International Journal of Food Microbiology 178 (2014) 76–86

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

Transcription profiling of interactions between Lactococcus lactis subsp. cremoris SK11 and Lactobacillus paracasei ATCC 334 during Cheddar cheese simulation Émilie Desfossés-Foucault, Gisèle LaPointe, Denis Roy ⁎ Institut sur la nutrition et les aliments fonctionnels (INAF), Département des Sciences des aliments et de nutrition, Université Laval, G1V 0A6 QC, Canada

a r t i c l e

i n f o

Article history: Received 5 September 2013 Received in revised form 27 February 2014 Accepted 3 March 2014 Available online 12 March 2014 Keywords: Transcription profile Lactic acid bacteria Cheddar cheese Interaction

a b s t r a c t The starter cultures (Lactococcus sp.) and non-starter lactic acid bacteria (mostly Lactobacillus spp.) are essential to flavor development of Cheddar cheese. The aim of this study was to elucidate the transcriptional interaction between Lactococcus lactis subsp. cremoris SK11 and Lactobacillus paracasei ATCC 334 in mixed cultures during simulated Cheddar cheese manufacture (Pearce activity test) and ripening (slurry). Reverse transcription quantitative PCR (RT-qPCR) was used to quantify the expression of 34 genes common to both bacteria and for eight genes specific to either L. lactis subsp. cremoris SK11 or L. paracasei ATCC 334. The multifactorial analysis (MFA) performed on fold change results for each gene revealed that the genes linked to stress, protein and peptide degradation as well as carbohydrate metabolism of L. paracasei ATCC 334 were especially overexpressed in mixed culture with L. lactis subsp. cremoris SK11 during the ripening simulation. For L. lactis subsp. cremoris SK11, genes coding for amino acid metabolism were more expressed during the cheese manufacture simulation, especially in single culture. These results show how complementary functions of starter and NSLAB contribute to activities useful for flavor development. © 2014 Elsevier B.V. All rights reserved.

1. Introduction In Cheddar cheese, both starter cultures (especially Lactococcus lactis subsp. cremoris in Canada) and non-starter lactic acid bacteria (NSLAB, mostly lactobacilli species such as Lactobacillus casei, Lactobacillus paracasei, Lactobacillus rhamnosus, Lactobacillus plantarum and Lactobacillus curvatus) are responsible for many of the biochemical changes that occur during ripening and therefore greatly influence flavor and texture development (Christiansen et al., 2005; Coeuret et al., 2003; Fox and McSweeney, 2004; Taïbi et al., 2010). L. lactis subspecies lactis and cremoris have been widely studied for their enzyme production in cheese, especially for those related to lactose and casein degradation. Lactobacillus species contribute to flavor through peptide and amino acid degradation and can be added as adjuncts (Smit et al., 2005). Although proteomic approaches are useful to detect and even quantify proteins in cheese (Gagnaire et al., 2009), other studies provide complementary transcriptomic results revealing specific bacterial responses to environmental conditions. Most of these gene expression studies were conducted in culture media or milk (Azcarate-Peril et al., 2009; Raynaud et al., 2005; Smeianov et al., 2007; Taïbi et al., 2011), but in situ studies of bacterial ecosystems ⁎ Corresponding author at: Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, 2440, Boul. Hochelaga, G1V 0A6 QC, Canada. E-mail address: [email protected] (D. Roy).

http://dx.doi.org/10.1016/j.ijfoodmicro.2014.03.004 0168-1605/© 2014 Elsevier B.V. All rights reserved.

are emerging with advances in methodology (Ndoye et al., 2011; Taïbi et al., 2011). The transcriptome response of lactococci has been analyzed in model cheeses (Bachmann et al., 2010; Cretenet et al., 2011). L. lactis subsp. lactis viability and metabolic activity were detected up to seven days in model cheese made with ultrafiltered milk with no mRNA degradation (Cretenet et al., 2011). In Gouda-type cheese, the response of mixed strain cultures revealed the induction of genes during cheese manufacturing and ripening (Bachmann et al., 2010). Between bacterial species, Even et al. (2009) analyzed the antagonist properties of L. lactis subsp. lactis on Staphylococcus aureus, showing the repression of several virulence genes. For yogurt cultures, HerveJimenez et al. (2009) showed coculture-specific proteome and transcriptome modifications of Streptococcus thermophilus LMG18311 cultivated in the presence of Lactobacillus delbrueckii subsp. bulgaricus ATCC 11842 in milk. Then, in yogurt, Sieuwerts et al. (2010) linked their interaction to purine, amino acid and long-chain fatty acid metabolism. In vitro, Kieronczyk et al. (2003) showed that the cooperation between L. lactis subsp. cremoris and GDH-positive L. paracasei strains can increase the production of carboxylic acids from amino acids and therefore improve flavor formation. However, the impact of L. paracasei on the transcriptional response of L. lactis, or the reverse, has never been studied, especially in cheese models. Such mixed-culture studies should bring new insights to understand the contribution of each species to cheese quality (Steele et al., 2013).

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In this study, the transcriptional profiles of L. lactis subsp. cremoris SK11 and L. paracasei ATCC 334 in mixed cultures were analyzed during simulation of Cheddar cheesemaking and ripening, demonstrating the interaction between both species. RT-qPCR was used to estimate differential expression of genes related to cellular metabolism and stress, carbon and amino acid catabolism, lipolysis and amino acid conversion at multiple stages during the process. 2. Material and methods 2.1. Bacterial strains, growth conditions and microbiological counts L. lactis subsp. cremoris SK11 was obtained from the NCIMB collection (National Collection of Industrial, Marine and Food Bacteria, UK), and L. paracasei ATCC 334 was obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA). L. lactis subsp. cremoris SK11 was grown aerobically for 16 h at 22 °C in 10 mL modified Elliker broth, composed of Elliker broth (Difco Laboratories, Sparks, MD, USA) supplemented with 0.5% yeast extract (Difco) and 0.08% Tween 80 (Sigma-Aldrich Canada Ltd., Oakville, ON, Canada). L. paracasei ATCC 334 was grown in anaerobic jars for 16 h at 37 °C in 10 mL MRS broth (Oxoid, Nepean, ON, Canada). Second sub-cultures for each strain were used to inoculate 40 mL of microfiltered 3.25% milk fat (MF) homogenized milk (Parmalat Canada, Toronto, ON, CA). The milk cultures were then incubated for 16 h overnight before being used to inoculate (at 3% v/v for L. lactis subsp. cremoris SK11 and at 0.1% v/v for L. paracasei ATCC 334) both the Pearce activity test samples and the Cheddar cheese slurries (T0 for both models). L. lactis subsp. cremoris SK11 and L. paracasei ATCC 334 populations were followed during the Pearce activity test and in slurry samples using standard microbiological methods. Ten grams of each sample was homogenized in 90 mL of sterile 0.1% peptone water and homogenized with the Stomacher® 400 Circulator (Seward, Worthing, West Sussex, UK) for 5 min at 260 rpm. The resulting suspension was serially diluted and plated on M17 agar (Oxoid) and acidified MRS agar (pH 5.4) (Oxoid). 2.2. Pearce activity test The Pearce activity test (Feirtag and McKay, 1987; Sheehan et al., 2005) was used with the following modifications. The test was repeated three times with both the separate strains (samples 1 to 5) and the combined strains (samples 6 to 10) in order to attain a final concentration of 109 CFU/g for L. lactis subsp. cremoris SK11 (3% v/v inoculation) and 106 CFU/g for L. paracasei ATCC 334 (0.1% v/v inoculation). The temperature profile was as follows: 120 min at 32 °C (with inoculation after 15 min (T0), addition of a CaCl2 solution (Fromagex, Rimouski, QC, CA) and of veal rennet (Fromagex) after 50 and 70 min (T1), respectively, to attain a final concentration of 0.02% (v/v) for each); increase to 38 °C over 30 min and held for 30 min (T2); decrease to 35 °C over 15 min and held at 35 °C for 60 min (T3); and 210 min (T5) at 35 °C (with the addition of salt to 2.0% w/w after 105 min (T4)). Samples (10 g for each analysis) were collected at T0 and at T1, T2, T3, T4 and T5 (samples 1 to 5 for single cultures and 6 to 10 for mixed cultures) for pH, microbiological counts and RNA extraction. Control milk samples without inoculum were also incubated and analyzed to confirm the absence of gene expression in those samples. 2.3. Cheese slurries The slurries were prepared from cheese manufactured following standard Cheddar cheesemaking procedures (Kosikowski, 1977). The resulting curd was lyophilized and grated, followed by γ-irradiation at 5000 Gy in order to inactivate all bacteria and RNA. Fifty grams of the resulting powder was mixed with 25 mL of sterile saline solution (initial concentration of 5% w/v) and inoculated with the separate strains

