The time-points were resampled at equidistant intervals of 15 min.

The value $P_{max}$ is characterizing the maximum aggregate rate of heat production (see **Figure 1C**). After reaching the maximum, the decrease of heat production rate starts. The time point where $P_{max}$ is measured is a convenient time for the end of the exponential growth phase(s).

The change of the number of viable cells over time was calculated using the equation (Stulova et al., 2013):

$$N = \frac{\Delta Q}{YQ} \qquad (3)$$

where $N$ (CFU mL$^{-1}$ h$^{-1}$) is the number of bacteria grown during the selected time interval, $\Delta Q$ (J mL$^{-1}$ h$^{-1}$) is the heat produced during the same time interval, and $Y_Q(J CFU^{-1})$ is the experimentally determined heat yield coefficient ($Y_Q$, J CFU$^{-1}$). Heat yield coefficient ($Y_Q$, J CFU$^{-1}$) was calculated on the basis of the results obtained from the experiments of ST12 growth in liquid RSM. The plate count was carried out at the end of exponential growth phase (6.31*10$^8$ CFU mL$^{-1}$)

and heat amount produced was calculated from the power-time curves (2.81 J). The value of $Y_Q$ (J CFU$^{-1}$) was determined to be $Y_Q = 2.81 J/6.31*10^8$ CFU mL$^{-1} = 4.45*10^{-9}$ J CFU$^{-1}$. Our earlier experiments (unpublished data) showed that the $Y_Q$ value depended remarkably on the temperature of the growth but not on the media composition or inoculation rate. The $Y_Q$ value indicated was used for the description of growth in the liquid cultures (RSM) as well as in the case of solid-state growth (in r-RSM).

The numbers of bacteria at the end of exponential growth phase ($N_{exp}$, CFU mL$^{-1}$) and the total numbers grown during the whole process ($N_{tot}$, CFU mL$^{-1}$), and also at “deviation moments” ($N^*$, CFU mL$^{-1}$)—see below, were calculated from heat evolution data $Q_{exp}(J mL^{-1})$ and $Q_{tot}(J mL^{-1})$, and $Q^*(J mL^{-1})$ using the indicated above $Y_Q$ value (Kabanova et al., 2012).

The radii of average spherical model colonies ($R_{col}$, µm) were calculated from average volumes of colonies using Kepler's conjecture of packing of determined by heat measurements numbers of bacteria in colonies (Hsiang, 1993; Hales, 1994)—see a detailed discussion of the use of the conjecture in Kabanova et al. (2012).

<table><caption>Table 1 | Growth characteristics of *Streptococcus thermophilus* ST12 in RSM and r-RSM at 40°C$^a$.</caption><thead><tr><th rowspan="2">Milk</th><th rowspan="2">Inoculation rate (cfu mL<sup>-1</sup>)</th><th colspan="2">μ<sub>max</sub> (Wh<sup>-1</sup>)</th><th>Q<sub>1-exp.ph</sub> (J mL<sup>-1</sup>)</th><th>Q<sub>exp.ph</sub> (J mL<sup>-1</sup>)</th><th>Q<sub>tot</sub> (J mL<sup>-1</sup>)</th><th>P<sub>max</sub> (μ W)</th><th>t<sub>Pmax</sub> (h)</th><th>λ (h)</th></tr><tr><th>μ<sub>max1</sub></th><th>μ<sub>max2</sub></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td rowspan="5">RSM</td><td>10<sup>6</sup></td><td>1.88 ± 0.03<sup>aA</sup></td><td>1.05 ± 0.01<sup>aA</sup></td><td>0.28 ± 0.00<sup>aA</sup></td><td>2.00 ± 0.05<sup>aA</sup></td><td>7.12 ± 0.01<sup>aAB</sup></td><td>362.95 ± 3.80<sup>aA</sup></td><td>5.50 ± 0.00<sup>aA</sup></td><td>0.02 ± 0.00<sup>aA</sup></td></tr><tr><td>10<sup>5</sup></td><td>1.85 ± 0.05<sup>aA</sup></td><td>1.18 ± 0.00<sup>bA</sup></td><td>0.32 ± 0.00<sup>bA</sup></td><td>2.10 ± 0.00<sup>bA</sup></td><td>7.21 ± 0.03<sup>aA</sup></td><td>362.77 ± 4.03<sup>aA</sup></td><td>6.17 ± 0.00<sup>bA</sup></td><td>0.98 ± 0.05<sup>bA</sup></td></tr><tr><td>10<sup>4</sup></td><td>1.91 ± 0.03<sup>aA</sup></td><td>1.17 ± 0.02<sup>bA</sup></td><td>0.34 ± 0.00<sup>bA</sup></td><td>2.11 ± 0.01<sup>bA</sup></td><td>7.12 ± 0.01<sup>aA</sup></td><td>363.10 ± 0.47<sup>aA</sup></td><td>7.33 ± 0.00<sup>cA</sup></td><td>2.08 ± 0.02<sup>cA</sup></td></tr><tr><td>10<sup>3</sup></td><td>1.85 ± 0.04<sup>aA</sup></td><td>1.14 ± 0.01<sup>bA</sup></td><td>0.32 ± 0.01<sup>bA</sup></td><td>1.97 ± 0.02<sup>aA</sup></td><td>7.24 ± 0.02<sup>aA</sup></td><td>359.57 ± 2.30<sup>aA</sup></td><td>8.67 ± 0.00<sup>dA</sup></td><td>3.05 ± 0.05<sup>dA</sup></td></tr><tr><td>10<sup>2</sup></td><td>1.83 ± 0.10<sup>aA</sup></td><td>1.13 ± 0.02<sup>bA</sup></td><td>0.35 ± 0.04<sup>bA</sup></td><td>2.00 ± 0.07<sup>aA</sup></td><td>7.23 ± 0.07<sup>aA</sup></td><td>345.52 ± 8.05<sup>aA</sup></td><td>9.92 ± 0.12<sup>eA</sup></td><td>4.07 ± 0.19<sup>eA</sup></td></tr><tr><td>10<sup>1</sup></td><td>1.61 ± 0.04<sup>bA</sup></td><td>1.01 ± 0.03<sup>a</sup></td><td>0.41 ± 0.02<sup>c</sup></td><td>2.26 ± 0.02<sup>cA</sup></td><td>7.36 ± 0.13<sup>aA</sup></td><td>353.31 ± 12.09<sup>aA</sup></td><td>10.83 ± 0.00<sup>fA</sup></td><td>4.98 ± 0.01<sup>fA</sup></td></tr><tr><td rowspan="5">r-RSM</td><td>10<sup>6</sup></td><td>1.89 ± 0.01<sup>abA</sup></td><td>1.00 ± 0.01<sup>aA</sup></td><td>0.28 ± 0.00<sup>aA</sup></td><td>2.41 ± 0.04<sup>bA</sup></td><td>7.02 ± 0.02<sup>abA</sup></td><td>240.67 ± 4.78<sup>abB</sup></td><td>6.50 ± 0.00<sup>aB</sup></td><td>0.04 ± 0.00<sup>aA</sup></td></tr><tr><td>10<sup>5</sup></td><td>2.01 ± 0.15<sup>aA</sup></td><td>1.06 ± 0.01<sup>bA</sup></td><td>0.33 ± 0.04<sup>bA</sup></td><td>2.36 ± 0.07<sup>bA</sup></td><td>6.92 ± 0.40<sup>aA</sup></td><td>260.32 ± 0.37<sup>bA</sup></td><td>7.17 ± 0.00<sup>aB</sup></td><td>1.15 ± 0.27<sup>bA</sup></td></tr><tr><td>10<sup>4</sup></td><td>1.89 ± 0.02<sup>abA</sup></td><td>0.94 ± 0.10<sup>aB</sup></td><td>0.45 ± 0.00<sup>bB</sup></td><td>2.26 ± 0.20<sup>aA</sup></td><td>6.89 ± 0.31<sup>aA</sup></td><td>247.32 ± 19.66<sup>abB</sup></td><td>8.17 ± 0.23<sup>bB</sup></td><td>1.96 ± 0.02<sup>cB</sup></td></tr><tr><td>10<sup>3</sup></td><td>1.88 ± 0.04<sup>abA</sup></td><td>0.93 ± 0.00<sup>aB</sup></td><td>0.44 ± 0.01<sup>bB</sup></td><td>2.28 ± 0.00<sup>aB</sup></td><td>7.12 ± 0.07<sup>aAB</sup></td><td>222.73 ± 2.02<sup>bB</sup></td><td>9.59 ± 0.12<sup>cB</sup></td><td>2.97 ± 0.04<sup>dA</sup></td></tr><tr><td>10<sup>2</sup></td><td>1.67 ± 0.09<sup>bA</sup></td><td>0.49 ± 0.03<sup>bB</sup></td><td>0.55 ± 0.01<sup>cB</sup></td><td>2.45 ± 0.11<sup>aB</sup></td><td>7.35 ± 0.56<sup>aA</sup></td><td>177.90 ± 1.63<sup>cB</sup></td><td>11.00 ± 0.24<sup>dB</sup></td><td>3.87 ± 0.07<sup>eA</sup></td></tr><tr><td>10<sup>1</sup></td><td>1.12 ± 0.01<sup>cB</sup></td><td>–</td><td>–</td><td>1.37 ± 0.32<sup>bB</sup></td><td>6.47 ± 0.06<sup>aB</sup></td><td>78.75 ± 6.77<sup>dB</sup></td><td>13.25 ± 0.59<sup>eB</sup></td><td>4.64 ± 0.15<sup>fB</sup></td></tr></tbody></table>

<table><caption>Table 2 | The t-test analysis denoting the differences between liquid and renneted RSM samples.</caption><thead><tr><th rowspan="2">Milk</th><th rowspan="2">Inoculation rate (cfu mL<sup>-1</sup>)</th><th colspan="2">μ<sub>max</sub> (Wh<sup>-1</sup>)</th><th>Q<sub>1-exp.ph</sub> (J mL<sup>-1</sup>)</th><th>Q<sub>exp.ph</sub> (J mL<sup>-1</sup>)</th><th>Q<sub>tot</sub> (J mL<sup>-1</sup>)</th><th>N<sub>exp</sub> (cfu mL<sup>-1</sup>)</th><th>P<sub>max</sub> (μW)</th><th>t<sub>Pmax</sub> (h)</th><th>λ (h)</th></tr><tr><th>μ<sub>max1</sub></th><th>μ<sub>max2</sub></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td rowspan="5">RSM vs. r-RSM</td><td>10<sup>6</sup></td><td>NS</td><td>*</td><td>NS</td><td>*</td><td>*</td><td>*</td><td>**</td><td>***</td><td>NS</td></tr><tr><td>10<sup>5</sup></td><td>NS</td><td>**</td><td>NS</td><td>*</td><td>NS</td><td>*</td><td>***</td><td>***</td><td>NS</td></tr><tr><td>10<sup>4</sup></td><td>NS</td><td>**</td><td>**</td><td>NS</td><td>NS</td><td>*</td><td>*</td><td>*</td><td>*</td></tr><tr><td>10<sup>3</sup></td><td>NS</td><td>**</td><td>**</td><td>**</td><td>NS</td><td>**</td><td>***</td><td>**</td><td>NS</td></tr><tr><td>10<sup>2</sup></td><td>NS</td><td>**</td><td>**</td><td>*</td><td>NS</td><td>*</td><td>**</td><td>*</td><td>NS</td></tr><tr><td>10<sup>1</sup></td><td>**</td><td>–</td><td>–</td><td>*</td><td>*</td><td>*</td><td>**</td><td>*</td><td>NS</td></tr></tbody></table>

<footnote>$^a$Data are means ± SD of maximal calorimetric growth rate ($\mu_{max}$) in the first and second exponential growth phase, the heat evolved during the first and second exponential phase ($Q_{1exp.ph}$ and $Q_{exp.ph}$), the total heat produced during the whole fermentation ($Q_{tot}$), maximum heat flow ($P_{max}$), the time of the maximum heat production rate ($t_{Pmax}$) and lag phase duration ($\lambda$) obtained from microcalorimetric power-time curves.</footnote>

<footnote>NS—not significant, *P* < 0.05, **P* < 0.01, ***P* < 0.001.</footnote>
## ANALYTICAL METHODS

Incubated in parallel to the microcalorimetric samples milk samples were mixed at chosen times 1:1 with isopropanol for sedimentation of proteins. The precipitate was removed by centrifugation at 14,000 × g for 10 min. The supernatant was filtered through a 13 mm diameter and 0.2-µm pore-size regenerated cellulose (RC) membrane filter (Whatman, Maidstone, UK) and diluted with water before analysis. High-performance liquid chromatography (HPLC) system (Alliance 2695 system, Waters Corp., Milford, MA) with a Refractive Index Detector 2414 and column BioRad HPX-87H 300 × 7.8 mm (Hercules, CA) was used for measuring lactose, glucose, galactose and lactate concentrations.

