (Natarajan et al., 2001). Protein Gcn4 is induced during amino acid starvation by a translation control mechanism. We found a slight increase in GCN4 transcript abundance on day 35 and so it would be interesting to determine if this gene is also regulated at the transcriptional level. BNA2 expression on day 35 was about 30 times higher than on day 5. This gene is involved in the de novo synthesis of NAD+ from tryptophan and was also shown to be up-regulated by several environment stresses (Gasch et al., 2000). It plays a role in chromosome end protection, since it is induced to a substantial extent when telomeres are uncapped (Greenall et al., 2008). A hypothesis may be advanced according to which the increase of NAD-dependent glutamate dehydrogenase production by the cells at the end of ripening requires more NAD+ production. In addition, NAD+ is also required for the activity of sirtuins, histone deacetylases involved in DNA repair, heterochromatin formation and lifespan determination (Lin et al., 2000; Michan and Sinclair, 2007). Interestingly, calorie restriction in S. cerevisiae extends chronological life span, i.e. the time that non-dividing cells remain viable, and this mechanism is believed to be related to the activation of NAD+-dependent protein deacetylase Sir2 by NAD+ (Lin et al., 2000). The increase of ATG2, ATG8 and ATG15 transcript abundances during ripening probably indicates the stimulation of autophagy, a membrane transport pathway from the cytoplasm to the vacuole for degradation and recycling. In some conditions, including starvation, autophagy increases, which is believed to aid in maintaining an amino acid pool for gluconeogenesis and for the synthesis of proteins essential to survival under starvation conditions (Gray et al., 2004). Transcriptional up-regulation of ATG8 in response to starvation has been observed in S. cerevisiae (Kirisako et al., 1999), where autophagy contributes to chronological longevity (Alvers et al., 2009). The increased of expression of G. candidum genes involved in vacuole proteolysis (PEP4 and PRC1) and reticulum-associated degradation of unfolded proteins (LHS1 and UBC6) may also contribute to recycling amino acids during starvation. Another clue of the quiescent state of G. candidum cells at the end of ripening is the substantial decrease in the expression of genes encoding subunits of mitochondrial F1F0 ATP synthase (ATP1, ATP2, ATP5 and ATP7), which probably reflects decreased production and consumption of ATP; quiescent cells in fact require less energy. Concerning genes involved in gluconeogenesis and glycolysis, there was no general trend of the expression levels which could indicate an increase or a decrease in the activity of these pathways. The abundance of MTH1 transcripts increased on day 35. This gene encodes a negative regulator that is induced when glucose is absent (Kim et al., 2006; Roy et al., 2013). The increase of MTH1 transcript abundance on day 35 can be explained by a reduced availability of energy substrates. The normalization of gene transcript abundances against stable reference genes is necessary to ascertain or identify transcription regulations. In the context of cheese ripening, normalized expression levels can be used to identify biological indicators reflecting cellular activity or physiological state, as illustrated above and in previous studies (Desfossés-Foucault et al., 2014; Monnet et al., 2012; Taïbi et al., 2011). When transcript abundances are normalized with reference to the quantity of cheese, gene expressions levels reflect the overall abundance of transcripts in the cheese. This is useful when studying the expression of genes that contribute to desirable or undesirable biochemical reactions in cheeses. For example, the ability to produce aromatic compounds from amino acids is closely related to the NADdependent glutamate dehydrogenase activity (Tanous et al., 2002), because the production of alpha-ketoglutarate favors the transamination of amino acids. The G. candidum GDH2 transcripts profile shows that, even if the transcript abundances (based on the quantity of cheese) of housekeeping genes such as TAF10, TFC1 and RPN2 decreased during ripening, there was an increase of GDH2, suggesting that more aromatic compounds are produced at the end of ripening (Fig. 5). As indicated in a prior study (Monnet et al., 2013), one possible bias in normalization against the quantity of cheese is that it does not compensate for