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(samples 11 to 14) or the combined strains (samples 15 to 18) in order to obtain an initial concentration of 109 CFU/g for L. lactis subsp. cremoris SK11 and 106 CFU/g for L. paracasei ATCC 334 before being blended three times for 1 min with a sterile stainless steel homogenizer (VWR, Mississauga, ON, CA). All these operations were conducted in a laminar flow hood to prevent contamination. The slurries (50 g) were then transferred into sterile headspace vials and flushed with nitrogen before being incubated at 30 °C for twelve days to simulate accelerated ripening equivalent to six months at 4–6 °C. Vials were collected after inoculation (T0) and after 3, 6, 9 and 12 days of ripening (samples 11 to 14 in single culture and 15 to 18 in mixed culture) for pH, microbiological counts and RNA extraction. The experiment was repeated three times and control slurries without inocula were incubated and analyzed in parallel to confirm the absence of gene expression in those samples. 2.4. RNA extraction and retrotranscription All RNA extractions were performed according to Ulvé et al. (2008). Sample temperature never exceeded 4 °C to limit nuclease activity. Briefly, all bacterial cells were first separated from the matrix: ten grams of cheese slurry was mixed with 90 mL of sterile 2% (w/v) trisodium citrate and homogenized with the Stomacher® 400 Circulator (Seward) for 5 min at 260 rpm. 10 mL of the suspension was centrifuged at 10,000 ×g for 10 min at room temperature, and fat layers were then removed with sterile swabs. When the recovered cell concentration was below 108 CFU/g on M17, 100 mL of suspension was centrifuged. RNA extractions were then carried out according to the RNeasy mini kit (Qiagen, Mississauga, ON, CA) instructions, with the following modifications. Bacterial pellets isolated from cheese were suspended in 500 μL of a solution composed of Tris EDTA (20 mM Tris–HCl pH 8.0, 2 mM EDTA pH 8.0) and RLT buffer (supplied with the RNeasy mini kit) before being transferred to a 2-mL straight-walled microtube containing 0.6 g of zirconium beads (0.1-mm diameter; BioSpec Products, Bartlesville, OK, USA), 50 μL of SDS (10%) and 500 μL of acid phenol (pH 4.3; Sigma Aldrich). The tubes were shaken three times for 1 min at maximum speed in a Mini-Beadbeater-16 (BioSpec Products) with chilling on ice for 2 min between each step to avoid RNA overheating. After adding 200 μL of chloroform, the samples were vigorously shaken and centrifuged for 20 min at 12,000 ×g at 4 °C. The aqueous phase was recovered and mixed with an equal volume of ethanol (70%). Samples were then purified according to Qiagen's instructions. RNA samples were eluted twice using 30 μL of RNase free water. DNase treatments were performed using the DNA-free™ kit (Ambion, Austin, TX, USA) according to the manufacturer's instructions, and the absence of residual DNA was routinely verified by PCR using universal rRNA 16S primers 27F (5′-AGAGTTTGATCCTGGCTCAG-3′) and 788R (5′-GGACTACCAGGGTATCTAA-3′) (Hartmann et al., 2005; Therese et al., 1998). RNA yield and quality (absence of degradation) were determined by electrophoresis profiling using a 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Retrotranscription was performed immediately after using the High Capacity cDNA archive kit with RNase Inhibitor (Applied Biosystems, Carlsbad, CA, USA) according to the manufacturer's instructions. The remaining RNA was stored at −80 °C, and cDNA samples were stored at −20 °C. 2.5. DNA extraction for qPCR optimization In order to verify primer and probe efficiency and specificity, total genomic DNA was extracted from L. lactis subsp. cremoris SK11 and L. paracasei ATCC 334, from five Lactobacillus species isolated from experimental cheese samples made in the same plant as the Cheddar cheese used to prepare slurry samples (accession numbers for the rDNA 16S gene partial sequences can be found in the Supplementary material, Table S2), and from two species obtained from the American Type Culture Collection (ATCC). All DNA extractions were carried out

