Microbial interactions within a cheese microbial
community

Jérôme Mounier, Christophe Monnet, Tatiana Vallaeys, Roger Arditi,
Anne-Sophie Sarthou, Arnaud Hélias, Françoise Irlinger

► To cite this version:

Jérôme Mounier, Christophe Monnet, Tatiana Vallaeys, Roger Arditi, Anne-Sophie Sarthou, et al. Microbial interactions within a cheese microbial community. Applied and Environmental Microbiology, 2008, 74 (1), pp.172-181. 10.1128/AEM.01338-07. hal-00556971

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Microbial Interactions Within a Cheese Microbial Community

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Jérôme Mounier¹, Christophe Monnet¹, Tatiana Vallaeks¹, Roger Arditi², Anne-Sophie

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Sarthou¹, Arnaud Hélias¹ and Françoise Irlinger¹*

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¹UMR782 Génie et Microbiologie des Procédés Alimentaires, INRA, AgroParisTech, 78850

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Thiverval Grignon, France.

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²Ecologie des Populations et Communautés, AgroParisTech, 75000 Paris, France

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<footnote>* Corresponding author. Mailing address: UMR782 Génie et Microbiologie des Procédés Alimentaires, INRA, AgroParisTech, 78850 Thiverval Grignon, France. Phone: +33 (0)1 30 81 54 91. Fax: +33 (0)1 30 81 55 97. E-mail: irlinger@grignon.inra.fr.</footnote>
12 Abstract

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14 The interactions that occur during the ripening of smear cheeses are not well understood.

15 Yeast-yeast interactions and yeast-bacteria interactions were investigated within a microbial

16 community composed of three yeasts and six bacteria found in cheese. The growth dynamics

17 of this community was precisely described during the ripening of a model cheese, and the

18 Lotka-Volterra model was used to evaluate species interactions. Subsequently, the effects of

19 yeast omissions in the microbial community on ecosystem functioning were evaluated. It was

20 found both in the Lotka-Volterra model and in the omission study that negative interactions

21 occurred between yeasts. *Yarrowia lipolytica* inhibited mycelial expansion of *Geotrichum*

22 *candidum*, and *Y. lipolytica*, and *G. candidum* inhibited *Debaryomyces hansenii* cell viability

23 during the stationary phase. However, the mechanisms involved in these interactions remain

24 unclear. It was also shown that yeast-bacteria interactions played a significant role in the

25 establishment of this multi-species ecosystem on the cheese surface. Yeasts were key species

26 in bacterial development, but their influence on the bacteria differed. It appeared that the

27 growth of *Arthrobacter arilaitensis* or *Hafnia alvei* relied less on a specific yeast function

28 because these species dominated the bacterial flora, regardless of which yeasts were present in

29 the ecosystem. For other bacteria such as *Leucobacter* sp. or *Brevibacterium aurantiacum*,

30 their growth relied on a specific yeast, i.e., *G. candidum*. Furthermore, *B. aurantiacum*,

31 *Corynebacterium casei* and *Staphylococcus xylosus* showed a reduced colonization capacity

32 compared with the other bacteria in this model cheese. Bacteria/bacteria interactions could not

33 be clearly identified.

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## 36 Introduction

Little is known about yeast-bacteria interactions, and smear ripened cheeses offer an interesting model to investigate them. Indeed, the smear cheese microbial community is composed of both yeast and bacteria, is of a known specific composition that constitutes the “inoculum”, and shows a reduced diversity and a high stability (12, 13, 25, 27, 34).

The smear is a red-orange, often viscous, microbial mat which is characterized by a succession of microbial communities including both yeast and bacteria. For example, the surface microflora of bacterial smear-ripened cheeses such as Reblochon, Tilsit and Limburger is composed of yeast, mainly *Debaryomyces hansenii* and *Geotrichum candidum*, and Gram-positive catalase-positive organisms such as coryneform bacteria and staphylococci (2, 9, 10, 35). During the first days of ripening, yeasts colonize the cheese surface and utilize lactate. This utilization progressively leads to the deacidification of the cheese surface, enabling the establishment of a bacterial community that is less acid-tolerant (8). These communities are relatively simple compared with other microbial communities such as soil communities. Indeed, they are composed of a limited number of mostly cultivable species, i.e., 10-20 species (12, 27). The microbial diversity of cheese was investigated using both cultivable and non-cultivable approaches such as rep-PCR, FT-IR spectroscopy, 16S rDNA sequencing, cloning and sequencing of 16S rDNA, SSCP, DGGE and TGGE (12, 13, 27, 28, 31).

While the succession of yeast and bacteria has been well described, the functional interactions in cheese between yeast and/or bacteria is not yet understood, and only a few interactions have been observed. An early study from Purko et al. (33) on the association between yeasts and *Brevibacterium linens* showed that *B. linens* did not grow on a vitamin-free agar medium. However, when the same medium was inoculated with yeast, it grew around the yeast colonies. Some yeast and bacterial strains have been selected for use by the
cheese industry because of their interesting technological properties such as aroma production or pigmentation. However, it has been shown that these commercial ripening cultures do not necessarily implant on the cheese surface, despite their massive inoculation in the early stages of ripening (7, 12, 27, 28). Mounier et al. (28) showed that the microorganisms that developed on the cheese surface were an adventitious microflora from the cheese environment (brine, ripening shelves and personnel), which rapidly outnumbered the commercial cultures. Several hypotheses have been advanced to explain these findings. These ripening cultures may be unfit for the cheese habitat, or negative interactions may occur between them and the adventitious microflora. Bacterial and yeast strains have also been selected for their anti-listerial activity (11, 25). Eppert et al. (11) found single strains of linocin-producing *B. linens* (a bacteriocin-like substance), which reduced *Listeria* spp. populations in cheeses but did not exert an inhibition comparable to that obtained with the ripening consortia from which these strains were isolated. Inversely, none of the 400 isolates from an effective anti-listerial ripening consortium evaluated in the study of Maoz et al. (25) exhibited anti-listerial activity in agar diffusion assays. This implies that the anti-listerial effect is probably not related to the production of inhibitory substances during growth.