Analysis of FAA was performed on an ultra-performance liquid chromatography (UPLC) system (Acquity UPLC; Waters Corp.) including a binary solvent manager, a sample manager and photodiode array (PDA) detector, connected to Waters Empower™ 2.0 software. Separations were performed on a 2.1 × 100 mm Waters Acquity UPLC AccQ•Tag Ultra Column operated at 55°C, the running time being 12 min. Sample derivatization procedure was as follows: 20 µL AccQ•Fluor reagent was added to the mixed solution of 70 µL AccQ•Fluor borate buffer and 10 µL sample or standard. The mixed solution was vortexed immediately for 10 s, transferred to an autosampler vial and allowed to stand at room temperature for 1 min. Then the vials were placed in a heating block at 55°C for 10 min, after which they were analyzed by the UPLC system. Empower software (Waters Corp.) was used for the data processing.

The pH of milk samples was measured with pH meter S20 Seven Easy equipped with InLab 413 electrode (Mettler-Toledo GmbH, Greifensee, Switzerland).

## CALCULATION OF GROWTH PARAMETERS CHARACTERIZING METABOLISM OF THE GROWING CELLS

The growth characteristics of the bacteria (yield coefficient values, growth rates etc.) during the growth in milk were calculated on the basis of the heat evolution converted to biomass concentrations or cell numbers and concentrations of organic acids etc. in the culture media as follows:

$$ \mu = \frac{\ln(N_2/N_1)}{t_2 - t_1} \quad (4) $$

<table><caption>Table 3 | The numbers of bacteria in the sample (cfu mL⁻¹) and in average colonies (cfu col⁻¹) at the “deviation moment,” at the end of exponential growth phase and at the end of cultivation at different inoculation rates from 10⁶ to 10¹ cfu mL⁻¹ in r-RSM.</caption><thead><tr><th rowspan="2">Inoculation rate, cfu mL<sup>-1</sup></th><th colspan="2">N* at power-time curves "deviation moment"</th><th colspan="2">N<sub>exp</sub> at the end of exponential phase</th><th colspan="2">N<sub>tot</sub> at the end of fermentation (at 22 h)</th></tr><tr><th>cfu mL<sup>-1</sup>, x 10<sup>7</sup></th><th>cfu col<sup>-1</sup></th><th>cfu mL<sup>-1</sup>, x 10<sup>8</sup></th><th>cfu col<sup>-1</sup></th><th>cfu mL<sup>-1</sup>, x 10<sup>9</sup></th><th>cfu col<sup>-1</sup></th></tr></thead><tbody><tr><td>10<sup>6</sup></td><td>6.23 ± 0.33</td><td>(6.23 ± 0.33) x 10<sup>1</sup></td><td>5.42 ± 0.08</td><td>(5.42 ± 0.08) x 10<sup>2</sup></td><td>1.58 ± 0.01</td><td>(1.58 ± 0.01) x 10<sup>3</sup></td></tr><tr><td>10<sup>5</sup></td><td>4.96 ± 0.24</td><td>(4.96 ± 0.24) x 10<sup>2</sup></td><td>5.30 ± 0.16</td><td>(5.30 ± 0.16) x 10<sup>3</sup></td><td>1.56 ± 0.09</td><td>(1.56 ± 0.09) x 10<sup>4</sup></td></tr><tr><td>10<sup>4</sup></td><td>4.81 ± 0.31</td><td>(4.81 ± 0.31) x 10<sup>3</sup></td><td>5.08 ± 0.44</td><td>(5.08 ± 0.44) x 10<sup>4</sup></td><td>1.55 ± 0.07</td><td>(1.55 ± 0.07) x 10<sup>5</sup></td></tr><tr><td>10<sup>3</sup></td><td>4.48 ± 0.90</td><td>(4.48 ± 0.90) x 10<sup>4</sup></td><td>5.13 ± 0.01</td><td>(5.13 ± 0.01) x 10<sup>5</sup></td><td>1.60 ± 0.01</td><td>(1.60 ± 0.01) x 10<sup>6</sup></td></tr><tr><td>10<sup>2</sup></td><td>1.86 ± 0.71</td><td>(1.86 ± 0.71) x 10<sup>5</sup></td><td>5.51 ± 0.25</td><td>(5.51 ± 0.25) x 10<sup>6</sup></td><td>1.65 ± 0.13</td><td>(1.65 ± 0.13) x 10<sup>7</sup></td></tr><tr><td>10<sup>1</sup></td><td>0.94 ± 0.08</td><td>(0.94 ± 0.08) x 10<sup>5</sup></td><td>3.12 ± 0.73</td><td>(3.12 ± 0.73) x 10<sup>7</sup></td><td>1.45 ± 0.01</td><td>(1.45 ± 0.01) x 10<sup>8</sup></td></tr></tbody></table>

$$ I_{\text{Lactose}} = \frac{d(C_{\text{Lactose}})}{d(X)} \quad (5) $$

$$ O_{\text{Lactose}} = \frac{d(C_{\text{Lactose}})}{d(X)} \quad (6) $$

$$ Y_{\text{Lactate Lactose}} = \frac{d(C_{\text{Lactate}})}{d(C_{\text{Lactose}})} \quad (7) $$

$$ Y_{XS} = \frac{d(X)}{(d(2 \cdot C_{\text{Lactose}}) - d(C_{\text{Gal}}) - d(Glc)) \times 180/1000} \quad (8) $$

$$ I_{AA} = \frac{d(C_{AA})}{d(X)} \quad (9) $$

where μ is the specific growth rate (h⁻¹), t is time (h), N is the number of cells calculated assuming that heat produced per cell formation is constant (Y_Q = 4.45 × 10⁻⁹ J per cell), X is the dry biomass (gdw L⁻¹), assuming that the mass of one cell is 0.2 × 10⁻¹² g (unpublished data), I_Lactose is the lactose consumption per biomass produced (mmol gdw⁻¹), O_Lactate is the lactate production per biomass produced (mmol gdw⁻¹); C designates the concentration of corresponding compound, for example C_Lactate (mM), Y_Lactate/Lactose shows the lactate yield per lactose consumed, YXS is the biomass yield per hexose consumed and I_AA

<table><caption>Table 4 | Mean radii of the colonies of *Streptococcus thermophilus* ST12 in r-RSM at the “deviation moment,” at the end of exponential growth phase and at the end of cultivations at different inoculation rates 10⁶–10¹ cfu mL⁻¹.</caption><thead><tr><th>Inoculation rate, cfu mL<sup>-1</sup></th><th>R<sub>col</sub> at power-time curves "deviation moment," µm</th><th>R<sub>col</sub> at the end of exponential phase, µm</th><th>R<sub>col</sub> at the end of fermentation (at 22 h), µm</th></tr></thead><tbody><tr><td>10<sup>6</sup></td><td>2.55 ± 0.01</td><td>5.22 ± 0.02</td><td>7.33 ± 0.01</td></tr><tr><td>10<sup>5</sup></td><td>4.94 ± 0.33</td><td>11.16 ± 0.11</td><td>15.95 ± 0.31</td></tr><tr><td>10<sup>4</sup></td><td>10.14 ± 0.10</td><td>23.69 ± 0.68</td><td>33.85 ± 0.51</td></tr><tr><td>10<sup>3</sup></td><td>22.29 ± 0.36</td><td>51.21 ± 0.02</td><td>73.72 ± 0.22</td></tr><tr><td>10<sup>2</sup></td><td>38.33 ± 0.92</td><td>113.05 ± 1.69</td><td>160.40 ± 4.13</td></tr><tr><td>10<sup>1</sup></td><td>64.68 ± 3.07</td><td>200.73 ± 15.77</td><td>331.12 ± 1.08</td></tr></tbody></table>

Radii of average colonies were calculated from average volumes of the colony determined using Kepler's conjecture of bacterial packing of colonies.

<footnote>*Number of bacteria at the moment of deviation of the microcalorimetric power-time curves obtained in liquid and in renneted reconstituted skim milk samples.</footnote>
is the consumption of corresponding amino acid per biomass produced (mmol gdw⁻¹).

## STATISTICAL ANALYSIS OF THE DATA

All the microcalorimetric experiments were repeated twice and measurements were carried out with two or three parallel samples. The power-time curves presented on the Figures and used in the calculations are combined curves of two parallel experiments.

The other analyses (HPLC, UPLC, pH) were carried out in triplicate. All values of the parallel experimental points were averaged and reported along with the value of standard deviation (SD). The experimental data were submitted to single-factor analysis of variance (ANOVA), and the differences of the means were evaluated by Fisher's least significant difference (LSD) test. The difference of the mean values was accepted at the significance level *p* < 0.05. The Student's *t*-test was performed to evaluate statistically significant differences between the mean calorimetric values obtained in RSM and r-RSM.

## RESULTS

### PECULIARITIES OF THE GROWTH OF ST. THERMOPHILUS ST12 IN r-RSM

Representative calorimetric power-time curves of the growth of ST 12 in RSM and r-RSM, at inoculation rate 10⁵ CFU mL⁻¹, are presented in **Figure 2**.

As was previously described (Stulova et al., 2013), a typical microcalorimetric power-time curve of inoculated RSM contained two peaks corresponding to two distinctively separated growth phases (first and second exponential growth phases). As seen in **Figure 2**, the general pattern with two major exponential growth phases was observed also in r-RSM. The growth curves of *St. thermophilus* ST12 in r-RSM and RSM were practically coinciding in the beginning, but starting from a certain number of the bacteria in the sample (N* = 0.63 × 10⁷ CFU mL⁻¹ at N₀ = 10⁵ CFU mL⁻¹), at the “deviation moment” marked by the arrow in **Figure 2** the maximal calorimetric growth rate (μmax1) in r-RSM

was decreased, branching from the curve of RSM, and the further growth in the coagulated matrix was clearly different from that in RSM. This led to the appearance of the second slower phase in the first exponential phase in comparison with the growth curve in milk (RSM). The splitting of the second exponential growth phase also into two was observed as well—see **Figure 2**.

As seen in **Figure 2**, acidification developed more slowly after 3.5 h of incubation in renneted milk compared to the liquid milk samples which was in a good correlation with the slower growth rate of the bacteria in r-RSM. Higher pH values in r-RSM samples accompanying the main part of the growth of the bacteria were observed in case of all inoculation rates studied (**Figure 3B**), in difference from a previous report (Favrot and Maubois, 1996) showing slower acidification in renneted curd only at lower level of inoculation (10² CFU mL⁻¹).

### Effect of inoculation rate on power-time curves of St. thermophilus ST12 in r-RSM

Calorimetric power-time curves of the growth of ST 12 in RSM and r-RSM from 10⁶ to 10¹ CFU mL⁻¹ with increments of 10-fold, are presented in **Figure 3** and processed results were summarized in **Table 1**. The *t*-test results denoting the differences between liquid and renneted RSM are presented in **Table 2**.

<figure><img src="image_57.png" /><figcaption>**FIGURE 4** | Calorimetric power-time curves (bold line), lactose (■), glucose (●), galactose (▲) and lactic acid (♦) profiles during the growth of *Streptococcus thermophilus* ST12 in liquid (gray) and renneted (black) reconstituted milk.</figcaption></figure>

<figure><img src="image_58.png" /><figcaption>**FIGURE 5** | Calorimetric power-time curves (bold line), lactose consumption (A) and lactate production (B) per biomass formed (mmol gdw⁻¹) of *Streptococcus thermophilus* in liquid (gray) and renneted (black) reconstituted milk.</figcaption></figure>

<figure><img src="image_59.png" /></figure>
According to **Table 2**, the first exponential growth phases of *St. thermophilus* ST12 in r-RSM were shorter and the calculated μmax values were higher than in the second phase like in the case of RSM, however, the noted differences were much more strongly expressed (see also Stulova et al., 2013). As seen from the data presented in **Tables 1, 2** the maximal calorimetric growth rates of the first exponential phase (μmax1) were similar (P > 0.05) in RSM and r-RSM at different inoculation rates ranging from 10⁶ to 10² CFU mL⁻¹, except 10¹ CFU mL⁻¹. The values of maximal calorimetric growth rates of the second exponential phase (μmax2) in r-RSM were significantly lower (P < 0.01) than in RSM. This is indicating that the growth of the bacteria in bigger colonies in case of r-RSM is most probably inhibited by the accumulating lactate.