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according to Licitra et al. (2007) with the following modifications. Bacterial pellets were suspended in 180 μL of buffer for enzymatic lysis (20 mM Tris HCl pH 8.0, 2 mM EDTA pH 8.0, 1.2% Triton X-100, 20 mg/mL lysozyme (Sigma-Aldrich), 10 μL/mL of 5 U/μL mutanolysin (Sigma-Aldrich)) and incubated at 37 °C for 1 h. Protein digestion was performed by adding 25 μL of proteinase K (20 mg/mL) and 200 μL of AL buffer from the DNeasy Blood and Tissue kit (Qiagen) and incubated at 70 °C for 30 min. The suspensions were then transferred to a 2-mL microtube containing 0.3 g of zirconium beads (1-mm diameter, BioSpec Products). Tubes were shaken twice for 90 s in a Mini-Beadbeater-16 (BioSpec Products), with chilling on ice for 2 min between each step to avoid DNA overheating, and were then centrifuged at 10,000 ×g for 10 min at room temperature. Finally, the nucleic acids were precipitated from the supernatant by adding 200 μL of ice-cold absolute ethanol. DNA purification was performed as specified in Qiagen's instructions. Residual RNA was removed using DNAse free RNase (Roche, Laval, QC, CA) according to the manufacturer's instructions. DNA yield and quality were determined using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). DNA samples were stored at − 20 °C. 2.6. Primer and probe design and specificity TaqMan® gene expression assays (containing both primers and TaqMan® probe) were manually designed with the Geneious Pro 5.3 software (Biomatters, New Zealand) for 34 genes common to both L. lactis subsp. cremoris SK11 and L. paracasei ATCC 334, along with eight specific genes for each species and five reference genes (see Table S1 in the Supplementary material for all primers and probes). Sequences for each gene locus were obtained from GenBank through the Geneious Pro 5.3 software interface, which also tested for hairpins and primer dimers. L. lactis subsp. cremoris SK11 gene sequences were selected by alignment with the corresponding sequences from L. lactis subsp. cremoris MG1363 and L. lactis subsp. lactis IL1403 to ensure detection of both L. lactis subspecies, while L. paracasei ATCC 334 gene sequences were aligned with L. paracasei BL23 and L. casei strain Zhang as well as with sequences from seven other Lactobacillus species (L. parabuchneri, L. rhamnosus, L. brevis, L. coryniformis, L. curvatus, L. delbrueckii subsp. lactis ATCC 12315, L. plantarum ATCC 14917) to confirm their ability to distinguish L. casei and L. lactis (see Table S2 in the Supplementary material). The PCR amplification was simulated with both primers (http://insilico.ehu.es/PCR/) and specificity was then verified in silico by blastn (http://blast.ncbi.nlm.nih.gov/) for both primers and probes.

amplification efficiencies were verified from raw fluorescence data using the DART-PCR v.1.0 software (Peirson et al., 2003). 2.8. Gene expression quantification Reverse transcription followed by quantitative PCR (RT-qPCR) analyses were performed with the Vii™A7 Real-Time PCR System (Applied Biosystems) using 384-well plates with the same reaction mixture and amplification parameters as the one for specificity assessment (see Section 2.7), but with 100 ng of cDNA as template. Each gene was analyzed in duplicate for each sample. Negative controls with no template cDNA were also included in each run, as well as a positive control sample (with genomic DNA as template). Following PCR amplification, cycle threshold (Ct) values were determined after manual adjustment of the threshold within the linear part of the logarithmic PCR amplification curve, the same threshold being used between RT-qPCR runs. 2.9. Relative target gene expression analysis Reference gene stability was then assessed with the Biogazelle GeNormPlus software (Biogazelle NV, Zwijnaarde, Belgium), revealing rpoB and atpD to be the most stable genes for normalization (data not shown). Gene expression for each sample was analyzed with the DataAssist software v3.0 (Applied Biosystems) (Livak and Schmittgen, 2001) and the fold difference for each gene was determined relative to the initial conditions for the Pearce activity test and slurries (T0) (see Tables S3 to S6 in the Supplementary material for detailed fold change results). 2.10. Statistical analysis Statistical significance (P b 0.05) by the mixed procedure was conducted using the JMP software v. 9.0.2 (SAS Institute Inc., 2010) with a full factorial design (variance components covariance structure) with assay (single or mixed culture) and cheese model (Pearce test or slurry) as fixed effects and replicates as the random classification variable. Double factor hierarchical clustering (with heat maps) of all genes was also performed with the JMP software. MFA was performed on base 10 logarithm transformed data with the R software (R Development core team, 2008) using the FactoMineR package (Husson et al., 2009) with each gene classification as main factors and cheese model (milk samples from the Pearce activity test or slurries), assay (single bacteria or mixed culture), temperature (30, 32, 35 or 38 °C), time (T1 to T5 and 3, 6, 9 and 12 days of ripening) and genes specific to only one of the bacterial species as supplementary variables.

2.7. Verification of assay specificity by quantitative PCR

3. Results

The specificity of each gene expression assay for L. lactis subsp. cremoris SK11 was tested against the genomic DNA of L. paracasei ATCC 334, and the specificity of L. paracasei ATCC 334 assays was tested against L. lactis subsp. cremoris SK11 genomic DNA (see Table S1 in the Supplementary material for Ct results). In addition, all assays were also tested using the genomic DNA of seven Lactobacillus species (see Table S2 in the Supplementary material) (data not shown). Quantitative PCR was performed with the Vii™A7 Real-Time PCR System (Applied Biosystems) using 384-well plates. PCR was performed in duplicate for each assay in 10 μL reaction mixtures containing 100 ng of genomic DNA, 5 μL TaqMan® Fast Advanced Master Mix and 1 × of the TaqMan® Gene Expression Assay Mix (containing both primers and TaqMan® probe). Negative controls with no template DNA were also included in each run, as well as a positive control sample (with genomic DNA from the target bacterial species as a template). Amplification consisted of a 20 s denaturation step at 95 °C, followed by 40 two-step cycles of 1 s for 95 °C and 20 s at 60 °C. PCR

3.1. Growth of L. lactis subsp. cremoris SK11 and L. paracasei ATCC 334 during the Pearce activity test and in the cheese slurries Both L. lactis subsp. cremoris SK11 and L. paracasei ATCC 334 viable counts went up 1 log during the cheesemaking simulation (Pearce activity test; Fig. 1), and pH values decreased from 6.6 ± 0.1 at T0 to 4.5 ± 0.1 at T5 (data not shown) for SK11 in single or mixed culture. For L. paracasei ATCC 334 in pure culture, pH did not decrease below 6.4 ± 0.1 (data not shown). In cheese slurry samples, pH remained at 5.0 ± 0.2 from the beginning (data not shown). Initial inoculations of both strains duplicated the same level achieved at the end of the Pearce activity test. L. lactis subsp. cremoris populations rapidly declined from 109 to 107 CFU/g after the first three days of ripening, followed by a total lack of detection on M17 agar after six days. L. paracasei ATCC 334 microbiological counts, however, remained high throughout the twelve days of ripening (Fig. 1).

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of mismatched nucleotides in the primers (1 or 2) and probe (1). Overall, the prtP nucleotide sequences of these two species are 95% identical (Ljungh and Wadström, 2009). Therefore, results for this gene should be considered as the total prtP transcription for both bacteria when in mixed culture.

A

8

6

3.3. Overall clustering of transcriptome profiles of L. lactis subsp. cremoris SK11 and L. paracasei ATCC 334 according to cheese model or type of culture

4

Log CFU/g

2

0 T0

T1

T2

T3

T4

T5

Cheesemaking step 10

79

B

8

6

4

2

0 0

3

6

9

12

Ripening time (days) Fig. 1. Viable counts for Lactococcus lactis subsp. cremoris SK11 (black) and Lactobacillus casei ATCC 334 (gray) during the Pearce activity test (T0 to T5) (A) and Cheddar cheese slurry over ripening time (from day 0 to day 12) (B). Single strains (—); mixed cultures (—).