In macrosystem ecology, several models that represent intra- and interspecies interactions in food webs have been established (see (3) for a review). The multispecies Lotka-Volterra model (22, 36) is a simple model used to measure interactions based on a linear relationship for a given species between growth rate and the populations of each member of the community. Such a model may be a good tool to investigate interactions within a microbial community.

Bonaiti et al. (5), using a three-step dichotomous approach, simplified an ecosystem of 83 strains from Livarot cheese, to four sub-ecosystems composed of nine species based on odor profile. One of these sub-ecosystems showed great similarities with the odor profile of
the 83-strain ecosystem, which had a very similar odor profile to the commercial cheese. This
sub-ecosystem of nine species was thought to be a good model ecosystem to reproduce cheese
surface diversity and to investigate microbial interactions.

The aim of this study was to identify interactions within this ecosystem in model
cheeses. In the first part of this study, the growth dynamics of each member of this
community were described, and the generalized Lotka-Volterra model (GLV) was used as a
preliminary approach to represent inter- and intraspecies interactions. In the second part,
specific strains of this community were omitted in order to evaluate the consequences of these
omissions on the further development of the rest of the community (species distribution,
substrate utilization, color of the cheese surface).
96 **Material and methods**

97

98 *Strains.* The starters used for cheese-making were frozen Flora Danica cultures (CHN 12 and

99 CHN 15, Chr Hansen, Arpajon, France). Flora Danica contains a mixture of *Lactococcus lactis* ssp. *lactis*, *L. lactis* ssp. *cremoris*, citrate-positive strains of lactococci and *Leuconostoc mesenteroides* ssp. *cremoris*.

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101

102 The nine microrganisms that composed the model ecosystem were *Arthrobacter arilaitensis*
103 3M03, *Brevibacterium aurantiacum* 2M23, *Corynebacterium casei* 2M01, *Hafnia alvei* 2E12,
104 *Leucobacter* sp. 1L36 and *Staphylococcus xylosus* 1L18 for the bacteria and, *Debaryomyces*
105 hansenii 1L25, *Geotrichum candidum* 3E17 and *Yarrowia lipolytica* 1E07 for the yeast. These
106 strains were obtained from the culture collection of the Food Microbiology Laboratory (LMA,
107 Caen, France). They were originally isolated from various batches of Livarot cheese.
108

108

109 *Growth properties of the microorganisms of the ecosystem on an agar-based media.* The
110 growth characteristics of the bacteria and yeast as a function of pH and NaCl were tested in a
111 media that contained 0.5 g yeast extract, 1 g casaminoacids, 0.1 g glucose and 1.5 g agar. Salt
112 content was 0, 30, 50, 100 and 150 g l⁻¹, while pH was 5, 5.5, 6, 6.5 and 7. Growth was
113 visually evaluated by checking for the presence of colonies after 2, 4 and 8 days incubation at
114 12°C.
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116 *Growth properties of the microorganisms found in the cheese ecosystem.* In this study, two
117 independent experiments were conducted at a five-month interval. In the first part of the
118 study, the growth dynamics of the nine species that composed the model ecosystem were
119 investigated on model cheeses (Exp. I). The cheeses were sampled in duplicate every day for
120 21 days for microbial enumeration, lactose and lactate content, and pH.

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In the second part of the study, the effects of single or multiple omissions of the yeast strains that originally composed the ecosystem were evaluated on model cheeses (Exp. II). All the possible combinations were tested. Cheeses were sampled in triplicate on day 0, 3, 11 and 21 for microbial enumeration, lactose, lactate ammonia and free amino acid content, surface-pH and color development.

Cheese production. Pilot-scale cheese production (coagulation, cutting, draining and molding of the curd) according to a process used for Livarot cheese was carried out under aseptic conditions in a sterilized, 2-m³ chamber as previously described by Leclercq-Perlat et al. (19). The milk used (~100 L) was pasteurized full-fat milk, standardized at 29 g/l fat with skim milk. The milk was pasteurized for 2.5 min at 75°C, and cooled at 37°C in the chamber. After 1 l of milk had been pumped into the tank, the milk was inoculated with the starter culture (Flora Danica, Chr Hansen, Arpajon, France). A filter-sterilized 10% CaCl₂ solution (100 ml) was added at the end of pasteurization. It was followed by the addition of the filter-sterilized coagulant containing 520 mg/l of chymosin at 30 ml/100 l of milk. Coagulation time was 20 min, and cutting of the curds took place after 30 min of hardening. The curd was then manually stirred for 5 min at a rate of 10 stirs/min. After standing for 15 min, 70 l of whey were removed prior to molding. Cheeses were shaped in circular polyurethane molds with a diameter of 9 cm and a height of 11 cm. Cheeses weighed approximately 350 g. The molds were inverted four times after 10 min, 2 h, 5 h and 15.5 h, with a temperature of 20°C in the chamber. After 17 h, cheeses were demolded, and after another hour, they were transferred to sterile bags and stored at -80°C until use.

Ripening culture. The yeast and bacteria were first precultured in 10 ml of Potato Dextrose Broth (PDB) or Brain Heart Infusion broth (BHI), respectively, in 50-ml flasks incubated at 25°C for 55 h at 150 rpm. 400 µl of each preculture were then used to inoculate 40 ml of PDB
or BHI in 150 ml flasks, which were incubated at 25°C for 66 h at 150 rpm. Five to 10 ml of each preculture were centrifuged at room temperature for 10 min at 4000 rpm. The supernatant was discarded and the cells resuspended in 9 g/l NaCl to obtain a concentration of 2 x 10⁹ CFU/ml and 2 x 10⁷ CFU/ml for the bacteria and the yeast, respectively. Subsequently, 1280 µl of each suspension were mixed and supplemented to make 20 ml with 9 g/l NaCl in a volumetric flask. This suspension was used to inoculate the model cheeses.