The integrating of the power-time curves allows measure the heat produced by the growing bacteria, where Qexp indicates the heat produced during the exponential growth phase and Qtot shows the total heat produced by the total biomass formed during fermentation. According to **Table 1** the amounts of heat produced during the exponential phase (Qexp, average) in r-RSM at inoculation rates ranging 10⁶–10² CFU mL⁻¹ were practically the same with average 2.35 J mL⁻¹ which corresponds to 5.30 × 10⁸ CFU mL⁻¹ (calculated using heat yield coefficient YQ). It was about 13% higher than the same value in RSM (2.04 J mL⁻¹). This fact

showed that during the exponential growth phase more bacteria were produced in r-RSM. Noting that the amount of lactate produced during the exponential growth was practically the same in the samples this fact allows to assume that pH was most probably the factor determining the end of the exponential growth. The amount of heat produced during the total growth (Qtot) was the same in r-RSM and RSM at inoculation rates ranging 10⁵–10² CFU mL⁻¹—7.14 ± 0.09 J mL⁻¹ that corresponds to the number of bacteria 1.62 × 10⁹ CFU mL⁻¹. The Qtot and Ntot values were the lowest in r-RSM at inoculation rate 10¹ CFU mL⁻¹—6.47 ± 0.06 J mL⁻¹ and 1.45 × 10⁹ CFU mL⁻¹ respectively. This fact showed that growth of the large colonies was clearly inhibited in coagulated milk (but the same amount of bacteria grew without inhibition in RSM).

Clear differences in the Pmax values between different inoculation rates in r-RSM are shown in **Figure 3**. The Pmax values were clearly different also if r-RSM and RSM values were compared with each other. As shown in Stulova et al. (2013) the values of Pmax were much higher (in average 350 μW mL⁻¹) and very similar at different inoculation rates (see **Figure 3**). The time to reach the maximum heat effect (tPmax) increased with decreasing amounts of inocula (**Table 1**). As seen from **Table 2**, there were significant differences noted in tPmax between r-RSM and RSM

<figure><img src="image_60.png" /><figcaption>FIGURE 6 | The changes of the concentrations of the individual free amino acids (FAA) during the growth of *Streptococcus thermophilus* ST12 at inoculation rate 10⁵ CFU mL⁻¹ in RSM (gray) and r-RSM (black).</figcaption></figure>
samples. Depending on the inoculation rate, 1–2.5 h more time was required to reach the maximum heat production rate ($t_{Pmax}$) in r-RSM compared with RSM (Table 1). These results showed that together with the decrease of the growth rate the length of the exponential phase was increased, which was the expected result.

The most likely explanation for the inhibition of *St. thermophilus* ST12 growth in r-RSM is that lactate accumulating in the colonies during growth in r-RSM was changing the pH locally and this led to the inhibition of growth. The peculiarities of colonial growth of bacteria will be discussed in more detail below (in section Discussion).

Lengths of the lag-phases ($\lambda$) in r-RSM, as in the case of RSM were changing with regular interval from one inoculation rate to another and became longer on decreasing of the initial inoculum. According to the statistical analysis (Table 2), no big differences were observed in lag-phase ($\lambda$) durations at the same inoculation rates in both media studied.

## Characterization of growth of colonies

Numbers of bacteria in average colonies in case of different inoculation rates were calculated using the data of the heat evolved during the different phases of the growth and $Y_Q$ value. The

radii of the average colonies were calculated using Kepler's conjecture of bacterial packing in colonies (Hsiang, 1993; Hales, 1994). The numbers of bacteria in average colonies (CFU/col) and mean sizes of the average colonies ($R_{col}$, µm) at the "deviation moments," at the end of exponential growth phase and at the end of fermentation at different inoculation rates (from $10^6$ to $10^1$ CFU mL$^{-1}$) in r-RSM are presented in Tables 3, 4, respectively.

It can be seen from the Table 3 that as expected from the measurements of the heat the total numbers of bacteria in samples at different inoculation rates were practically equal at the end of the cultivations, which automatically means that the grown colonies contained different number of cells. The same was true for the data from the end of the exponential growth.

In agreement with the same type of logic the radii of average colonies at the end of fermentation were varying from 7 µm at the highest inoculation rate $10^6$ CFU mL$^{-1}$ to 331 µm at the inoculation rate $10^1$ CFU mL$^{-1}$ (Table 4). Average size of colonies at inoculation rate $10^5$ CFU mL$^{-1}$ after 22 h of fermentation was similar to that measured in a model cheese by Floury et al. (2013)—average diameters of colonies being approximately 30 µm and 30–50 µm, respectively.

<figure><img src="image_61.png" /></figure>

a

<figure><img src="image_62.png" /></figure>

<figure><img src="image_63.png" /></figure>

b

<figure><img src="image_64.png" /></figure>

**FIGURE 7** | (A) Initial concentrations of free amino acid (FAA) and concentrations of FAA at the end of the exponential phase and at the end of growth in RSM (a) and r-RSM(b). (B) Radar-diagram showing the concentrations of FAA at the end of growth (at 22 h) in RSM (a) and r-RSM (b).
It should be emphasized that only one-third of biomass was synthesized by the end of the second exponential growth phase in r-RSM whereas most of the biomass was synthesized during the post-exponential growth phase (Table 3). In contrast to that radii of the colonies at the ends of the second exponential phases reached approximately 70% of the final values.

If the data obtained showed that the mechanisms determining the end of the exponential phase and end of cultivation in our experiments at 22 h were not depending on the inoculation rates, then the mechanism leading to the “deviation moment” is more complicated. As seen from the Tables 3, 4 neither the total numbers of bacteria N* nor the sizes of average colonies were equal at different inoculation rates at the moment when the growth rate of bacteria decreased in the r-RSM in comparison with that in RSM.

## UTILIZATION OF CARBOHYDRATES AND PRODUCTION OF LACTIC ACID BY THE BACTERIA IN r-RSM

The studies of consumption of lactose and formation of organic acids were carried out in the case of inoculation rate of 10⁵ CFU mL⁻¹.

The changes of the concentration of carbohydrates (lactose, glucose, galactose) and lactic acid during fermentation of RSM

and r-RSM with *St. thermophilus* ST12 at inoculation rate 10⁵ CFU mL⁻¹ are presented in **Figure 4**. As seen from this figure, the patterns of hydrolysis of lactose, release of glucose and galactose and formation of lactic acid were practically identical in both RSM and r-RSM (**Figure 4**). Approximately 130 mmol L⁻¹ of lactose was hydrolysed at the end of the second exponential growth phase (during active growth), 127 mmol L⁻¹ of glucose and 58 mmol L⁻¹ of galactose was utilized by the bacteria, and about 20 mmol L⁻¹ of lactic acid was produced.

Lactose consumption and lactate production per biomass formed (mmol gdw⁻¹) were calculated (see Section Calculation of Growth Parameters Characterizing Metabolism of the Growing Cells) and are presented in **Figures 5A,B**. During the growth of *St. thermophilus* ST12, lactose was consumed very fast—up to 2200 and 1800 mmol gdw⁻¹ in liquid and renneted RSM, respectively. Lactose consumption led to the accumulation of galactose, as well as glucose (Figure 5), due to low glycolytic activity of the bacteria, reflected by the low lactate yield per lactose below 0.2 mol mol⁻¹ in RSM as well as in r-RSM (theoretical maximum 4 mol mol⁻¹). It can be assumed that galactose was not metabolized by this strain; however, the accumulation of galactose was lower than predicted compared to the consumption of lactose. This indicated that either galactose was

<figure><img src="image_65.png" /><figcaption>**FIGURE 8** | Calorimetric power-time curves (bold line) and the consumptions (line with circles) of the individual FAA during the exponential growth of *Streptococcus thermophilus* ST12 at inoculation rate 10⁵ CFU mL⁻¹ in RSM (gray) and r-RSM (black).</figcaption></figure>
consumed through glycolysis, or it was used for polysaccharide production. Usually polysaccharides are produced in growth-restricted conditions to synthesize carbon storage substances for the future.

## CHANGE OF FREE AMINO ACIDS DURING FERMENTATION IN r-RSM

The changes of the concentrations of the individual FAA during fermentation of liquid and renneted RSM with *St. thermophilus* ST12 at inoculation $10^5$ CFU mL$^{-1}$ were evaluated and presented in **Figure 6**. As seen from **Figure 6**, glutamic acid was initially the most abundant free amino acid in both, RSM and r-RSM, accounting for 46% of the total.

There was a dramatic reduction of asparagine, glycine and valine at the end of exponential phase in both RSM and r-RSM. Leucine showed a clear decline in RSM during fermentation, whereas in the case of r-RSM a larger increase of this amino acid as well as phenylalanine was observed. It can be seen from the data presented in **Figure 7** that the intensive liberation of the majority of amino acids in RSM and r-RSM was observed during the period from the end of exponential phase till the end of fermentation (from 5 h till 22 h).

The concentrations of the total free amino acids (TFAA) were 15% higher in r-RSM than those determined in RSM at the end of cultivations, 2024 and 1713 µmol L$^{-1}$, respectively. Glutamic acid, proline, lysine and aspartic acid were the most predominant FAA in RSM at the end of fermentation (at 22 h), accounting for 22, 12, 9, and 8% of the total, respectively. In contrast, leucine, glutamic acid, phenylalanine, arginine and proline, accounting

for 18, 17, 12, 11, and 8% of the total were the dominant amino acids in r-RSM—see **Figure 8**.

## METABOLISM OF AMINO ACIDS AND GROWTH OF THE BACTERIA

Proteolysis in rennet curd is catalyzed by enzymes from coagulant and enzymes from the inoculated *St. thermophilus*. The initial hydrolysis of caseins is caused by the chymosin which results in the formation of large (water-insoluble) and intermediate-sized (water-soluble) peptides which are degraded subsequently by the coagulant and enzymes from the starters (Sousa et al., 2001). The final products of proteolysis are FAA and their concentration in curd is the net result of the liberation of amino acids from casein, their degradation to catabolic products and also utilization and synthesis by the starter bacteria. Free amino acid concentrations changes were evaluated in liquid and renneted milk samples during the growth of *St. thermophilus* ST12—see **Figures 6–8**. Amino acid consumption during the 1st and 2nd exponential growth phases is presented in **Table 5** Although *St. thermophilus* has not been shown to be auxotrophic for any single amino acid, growth is abolished when both Glu/Gln and Cys/Met are removed (Letort et al., 2002). In addition, the exhaustion of Leu and Val reduces the growth rate more than three times and only six amino acids (Asn, Ala, Ile, Gly, Ser, and Thr) can be individually omitted without any effect on the growth rate.