3.2. Specificity of gene expression assays For all gene expression assays except prtP, lack of cross-reactivity to the non-targeted bacteria genomic DNA was demonstrated (L. paracasei ATCC 334 for lack of reactivity to L. lactis subsp. cremoris SK11, and L. lactis subsp. cremoris SK11 for lack of reactivity to L. paracasei ATCC 334). Ct results were at least 10 to 16 Ct higher, confirming assay specificity (see Table S1 in the Supplementary material). All assays showed that Ct was over 30 (data not shown) for five other Lactobacillus species commonly found in Canadian Cheddar cheese samples (see Table S2 in the Supplementary material). However, specificity between L. lactis subsp. cremoris SK11 and L. paracasei ATCC 334 could not be achieved for the prtP gene (similar Ct values of 16 to 18), due to the low number

All 34 genes common to both L. lactis subsp. cremoris SK11 and L. paracasei ATCC 334 were classified in six functional categories, with a seventh category dedicated to the eight specific genes in order to perform multivariate statistical analysis. For L. lactis subsp. cremoris SK11, about the same number of genes was significantly either over- or underexpressed during the cheesemaking simulation (26 out of 42 genes for the Pearce activity test), in comparison to the ripening simulation (23 out of 42 genes for slurry samples) (Table 1). For L. paracasei ATCC 334, 27 genes out of 42 were differentially expressed between single and mixed culture conditions during the cheesemaking simulation, as opposed to only 16 during the ripening simulation (Table 1). Samples were clustered according to fold change in gene expression calculated with T0 (for Pearce or slurry) as the reference state to determine the impact of single and mixed culture on L. lactis subsp. cremoris SK11 and L. paracasei ATCC 334 transcription profiles during both cheesemaking simulation (Pearce activity test) and ripening simulation (slurry) (Fig. 2). For L. lactis subsp. cremoris SK11 (Fig. 2A), two principal clusters differentiated the samples by cheese model. Corresponding samples of the single and mixed culture assays clustered together for each stage of the Pearce activity test (T1 with T1, T2 with T2, etc.), but did not cluster for slurry samples. Genes showing overexpression either during the Pearce activity test or during ripening in the cheese slurry clustered separately while genes showing underexpression in either cheese model were clustered together. For L. paracasei ATCC 334 (Fig. 2B), samples were grouped according to single or mixed culture, with most over-expressed genes clustering in the mixed culture samples. To discriminate between the impact of cheese model (milk or slurry) and that of the assay (single or mixed culture) on transcription profiles for both bacteria, multiple factor analysis (MFA) was conducted on all gene expression data (see Fig. 3 and Figs. S1 and S2 in the Supplementary material). Factor 1 explained 16.53% of the variation and was linked to the impact of mixed culture on L. paracasei ATCC 334 gene expression, factor 2 was related to the impact of the cheese model and explained 10.79% of the variation, and factor 3 represented the impact of mixed culture on L. lactis subsp. cremoris SK11 and explained 9.36% of the variation, all three factors explaining 36.68% of the variation between samples.

Table 1 Number of differentially expressed genes between single and mixed cultures during cheesemaking and ripening simulations. Metabolic pathway

Number of genes analyzed

Number of genes significantly under- or over-expressed between single and mixed cultures Cheesemaking

Stress response Carbohydrate metabolism Protein, peptide and amino acid degradation Amino acid metabolism Cellular functions Lipolysis Specific genes

5 7 8 8 5 1 8

Ripening

L. paracasei ATCC 334

L. lactis subsp. cremoris SK11

L. paracasei ATCC 334

L. lactis subsp. cremoris SK11

4 5 7 3 2 1 5

2 2 6 7 5 0 4

1 3 6 2 2 1 2

3 4 4 3 4 1 5

80

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A

1 6 2 7 4 9 3 8 5 10 11 12 13 14 18 15 17

acpD estA serC busAA glyA trpG cysK groEL dnaJ dnaG glmS prtP araT aspB luxS deoC gapA argH metC holin pepX bcaT oppD purD cysE clpC pfk nifS htrA pepQ adhA dacA hisC ldh optD pepM pepN serA argD galK fstE groES

16

1

B

2 11 12 4 3 5 13 14 6 9 7 15 8 10 16 17

adhA aldB metC lacG fstE groES fpb gdh araT clpC dnaG cysK lipA purD aspB glyA htrA nifS gapA aspA prtP pfk bcaT estA ldh glmS pepQ pepM ycjM hisC luxS oppD serA argH pepX dnaJ dacA galK groEL deoC pepN opuA

18

Fig. 2. Double factor hierarchical clustering (Ward's method) of gene expression by (A) Lactococcus lactis subsp. cremoris SK11 and (B) Lactobacillus casei ATCC 334 during the five steps of the Pearce activity test (empty symbols) in single culture (samples 1 to 5) or in mixed culture (samples 6 to 10) and in cheese slurry (full symbols) samples in single (samples 11 to 14) or in mixed culture (samples 15 to 18). Fold change in gene expression relative to T0 is color-coded, with red indicating up-regulation (between 0.5 and 12.0) and green indicating downregulation (−0.5 to −4), intermediate colors representing 0 to 0.5 or 0 to −0.5 and black representing results that did not vary from the control point (T0).

Furthermore, MFA describes the impact of each variable (cheese model or single vs. mixed culture) on each gene category to better understand the changes to selected metabolic pathways for L. lactis subsp. cremoris SK11 and L. paracasei ATCC 334 (Fig. 3). Each gene is represented on a factorial map, which is an ordination plot obtained from a principal component analysis (PCA) that indicates the value of each gene (vector) for the corresponding factor or factors (see Fig. S2 in the Supplementary material). Only the most significant genes (cos2 N 0.2) are displayed, and most vectors are found in the two right quadrants (Fig. S2A), indicating their link to L. paracasei ATCC 334 in mixed culture (see factor 1 of Fig. S1 in the Supplementary material),

or in the two upper quadrants (Fig. S2B), linking their differential expression to L. lactis subsp. cremoris SK11 in mixed culture (see factor 3 of Fig. S1 in the Supplementary material). 3.4. Changes in SK11 and ATCC 334 gene expression during cheesemaking and ripening simulations According to MFA, the L. paracasei ATCC 334 genes that were more over-expressed in mixed culture (factor 1) were the stress response genes, as well as genes related to protein, peptide and amino acid degradation and those linked to carbohydrate metabolism (Fig. 3A). The

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(busAA) specific to the strain was mostly over-expressed at the end of cheesemaking in mixed culture (P = 0.0002). In slurry samples, groEL was under-expressed in single culture, but was significantly overexpressed in mixed culture after six days of ripening (P b 0.0001) (Table S6). 3.4.2. Carbohydrate metabolism For L. paracasei ATCC 334 carbohydrate metabolism gene expression in cheesemaking simulation (Table S3), the most notable differentially expressed genes were deoC (P b 0.0001) and the phosphofructokinase gene pfk (P = 0.0068), which were down-regulated in single culture and up-regulated in mixed culture from T3 to T5 and T4 to T5, respectively. During ripening simulation (Table S4) the ldh gene was over-expressed in single culture but under-expressed in mixed culture (P b 0.0001). The galK gene expression, however, increased throughout ripening time in single culture, but remained stable or under-expressed in mixed culture (P = 0.0289). During the cheesemaking simulation (Pearce activity test), only two L. lactis subsp. cremoris SK11 genes were differentially expressed for these pathways (Table S5). The gapA gene, encoding the glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was especially overexpressed after heating and cooling to 35 °C (T3) and at the end of the cheesemaking process (T5) for both single and mixed cultures (P = 0.0295). The galactokinase gene (galK), however, was only over-expressed in mixed culture at the end of the Pearce test from T3 to T5 (P b 0.0001). In ripening simulations, four L. lactis subsp. cremoris SK11 genes were either under- (deoxyribose-phosphate aldolase deoC) or over-expressed (ldh, adh, galK) through time, especially after 9 days for the lactate dehydrogenase gene ldh (P = 0.0217) and in mixed culture for adhA, which codes for alcohol dehydrogenase (P = 0.0067) (Table S6).