### *Curd inoculation*
Under sterile conditions, 57 ml of a saline solution containing 92 g/l NaCl were added to 246 g of unsalted curd and mixed three times for 10 s at maximum speed using a Warring blender. 2.4 ml of the ripening culture were then added and mixed, yielding 10⁴ CFU/g and 10⁶ CFU/g of cheese, for the yeast and the bacteria, respectively. Thirty grams were then transferred to sterile crystallizing basins with a diameter of 5.6 cm, and incubated at 12°C for 21 days. Two or three cheeses were used at each time point analyzed. Salt content of the cheeses was ~17 g/kg.

### *Analyses*
Surface pH was measured using a surface electrode Blue line 27 (Schott). The pH values were the arithmetic means of three measurements. Surface color was measured using a CM-2002 spectrocolorimeter (Minolta, Carrières sur Seine, France) as described by Mounier et al. (29). The data was processed using the three-dimensional L * a * b response, and logged into the L * and C * system. L * ranges from 0 (black) to 100 (white) and indicates lightness, a * and b * are the chromaticity coordinates indicating the color directions; + a * is the red direction at 0°, - a * is the green direction at 180°, + b * is the yellow direction at 90° and - b * is the blue direction at 270°. Cheese surfaces were photographed using a digital camera. Lactose and lactate content were determined on the whole cheese using HPLC as previously described by Leclercq-Perlat et al. (19). The release of free amino acids was measured on the whole cheese as described by Grunau et al. (14). Ammonia content of the whole cheese was
measured using the Nessler reagent.

Microbiological analyses. Cheese was homogenized using a mortar and pestle, and ~1 g of the cheeses was sampled and transferred into a sterile container. A sterile saline solution (8.5 g/l NaCl) was added to yield a 1:10 dilution, and the mixture was homogenized with an Ultra Turrax® (Labortechnik) at 8000 rpm/min for 1 min. Total bacteria except lactic acid bacteria were enumerated by surface plating in duplicate on BHI agar supplemented with 50 mg/l amphotericin B after five days incubation at 25°C. Yeast population was determined by surface plating in duplicate using Yeast-Glucose-Chloramphenicol agar (YGC) supplemented with 0.01 g/l tetrazolium chloride (TTC) after three days incubation at 25°C. Lactic acid bacteria were enumerated by surface-plating in duplicate on MRS agar after two days incubation at 30°C.

Enumeration of yeast and bacterial species. Each yeast species had a distinct morphotype on YGC supplemented with TTC, which allowed their direct enumeration. For the bacteria, 250 colonies of each cheese sample were removed at random with sterile toothpicks and transferred onto 96-well microtiter plates containing 100 µl of BHI supplemented with 10% (v/v) glycerol and incubated three days at 25°C. The plates were stored at -80°C until use. For bacterial identification, the isolates that grew in microtiter plates were replicated onto five media, i.e., BHI agar containing 20 mg/l erythromycin, 1 or 5 mg/l novobiocin, 1 mg/l vancomycin or 1 g/l TTC. After incubation for three days at 25°C, the isolates were checked for their ability to grow in the presence of the various selective agents. The combination of the five media was discriminative for each bacterium (Table 1). The counts of each bacterium ($C_i$) were estimated as follows:
$$ \frac{dC}{dt} = k \frac{N_0 - N}{N} $$

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199

200 where $C_0$ is the total bacterial count in CFU/g, $N_t$ is the number of clones replicated, and $N_i$ is

201 the number of clones identified as bacterium i.

202

203 Statistical analysis

204 The data with repeated measurements (bacterial and yeast population, pH, color, lactate) were

205 compared and statistically assessed using an analysis of variance (ANOVA). When

206 differences were detected by ANOVA, a Student-Newman-Keuls test was used to determine

207 which means were different. Statistical significance was set at $P < 0.05$.

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209 Lotka-Volterra modeling

210 The multispecies Lotka-Volterra model was used in this study. Taking $n$ species, the dynamic

211 of the species $i$ ($i = 1, ..., n$) is the following:

$$ \frac{d\mathcal{K}}{dt} = \mathcal{K} \left\{ \beta_i + \sum_{j=1}^{n} \alpha_{ij} \mathcal{X}_j \right\} $$

212

213 where $\beta_i$ represents the intrinsic growth rate of the species $i$, and $\alpha_{ij}$ the influence of the

214 species $j$ on the growth rate of species $i$. This influence is positive or negative according to the

215 sign of $\alpha_{ij}$. In this model, the interactions are assumed constant for a given species $j$

216 abundance. To determine the interaction coefficients, the multispecies Lotka-Volterra system

217 can be expressed as a multi-linear regression:

$$ \frac{d\mathcal{K}}{dt} = \beta_0 + \beta_1 \sum_{j=1}^{n} \alpha_{ij} \mathcal{X}_j $$

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219 The left part of this equation was obtained by deriving the logarithm of the species

220 concentration according to time using the cubic spline function without smoothing (Matlab®).
In a linear regression model, the correlations between explicative variables have a high impact on parameter identification. The design of experiments makes it possible to avoid the correlations, but this approach is not possible in the present study. Consequently, to avoid too many correlations, the model was not used on each species but on clusters that grouped the different organisms obtained from a squared correlation coefficient with a 0.75 threshold value. For a given cluster, the sum of abundance of the different species was used in the linear model. Inside this simplified system, an interaction coefficient $\alpha_{ij}$ was considered to be significant when $P(\alpha_{ij} \neq 0) > 90\%$.
229 **Results**