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differences of RNA extraction efficiencies from one cheese to another, but this problem may be minimized by using a well-standardized protocol, in which there is efficient cell lysis. In this study, we were able to obtain a consistent and repeatable quantification of numerous G. candidum gene transcripts during the ripening of Reblochon-type cheeses. These data were used to characterize the activity and physiological state of the cells. We have also illustrated how the quantification of gene transcripts (e.g. NAD-dependent glutamate dehydrogenase GDH2) and the normalization of data against the quantity of cheese can be used to identify bioindicators relevant for cheesemaking. Acknowledgments This work was supported by the ExEco program (a joint metatranscriptomic and biochemical approach of the cheese ecosystem: for an improved monitoring of the expression of a complex food ecosystem) (ANR-09-ALIA-012-01), funded by the French National Research Agency (ANR). We thank Serge Casaregola (INRA, UMR1319 Micalis, Thiverval-Grignon, France) for providing the sequences of G. candidum ATCC 204307 genes, and Nour-Elhayate Baddazi and Brigitte Pollet (INRA, UMR782 GMPA, Thiverval-Grignon, France) for the analysis of free amino acids. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.ijfoodmicro.2014.11.009. References Ablain, W., Hallier Soulier, S., Causeur, D., Gautier, M., Baron, F., 2009. A simple and rapid method for the disruption of Staphylococcus aureus, optimized for quantitative reverse transcriptase applications: application for the examination of Camembert cheese. Dairy Sci. Technol. 89, 69–81. Alvers, A.L., Fishwick, L.K., Wood, M.S., Hu, D., Chung, H.S., Dunn, W.A., Aris, J.P., 2009. Autophagy and amino acid homeostasis are required for chronological longevity in Saccharomyces cerevisiae. Aging Cell 8, 353–369. Aouida, M., Khodami-Pour, A., Ramotar, D., 2009. Novel role for the Saccharomyces cerevisiae oligopeptide transporter Opt2 in drug detoxification. Biochem. Cell Biol. 87, 653–661. Bärtschi, C., Berthier, J., Valla, G., 1994. Evolution of the surface fungal flora of Reblochon cheese. Lait 74, 105–114. Boutrou, R., Gueguen, M., 2005. Interests in Geotrichum candidum for cheese technology. Int. J. Food Microbiol. 102, 1–20. Bustin, S.A., Nolan, T., 2009. Analysis of mRNA expression by real-time PCR. In: Logan, J., Edwards, K., Saunders, N. (Eds.), Real-time PCR. Current Technology and Applications. Caister Academic Press, Norfolk. Cankorur-Cetinkaya, A., Dereli, E., Eraslan, S., Karabekmez, E., Dikicioglu, D., Kirdar, B., 2012. A novel strategy for selection and validation of reference genes in dynamic multidimensional experimental design in yeast. PLoS ONE 7, e38351. Chiocchetti, A., Zhou, J., Zhu, H., Karl, T., Haubenreisser, O., Rinnerthaler, M., Heeren, G., Oender, K., Bauer, J., Hintner, H., Breitenbach, M., Breitenbach-Koller, L., 2007. Ribosomal proteins Rpl10 and Rps6 are potent regulators of yeast replicative life span. Exp. Gerontol. 42, 275–286. Cholet, O., Henaut, A., Casaregola, S., Bonnarme, P., 2007. Gene expression and biochemical analysis of cheese-ripening yeasts: focus on catabolism of L-methionine, lactate, and lactose. Appl. Environ. Microbiol. 73, 2561–2570. Cocolin, L., Alessandria, V., Dolci, P., Gorra, R., Rantsiou, K., 2013. Culture independent methods to assess the diversity and dynamics of microbiota during food fermentation. Int. J. Food Microbiol. 167, 29–43. Cogan, T.M., Goerges, S., Gelsomino, R., Larpin, S., Hohenegger, M., Bora, N., Jamet, E., Rea, M.C., Mounier, J., Vancanneyt, M., Guéguen, M., Desmasures, N., Swings, J., Goodfellow, M., Ward, A.C., Sebastiani, H., Irlinger, F., Chamba, J.F., Beduhn, R., Scherer, S., 2014. Biodiversity of the surface microbial consortia from Limburger, Reblochon, Livarot, Tilsit, and Gubbeen cheeses. In: Donnelly, C.W. (Ed.), Cheese and Microbes. ASM Press, Washington, DC, pp. 219–250. Coschigano, P.W., Miller, S.M., Magasanik, B., 1991. Physiological and genetic analysis of the carbon regulation of the NAD-dependent glutamate dehydrogenase of Saccharomyces cerevisiae. Mol. Cell. Biol. 11, 4455–4465. Cretenet, M., Laroute, V., Ulvé, V., Jeanson, S., Nouaille, S., Even, S., Piot, M., Girbal, L., Le Loir, Y., Loubiere, P., Lortal, S., Cocaign-Bousquet, M., 2011. Dynamic analysis of the Lactococcus lactis transcriptome in cheeses made from milk concentrated by ultrafiltration reveals multiple strategies of adaptation to stresses. Appl. Environ. Microbiol. 77, 247–257. Daval, S., Lebreton, L., Gracianne, C., Guillerm-Erckelboudt, A.Y., Boutin, M., Marchi, M., Gazengel, K., Sarniguet, A., 2013. Strain-specific variation in a soilborne phytopathogenic fungus for the expression of genes involved in pH signal transduction pathway,

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