Fig. 3. MFA group representation. Each group of variables is projected on the factor map created by the MFA. Active groups of genes (red) and supplementary sample groups (green) are plotted. (A) MFA representation for the first two factors (factor 1 linked to the effect of the mixed culture on ATCC 334 gene expression and factor 2 linked to the cheese model (slurry or milk)); (B) MFA representation for the second factor (linked to the model) and third factor (linked to the effect of the mixed culture on SK11 gene expression).

genes linked to amino acid metabolism, however, were more highly expressed in the cheesemaking simulation than in the ripening model. For L. lactis subsp. cremoris SK11, the genes linked to amino acid metabolism were also more highly expressed during the cheesemaking simulation. Genes related to stress, carbohydrate metabolism, protein, peptide and amino acid degradation and cellular functions, however, were more highly expressed in the slurry samples of the ripening model. 3.4.1. Stress response genes For L. paracasei ATCC 334, most stress response genes were differentially expressed between single and mixed cultures during cheesemaking (Table S3). For example, the glycine betaine transporter gene (opuA), specific to L. paracasei ATCC 334, was overexpressed during cheesemaking in the presence of L. lactis subsp. cremoris SK11. For L. lactis subsp. cremoris SK11, the chaperone gene htrA was most highly expressed after heating during cheesemaking (T3) in both single and mixed cultures (Table S5), while the glycine betaine transport gene

3.4.3. Protein, peptide and amino acid degradation For L. paracasei ATCC 334, the peptide transporter oppD was significantly over-expressed at T5 in both single and mixed cultures (P = 0.0098) (Table S3). The prtP gene was over-expressed in single culture (P b 0.0148) from 6 to 9 days of ripening (Table S4). Two aminopeptidases (pepX and pepN) were over-expressed throughout cheesemaking in mixed culture (P b 0.0001 and P = 0.0003, respectively), whereas the pepQ, pepM and pepX genes were mostly overexpressed in mixed culture at the end of ripening (Tables S3 and S4). The bcaT gene, a branched chain amino acid aminotransferase, was especially under-expressed in mixed culture (P b 0.0001) at the beginning of ripening (Table S4). Six of the eight L. lactis subsp. cremoris SK11 genes for these pathways were significantly differentially expressed during cheesemaking simulation. Genes encoding the aromatic aminotransferase araT and the oligopeptide ABC transporter oppD were under-expressed, especially after cooling to 35 °C (T3) (Table S5). In slurry samples, four genes of the amino acid and peptide degradation pathways were differentially expressed (Table S6). For example, the bcaT gene was mostly under-expressed at the beginning of ripening. The prtP gene was highly over-expressed in pure culture (P = 0.0073) at the beginning of ripening (days 3 and 6), then remained slightly overexpressed from 9 to 12 days. The specific L. lactis subsp. cremoris SK11 gene optD (CodY-regulated oligopeptide ABC transporter gene) was also highly over-expressed after 9 days of ripening in mixed culture. This higher expression of optD was accompanied by the high expression of several peptidases (pepM, pepX and pepN) in slurry samples. 3.4.4. Amino acid metabolism: sulfur and nitrogen Variation in amino acid metabolism gene expression occurred mainly during the Pearce activity test rather than during ripening (Fig. 3). For L. paracasei ATCC 334, the cystathionine β-lyase metC and argH gene (arginine conversion from glutamate) were overexpressed in mixed culture (Table S3), but the alpha-acetolactate

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decarboxylase gene (aldB), specific to L. paracasei ATCC 334, was more over-expressed in single culture. During ripening, the glutamate dehydrogenase (gdh) gene, specific to L. paracasei ATCC 334, was significantly over-expressed, as was the luxS gene, which codes for a S-ribosylhomocysteine lyase (Table S4). For L. lactis subsp. cremoris SK11, the cystathionine β-synthase cysK and the histidinol-phosphate aminotransferase hisC were significantly over-expressed, especially after cooling to 35 °C (T3) (Table S5). The luxS gene was over-expressed at T4, especially in single culture. During ripening, only three genes (aspB, metC and cysK) were differentially expressed (Table S6). The cysK and metC genes showed opposite patterns between single and mixed culture conditions (down-regulation in single culture vs. over-expression in mixed culture for cysK (P b 0.0001) and an increase in expression in single culture vs. a decrease in mixed culture for metC (P = 0.005)). 3.4.5. Cellular function genes In order to indicate bacterial growth potential in a Cheddar cheese environment, cellular division (ftsE), DNA replication (dnaG, purD) and cell wall formation (dacA, glmS) genes were analyzed. For L. paracasei ATCC 334, purD was over-expressed in mixed culture (Table S3). During ripening simulation, glmS and dacA were significantly over-expressed in mixed culture (Table S4). During cheesemaking (Table S5), only the dacA gene was always over-expressed, especially after the 35 °C cooling step (T3) up to the end of cheesemaking (T5) in single and mixed cultures for L. lactis subsp. cremoris SK11. During ripening simulation, dacA and ftsE were over-expressed in mixed culture whereas the glmS gene, however, was over-expressed in single culture, but under-expressed in mixed culture (Table S6). 3.4.6. Lipolysis genes For L. paracasei ATCC 334, the estA gene was mostly under-expressed in all conditions and models (Tables S3 and S4). However, the lipA gene, specific to L. paracasei ATCC 334, was under-expressed in single culture, but over-expressed in mixed culture during the Pearce activity test. For L. lactis subsp. cremoris SK11, the estA gene was differentially expressed between single (down-regulation) and mixed (up-regulation) cultures during ripening, but with little variation over time (Table S6). 4. Discussion The mRNA pool is the sum of transcriptional activity, mRNA degradation by ribonucleases and dilution of transcripts by growth (Cocaign-Bousquet et al., 2002). The cellular mRNA is thus predominantly determined by the balance between the two rates of mRNA synthesis and decay, as dilution rate can generally be considered negligible compared to the degradation rate (Even et al., 2002). Hence, changes in the mRNA pool can occur either by transcriptional control or by modification of the susceptibility of the mRNA to degradation (Redon et al., 2005). During cheesemaking, multiple and simultaneous changes occur in the environment and the cells must constantly adapt. Stability of mRNA is a significant part of gene expression in response to such stress conditions. The growth rate regulates mRNA stability since a decrease in growth rate is accompanied by a decrease in transcription rate. Stabilization of L. lactis transcripts has been observed in response to carbon starvation (Redon et al., 2005). Stabilization of mRNA may partially compensate for the decrease in transcription rate in order to maintain transcript concentration (Dressaire et al., 2013). On the other hand, higher mRNA degradation rates could occur at high growth rates. Such conditions can occur during the cheesemaking process. During cheese ripening, lactococci are known to remain metabolically active even when they are not detected by viable counts (Ganesan et al., 2007). Under these conditions, some transcripts may have greater