230 **Growth properties of the ecosystem microorganisms**

231 The growth characteristics of the bacteria as a function of NaCl content and pH on an agar-
232 based media are compared in Figure S1 (supplementary material). The bacteria could be
233 divided into three groups based on their growth abilities. The first group was comprised of *H. alvei* and *S. xylosus*, which grew under all the conditions tested, except at pH 5 and 0% NaCl
234 in which *S. xylosus* did not grow. The second group was comprised of *A. arilaitensis*, which
235 grew at a pH equal or greater than 5.5, except in the presence of 0 and 30 g l⁻¹ NaCl where it
236 grew at a pH equal or greater than 6.5 and 6, respectively. The third group was comprised of
237 *Leucobacter* sp., *B. aurantiacum* and *C. casei*, which only grew at a pH equal or greater than
238 6, except for *B. aurantiacum*, which grew in the presence of 100 and 150 g l⁻¹ NaCl at pH 5.5.
239 In some cases, *C. casei* only grew at a pH equal or greater than 6.5. The bacteria generally
240 grew better in the presence of increased concentrations of NaCl. Yeast grew under all the
241 conditions tested (data not shown).
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243

244 **Microbial and physico-chemical dynamics during the development of the ecosystem on**
245 **model cheese**

246 *Reproducibility of microbial dynamics.* The growth of the three yeasts and six bacteria
247 during cheese ripening are shown in Figures 1A and 1B, respectively. There was a good
248 reproducibility (a difference of less than 0.5 log₁₀ units) between duplicates in the numbers of
249 the yeast and the three dominant bacterial species, i.e., *A. arilaitensis*, *Leucobacter* sp. and *H. alvei* (data not shown). The three other bacterial species were **only** detected occasionally on
250 one or two of the cheeses analyzed because these bacteria had numbers below the detection
251 limit of our method of analysis (approximately 2 log₁₀ units below the total count). *S. xylosus*
252 was not isolated on day 12, 16, 17, 18 and 20; *B. aurantiacum* on day 10, 12, 14 and 20 and
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254 C. casei on day 20.

255 Yeast growth. D. hansenii and Y. lipolytica grew during the first days of ripening and
256 had almost similar growth rates (Figure 1A); in contrast, G. candidum grew only after two
257 days. A possible explanation for the absence of the increase in cell numbers of G. candidum
258 may be that G. candidum had a longer lag phase or formed mycelium at the start of ripening.
259 Indeed, mycelium with hyphae consists of different cells but would give only 1 CFU per agar
260 plate. The growth of G. candidum coincided with a slowing down of D. hansenii and Y.
261 lipolytica growth. Overall, D. hansenii dominated the cheese surface until day 5; then,
262 between day 6 and 9, the three yeasts had similar cell numbers, after which D. hansenii
263 became progressively subdominant compared with Y. lipolytica and G. candidum. Indeed, G.
264 candidum and Y. lipolytica numbers remained constant or increased slightly, while the D.
265 hansenii population decreased by 1.5 log₁₀ units between day 6 and day 21.

266 Bacterial growth. During the first days of ripening, the counts of H. alvei, A.
267 arilaitensis, Leucobacter sp. and S. xylosus remained constant, while the populations of C.
268 casei and B. aurantiacum decreased by approximately 1 log unit between day 0 and 4 (Figure
269 1B). Growth of all the organisms occurred after day 5-6. A. arilaitensis, followed by H. alvei,
270 dominated the cheese surface between day 6 and day 9. After day 9, Leucobacter sp. counts
271 increased, and this species also became dominant on the cheese surface. S. xylosus, C. casei
272 and B. aurantiacum remained subdominant throughout the entire ripening period. Lactic acid
273 bacteria counts decreased slightly from ~10⁸ CFU/g on day 0 to 2 x 10⁷ CFU/g at the end of
274 ripening (data not shown).

275 Lactose, lactate and pH dynamics during ripening. Lactose, lactate and pH variations
276 during ripening are shown in Figure 2. Lactose was used first and was totally depleted on day
277 8. After an increase during the first days of ripening, probably due to a slight acidification by
278 the lactic acid bacteria, lactate was consumed from day 5 to day 9, but was not depleted. Sixty
percent **of the lactate** was used during growth, which indicates that lactate was not a limiting carbon source. The surface deacidification occurred between day 2 and day 6, with a pH increase from approximately 5.0 to 8.0. This deacidification was highly correlated with the utilization of lactate and the growth of *G. candidum* on the cheese surface (data not shown).

*Generalized Lotka-Volterra modeling.* The dendogram of the different species according to their squared correlation coefficient during growth is shown in Figure S2 (supplementary material). With a threshold value of 0.75, each yeast was considered to have a specific growth dynamic. In contrast, except for *Leucobacter* sp., the growth dynamics of the bacteria were considered to be correlated. Consequently, GLV modeling was performed on the growth dynamics of five distinct groups that comprised four individual species, i.e., *Y. lipolytica*, *G. candidum*, *D. hansenii* and *Leucobacter* sp., and a group of bacteria including *A. arilaitensis*, *B. aurantiacum*, *C. casei*, *H. alvei* and *S. xylosus*.

The main interactions according to GLV modeling are shown in Figure 3. Yeast-yeast interactions were found to be only negative, while yeast-bacteria interactions were found to be only positive. *G. candidum* interacted negatively with *D. hansenii* and *Y. lipolytica*, while it interacted positively with *Leucobacter* sp. and the group of bacteria. *D. hansenii* was found to have a negative interaction with *Y. lipolytica*, while it had a positive interaction with the group of bacteria. Self-inhibition of *G. candidum* and *D. hansenii* were also found in the model.

The model succeeded in representing the growth of the different microbial populations as shown in Figures S3 and S4 (supplementary material), which compare measured and estimated values for the two data sets. Total residual error between estimated and measured values was 0.1 ± 0.4 log CFU/g for both data sets.

## Effects of single and multiple omissions of yeast in the ecosystem

We aimed at identifying yeast-yeast or yeast-bacteria interactions by comparing the
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growth of each individual microorganism in the absence or presence of one, two or three
yeasts. The utilization of lactose and lactate, the deacidification rate and the color
development of the cheese surface were also compared for each inoculum tested.

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*Reproducibility.* There was good reproducibility between triplicates in terms of lactose and lactate utilization, deacidification and the growth of the microorganisms of the ecosystem as well (data not shown). There was also a good reproducibility between the data of the dynamic study and the omission study in which all the members of the community were inoculated (data not shown).