Most of the trends in consumption of FAA during the exponential growth of *St. thermophilus* ST12 in RSM and r-RSM were similar—see **Tables 5, 6**. The only amino acids consumed in amounts corresponding to the calculated requirements for

<table><caption>**Table 5** | Relative consumption of free amino acids (r-l_FAA) compared to that required for the synthesis of biomass proteins (%).</caption><thead><tr><th colspan="2">Time of growth, h</th><th colspan="6">RSM</th><th colspan="6">r-RSM</th></tr><tr><th>r-l_FAA<br>mmol g<sup>-1</sup></th><th>Content in<br>biomass, mmol g<sup>-1</sup></th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th></tr></thead><tbody><tr><td></td><td>μ</td><td>1.71</td><td>0.90</td><td>0.59</td><td>0.77</td><td>0.46</td><td>0.24</td><td>1.68</td><td>0.88</td><td>0.47</td><td>0.47</td><td>0.52</td><td>0.43</td></tr><tr><td></td><td>r-l_His</td><td>0.08</td><td>-579</td><td>-294</td><td>-395</td><td>-160</td><td>-41</td><td>-18</td><td>-35</td><td>-260</td><td>-587</td><td>-488</td><td>-159</td><td>-38</td></tr><tr><td></td><td>r-l_Asn</td><td>0.22</td><td>2014</td><td>521</td><td>305</td><td>82</td><td>-1</td><td>-9</td><td>402</td><td>555</td><td>231</td><td>119</td><td>-22</td><td>-24</td></tr><tr><td></td><td>r-l_Gln</td><td>0.19</td><td>61</td><td>-23</td><td>-104</td><td>-33</td><td>-4</td><td>8</td><td>3</td><td>-36</td><td>-111</td><td>-43</td><td>2</td><td>8</td></tr><tr><td></td><td>r-l_Org</td><td>0.22</td><td>-1550</td><td>-344</td><td>-243</td><td>-81</td><td>-25</td><td>-17</td><td>-287</td><td>-354</td><td>-251</td><td>-297</td><td>-177</td><td>-100</td></tr><tr><td></td><td>r-l_Gly</td><td>0.45</td><td>109</td><td>147</td><td>251</td><td>87</td><td>12</td><td>-5</td><td>39</td><td>188</td><td>257</td><td>205</td><td>37</td><td>4</td></tr><tr><td></td><td>r-l_Aspt</td><td>0.22</td><td>-249</td><td>-85</td><td>-89</td><td>-125</td><td>-107</td><td>-134</td><td>-38</td><td>2</td><td>31</td><td>-65</td><td>-148</td><td>-128</td></tr><tr><td></td><td>r-l_Glu</td><td>0.19</td><td>-302</td><td>133</td><td>375</td><td>70</td><td>-49</td><td>-108</td><td>69</td><td>163</td><td>13</td><td>-182</td><td>-241</td><td>-205</td></tr><tr><td></td><td>r-l_Thr</td><td>0.30</td><td>9</td><td>27</td><td>51</td><td>23</td><td>6</td><td>-2</td><td>9</td><td>33</td><td>40</td><td>50</td><td>19</td><td>8</td></tr><tr><td></td><td>r-l_As</td><td>0.64</td><td>-11</td><td>4</td><td>11</td><td>4</td><td>0</td><td>-2</td><td>5</td><td>26</td><td>38</td><td>42</td><td>12</td><td>1</td></tr><tr><td></td><td>r-l_Pro</td><td>0.18</td><td>-498</td><td>-216</td><td>-241</td><td>-160</td><td>-102</td><td>-121</td><td>-76</td><td>-162</td><td>-200</td><td>-244</td><td>-140</td><td>-85</td></tr><tr><td></td><td>r-l_Cys</td><td>0.03</td><td>-5186</td><td>-1008</td><td>-381</td><td>82</td><td>119</td><td>91</td><td>-916</td><td>-1082</td><td>-692</td><td>-349</td><td>134</td><td>247</td></tr><tr><td></td><td>r-l_Lys</td><td>0.42</td><td>69</td><td>-17</td><td>-56</td><td>-53</td><td>-41</td><td>-49</td><td>22</td><td>27</td><td>0</td><td>-38</td><td>-44</td><td>-34</td></tr><tr><td></td><td>r-l_Tyr</td><td>0.12</td><td>-52</td><td>-80</td><td>-142</td><td>-104</td><td>-69</td><td>-79</td><td>0</td><td>-9</td><td>-33</td><td>-73</td><td>-66</td><td>-52</td></tr><tr><td></td><td>r-l_Met</td><td>0.07</td><td>179</td><td>12</td><td>-28</td><td>-44</td><td>-44</td><td>-58</td><td>15</td><td>30</td><td>18</td><td>-8</td><td>-39</td><td>-38</td></tr><tr><td></td><td>r-l_Val</td><td>0.36</td><td>109</td><td>49</td><td>64</td><td>4</td><td>-19</td><td>-32</td><td>26</td><td>70</td><td>79</td><td>59</td><td>-7</td><td>-19</td></tr><tr><td></td><td>r-l_Ile</td><td>0.27</td><td>105</td><td>36</td><td>26</td><td>2</td><td>-8</td><td>-14</td><td>31</td><td>53</td><td>22</td><td>-1</td><td>-19</td><td>-15</td></tr><tr><td></td><td>r-l_Leu</td><td>0.39</td><td>122</td><td>32</td><td>19</td><td>-7</td><td>-19</td><td>-30</td><td>1</td><td>-58</td><td>-125</td><td>-155</td><td>-115</td><td>-81</td></tr><tr><td></td><td>r-l_Phe</td><td>0.18</td><td>-9</td><td>-36</td><td>-69</td><td>-59</td><td>-38</td><td>-43</td><td>-24</td><td>-191</td><td>-335</td><td>-423</td><td>-236</td><td>-144</td></tr><tr><td></td><td>r-l_Trp</td><td>0.07</td><td>34</td><td>-29</td><td>-76</td><td>-54</td><td>-31</td><td>-27</td><td>10</td><td>-93</td><td>-207</td><td>-234</td><td>-99</td><td>-59</td></tr></tbody></table>

Green color shows that free amino acids were consumed more than required for biomass synthesis. Amino acid composition of biomass is taken from the literature (Adamberg et al., 2012).
<table><caption>**Table 6 | Milk composition, maximal amount of biomass that can be obtained from free amino acids (FAA) in milk and relative amount of FAA supporting the synthesis of biomass proteins.**</caption><thead><tr><th rowspan="2">Aminoacid</th><th colspan="2">Composition of milk (C<sub>FAA</sub>), mM</th><th rowspan="2">Biom.(B<sub>AA</sub>),mmol g<sup>-1</sup> <sup>a</sup></th><th colspan="2">Theoretical amount of biomass from FAA (Xtheor), g L<sup>-1</sup> <sup>b</sup></th><th colspan="2">Experimentally determined biomass (Xexp) at 8 h, g L<sup>-1</sup> <sup>c</sup></th><th colspan="2">Max amount of FAA (r-I_FAA max), %<sup>d</sup></th></tr><tr><th>RSM</th><th>r-RSM</th><th>RSM</th><th>r-RSM</th><th>RSM</th><th>r-RSM</th><th>RSM</th><th>r-RSM</th></tr></thead><tbody><tr><td>His</td><td>10.4</td><td>10.5</td><td>0.08</td><td>0.12</td><td>0.12</td><td>0.12</td><td>0.18</td><td>100</td><td>70</td></tr><tr><td>Asn</td><td>70.1</td><td>69.9</td><td>0.22</td><td>0.33</td><td>0.32</td><td>0.12</td><td>0.18</td><td>262</td><td>181</td></tr><tr><td>Gln</td><td>14.6</td><td>14.4</td><td>0.19</td><td>0.08</td><td>0.08</td><td>0.12</td><td>0.18</td><td>64</td><td>43</td></tr><tr><td>Arg</td><td>14.4</td><td>15.0</td><td>0.22</td><td>0.06</td><td>0.07</td><td>0.12</td><td>0.18</td><td>52</td><td>37</td></tr><tr><td>Gly</td><td>55.7</td><td>56.0</td><td>0.45</td><td>0.12</td><td>0.12</td><td>0.12</td><td>0.18</td><td>99</td><td>69</td></tr><tr><td>Asp</td><td>26.9</td><td>26.5</td><td>0.22</td><td>0.12</td><td>0.12</td><td>0.12</td><td>0.18</td><td>101</td><td>69</td></tr><tr><td>Glu</td><td>270.0</td><td>271.0</td><td>0.19</td><td>1.46</td><td>1.46</td><td>0.12</td><td>0.18</td><td>1176</td><td>817</td></tr><tr><td>Thr</td><td>10.2</td><td>10.1</td><td>0.3</td><td>0.03</td><td>0.03</td><td>0.12</td><td>0.18</td><td>27</td><td>19</td></tr><tr><td>Ala</td><td>33.6</td><td>33.6</td><td>0.64</td><td>0.05</td><td>0.05</td><td>0.12</td><td>0.18</td><td>42</td><td>29</td></tr><tr><td>Pro</td><td>20.7</td><td>20.8</td><td>0.18</td><td>0.11</td><td>0.11</td><td>0.12</td><td>0.18</td><td>92</td><td>64</td></tr><tr><td>Cys</td><td>2.2</td><td>2.5</td><td>0.03</td><td>0.08</td><td>0.10</td><td>0.12</td><td>0.18</td><td>67</td><td>53</td></tr><tr><td>Lys</td><td>15.9</td><td>15.9</td><td>0.42</td><td>0.04</td><td>0.04</td><td>0.12</td><td>0.18</td><td>30</td><td>21</td></tr><tr><td>Tyr</td><td>2.7</td><td>2.7</td><td>0.12</td><td>0.02</td><td>0.02</td><td>0.12</td><td>0.18</td><td>18</td><td>12</td></tr><tr><td>Met</td><td>3.0</td><td>3.0</td><td>0.07</td><td>0.04</td><td>0.04</td><td>0.12</td><td>0.18</td><td>36</td><td>25</td></tr><tr><td>Val</td><td>15.1</td><td>14.9</td><td>0.36</td><td>0.04</td><td>0.04</td><td>0.12</td><td>0.18</td><td>33</td><td>23</td></tr><tr><td>Ile</td><td>6.0</td><td>6.0</td><td>0.27</td><td>0.02</td><td>0.02</td><td>0.12</td><td>0.18</td><td>18</td><td>12</td></tr><tr><td>Leu</td><td>8.3</td><td>8.1</td><td>0.39</td><td>0.02</td><td>0.02</td><td>0.12</td><td>0.18</td><td>17</td><td>12</td></tr><tr><td>Phe</td><td>3.0</td><td>2.9</td><td>0.18</td><td>0.02</td><td>0.02</td><td>0.12</td><td>0.18</td><td>13</td><td>9</td></tr><tr><td>Trp</td><td>3.2</td><td>3.3</td><td>0.07</td><td>0.04</td><td>0.05</td><td>0.12</td><td>0.18</td><td>36</td><td>26</td></tr></tbody></table>

biomass synthesis in the 1st exponential phase were Gly, Glx (Gln+Glu) and Asx. These amino acids were also the only amino acids whose concentrations in milk exceeded the amount required for biomass synthesis (Table 6). The amount of free His was also sufficient to satisfy biomass requirements, but it should be noted that the amount of this amino acid did not decrease during the exponential growth of the bacteria. Most of the concentrations of other FAA could not support the growth of cells more than 30%. Free Arg and Pro in milk could support amino acid requirements for over 40% but instead of decrease the concentrations of these amino acids increased during the 1st exponential growth phase. This pattern was quite general. Accumulation of amino acids in the media, especially in renneted milk samples due to their release from proteins, and most noticeably during the second exponential growth phase, made it impossible to analyse consumptions of amino acids in this growth phase.

Overconsumption of Glu 5–7 times exceeding the need for biomass synthesis indicated the potential of the synthesis of other amino acids from it. In total, 4.6 mmol g⁻¹ of amino acids were incorporated into the biomass. Of the total, 30% was covered by the consumption of FAA (excluding Glu). If we take into account Glu overconsumption and amino group transfer to other amino acids, Glu can provide ammonia for 25% of amino acids of the

biomass. The remaining 45% of amino acids must be have been derived from peptides.

## DISCUSSION

During the last years, there has been a significant increase of interest in study of growth of bacterial colonies in dairy food matrices (especially in cheese matrices). It has been shown that location, distribution of the cells in different zones of the matrices as well as size of the colonies and distances between colonies are important factors determining peculiarities of cheese ripening processes, influencing utilization of the nutrients and diffusion of metabolites within the cheese matrices (Ercolini et al., 2003; Jeanson et al., 2011; Floury et al., 2013). However, the first systematic comparative study of quantitative peculiarities of growth and metabolism of a LAB *St. thermophilus* ST12 at different inoculation rates in liquid and renneted milk during the whole growth of the bacteria was carried out in this paper to our knowledge. A novel non-invasive method of microcalorimetry developed by us was used throughout the study. The method developed and used allowed obtain the first time quantitative data on the growth kinetics of colonies of different sizes in renneted milk.