stability than transcripts which are required to adapt to changing conditions. To our knowledge, no study has been conducted on transcript stability under conditions of cell lysis, such as autolysis of lactococci. As RT-qPCR measures the mRNA pools, the results may not directly reflect the regulatory mechanisms involved and co-transcribed genes may not have comparable changes in mRNA levels. The aim of this discussion is not to corroborate well-known regulatory mechanisms, but to highlight how mixed culture influences gene expression over the course of cheesemaking and ripening. Overall clustering of transcriptome profiles showed that the three factors of cheese model, single and mixed cultures contribute to explaining close to 37% of the variation in gene expression, while other processing factors such as time, temperature and addition of salt did not seem to have significant impact in comparison. Other factors influencing gene expression that are not included in this model are nutrient availability, metabolic intermediates and basic gene regulation mechanisms. Thus, the future challenge is to include additional factors in multifactorial models of microbial interactions in the context of cheese processing, as has been done for single species milk fermentations (Yvon et al., 2011; Dhaisne et al., 2013). One example is the inclusion of metabolomic data, such as those obtained in cheese made with ultrafiltered milk inoculated only with lactococci (Yvon et al., 2011). In that study, both proteomic and metabolite analysis were carried out up to 7 days of ripening at 12 °C. The two factors of ripening time and strain explained 68.5% of the variation in metabolite levels. Dhaisne et al. (2013) were able to explain 68% of the variation in fermented milk samples among nine L. lactis subsp. lactis strains using 82 phenotypic variables. The 20 variables contributing most to strain discrimination were identified as volatile compounds. These studies illustrate the potential for the next stage necessary for studying microbial interactions, integrating multiple approaches in order to carry out multifactorial analysis. 4.1. Growth of L. lactis subsp. cremoris SK11 and L. paracasei ATCC 334 during cheesemaking and ripening During the cheesemaking simulation (Pearce activity test), viable counts and pH decrease were consistent with results obtained by Taïbi et al. (2011) for L. lactis subsp. cremoris strains in single culture. The lack of pH decrease for L. paracasei ATCC 334 can be explained by the lower level of inoculation and the lower acidification properties of this species. During ripening, the decrease in viable counts of L. lactis subsp. cremoris was also obtained by Muehlenkamp-Ulate and Warthesen (1999), although they detected L. lactis subsp. cremoris SK11 for twelve days at 30 °C (10 3 CFU/g), but starting with a lower initial inoculum (108 CFU/g). That study did not find any significant difference between control slurries (starter only) and those with added lactobacilli, but the pH was slightly lower than that typically found in cheese. Their explanation was that the higher moisture levels and temperature found in slurries could increase residual lactose utilization, and therefore lower the pH values. Considering the combination of higher temperature and lower pH, this could have contributed to higher lactococci autolysis rates within the first 6 days of accelerated ripening in the present study. Wijesundera et al. (1997) also did not detect several strains of L. lactis subsp. cremoris and lactis after ten days in a mixed culture with L. casei NCDO151 in slurries ripened at 30 °C. Lactococci have been found to retain some transcriptional ability for six months in ripening Cheddar cheese even though their cDNA copy numbers decreased over time (Desfossés-Foucault et al., 2013). Within the first month of ripening, autolysis of lactococci was posited in this previous study because of the decrease in viable counts concomitant with a reduction in the number rDNA copies representing dead intact cells versus the number of cDNA copies of rRNA. Over the 6month ripening period, a decrease in cDNA equivalent to 2 logs of culturable cells was obtained. In the present study, cDNA transcripts

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were detected even in the absence of viable cell counts, indicating that mRNA is being maintained, most probably by residual intact cells, so autolysis must not be complete. However, the relative gene expression approach does not allow us to estimate how many copies are present, so the degree of autolysis versus intact cells cannot be estimated. For Lactobacillus, Muehlenkamp-Ulate and Warthesen (1999) obtained similar results for pure cultures of four strains of L. paracasei in cheese slurries incubated at the same temperature, with a stable high count over time. In pasteurized and thermized milk Cheddar cheese samples with no adjunct Lactobacillus inoculation, transcriptionally active L. paracasei cells were also detected at high levels (up to 11 log cDNA copy numbers) throughout ripening at 4, 7 and 12 °C (Desfossés-Foucault et al., 2013). 4.2. Stress response Over-expression of stress response genes in mixed culture during cheesemaking indicates that L. paracasei ATCC 334 reacted more to stress in the presence of L. lactis subsp. cremoris SK11. The opuA gene encoding glycine betaine transport was over-expressed, which is consistent with compatible solute accumulation in response to osmotic stress. In L. casei Zhang, stress response is accentuated upon entering the stationary phase (Wu et al., 2009), which was presumed to correlate with increasing acidic conditions. The lower expression of stress response genes during ripening when in mixed culture indicates that L. paracasei ATCC 334 appears less stressed during ripening, which could be a consequence of the decrease of viable L. lactis subsp. cremoris SK11 over time. Lysing starter cells could release substrates that can be catabolized by NSLAB (Steele et al., 2013). The higher expression of stress genes for L. lactis at the end of cheesemaking indicates a response to the cumulative temperature and osmotic stresses found in cheese (Cretenet et al., 2011; Taïbi et al., 2011). Higher expression of htrA after heating during cheesemaking is consistent with a role for this gene product in response to heat shock, among other stresses (Foucaud-Scheunemann and Poquet, 2003). Although Taïbi et al. (2011) found an increase in htrA gene expression for L. lactis subsp. cremoris SK11 after 4% salting at the end of the Pearce activity test, we found no such result with 2% salt. In effect, htrA transcription does respond more to higher levels of salt (Foucaud-Scheunemann and Poquet, 2003). The over-expression of busAA is also coherent with a cumulative response to osmotic and heat stresses (Xie et al., 2004). The fact that busAA is slightly more highly expressed in mixed culture at the end of cheesemaking may indicate a response to competition with L. paracasei for compatible solute accumulation. During ripening, however, L. lactis subsp. cremoris SK11 showed a higher level of general stress (groEL) in mixed culture compared to single culture in the slurry model after six days of ripening. Mixed culture thus appears to amplify the response of both L. paracasei and L. lactis subsp. cremoris to cumulative heat and osmotic stresses, perhaps hastening entry into stationary phase. 4.3. Carbohydrate metabolism Over the course of cheesemaking, lactose availability decreases in proximity to cells while lactic acid accumulates so that carbon and energy limitation provoke entry into stationary phase. The glycolytic flux, including the shift from homolactic to mixed acid fermentation, is mainly controlled by the equilibrium of glycolytic intermediates, cofactors (NADH/NAD + ratio) as well as ATP–ADP pools through their action on enzyme activities (Cocaign-Bousquet et al., 2002; Garrigues et al., 1997). Nonetheless, growth rate and availability of carbon sources do have some influence on transcriptional activity. Higher levels of glycolytic intermediates increase CcpA binding to promoter sequences, which, depending on their position, either