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*Yeast-yeast interactions.* The viability of *D. hansenii* during the stationary phase was affected in the presence of the other yeasts (see Figure S5 in supplementary material). Populations of *D. hansenii* were significantly lower (p<0.05) on day 11 when *D. hansenii* was grown in the presence of *G. candidum* or *G. candidum* and *Y. lipolytica*. Indeed, populations of *D. hansenii* were 0.5 and 0.7 log₁₀ units lower than the *D. hansenii* monoculture in the presence of *G. candidum* or *G. candidum* and *Y. lipolytica*, respectively. Moreover, between day 11 and day 21, *D. hansenii* populations decreased from 1 to 1.7 log₁₀ units when this organism was co-cultivated with *G. candidum* and/or *Y. lipolytica*, whereas it remained constant in the monoculture. This inhibitory effect was similar regardless of whether *Y. lipolytica* or *G. candidum* were present, but was more pronounced in the presence of both species. Populations of *Y. lipolytica* and, to a lesser extent, populations of *G. candidum*, were significantly lower (p<0.05) on day 11 when they were grown in the presence of other yeasts (data not shown). Their respective counts were 0.4 and 0.7 log₁₀ units lower than those observed in monoculture. However, there was not any loss in viability of *Y. lipolytica* and *G. candidum* during the stationary phase.

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Interestingly, *Y. lipolytica* but not *D. hansenii* greatly influenced the mycelium
formation of *G. candidum*. In the monoculture or in the sole presence of *D. hansenii*, *G. candidum* grew in the form of white mycelium, which covered the surface of the model cheeses (Figure 4A and 4B), whereas in the presence of *Y. lipolytica*, growth occurred as spaghetti-like structures without formation of pseudohyphae (Figure 4C). This inhibition of mycelial development did not influence cellular growth since only small differences in numbers of *G. candidum* were found (Figure 4D). This phenomenon was also observed in the presence of both *Y. lipolytica* and *D. hansenii*. The idea that an interaction of *Y. lipolytica* on *G. candidum* occurred was also reinforced because the rate of utilization of lactate in the cheese containing *G. candidum* and *Y. lipolytica* was decreased in the presence of *Y. lipolytica* compared with the monoculture or in co-culture with *D. hansenii* (Figure 4D). Ninety percent of the lactate was used after 21 days when *G. candidum* grew as the sole yeast or in the presence of *D. hansenii*, while only 44% was used when this organism was co-cultivated with *Y. lipolytica*.

342

### Chemical characteristics of the cheese.

*G. candidum* showed the highest deacidification rate, followed by *D. hansenii* and *Y. lipolytica*, which had similar deacidification rates (Figure 5A). The pH reached its maximal value, i.e., 8.0, after 11 days when *G. candidum* was present in the ecosystem, whereas pH ranged from 6 to 6.5 for *D. hansenii* and *Y. lipolytica* (Figure 5A) or a combination of both species (data not shown). After 21 days, pH ranged from 7.4 to 8.0. The higher pH of cheese containing *G. candidum* may be attributable to the fact that *G. candidum* utilized more lactate than *D. hansenii* between d 0 and 11. *D. hansenii* produced a small amount of NH₃ (data not shown). *Y. lipolytica* did not utilize lactate but produced large amounts of NH₃ (data not shown). Amino acids and compounds such as ornithine and γ-amino-n-butyric acid (GABA), differed between cheeses (data not shown). After 21 d, the cheese inoculated with *Y. lipolytica*
354 had 2-15 times more free amino acids, depending on the amino acid considered, than the
355 cheeses inoculated with *D. hansenii* or *G. candidum* and the cheese with no yeast. Except for
356 asparagine, cysteine, ornithine and GABA, all amino acids were produced in large quantities
357 in the cheese inoculated with *Y. lipolytica* compared with the two other yeasts (data not
358 shown).

359

360 *Development of the bacterial community.*

361 The growth of the bacteria in the cheese model was considerably influenced by the yeasts that
362 were either present or not in the initial inocula. Growth of the bacteria did not occur when
363 yeasts were not inoculated (Figure 6A). After 11 and 21 days, the cheeses that contained *G.*
364 *candidum* showed significantly higher surface-pH than the cheeses inoculated with *D.*
365 *hansenii* and/or *Y. lipolytica*. The differences in surface pH between cheeses inoculated with
366 *D. hansenii* and/or *Y. lipolytica* were much lower when *D. hansenii* and *Y. lipolytica* were
367 combined than when they were the sole yeasts inoculated (Figures 6A and 6B). After 11 days,
368 the bacterial count of the cheese inoculated with *G. candidum* by itself was significantly
369 higher (p<0.05) than the cheese inoculated with *D. hansenii* or *Y. lipolytica* by itself (Figure
370 6A). In contrast, with two or three yeasts in the community, total bacterial counts were
371 statistically similar (p<0.05) despite the fact that surface pH was significantly lower (p<0.05)
372 on the cheese containing *D. hansenii* and *Y. lipolytica* (Figure 6B). After 21 days, total
373 bacterial counts were not statistically different in all cheeses except the cheeses that contained
374 *Y. lipolytica* as the sole yeast, and *D. hansenii* and *Y. lipolytica*, which had counts 1.5 and 1
375 log₁₀ units lower, respectively (Figures 6A and 6B).