It was shown that the growth patterns of *St. thermophilus* ST12 in r-RSM during fermentation at 40°C were clearly multiphase

<footnote><sup>a</sup>Biomass composition is taken from literature (Adamberg et al., 2012).</footnote>

<footnote><sup>b</sup>Calculated as $X_{theor} = (C_{AA}/1000)/B_{AA}$ indicating how much biomass can be produced from free amino acid present in milk assuming that amino acid is used only for the synthesis of biomass proteins. AA refers to corresponding amino acid.</footnote>

<footnote><sup>c</sup>Experimentally determined biomass concentration assuming that heat produced per cell is $4.45 \times 10^{-9}$ J per cell and cell mass is $0.2 \times 10^{-12}$ g.</footnote>

<footnote><sup>d</sup>Calculated as $r_I\_FAA \max = X_{theor} \times 100/X_{exp}$ indicating relative amount of amino acid in biomass proteins that can be supported by consumed free amino acids. Percentage below indicates that free amino acid was less consumed than required for synthesis of biomass proteins.</footnote>
in comparison with diauxic growth of *St. thermophilus* ST12 in RSM. Decrease of the maximal calorimetric growth rates μmax during the first and the second exponential growth phases as well as remarkably lower values of maximum heat flows Pmax (W mL⁻¹) of the thermophilic starters growing in r-RSM were observed. The noted inhibition of growth rates in the r-RSM could be explained most probably by the accumulation of lactate in the colonies leading to the change of pH. This conclusion was supported also by the fact that lactate inhibition was not observed in RSM where the buffering capacity of liquid milk was sufficient for alleviating of the influence of lactate (see **Figure 3**).

The data obtained showed that the numbers of bacteria in the end of the exponential phase (combined exponential phase, see above), as well as in the end of the cultivations were practically the same at different inoculation rates, see **Table 3**. This allowed introduce a very simple calculation scheme for the determination of the sizes of the colonies, including the radii, in these impor- tant points at different inoculation rates presented in **Table 3**. However, the total numbers of bacteria in the end of the expo- nential growth in r-RSM were higher than in RSM for about 13% at all except at low inoculation rates. This fact could be explained by the lower rate of decrease of pH in the r-RSM in comparison with RSM indicating that buffering capacity of the matrices is important in determining the end of the exponen- tial growth of the bacteria in milk, not accumulation of lactate directly.

It should be noted that the total numbers of bacteria in the “deviation points” were not the same at different inoculation rates as expected from the data presented above. This was indicating that these points were determined not by a “uniform” mechanism like the end of exponential growth phases and the end of growth during the cultivation but in this case most probably also the sizes of colonies were playing more important role in determining the local buffering capacity in the colonies. Another factor derived from the size of colonies is the availability of nutrients (essential amino acids) for bacteria as proteases are cell bound and can- not freely move in the environment to deliberate peptides from casein.

An observation showing that the growth rates of bacteria in small colonies in the beginning of the cultivation at all inoculation rates were the same as in liquid milk indicated that diffusion rate of glucose was practically the same in these two types of samples.

## CONCLUSIONS

The present study clearly showed potential of non-invasive method of microcalorimetry in combination with the other ana- lytical methods in study of quantitative peculiarities of growth and metabolism of bacteria in opaque and solid state media—see a detailed comparison of the methods in Lobete et al. (in press). It was possible to elucidate the quantitatively characterized growth patterns of *Streptococcus thermophilus* ST12 in renneted milk at different inoculation rates and compare them with the patterns of growth of the bacteria in milk. Clear discriminative results of growth and metabolic patterns of cells in renneted and liquid milk encourage use of microcalorimetry more widely as a screen- ing tool for strain selection in their natural environment (milk or curd).

## ACKNOWLEDGMENTS

The financial support for this research was financially supported through the grant ETF7636 of Estonian Science Foundation, through the institutional research funding (IUT 1927) of Estonian Ministry of Education and Research, and by the EU Regional Development Fund projects EU28912, EU29994 and 3.2.0701.11-0018.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 06 October 2014; accepted: 22 January 2015; published online: 10 February 2015.

Citation: Stulova I, Kabanova N, Kriščiunaite T, Adamberg K, Laht T-M and Vilu R (2015) Microcalorimetric study of the growth of *Streptococcus thermophilus* in renneted milk. *Front. Microbiol.* 6:79. doi: 10.3389/fmicb.2015.00079

This article was submitted to Food Microbiology, a section of the journal Frontiers in Microbiology.

Copyright © 2015 Stulova, Kabanova, Kriščiunaite, Adamberg, Laht and Vilu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
# Measurement of pH micro-heterogeneity in natural cheese matrices by fluorescence lifetime imaging

Zuzana Burdikova<fnref n="1" />*, Zdenek Svindrych², Jan Pala³, Cian D. Hickey<fnref n="1" />, Martin G. Wilkinson⁴, Jiri Panek⁵, Mark A. E. Auty<fnref n="1" />, Ammasi Periasamy² and Jeremiah J. Sheehan<fnref n="1" />

**OPEN ACCESS**

**Edited by:**
Sophie Jeanson,
Institut National de la Recherche
Agronomique, France

**Reviewed by:**
Carmen Wacher,
Universidad Nacional Autónoma de
México, Mexico
Christelle Lopez,
Institut National de la Recherche
Agronomique, France

\*Correspondence:
Zuzana Burdikova,
Teagasc Food Research Centre
Moorepark, Fermoy, Co. Cork, Ireland
zuzana.burdikova@teagasc.ie

**Specialty section:**
This article was submitted to Food Microbiology, a section of the journal Frontiers in Microbiology

**Received:** 28 September 2014

**Accepted:** 19 February 2015

**Published:** 06 March 2015

**Citation:**
Burdikova Z, Svindrych Z, Pala J, Hickey CD, Wilkinson MG, Panek J, Auty MAE, Periasamy A and Sheehan JJ (2015) Measurement of pH micro-heterogeneity in natural cheese matrices by fluorescence lifetime imaging. *Front. Microbiol.* 6:183. doi: 10.3389/fmicb.2015.00183

Cheese, a product of microbial fermentation may be defined as a protein matrix entrapping fat, moisture, minerals and solutes as well as dispersed bacterial colonies. The growth and physiology of bacterial cells in these colonies may be influenced by the microenvironment around the colony, or alternatively the cells within the colony may modify the microenvironment (e.g., pH, redox potential) due to their metabolic activity. While cheese pH may be measured at macro level there remains a significant knowledge gap relating to the degree of micro-heterogeneity of pH within the cheese matrix and its relationship with microbial, enzymatic and physiochemical parameters and ultimately with cheese quality, consistency and ripening patterns. The pH of cheese samples was monitored both at macroscopic scale and at microscopic scale, using a non-destructive microscopic technique employing C-SNARF-4 and Oregon Green 488 fluorescent probes. The objectives of this work were to evaluate the suitability of these dyes for microscale pH measurements in natural cheese matrices and to enhance the sensitivity and extend the useful pH range of these probes using fluorescence lifetime imaging (FLIM). In particular, fluorescence lifetime of Oregon Green 488 proved to be sensitive probe to map pH micro heterogeneity within cheese matrices. Good agreement was observed between macroscopic scale pH measurement by FLIM and by traditional pH methods, but in addition considerable localized microheterogeneity in pH was evident within the curd matrix with pH range between 4.0 and 5.5. This technique provides significant potential to further investigate the relationship between cheese matrix physico-chemistry and bacterial metabolism during cheese manufacture and ripening.

**Keywords:** cheese matrix, C-SNARF-4, Oregon Green, pH micro-heterogeneity, fluorescence lifetime, FLIM, natural cheese

<footnote n="1">¹ Teagasc Food Research Centre, Moorepark, Fermoy, Ireland, ² Department of Biology, W. M. Keck Center for Cellular Imaging, University of Virginia, Charlottesville, VA, USA, ³ Department of Sport Medicine, Third Faculty of Medicine, Charles University, Prague, Czech Republic, ⁴ Department of Life Sciences, Faculty of Science and Engineering, University of Limerick, Limerick, Ireland, ⁵ Institute of Macromolecular Chemistry, Academy of Sciences of the Czech Republic, Prague, Czech Republic</footnote>
Introduction

Cheese is a complex physiochemical and microbial system con-
taining many interacting components. It may be defined as a pro-
tein matrix entrapping fat, moisture, minerals and other solutes
as well as dispersed bacterial colonies. Irrespective of cheese type,
both starter and non-starter bacteria are immobilized and iso-
lated within the curd matrix during the coagulation step of cheese
manufacture (Fox et al., 2000; McSweeney, 2004b; Jeanson et al.,
2011). Each inoculated bacterial cell is assumed to grow, generat-
ing colonies which are dispersed within the cheese curd. Bacterial
colonies within the cheese matrix have been shown to be located
on or within milk fat globular membrane material (MFGM) and
at fat-protein interfaces with the curd (Laloy et al., 1996; Lopez
et al., 2006) and are considered to interact with the cheese matrix
during ripening (Feeney et al., 2002; McSweeney, 2004a). The
growth and physiology of bacterial cells in these colonies may be
influenced by the microenvironment around the colony, or alter-
natively the cells within the colony may modify the microenvi-
ronment (e.g., pH, redox potential) due to their metabolic activity
(McSweeney, 2004a).

The pH of cheese curd is determined by the extent of acidi-
fication during manufacture, by the availability of substrate for
fermentation, principally lactose, by the buffering capacity of the
cheese curd and, in some cases, by the degree of deacidification
during ripening. Cheese pH affects the texture of curd directly by
influencing the degree of casein hydration, which in turn influ-
ences the visco-elastic behavior of the protein matrix (Euston
et al., 2002; Kilcast and Angus, 2007). pH also affects texture and
flavor indirectly by influencing the activity of enzymes impor-
tant to ripening, e.g., plasmin (Grufferty and Fox, 1988) and, in
the case of the coagulant, both the retention of and the activity
of the enzyme in the curd during manufacture and subsequent
ripening (Holmes et al., 1977; Stadhouders et al., 1977; Visser,
1977; Creamer et al., 1985; Garnot et al., 1987). The pH also influ-
ences the metabolic activity of lactic acid bacteria (Meldrum et al.,
2003; Kajfasz and Quivey, 2011; Jeanson et al., 2013). The pH is a
key factor influencing amino acid decarboxylase activity (Gardini
et al., 2001) and bacteriocin production (Foulquié Moreno et al.,
2003).

Development of a method capable of precisely measuring
pH at localized level within the cheese matrix will facilitate a
new understanding of the relationship between cheese matrix
physico-chemistry and bacterial metabolism during cheese man-
ufacture and ripening.

Until now different techniques have been used to mon-
itor pH in and around colonies. Microelectrodes were first
used to measure pH in and around submerged colonies of
S. typhimurium (Wimpenny et al., 1995; Jeanson et al., 2013),
however, these cannot map the spatial variation of pH to a res-
olution of a micrometer. Standard methodologies to measure
the pH in cell biology include the use of pH sensitive fluores-
cent dyes—fluorescein based pH indicator, benzoxanthene dyes,
cyanine-based pH indicators, etc. (Han and Burgess, 2010). In
the vast majority of these experiments Confocal Laser Scanning
Fluorescence Microscopy (CLSM) is employed, mainly due to
its ability to perform optical sectioning and provide accurate

three-dimensional representation of the fluorophore distribution
(Inoue, 1995).

Seminaphthorhodafluor-4F 5-(and-6) carboxylic acid (C-SNARF-4) is a long wavelength fluorescent pH indicator (Haugland, 2002). The dye exhibits two emission peaks whose intensities display different pH dependencies, thus it is suitable as a ratiometric fluorescent pH probe. The results of the fluorescence intensity ratio of these two peaks have been confirmed as a reliable indicator of pH at values 5.0 and above (Hunter and Beveridge, 2005). However, in more acidic environments the pH sensitivity of the fluorescence ratio decreases rapidly.

Oregon Green 488 is a fluorinated analog of fluorescein. It
exhibits higher photostability and lower pKₐ (pKₐ = 4.7 vs.
6.4 for fluorescein), making it a useful pH indicator in the
weakly acidic range (pH 4–6). However, to use Oregon Green
as a ratiometric pH probe, two excitation wavelengths are nec-
essary (Whitaker et al., 1991). Oregon Green carboxylic acid has
been widely employed to measure lysosomal and endosomal pH
(Dunn et al., 1991). Bioconjugate prepared from Oregon Green
488, dextran, has the advantage of high stability and low affinity
to cheese matrix components (proteins).