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enhance or repress gene transcription. During growth on easily metabolized carbon sources such as glucose, CcpA represses the gal operon while inducing the las operon, most notably during the exponential growth phase (Luesink et al., 1998; Zomer et al., 2007). The shift to mixed acid fermentation is observed when pyruvate kinase and L-lactate dehydrogenase activities are lower due to sugar limitations and the decreased pool of triose phosphates relaxes control over pyruvate metabolism, leading to enhanced production of acetate and ethanol (Garrigues et al., 1997). Under sugar limiting conditions, high glyceraldehyde phosphate dehydrogenase (GAPDH, gapA) activity reduces the triose phosphate pool, allowing higher pyruvate formate lyase activity and shifting fermentation from homolactic (ldh) to mixed acid. Underexpression of ldh might indicate that L. paracasei switches from homolactic fermentation to mixed acid fermentation after cheesemaking and during ripening (Laakso et al., 2011) in response to carbon limitation in the presence of L. lactis subsp. cremoris SK11. The higher galK expression in single culture is consistent with reduced catabolite repression linked to carbon limitation. As galK expression was lower in mixed culture (no induction by carbon limitation), L. paracasei ATCC 334 may be using other sources of carbon when in competition with L. lactis subsp. cremoris SK11 (Budinich et al., 2011). As NADH is needed for lactate production from pyruvate, under NADH-limiting conditions, pyruvate is more likely catabolized to 2acetolactate, which can then be converted to acetoin by acetolactate decarboxylase coded by the aldB gene. Thus, the aldB gene product can contribute to maintaining the NAD +/NADH balance as well as volatile compound production. When aldB is more highly expressed, less acetolactate would be available for branched chain amino acid biosynthesis as well. In single culture of L. paracasei ATCC 334, higher aldB gene expression seems to favor volatile compound production rather than BCAA synthesis during both cheesemaking and ripening. The higher expression of aldB is also consistent with higher availability of branched chain amino acids in single culture compared to mixed culture. For L. lactis, genes of the Leloir pathway (galK) are known to be over-expressed at the end of the Pearce activity test (Taïbi et al., 2011) and in cheese models. In a model cheese made from ultrafiltered milk, adh and gapA gene expressions were induced due to carbon starvation (Cretenet et al., 2011). For L. lactis, gapA expression was higher during cheesemaking than during ripening. For both bacterial strains in our study, mixed culture negatively affected gapA expression during cheesemaking, but not significantly during ripening. The ldh gene is also known to be over-expressed by L. lactis after 25 days of ripening under energy starvation conditions (Bachmann et al., 2010). In our study, mixed culture had a positive effect on L. lactis ldh and galK expressions but had a negative or no effect on the expression of these genes in L. paracasei. This indicates that carbon limitation may be more of a problem for L. lactis than for L. paracasei. Phosphofructokinase activity determines the level of the glycolytic intermediate fructose-1,6-diphosphate (FDP) while deoxyribosephosphate aldolase deoC couples the glycolytic pathway to deoxyribonucleoside synthesis. Nucleosides can thus be scavenged as an alternative carbon source or salvaged for nucleic acid synthesis. In mixed culture, over-expression of deoC and pfk by L. paracasei during cheesemaking could indicate higher glycolytic flux in competition with L. lactis subsp. cremoris SK11 under the heat and salt stresses found at the end of cheesemaking. In L. lactis subsp. cremoris SK11, mixed culture had no effect on the expression of these two genes during cheesemaking, but did have a significantly negative effect on the expression of deoC during ripening. Throughout cheesemaking and ripening, mixed culture does not appear to affect FDP production in L. lactis subsp. cremoris SK11, but deoC expression was significantly lowered during ripening. For both bacteria, lowered expression of deoC during ripening may be associated with lower nucleic acid synthesis. Other

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scavenging activities may provide alternate carbon and nitrogen sources following starter cell lysis (Budinich et al., 2011). 4.4. Protein, peptide and amino acid degradation Typical expression patterns were observed for lactobacilli grown in milk in pure culture (Smeianov et al., 2007). High peptidase expression provides a selective advantage in response to the proteolytic activity of L. lactis subsp. cremoris SK11 in the high protein environment of milk, which is initially low in free amino acids (Cai et al., 2009). This major peptide degradation by L. paracasei ATCC 334 indicates potential debittering activity that could positively contribute to flavor development (Smeianov et al., 2007). The fact that peptide degradation is mainly detected in mixed culture points to a synergy between lactococci and lactobacilli for the conversion of milk proteins that could contribute to flavor formation during Cheddar cheese ripening. However, competition for peptides and amino acids is suggested by the over-expression of the cell wall proteinase prtP during the cheesemaking step compared to the ripening phase by L. paracasei ATCC 334. At the beginning of ripening, the under-expression of both oppD and bcaT in L. paracasei may indicate the availability of free amino acids, reducing the need for peptide transport and amino acid biosynthesis. However, throughout cheesemaking and ripening, bcaT was underexpressed in mixed culture while oppD was under-expressed in single culture. In a study on Lactobacillus helveticus, while oppD was upregulated in milk compared to MRS medium, the expression of bcaT did not change (Smeianov et al., 2007). A system equivalent to CodY control has not been demonstrated for L. paracasei, even if it has been proposed due to its presence in Bacillus species. Most significantly, a codY homolog is not found in the genome of L. paracasei strain ATCC 334. Thus, interpretations based on CodY control cannot yet be applied. For L. lactis, the expression of genes under CodY transcriptional control (such as oppD) is known to decrease in milk as branched chain amino acids accumulate after the initial growth phase is completed (Raynaud et al., 2005), which is also the case in cheese models (Cretenet et al., 2011). In accordance with increasing peptide and amino acid availability as casein is degraded, prtP expression starts high at the beginning of ripening, and then decreases towards the end of the ripening phase. While the presence of L. paracasei did not influence the expression of optD during cheesemaking, mixed culture had a positive effect on optD over ripening, possibly indicating higher competition for peptides and lower intracellular branched amino acid pools in mixed culture. The majority of peptidase genes were under-expressed by L. lactis subsp. cremoris SK11, except for pepM and pepN, which increased in expression at the end of ripening. In mixed culture, L. lactis subsp. cremoris SK11 showed higher transcription of three peptidases than in single culture after 9 days of ripening, so mixed culture could contribute to bitter peptide degradation in the later phases of ripening (Taïbi et al., 2011). At the beginning of ripening, under-expression of bcaT could indicate a high initial intracellular content of free branched chain amino acids (Yvon and Rijnen, 2001). As bcaT is involved in the first biosynthetic step, this gene is known to be repressed by CodY in the presence of branched chain amino acids. However, high intracellular levels of branched chain amino acids lead to growth inhibition by weakening the proteolytic system, so bcaT activity is beneficial for degrading excess levels of intracellular BCAAs. In addition, in auxotrophic lactococci, the catabolic role of bcaT contributes to conversion of branched chain amino acids to α-keto acids as the first step of producing flavor precursors. As bcaT gene expression was higher at the end of ripening in mixed culture than in pure culture, the intracellular branched chain amino acid pool may be lower under these conditions, perhaps as a result of competition with L. paracasei for peptides. Amino acid conversion appears to be stimulated by mixed culture for L. lactis, but not for L. paracasei, while mixed culture stimulated peptide import and degradation by L. lactis and L. paracasei.