376 As shown in Figure 7, there were only small differences in the distribution of the bacterial
377 species on the different cheeses after 11 days, except for the cheese inoculated with *D.*
378 *hansenii* and *G. candidum*. Except in the cheese inoculated with *G. candidum* and *D.*
hansenii, the cheeses were dominated by *A. arilaitensis*, which represented between 66 and 86% of the total isolates, followed by *H. alvei* (5-25%), *Leucobacter* sp. (2-10%), *S. xylosus* (3-10%), *C. casei* and *B. aurantiacum* (0.4-2%). *H. alvei* (70%), followed by *A. arilaitensis* (26%) and *Leucobacter* sp. (11%), dominated the cheese inoculated with *D. hansenii* and *G. candidum*. After 21 d, differences and common patterns were found in the distribution of the bacterial community. *Leucobacter* sp. grew in all the cheeses inoculated with *G. candidum* and represented between 26 and 60%, whereas this species was subdominant (less than 5% of the total isolates) in all cheeses in which *G. candidum* was absent. *A. arilaitensis* dominated in the cheese inoculated with *D. hansenii* or *G. candidum* as the sole yeast (70% of the isolates), while *H. alvei* dominated in cheeses inoculated with *Y. lipolytica* or *Y. lipolytica* and *D. hansenii* (70% of the isolates). After 21 days, *S. xylosus*, *B. aurantiacum* and *C. casei* remained subdominant, except in the cheese inoculated with *G. candidum* as the sole yeast in which *B. aurantiacum* represented 10% of the isolates taken in this cheese.

392

393 Color development of the cheese surface.

There were only small differences in the color development of all the cheeses after 11 days, except the cheese inoculated with no yeast and the cheeses inoculated with *G. candidum* or *G. candidum* and *D. hansenii*, which had a lower b* (yellow dimension) probably because *G. candidum* formed white mycelia on the surface (data not shown). In contrast, all the cheeses differed considerably in terms of color development after 21 days (Figure 8). The consortium that contained the three yeasts showed the highest a* and b* values, followed by the two other cheeses inoculated with *Y. lipolytica* and *D. hansenii* or *Y. lipolytica* and *G. candidum*. The cheeses inoculated with *G. candidum* by itself and *G. candidum* and *D. hansenii* had high a* but low b* values, while the cheeses inoculated with only *D. hansenii* or *Y. lipolytica* had high b* but low a* values.

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401

402

403
404 **Discussion**

405 In this study, the dynamics of a nine-species cheese ecosystem and the effects of the
406 omission of one, two or three yeasts on the growth of this community were investigated in
407 model cheeses. To our knowledge, all the studies about the growth behavior of
408 microorganisms isolated from cheese have been done on mixed cultures with only two
409 microorganisms, generally a yeast and a bacteria, on cheese agar (23, 24) or on curd made
410 under aseptic conditions (4, 20, 29). Despite the fact that such studies provide interesting
411 information on the individual growth characteristics of these organisms and their contribution
412 to ripening, they do not take account of the fact that the cheese microflora is much more
413 diverse and that interactions may exist between the members of these communities. These
414 interactions may strongly influence their implantation and colonizing capacity in cheese, as
415 shown in this study.

416

417 *Yeast-yeast interactions.* *G. candidum* was isolated from nearly all smear-ripened cheeses.
418 This organism imparts a uniform, white velvety coat on the surface of some cheeses such as
419 St. Marcellin, while on others such as Livarot, it is not the case (6). In this study, it was found
420 that when *Y. lipolytica* was grown in association with *G. candidum*, hyphal formation was
421 inhibited and that *G. candidum* grew as spaghetti-like structures instead (Figure 4). Numerous
422 chemical and environmental parameters have been reported to influence the yeast-mycelium
423 formation, such as temperature, glucose levels, pH, nitrogen sources and inoculum size (30).
424 Among these, ammonia and proline, which were produced in greater quantities by *Y.
425 lipolytica* than *D. hansenii*, may be an explanation for this observation. Palkova and Forstova
426 (32) showed that, between different yeast taxa, ammonia induction triggered changes in
427 colony morphology in which pseudohyphae decomposed into non-dividing yeast cells.
428 Kulkarni and Nickerson (17) showed that proline (10 mM) induced the yeast morphology in
429 *Ceratosystis ulmi* in defined liquid media, and that budding yeasts were only formed above
430 10⁶ blastidiospores per ml. However, in our study, other factors may be involved, and further
431 investigations are being pursued to understand this interaction. *G. candidum* was also less
432 metabolically active or its metabolism was differently orientated in the presence of *Y.
433 lipolytica* because *G. candidum* was less effective in utilizing lactate in spaghetti-like
434 structures than in mold-like structures. Indeed, mycelium-like structures may provide a better
435 access to substrates in the cheese matrix.

436 The presence of other yeasts in the cheese had only a small effect on the growth of
437 each individual yeast. This may be explained by the fact that each yeast utilized different
438 energy sources for growth. Barnett et al. (1) showed that *D. hansenii* assimilates lactose and
439 lactate while *G. candidum* and *Y. lipolytica* only assimilate lactate. In this study, *Y. lipolytica*
440 did not utilize lactate. The energy source of *Y. lipolytica* remained unclear, but nitrogen
441 compounds are likely to be its main energy source. *D. hansenii* populations were found to
442 significantly decrease in the presence of other yeasts. This indicates that competition for
443 nutrients or negative interactions (inhibition) occurred between yeasts, which affected cell
444 maintenance of *D. hansenii* during its stationary phase.

445

446 *Yeast-bacteria interactions*. In this study, it was demonstrated that the bacterial development
447 and distribution of the different species were modified depending on the yeast present in the
448 ecosystem. It is obvious, because of the different levels of acid-sensitivity of the bacteria, that
449 the deacidification rate of the yeasts influenced the bacterial development on the cheese
450 surface. Indeed, in most cases, the bacteria reached higher population levels when the
451 deacidification was more rapid. The growth characteristics of each bacterial strain as a
452 function of pH determined in agar-based media gave us an insight into the growth ability of
453 each bacterium. For example, *Leucobacter* sp. was much more acid-sensitive than *H. alvei*
454

and developed later in the ripening process. *C. casei* and *B. aurantiacum* were also quite acid-
sensitive and did not hold up well under the acidic stress that occurred at the start of ripening,
compared to the other members of the bacterial community. This may be responsible for their
subdominance in almost all the cheeses.