Besides fluorescence intensity, fluorescence lifetime, a proba-
bilistic timescale of fluorescence emission (Sun et al., 2011), may
provide additional information about the chemical environment
(e.g., pH) of the probe. The most common example is fluores-
cein, whose fluorescence lifetime changes from 4 ns at pH 10
to 2 ns at pH 7 (Lakowicz, 2006), and many other fluorophores
were probed for lifetime–pH dependence, including some of the
SNARF family (Szmacinski and Lakowicz, 1993). Oregon Green
dyes were also probed for lifetime–Ca²⁺ sensitivity (Agronskaia
et al., 2004).

Fluorescence Lifetime Imaging (FLIM)

Fluorescence of a fluorophore is characterized by its absorption
spectrum (a probability that a photon of given wavelength is
absorbed by the molecule), quantum yield (a probability that
the excited molecule emits a photon during transition to the
ground state), its emission spectrum (a distribution of wave-
lengths of the emitted photon) and the fluorescence lifetime
(a characteristic time the molecule spends in the excited state
before emitting a photon). Both excitation and emission spec-
trum are used extensively to distinguish different fluorophores
and to sense the local environment of a fluorophore (local ion
concentration, electric potential, pH, etc., Lakowicz, 2006). How-
ever, only a limited number of fluorophores, often specifically
designed, display useful changes in excitation and emission spec-
tra. Here we note that changes in fluorescence intensity alone
cannot be exploited in most cases, because the fluorescence inten-
sity depends predominantly on the concentration of the fluores-
cent molecules. When such fluorophores are not available or do
not perform optimally in specific experimental conditions, one
may explore the dependence of fluorescence lifetime on the local
environment.

The fluorescence lifetime has only recently been widely
adopted, mainly because of the technical challenges associated
with the measurement of such fast phenomena (typically in
the nanosecond range). One of the most efficient precise and
reliable methods used in conjunction with confocal microscopy is Time Correlated Single Photon Counting (TCSPC). This method relies on sub-nanosecond pulsed illumination and fast photon-counting detectors to measure the delay between the excitation pulse and the photon detection event (Becker, 2005). The intensity of fluorescence following a short excitation pulse (usually much shorter than 1 ns) decays exponentially on a nanosecond timescale (Figure 1A). This decay, however, cannot be digitized directly with current instrumentation; moreover, in a typical confocal microscopy experiment there is less than one photon detected in a hundred of laser pulses. Instead, the time delay between the laser pulse and photon detection event is measured (Figure 1B) for each photon and a histogram (Figure 1C) is built from these delays in each pixel of a confocal image. By fitting an exponential model (Equation 1) to these decays, the information about the lifetime of the constituents is recovered.

In simple cases (single molecular species with single ground and excited state configuration, or with configurations changing in sub-nanosecond timescale) single exponential decay completely describes the experimental data. However, often more exponential components are needed to achieve acceptable fit between the model (Equation 1) and the recorded decay. Although the detailed mechanism is often poorly understood, multi-exponential decay suggest the existence of more distinct fluorescent species or several stable configurations of the fluorophore on long timescales (relatively to the fluorescence lifetime). Despite the inherent difficulties and numerical instability of multi-exponential fitting, when performed cautiously, the individual lifetime components can provide valuable information regarding local environment of the fluorophore (Becker, 2005).

The objectives of this study were (i) to assess the potential of C-SNARF-4 and Oregon Green 488 fluorescent dyes to perform as ratiometric pH indicators in cheese matrices at microscale, (ii) to extend the useful pH range of the

probes using lifetime measurements, and (iii) to determine the level of pH microheterogeneity within a natural cheese matrix. To our knowledge it is the first time that advanced microscopic technique fluorescent lifetime imaging microscopy (FLIM) of C-SNARF-4 and Oregon Green 488 has been applied to determine localized pH of different types of natural cheese.

# Materials And Methods

## Cheese Preparation And Characterization
### Starter Strains

Thermophilic starter cultures typically used in Swiss-type cheese manufacture (Scott, 1981), i.e., *S. thermophilus* TH3 (ST) and *L. helveticus* LHB02 (LH) were purchased from Chr. Hansen Ltd. (Little Island, Co. Cork, Ireland) as individual frozen concentrates and stored at -80°C until cheese manufacture.

### Cheese Manufacture

Two cheese making trials consisting of four vats of 454 kg cheese milk were undertaken over a 6 month period. Raw milk was obtained from a local dairy company, standardized to a protein to fat ratio of 1.01:1, held overnight at 4°C, pasteurized at 72°C for 15 s, and pumped at 32°C into cylindrical, jacketed, stainless steel vats (500-1) with automated variable speed cutting and stirring equipment (APV) Schweiz AG, Worb, Switzerland). Cheesemilk was heated to 34°C and inoculated with 0.1 g/l ST (10⁶ cfu/ml) and 0.05 g/l LH (5·10⁵ cfu/ml). After a 60 min ripening period, chymosin (Chymax plus, Chr. Hansens Ltd.), diluted 1:6 with deionized water, was added at a level of 18 ml per 100 kg milk. A coagulation time of 35 min was allowed prior to the cutting of the coagulum. After a 10 min healing period, the curd/whey mixture was cooked by steam injection into the jacket of the vat with constant stirring. Maximum scalds employed were 50 or 40°C

<figure><img src="image_66.png" /><figcaption>**FIGURE 1** | The Fluorescence decay and its measurement. (A) The fluorescence decay of an ensemble of identical molecules after a laser pulse. The Instrumental Response Function (IRF) combines the excitation pulse duration and the time resolution of the detector and an associated hardware. The intensity of a single fluorescent species that can only assume single ground and excited state follows an exponential decay with a characteristic lifetime τ. (B) In a practical TCSPC experiment there is typically much less</figcaption></figure>

than one photon detected for every laser pulse. The time of photon arrival *t* within the laser pulse period *T* is measured for every photon. In our case of 80 MHz laser pulse repetition rate the period *T* = 12.5 ns. (C) A histogram of photon arrival times that is built from the single photon events approaches the theoretical ensemble decay curve of (A). This histogram can be built in each pixel of a confocal image and the lifetime information at each pixel is then extracted by fitting an exponential model (Equation 1).
<table><caption>TABLE 1 | Manufacture parameters used during cheese making trials.</caption><thead><tr><th>Treatment</th><th>50°C, BS</th><th>50°C, DS</th><th>40°C, BS</th><th>40°C, DS</th></tr></thead><tbody><tr><td>Starter cultures</td><td>S. thermophilus<br>L. helveticus</td><td>S. thermophilus<br>L. helveticus</td><td>S. thermophilus<br>L. helveticus</td><td>S. thermophilus<br>L. helveticus</td></tr><tr><td>Max scald</td><td>50°C</td><td>50°C</td><td>40°C</td><td>40°C</td></tr><tr><td>Curd handling</td><td>Pre-press and mold</td><td>Cheddaring of curd</td><td>Pre-press and mold</td><td>Cheddaring of curd</td></tr><tr><td>Salting method</td><td>Brine</td><td>Dry salt at 1.45% (w/w)</td><td>Brine</td><td>Dry salt at 1.45% (w/w)</td></tr></tbody></table>

(Table 1). Curds were cooked at a rate of 0.5°C/min from 34 to 45°C and at 1°C/min from 45°C to maximum scald (50°C) where appropriate. Curds were pitched at pH 6.3.

Brine salted (BS) cheeses were pre-pressed under whey at 5.4 kPa for 10 min after which the curd was placed in 10 kg round molds and pressed under increasing pressure up to 35.2 kPa. At pH 5.3, the cheeses were de-molded and placed for 21 h in a saturated brine solution [23% (w/w) NaCl, 0.2% (w/w) Ca, pH 5.2, 10°C].

Dry salted (DS) cheeses were drained and the curd cheddared, milled at pH 5.3, salted at a rate of 1.45% (w/w) and pressed overnight on a horizontal press at 264.6 kPa in 20 kg blocks. All cheeses were vacuum packed post brine/pressing and ripened at 8°C for up to 230 days.

## Macroscopic pH Measurement

A standard procedure to measure the pH of cheese (BS 770-5:1976) was used. Briefly, a slurry was created from a cheese sample using distilled water at 20°C and the pH of the cheese suspension was recorded with a standard laboratory electrode-based pH meter. All samples were measured in duplicate and the mean values are provided.

## Fluorescence Microscopy
Sample Preparation

C-SNARF-4 calibration samples were prepared by mixing 10 µl of 1 mM C-SNARF-4 in DMSO stock solution with 90 µl of 100 mM sodium citrate buffer adjusted to pH 3.0-7.0. A 20 µl drop of the solution was placed on a glass coverslip and imaged with an inverted confocal microscope.

C-SNARF-4 stained cheese samples were prepared by cutting a small piece (about 5 × 5 × 0.5 mm) from the bulk of the cheese, the piece was then placed on a glass coverslip with a 20 µl drop of fluorophore solution (10 µl of 1 mM C-SNARF-4 in DMSO stock solution mixed with 90 µl of deionized water). Additionally, cheese samples without the fluorescent probe were prepared to assess the amount of cheese autofluorescence.

Oregon Green 488 (Molecular Probes, D-7172) stained calibration samples were prepared by immersing a small piece of cheese in a 40 µl drop of Oregon Green 488 buffered solution (20 µl of 50 µM dye in deionized water, mixed with 20 µl of 100 mM sodium citrate buffer adjusted to pH 3.0-6.0) on a glass coverslip and imaged with an inverted microscope.

For cheese pH measurements the same protocol was followed, but the citrate buffer was replaced by an equal amount of deionized water to maintain the same dye concentration as

in the calibration samples. To assess the cheese autofluorescence, cheese samples without the fluorescent probe were also prepared.

## Fluorescence Image Acquisition

Spectrally resolved fluorescence measurements and excitation-emission spectra measurements of C-SNARF-4 were performed with Leica TCS SP8 X confocal laser scanning microscope based on Leica DMI6000 inverted fluorescent microscope (Leica Microsystems CMS GmbH) equipped with White Light Laser (freely tunable excitation in the spectral range 470-670 nm), acousto-optical beam splitter and hybrid detectors with a LightGate option. The Acousto-Optical Beam Modulator (AOBM) was set in the mode of constant output power over the whole working interval that allows the constant intensity of the excitation laser beam for all excitation wavelengths impinging on the sample. The emission spectra were collected sequentially with internal spectral detector, with spectral bandwidth of 20 nm and step size 20 nm in the range 500-720 nm with the starting offset 10 nm from the excitation wavelength. We did not take into account cross-excitation and possible fluorescence emitted in the range less than 10 nm from the excitation wavelength. The image acquisition and analysis were performed in software LAS AF 3.1.2 (Leica Microsystems GmbH). Data were averaged from 200 × 200 µm area, approximately 30 µm deep in the calibration solution with a 20×/0.75 immersion objective lens.

## Fluorescence Lifetime Measurements

Lifetime measurements of C-SNARF-4 calibration samples and C-SNARF-4 stained cheese samples were performed with Olympus FV1200 confocal laser scanning microscope based on Olympus IX83 inverted fluorescence microscope (Olympus Europe GmbH) equipped with two single photon counting hybrid detectors, two channel time correlator and 485 nm pulsed picosecond laser (PicoQuant GmbH). The laser repetition rate was set to 40 MHz and the laser power was adjusted to keep the mean detected photon rate below 2% of the laser repetition rate. Two spectral ranges were measured simultaneously (channel 1 corresponding to 570-620 nm, channel 2 corresponding to 650-700 nm). Typically, calibration data were averaged from 100 × 100 µm area, approximately 30 µm deep in the calibration solution with a 60×/1.2 water immersion objective lens. Typically, 120 s acquisition was necessary to accumulate 10⁵ detection events. When measuring cheese samples the focus was set to the cheese surface, i.e., the plane of the strongest fluorescence. The measured fluorescence decay curves were fitted to a two- or three-component
exponential model (a sum of two or three exponential decays) according to

$$I(t) = \sum_{n=1}^{N} a_n e^{-t/\tau_n} + b \quad (1)$$

using the PicoQuant SymPhoTime64 TCSPC fitting software. Here $I(t)$ is the model fluorescence intensity, $N$ is the number of components of the model, $I_n$ and $\tau_n$ are the intensity and the lifetime of the $n$-th component of the model respectively ($n = 1, 2, 3$; lifetimes $\tau_n$ arranged in ascending order) and $b$ is the mean background. The fluorescence images were acquired at $512 \times 512$ pixels that were binned to $256 \times 256$ pixels for lifetime calculations.