4.5. Amino acid metabolism: sulfur and nitrogen According to Raynaud et al. (2005), de novo biosynthesis may be induced in the Cheddar cheese environment as milk is initially very low in free amino acids (Raynaud et al., 2005). However, even the low methionine content of milk may be sufficient to repress metC (encoding cystathionine β-lyase) and cysK (encoding cystathionine β-synthase) expression in lactococci (Dias and Weimer, 1998). Low amino acid content is supported by the generally positive cysK expression during cheesemaking. As cysK was under-expressed during ripening, at least in single culture of L. lactis subsp. cremoris SK11, amino acids may not be limiting. For L. lactis, cysK over-expression in mixed culture could be explained by competition for sulfur compounds such as methionine and cysteine (both transcription inhibitors of cysK in Lactococcus sp.) with L. paracasei ATCC 334 during ripening (Yvon and Rijnen, 2001). As mixed culture had a positive effect on cysK expression during ripening, there appears to be more competition for amino acids. For L. paracasei, the metC gene is overexpressed in mixed culture compared to single culture, indicating cystathionine conversion (Yvon and Rijnen, 2001) and further supports sulfur compound limitation in mixed culture. During ripening, two genes coding for amino acid conversion (glmS and luxS) were over-expressed in mixed culture compared to single culture for L. paracasei. The glmS gene is associated with hexosamine production for cell wall biosynthesis. LuxS activity contributes to detoxifying byproducts of methyltransferase activities of the cells by producing homocysteine, which can lead either to cysteine or methionine synthesis. The L. lactis subsp. cremoris SK11 transcription response confirms the under-expression of glmS during ripening, suggesting less precursor synthesis for cell wall growth. This concords with a succession of bacterial species, L. lactis subsp. cremoris SK11 declining through time while L. paracasei ATCC 334 populations are maintained at high levels. Mixed culture had a positive effect on luxS expression for L. paracasei but a slightly negative impact for L. lactis in both types of cheese simulations. For L. casei Zhang, luxS expression increases in stationary phase (Wu et al., 2009). The gdh gene is linked to amino acid conversion to flavor compounds (Tanous et al., 2002) and was significantly over-expressed in mixed culture compared to single culture in our study. The positive influence of lactococci on lactobacilli is exemplified by the up-regulation of the L. paracasei ATCC 334 glutamate dehydrogenase (GDH) as well as an increased peptidase activity during both cheesemaking and ripening phases. Alpha-ketoglutarate is the limiting step in the production of aroma compounds from amino acids, especially since not many lactococci have GDH activity, but they can contribute to the downstream steps of amino acid degradation by producing carboxylic acids from α-keto acids (Kieronczyk et al., 2003). GDH-positive bacteria increase the availability of α-keto and hydroxy acids by converting glutamate, which is generally present in high amounts in cheese. Flavor development in cheese is thus stimulated by the cooperation between the starter lactococci and the NSLAB.

4.6. Other cellular functions The milk environment is especially low in purines, which therefore need to be synthesized by lactic acid bacteria (Smeianov et al., 2007). During the cheesemaking step, higher expression of purD by L. paracasei ATCC 334 in mixed culture indicates a greater response to synthesize more purines in competition with the starter. Indeed, mixed culture appears to slightly stimulate the expression of the purD gene by L. lactis subsp. cremoris SK11 compared to single culture during cheesemaking. During ripening, however, mixed culture has a negative effect on purD expression by both strains. Thus, both L. lactis subsp. cremoris SK11 and L. paracasei ATCC 334 may not need

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to synthesize purines as much in mixed culture during ripening, perhaps due to scavenging activities. DD-carboxypeptidases contribute to determining bacterial shape as they are proposed to play a regulatory role in peptidoglycan synthesis by removing surplus pentapeptides in recently added peptidoglycan (Typas et al., 2012). The lack of activity of the D -alanyl-D alanine carboxypeptidase has been associated with autolysis in the stationary phase in Escherichia coli (Templin et al., 1999). For both strains in this study, mixed culture had a positive impact on dacA expression compared to single culture. In addition, L. paracasei had a greater effect on dacA expression by L. lactis subsp. cremoris SK11 during ripening than during cheesemaking. Thus, mixed culture appears to favor peptidoglycan recycling, which is especially important during stationary phase. This may not be sufficient to avoid starter lysis and the decline of the L. lactis subsp. cremoris SK11 population. 4.7. Lipolysis and fatty acid conversion Although lipid and fatty acid degradation is a major flavor formation pathway for various types of cheese through the production of esters and aldehydes, lipolysis is less important in Cheddar cheese (Smit et al., 2005; Yvon and Rijnen, 2001) and has generally been underestimated in milk fermentations (Dhaisne et al., 2013). Nevertheless, esterase activity can contribute to the conversion of esters into flavor components (Nardi et al., 2002). Mixed culture had a positive influence on the expression level of the esterase gene estA by L. lactis subsp. cremoris SK11 and on the lipase gene lipA by L. paracasei ATCC 334. However, under-expression of estA by L. paracasei ATCC 334 indicates low potential for ester synthesis or hydrolysis. Thus, starter and NSLAB may collaborate in order to produce or convert flavor related compounds such as esters. 5. Conclusion Growth in mixed culture had a significant impact on the L. paracasei ATCC 334 transcriptional profile and the presence of L. paracasei ATCC 334 had a greater impact on L. lactis subsp. cremoris SK11 during ripening than during the cheesemaking simulation. This suggests little transcriptional activity by L. lactis subsp. cremoris SK11 during ripening, but some of the components liberated in the cheese environment might contribute to the higher gene expression of L. paracasei ATCC 334 in mixed culture, and points to the complementarity of both bacterial species for flavor development in cheese. In this study, the application of RT-qPCR for mixed culture analysis has therefore been validated and contributes to establishing the interaction between starter cultures and NSLAB during Cheddar cheesemaking and ripening. Acknowledgments This work was possible thanks to Denis Roy's NSERC-Dairy Sector Industrial Research Chair in Cheese Technology and Typicity, to which Gisèle LaPointe widely contributed through her strategic NSERC grant. The authors gratefully acknowledge the financial contribution of industrial partnerships (Agropur Coopérative, Fromagerie Clément/Damafro Inc., Novalait Inc., Parmalat Canada, Dairy Farmers of Canada, Groupe Saputo Inc.). We also thank Marie Filteau for her scientific advice on statistical analysis and Geneviève Masson for her technical assistance. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.ijfoodmicro.2014.03.004.

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