455

456

457

458

Surface-pH was not the only factor that influenced bacterial development. For
example, *S. xylosus*, which is able to grow at relatively low pH on agar, did not well colonize
the cheese surface compared to *A. arilaitensis*, *H. alvei* or *Leucobacter* sp. This also indicates
that growth abilities obtained in pure culture on agar-based media cannot be extrapolated to
more complex media and multi-species ecosystems. *S. xylosus* may have a limited
colonization capacity in cheese because the nutrients available may not have been sufficient to
support growth, or competition may have occurred between this strain and the different yeasts
and bacteria. In biodiversity studies, it has been reported that *Staphylococcus* spp. were often
predominant in the early stages of ripening, but were rapidly outnumbered by other bacteria at
the later stages of ripening (15, 28, 34). However, in co-cultures studies, *S. saprophyticus* was
able to reach high numbers, i.e., 10¹⁰ CFU/g with *D. hansenii* in model cheese curd (29),
while it did not reach such numbers in cheese (28). Therefore, *Staphylococcus* spp. strains
may have a limited colonization capacity of this type of cheese, especially when the
microflora is much more complex.

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472

*Leucobacter* sp. only grew in the cheeses that contained *G. candidum*. This would imply that
*Leucobacter* sp. was highly dependent on *G. candidum* activities either because *G. candidum*
rapidly deacidified the surface or because it produced metabolites that enhanced *Leucobacter*
sp. growth. Similarly, *B. aurantiacum* represented ~10% of the clones isolated after 21 d in
the cheese inoculated with *G. candidum* as the sole yeast, whereas *B. aurantiacum* was
subdominant in the other microbial communities. It is possible that *G. candidum* detoxified
the environment and released substrates that promoted growth of *B. aurantiacum* under these

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478
479 conditions.

480 *A. arilaitensis* and, in most cases, *H. alvei*, were found to represent a large part of the bacteria
481 under all the conditions tested. Therefore, these species may not be highly dependent on a
482 specific yeast interaction, with the exception of surface deacidification. *A. arilaitensis* has
483 been found to dominate the microflora of many European cheeses (12, 13, 16). This shows the
484 high colonization capacity of this species compared with others, such as *B. linens* or *B.
485 aurantiacum*.

486 *Color development of the cheese surface.* The color differentiation that occurred between d 11
487 and d 21 is probably due to the production of pigments by the bacteria. Interestingly, in some
488 cases, if we compare two cheeses with almost similar bacterial distribution and population,
489 such as the ecosystems that contained only *Y. lipolytica* or *Y. lipolytica* and *D. hansenii*, color
490 differed considerably between the two ecosystems. This would imply that, depending on the
491 yeasts present, species-specific bacterial pigmentation was different in these two cheeses. This
492 is in agreement with a previous study of Leclercq-Perlat et al. (18) in which it was shown that
493 *B. linens* pigmentation differed depending on the yeast used for deacidification. The
494 ecosystem that contained the three yeasts yielded the strongest color development. This
495 suggests that each yeast would have different ecosystem functions in terms of color
496 development and that the combination of the three yeasts led to the highest pigment
497 production by the bacteria.

498 *Lotka-Volterra modeling.* In this study, Lotka-Volterra modeling was used for the first time
499 on a microbial ecosystem as a preliminary approach to represent inter- and intraspecies
500 interactions. This approach made it possible to identify the positive interactions between the
501 bacteria and the yeast during ripening, i.e., the positive effect of *G. candidum* on *Leucobacter*
502 sp. and on the rest of the bacteria that were confirmed in this study. Similarly, the negative
503 interaction between yeasts, such as the inhibition of *G. candidum* on *D. hansenii*, was also
found in the Lotka-Volterra model. However, this model showed interactions such as a
negative interaction of the bacteria on *D. hansenii*, which could not be explained by the
omission study. Inversely, other interactions such as a negative interaction from *Y. lipolytica*
on *D. hansenii* were not significant in the model but observed *in situ*. Further data would be
necessary to confirm or invalidate the interactions observed in the model. Because growth of
most of the bacteria was highly correlated, we could not measure interactions between each
individual bacterial species and the yeasts. Despite the limits of this approach, the use of the
GLV model on only one set of data provided us with an insight into the main interactions.
Therefore, GLV modeling may be useful as a preliminary step to orientate interaction studies.

The smear cheese microbial community is a beneficial biofilm because it is
responsible for the flavor and appearance of this type of cheese. For a better understanding of
the interactions that occur, it would be interesting to investigate the spatial distribution of
these microorganisms on the cheese surface using fluorescence *in situ* hybridization, for
example.
518 Acknowledgements

519 J. Mounier would like to thank INRA for awarding him a post-doc fellowship. The authors

520 thank M.-N. Leclercq-Perlat and F. Lecornue for cheese production under aseptic conditions.

521 The authors also thank A. Antoinette and A. Delile for their excellent technical assistance.
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<caption>632 Table 1. Selective media used to identify the different bacterial clones.</caption>

633

<table><thead><tr><th rowspan="2"></th><th colspan="2">Novobiocin</th><th>Erythromycin</th><th>Vancomycin</th><th>TTC<sup>‡</sup></th></tr><tr><th>1 mg/l</th><th>5 mg/l</th><th>20 mg/l</th><th>1 mg/l</th><th>0.1 g/l</th></tr></thead><tbody><tr><td>Arthrobacter arilaitensis</td><td>-<sup>†</sup></td><td>-</td><td>-</td><td>-</td><td>+</td></tr><tr><td>Brevibacterium aurantiacum</td><td>+</td><td>-</td><td>-</td><td>v</td><td>v</td></tr><tr><td>Corynebacterium casei</td><td>+</td><td>+</td><td>-</td><td>-</td><td>v</td></tr><tr><td>Hafnia alvei</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td></tr><tr><td>Leucobacter sp.</td><td>-</td><td>-</td><td>-</td><td>+</td><td>+</td></tr><tr><td>Staphylococcus xylosus</td><td>+</td><td>+</td><td>-</td><td>+</td><td>+</td></tr></tbody></table>

634

635 † -, absence of growth; +, growth; v, variable growth.