Lifetime measurements of Oregon Green 488 stained cheese samples were performed with Zeiss LSM780 NLO confocal microscope based on Axio Observer. Z1 inverted microscope (Carl Zeiss MicroImaging GmbH) equipped with Chameleon Vision femtosecond pulsed IR laser (Coherent Inc.) tuned to 860 nm and Becker and Hickl hybrid detector (HPM-100-40) and TCSPC hardware and software (SPC-150, Becker & Hickl GmbH). The repetition rate of the pulsed laser was fixed to 80 MHz. Fluorescence was collected through an IR blocking filter and 580-640 nm emission filter with a Plan Apo $63 \times /1.4$ oil IR objective lens. Fluorescence lifetimes were calculated from $512 \times 512$ pixel images binned to $256 \times 256$ pixels. The measured fluorescence decay curves were analyzed in terms of two-exponential model according to Equation (1) and the mean lifetime was then calculated according to

<table><caption>TABLE 2 | Macroscopic pH values of cheese trials at day 223 of ripening.</caption><thead><tr><th>Sample</th><th>vat 1</th><th>vat 2</th><th>vat 3</th><th>vat 4</th></tr></thead><tbody><tr><td>Treatment</td><td>50°C, BS</td><td>50°C, DS</td><td>40°C, BS</td><td>40°C, DS</td></tr><tr><td>pH</td><td>5.20 ± 0.01</td><td>5.31 ± 0.01</td><td>4.99 ± 0.01</td><td>5.09 ± 0.01</td></tr></tbody></table>

$$\tau_m = \frac{\sum_{n=1}^{N} a_n \tau_n}{\sum_{n=1}^{N} a_n} \quad (2)$$

using the Beker & Hickl SPCImage software. Here $\tau_m$ is the mean lifetime, other symbols are defined in Equation (1).

## Results

### Macroscopic pH Of Natural Cheese Samples

The results obtained through the standard measurement with a pH electrode of homogenized slurry samples are summarized in Table 2. It is evident that the cheese pHs vary depending on the cheese manufacture treatments but all are within the range 4.99-5.31 and are typical of the pH of many natural cheese types. However, what this method does not measure is the degree and pattern of variability in pH within each cheese matrix. It is possible to get approximate measurement by direct measurement with a pH electrode of points within the cheese matrix but this is not sufficiently accurate to create a full pH map of the matrix and to relate the pH map to localized compositional, biochemical and microbial differences within the cheese matrix.

### C-SNARF-4 Emission Spectra

The pH dependence of the probe's emission spectrum is summarized in Figure 2 for two excitation wavelengths (480 nm—the wavelength used for subsequent lifetime measurements; and 530 nm—the wavelength of most efficient excitation) in the pH range 3.0-7.0. The pH dependence of the ratio of the intensities of the two emission peaks (centered at 590 and 660 nm, respectively) is shown in Figure 3.

### C-SNARF-4 Excitation-Emission Spectra

The excitation-emission scans (Figure 4) indicate almost no pH sensitivity of the dye in the pH range of 3.7-5.0 (the Figures 4A-F show the same spectral footprint regardless of pH). At pH 5 and above (Figures 4F-J) there is an apparent trend

<figure><img src="image_67.png" /><figcaption>FIGURE 2 | The pH-dependent emission spectra of C-SNARF-4 in water. Measured in the pH range 3.0-6.5 at two excitation wavelengths: (A) 488 nm, (B) 530 nm. Note that the spectra do not change significantly below pH 4.5.</figcaption></figure>
in the spectra that manifests the pH sensitivity of the probe. However, introducing time gating (discarding photons that arrive within 1 ns of the excitation pulse) modifies the spectral footprint of the (Figure 5) suggesting that lifetime imaging (FLIM) may enhance pH sensitivity at lower pH.

## C-SNARF-4 Fluorescence Lifetimes In Aqueous Solution

We performed calibration measurements of C-SNARF-4 dye in aqueous pH buffers in the range of pH 3.0–7.0 and measured the fluorescence lifetimes in two spectral bands: channel 1 corresponds to 570–620 nm, and channel 2 corresponds to

650–700 nm. These two spectral bands were chosen according to the two emission peaks of the dye used for ratiometric pH imaging.

While a two-exponential model fits sufficiently well the decay curves of channel 2 (far red fluorescence), three-exponential model is necessary for analyzing channel 1 (orange fluorescence). The results of the multi-exponential fitting procedure, i.e., pH dependence of the individual lifetime components are shown in Figure 6A (channel 1) and Figure 6B (channel 2). While the microscopic origin of the individual components is unknown, they can be used empirically to measure local pH, as long as they display sufficient sensitivity (the lifetime changes monotonically with pH and this change is bigger than the statistical error of the measurement). None of the channel 1 lifetime components displayed pronounced sensitivity toward pH. On the other hand, the long lifetime component of the channel 2 decay curves shows

<figure><img src="image_68.png" /><figcaption>**FIGURE 3** | The ratio of fluorescence intensities *I*<sub>590</sub>/*I*<sub>660</sub> of C-SNARF-4 ratiometric pH probe in water. Measured in the pH range of 3.0–7.0, excited at 480 nm (solid symbols) ad at 530 nm (open symbols). The widths of the two emission bands (centered at 590 and 660 nm, respectively) are 60 nm.</figcaption></figure>

<figure><img src="image_69.png" /><figcaption>**FIGURE 5** | Excitation-emission lambda scans of the C-SNARF-4 in water. Measured at pH 5.5 (A) without timegate and (B) with timegate 1 ns, i.e., all photons arriving within the first nanosecond after the excitation pulse are discarded. Vertical axis shows excitation wavelengths of White Light Laser (470–670 nm), horizontal axis displays emission spectra (480–730 nm).</figcaption></figure>

<figure><img src="image_70.png" /><figcaption>**FIGURE 4** | Excitation-emission lambda scans of the C-SNARF-4 in water buffered to different pH. Vertical axis shows excitation wavelengths of White Light Laser (470–670 nm), whereas horizontal axis displays emission spectra (480–730 nm) of C-SNARF-4 in different pH: (A) 3.7, (B) 4.0, (C) 4.3,</figcaption></figure>

(D) 4.5, (E) 4.8, (F) 5.0, (G) 5.5, (H) 5.8, (I) 6.1, and (J) 6.5. Increase of pH value results in moving maximum emission from the blue part of spectra toward the red part of spectra with forming two peaks horizontally for pH 5.5 and quasi-symmetrical emission for pH 6.1 and 6.5.
<figure><img src="image_71.png" /><figcaption>**FIGURE 6** | The effect of pH on the fluorescence lifetime of C-SNARF-4 in water. (A) Channel 1 (570–620 nm emission), three-component model, none of the lifetime components shows</figcaption></figure>

<figure><img src="image_72.png" /><figcaption>pronounced dependence. (B) Channel 2 (650–700 nm emission), two-component model, the longer lifetime component τ₂ shows pronounced pH dependence.</figcaption></figure>

<table><caption>**TABLE 3** | The fluorescence lifetimes of cheese matrix stained with C-SNARF-4 fluorescent dye, channel 2, three-component exponential model.</caption><thead><tr><th></th><th>τ<sub>1</sub> [ns]</th><th>τ<sub>2</sub> [ns]</th><th>τ<sub>3</sub> [ns]</th></tr></thead><tbody><tr><td>vat 1</td><td>0.62 ± 0.05</td><td>1.65 ± 0.09</td><td>3.71 ± 0.18</td></tr><tr><td>vat 2</td><td>0.61 ± 0.02</td><td>1.63 ± 0.03</td><td>3.69 ± 0.16</td></tr><tr><td>vat 3</td><td>0.56 ± 0.01</td><td>1.59 ± 0.02</td><td>3.65 ± 0.08</td></tr><tr><td>vat 4</td><td>0.59 ± 0.03</td><td>1.60 ± 0.04</td><td>3.62 ± 0.09</td></tr></tbody></table>

Average lifetime ± standard deviation calculated from 6 to 8 measurements.

pronounced pH dependence in the whole measured pH range (the upper curve in **Figure 6B**). Mean fluorescence lifetime τₘ calculated according to Equation (2) does not show pronounced pH dependence, thus two-exponential fitting is necessary to extract the pH information.

## Microscopic pH Of Cheese Samples Measured With C-SNARF-4

Based on calibration data we tried to utilize the pH dependence of C-SNARF-4 fluorescence in the red spectral channel for cheese pH measurements on the microscopic scale. However, both the fluorescence intensity and the fluorescence lifetime distribution were essentially homogeneous across the sample. We have also observed a strong affinity of the dye toward the cheese surface.

We measured a total of 4 cheeses (designated vat 1 to vat 4), each multiple times. Surprisingly, a new decay component with relatively long lifetime (τ₃ ≥ 3.5 ns) appeared (Table 3). Moreover, the τ₂ component, which is assumed to correspond to the pH dependent component of the calibration data, has significantly shorter lifetime than the value deduced from calibration (expected value is about 2.2 ns for pH 4.5). Also the fastest component (τ₁ ≈ 0.7 ns) is significantly shorter, than the corresponding lifetime from calibration (about 0.9 ns). Thus, in

our scenario the C-SNARF-4 probe did not prove itself as a useful probe of the local pH.

Control experiments performed on cheese samples without fluorescent probe revealed, that the cheese autofluorescence is almost two orders of magnitude weaker than C-SNARF-4 fluorescence, and its lifetime is significantly longer (over 6 ns) than that of the fluorescent probe, so that the autofluorescence does not interfere with our measurements.

## Oregon Green 488 Fluorescence Lifetimes And Microscopic pH Measurements

Oregon Green 488 displays single emission peak whose position is independent of local environment. Thus fluorescence lifetime (FLIM) must be employed in order to measure the local pH variation. The mean fluorescence lifetime of the Oregon Green 488 excited by 860 nm pulsed laser at 80 MHz and detected in the red emission band was determined to be 3.9 ± 0.1 ns in aqueous buffer at pH 7. When the dye is applied to the cheese matrix, the lifetime shortens significantly, but the pH sensitivity of the dye is preserved. **Figure 7A** shows representative FLIM images of cheese matrix buffered to pH 3.0–6.0 and stained with Oregon Green 488. The pH sensitivity of the dye is immediately apparent from the change of the pseudocolor encoding local fluorescence lifetime. The mean lifetime τₘ calculated as a mean value from several fields of view is plotted against the buffer pH in **Figure 7B** (solid symbols and a linear least-squares fit). The mean lifetime of the dye changes by more than 1 ns in the studied pH range.

To measure the local pH of cheese the dye is applied to the cheese matrix without a pH buffer and the same procedure is followed. From the acquired FLIM image and the linear fit of the calibration series the average pH of vat 2 cheese sample after 223 days of ripening was determined to 5.2 ± 0.2 (Figure 7B, open symbol). The local variations of the fluorescence lifetime (and thus the pH) in a natural cheese sample are shown in greater detail in **Figure 8**. We note that application of the dye to the non-buffered cheese produced no measurable macroscopic
<figure><img src="image_73.png" /><figcaption>**FIGURE 7** | Lifetime measurements of cheese stained with Oregon Green 488 buffered to pH 3.0–6.0. (A) FLIM images, the pseudocolor represents the mean lifetime τ<sub>m</sub>, intensity represents photon count, captions denote the pH of the buffer solution (–.- denotes unbuffered sample). Scalebar 50 µm. (B) The pH dependence</figcaption></figure>

of mean lifetime τ<sub>m</sub> calculated from the FLIM images (solid symbols), and the mean lifetime of stained cheese without pH buffer (open triangle). Error bars represent the standard deviation, mainly caused by sample inhomogeneity. The inset shows the pseudocolor scale of the FLIM images calibrated both in lifetime and pH values.