636 ‡ TTC: Tetrazolium chloride

637

638

639

640
641 **Figure legends.**

642 **Figure 1.** Yeast (A) and bacterial (B) dynamics of the cheese ecosystem on model cheeses.

643 (A): ♦, *Debaryomyces hansenii*; ■, *Yarrowia lipolytica*; ▲, *Geotrichum candidum*. (B): ●, *Arthrobacter arilaitensis*; ■, *Hafnia alvei*, □, *Leucobacter* sp., ♦, *Corynebacterium casei*; ○, *Brevibacterium aurantiacum*, ▲, *Staphylococcus xylosus*.

646

647 **Figure 2.** Lactose (△), lactate (■) and pH (●) variations during the growth of the cheese ecosystem on model cheese.

649

650 **Figure 3.** Main interactions (→, positive; →, negative) according to generalized Lotka-Volterra modeling between the members of the multi-species ecosystem. *Dh*, *Debaryomyces hansenii*; *Yl*, *Yarrowia lipolytica*; *Gc*, *Geotrichum candidum*; *Ls*, *Leucobacter* sp; C, group including *Arthrobacter arilaitensis*, *Hafnia alvei*, *Corynebacterium casei*, *Brevibacterium aurantiacum* and *Staphylococcus xylosus*.

655

656 **Figure 4.** Macromorphology of *Geotrichum candidum* grown as a monoculture (A) or in the presence of *Debaryomyces hansenii* (B) or *Yarrowia lipolytica* (C) and (D) Lactate utilization (closed symbols) and *G. candidum* counts (open symbols) in model cheeses containing *G. candidum* (●, ○), *G. candidum* and *D. hansenii* (■, □) or *G. candidum* and *Y. lipolytica* (▲, △).

661

662 **Figure 5.** Lactate consumption (closed symbols) and pH increase (open symbols) during ripening in cheeses inoculated with *Debaryomyces hansenii* (▲, △), *Geotrichum candidum* (●, ○) or *Yarrowia lipolytica* (■, □).

665
**Figure 6.** Total bacterial growth and surface pH increase during ripening in cheeses inoculated with (A) no yeast (○), *Debaryomyces hansenii* (■), *Yarrowia lipolytica* (♦), *Geotrichum candidum* (▲) or (B) *D. hansenii* and *Y. lipolytica* (◊), *D. hansenii* and *G. candidum* (△), *Y. lipolytica* and *G. candidum* (□), *D. hansenii*, *Y. lipolytica*, and *G. candidum* (●).

**Figure 7.** Distribution of the bacterial species in the model cheese after 11 and 21 days as a function of the yeast inoculated. DH, *Debaryomyces hansenii*; YL, *Yarrowia lipolytica*; GC, *Geotrichum candidum*.

**Figure 8.** Color of the cheese surface after 21 d as a function of the chromaticity coordinate a* (red dimension) and b* (yellow dimension) values. Cheeses were inoculated with no yeast (♦), *Debaryomyces hansenii* (■), *Geotrichum candidum* (▲), *Yarrowia lipolytica* (●), *D. hansenii* and *Y. lipolytica* (◊), *Y. lipolytica* and *G. candidum* (□) and *D. hansenii*, *Y. lipolytica*, and *G. candidum* (○).
<caption>Figure 1.</caption>

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<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>
706 Figure 2.

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722 Figure 3.

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<figure><img src="image_5.png" /></figure>

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<figure><img src="image_6.png" /><figcaption>Figure 4.</figcaption></figure>

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<figure><img src="image_7.png" /></figure>

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Figure 5.

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<figure><img src="image_8.png" /></figure>

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<figure><img src="image_9.png" /><figcaption>Figure 6.</figcaption></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>
<figure><img src="image_13.png" /><figcaption>Figure 7.</figcaption></figure>
798 Figure 8.

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<figure><img src="image_14.png" /></figure>

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801 Supplementary material

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803 **Figure legends.**

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805 **Figure S1.** Growth properties of the bacteria of the cheese ecosystem as a function of pH and NaCl content at 12°C. Growth after ■ 2 days, ■ 4 days and □ absence of growth after incubation for 8 days.

808 **Figure S2.** Dendrogram of the different species according to their squared correlation coefficient during growth in model cheese used for Lotka-Volterra modelling.

809

810 **Figure S3.** Comparison of experimental populations (○) of experiment I and estimated populations (—) using Lotka-Volterra modelling of *Debaryomyces hansenii* (Dh), *Yarrowia lipolytica* (Yl), *Geotrichum candidum* (Gc), *Leucobacter* sp. (Le) and a group including *Arthrobacter arilaitensis*, *Hafnia alvei*, *Corynebacterium casei*, *Brevibacterium aurantiacum* and *Staphylococcus xylosus* (C).

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815 **Figure S4.** Comparison of experimental populations of experiment II (○) and estimated populations (—) using Lotka-Volterra modelling of *Debaryomyces hansenii* (Dh), *Yarrowia lipolytica* (Yl), *Geotrichum candidum* (Gc), *Leucobacter* sp. (Le) and a group including *Arthrobacter arilaitensis*, *Hafnia alvei*, *Corynebacterium casei*, *Brevibacterium aurantiacum* and *Staphylococcus xylosus* (C).

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820 **Figure S5.** Growth of *Debaryomyces hansenii* when cultivated as a monoculture (■) or in co-culture with *Yarrowia lipolytica* (▲), *Geotrichum candidum* (○) or *Y. lipolytica* and *G. candidum* (□).

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<figure><img src="image_15.png" /><figcaption>Figure S1.</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>Figure S2.</figcaption></figure>
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<caption>869 **Figure S3.**</caption>

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<figure><img src="image_17.png" /></figure>
<figure><img src="image_18.png" /></figure>
875 **Figure S5.**

<figure><img src="image_19.png" /></figure>

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