<figure><img src="image_74.png" /><figcaption>**FIGURE 8** | FLIM image of natural cheese sample stained with Oregon Green 488. Shown are apparent local variations of fluorescence lifetime and thus the pH. Localized spots with pH as low as 4.0 are observed. The dark areas most likely represent fat within the cheese matrix. The pseudocolor scale of the FLIM images is calibrated both in lifetime and pH values.</figcaption></figure>

pH change, as measured by a macroscopic pH electrode method.

Discussion

We have confirmed that the C-SNARF-4 fluorescent pH probe may be used as a reliable ratiometric pH indicator at pH 5.5 and above (Figure 3). Essentially, the emission spectra do not depend on the excitation wavelength in the range 480–530 nm, as illustrated in Figure 2. However, the range of interest in cheese research is usually at lower pH values, generally around 4.8–6.0 (with some exceptions). In this range the traditional ratiometric approach does not provide sufficient sensitivity.

Excitation-emissions scans (Figure 4) show that the results of ratiometric pH measurements are largely insensitive to the excitation wavelength, but utilizing the lifetime information may extend the useful range of the dye toward lower pH's (Figure 5).

We have observed and measured the pH dependence of fluo-
rescence lifetime of C-SNARF-4 dye, which extends through the
pH range that can be sensed toward more acidic values (down
to pH ≈ 3), part of this range which may be encountered in
cheese research. From the calibration curve τ₂ in **Figure 6B**) one
can clearly see the relationship between pH and fluorescence
lifetime. But the calibrated lifetime–pH dependence performed
in aqueous solutions is not directly applicable to cheese matrix
results, as a new long-lifetime component appears τ₃ in **Table 3**)
and the lifetimes of the other two components are significantly
shortened in such a protein-rich environment. More specifically,
the τ₂ component measured on cheese samples (about 1.7 ns,
**Table 3**) would correspond to pH below 4.0 (according to the cal-
ibration curve τ₂ in **Figure 6B**), which is much lower than the
reference macroscopic value (about pH 5.0, **Table 2**). A similar
interaction of SNARF-based dye with proteins was described pre-
viously (Srivastava and Krishnamoorthy, 1997). We also note that
we observed strong affinity of the fluorescent probe toward the
protein-rich cheese surface, which changes the dye's photophys-
ical properties significantly and alter its pH response. Both the
spectral and lifetime signatures of the stained protein matrix do
not correspond to the calibration values at any pH.

Moreover, we observed that both the fluorescence intensity
and lifetimes are essentially homogeneous across the sample
surface (as shown by the small standard deviations in **Table 3**).
The expected pH variation between samples is less than 0.5 and
cannot be reliably detected by the C-SNARF-4 dye.

Similarly, we also applied the same methodology to other
pH sensitive fluorescent dyes, namely BCECF-AM, FITC, and
Acridine Orange, but again we observed little to no lifetime-pH
sensitivity when applied to cheese matrix (data not shown).

On the other hand, our results suggest that applying the FLIM
microscopic technique to the Oregon Green 488 fluorescent dye
resulted in an accurate method of determination of pH in the
cheese samples (the main results are summarized in **Figure 7**).
The average value of pH 5.2 ± 0.2 is in good agreement with the
macroscopic pH 5.3 (Table 2). In addition micro-heterogeneity in pH is apparent in the cheese matrix as shown in Figure 8 with pH ranging between 4.0 and 5.5 depending on location. This is particularly interesting as it shows the pH of cheese matrix is not homogeneous but contains localized variation of pH. This may be due to localized differences in the aqueous phase or concentrations of constituents of the aqueous phase including lactose, lactate, minerals or salt. It may also be influenced by variations in buffering capacity of the surrounding cheese matrix. It also poses questions regarding the influence of microbial colonies on pH micro heterogeneity. Other authors (Lopez et al., 2006, 2007; Pereira et al., 2009) reported bacterial colonies to be located at the protein fat interface, with the bacterial cells always being in contact with the fat globule membrane. Although the study of (Jeanson et al., 2013) showed no differences in pH around microbial colonies, that study differed in that it was conducted on an unripened non-fat UF model cheese system rather than on a

ripened natural cheese matrix, and the microbial colonies were lactococci rather than thermophilic species.

The current study has focused on applying the developed method to determining the micro heterogeneity within one of the cheese types (vat 2). It is envisaged that future work would focus on using the developed method to determine whether manufacture processes influence pH at local level within different cheese matrices and whether different cheese types may have different patterns of micro heterogeneity. We also expect that this methodology can be employed to examine also wide range of different types of food products with sufficient moisture content.

## Acknowledgments

We acknowledge funding from the Dairy Levy Trust and the Czech Science Foundation (grant no. 14-15272P).

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2015 Burdikova, Svindrych, Pala, Hickey, Wilkinson, Panek, Auty, Periasamy and Sheehan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
# Diffusion of solutes inside bacterial colonies immobilized in model cheese depends on their physicochemical properties: a time-lapse microscopy study

Juliane Floury¹,²*, Ilham El Mourdi¹,², Juliana V. C. Silva¹,², Sylvie Lortal¹,², Anne Thierry¹,² and Sophie Jeanson¹,²

¹INRA, UMR1253 Science and Technology of Milk and Eggs, Rennes, France, ²Agrocampus Ouest, UMR1253 Science and Technology of Milk and Eggs, Rennes, France

OPEN ACCESS

**Edited by:**
Jean-christophe Augustin,
Ecole Nationale
Vétérinaire d'Alfort, France

**Reviewed by:**
Antonio Valero,
University of Cordoba, Spain
Louis Coroller,
Université de Brest, France

**\*Correspondence:**
Juliane Floury,
UMR Science and Technology of Milk
and Eggs, INRA/Agrocampus Ouest,
65 rue de Saint Brieuc,
35042 Rennes Cx, France
juliane.floury@agrocampus-ouest.fr

**Specialty section:**
This article was submitted to
*Food Microbiology,*
a section of the journal
*Frontiers in Microbiology*

**Received:** 05 February 2015

**Paper pending published:**
18 March 2015

**Accepted:** 10 April 2015

**Published:** 30 April 2015

**Citation:**
Floury J, El Mourdi I, Silva JVC, Lortal S, Thierry A and Jeanson S (2015) *Diffusion of solutes inside bacterial colonies immobilized in model cheese depends on their physicochemical properties: a time-lapse microscopy study*. Front. Microbiol. 6:366.
doi: 10.3389/fmicb.2015.00366

During cheese processing and ripening, bacteria develop as colonies. Substrates and metabolites must then diffuse either from or into the colonies. Exploring how the inner cells of the colony access the substrates or get rid of the products leads to study the diffusion of solutes inside bacterial colonies immobilized in cheese. Diffusion limitations of substrates within the bacterial colony could lead to starvation for the cells in the center of the colony. This study aimed at better understands ripening at the colony level, by investigating how diffusion phenomena inside colonies vary depending on both the physicochemical properties of the solutes and *Lactococcus lactis* strain. Dextrans (4, 70, and 155 kDa) and milk proteins (BSA, lactoferrin and αS₁-casein) of different sizes and physicochemical properties were chosen as model of diffusing solutes, and two *L. lactis* strains presenting different surface properties were immobilized as colonies in a model cheese. Diffusion of solutes inside and around colonies was experimentally followed by time-lapse confocal microscopy. Dextran solutes diffused inside both lactococci colonies with a non-significantly different effective diffusion coefficient, which depended mainly on size of the solute. However, whereas flexible and neutral hydrophilic polymers such as dextran can diffuse inside colonies whatever its size, none of the three proteins investigated in this study could penetrate inside lactococci colonies. Therefore, the diffusion behavior of macromolecules through bacterial colonies immobilized in a model cheese did not only depends on the size of the diffusing solutes, but also and mainly on their physicochemical properties. Milk caseins are probably first hydrolyzed by the cell wall proteases of *L. lactis* and/or other proteases present in the cheese, and then the generated peptides diffuse inside colonies to be further metabolized into smaller peptides and amino acids by all the cells located inside the colonies.

**Keywords:** bacterial colony, *Lactococcus lactis*, cheese, confocal microscopy, diffusion, milk protein
Introduction

During cheese making, regardless of the cheese type, bacteria are
immobilized in the curd during the coagulation step, and then
grow as colonies spread within the cheese curd. Jeanson et al.
(2011) showed that the distribution of Lactococcus lactis colonies
was random in a non-fat model cheese. Lactococci are the most
used starters in the cheese industry. They are a major actor of
ripening which gives the cheese its final sensorial properties.
During ripening, they are responsible for the proteolysis, the milk
protein breakdown, leading to peptides and amino acids. It is
then obvious that the access to nitrogen sources, i.e., proteins
and derivates, is of major importance for the proteolysis activity
and the bacterial metabolism of the cells within the colony.
However, the way the bacteria interact with dairy components
is still poorly understood (Burgain et al., 2014). It is highly
probable that, on one hand, milk proteins have to diffuse from
the cheese matrix (a fat-protein network) into the colony to reach
the bacterial cells in the center of the colony. Indeed, nutrients
have to reach the center cells of the colony; otherwise these center
cells may be starved. On the other hand, proteolysis end-products
(small peptides and amino acids) have to diffuse from the
bacterial colonies into the cheese matrix. If diffusion limitations
occur inside the bacterial colony, gradients of concentration
of both nitrogen sources (low concentrations in the center of
the colony) and nitrogen end-products (high concentrations in
the center of the colony) may be generated and may affect the
metabolic activity of microbial cells, and thus the kinetics of
the ripening process. The mean diameters of colonies and the
mean distance between them in a model cheese were shown to be
strongly influenced by the initial inoculation level. The lower the
inoculation level was, the larger the colonies were, and then the
further away they were from each other (Jeanson et al., 2011). It
has previously been observed that low concentration of substrates
could generate different physiological states or different growth
rates in pathogenic bacterial colonies when colonies were bigger
than 400 µm diameter (McKay et al., 1997; Kreft et al., 1998).
The main hypothesis for these observations was that the diffusion
limitations of substrates within the bacterial colony lead to
starvation for the cells in the center of the colony. As a
consequence, lysis could be higher for the center cells in Vibrio
cholera colonies (Wimpenny, 1992). If lysis occurs at the center of
the colony, it is also very important to know if bacterial enzymes
could diffuse out of the colony to determine how far from the
colony proteolytic enzymes could diffuse outside the colony, in
the cheese matrix.

However, Floury et al. (2010) reported a strong lack of data
about the diffusion properties of key molecules like sugars,
organic acids, proteins, and peptides in cheese. The first effective
diffusion coefficient was determined for nisin in model cheeses
(Aly et al., 2011). Using fluorescently labeled solutes, Silva et al.
(2013) showed that dextran macromolecules up to 2 MDa,
and different dairy proteins, were able to diffuse through the
model cheese. Quite interestingly, the proteins tested (rigid and
negatively charged molecules) were hindered to a greater degree
than the dextrans (flexible and neutral molecules) in the model
cheese, due to specific interactions between the protein matrix

and the diffusing proteins. So far, only our previous study
(Floury et al., 2013) has investigated the diffusion of molecules
within bacterial colonies. Even if it has been demonstrated that
viral particles such as bacteriophages (≈100 nm head) could
diffuse inside biofilms (Lacroix-Gueu et al., 2005; Briandet
et al., 2008), we consider that the structure of biofilms (with
exopolysaccharide matrix) and colonies is not really comparable.
It was then very important to understand ripening at the
colony level by investigating the diffusion of model nutrient
macromolecules, such as polysaccharides and milk proteins,
inside lactococci colonies.

In our previous study (Floury et al., 2013), we developed
a specific experimental design and we demonstrated for
the first time that model solutes of different sizes (dextran
macromolecules from 4.4 to 155 kDa) were able to diffuse inside
bacterial colonies of L. lactis, immobilized in two different models
of solid food matrices (model cheese and agar). The principle of
this static design was to deposit a solution of the fluorescently-
labeled diffusing solute on the upper side of a gel cassette
(Brocklehurst, 1995) filled with the solid medium, previously
inoculated with L. lactis. The gel cassettes were then directly
observed by confocal laser microscopy and the corresponding
relative fluorescence intensity profiles within the colony vs. in the
surrounding media were quantified after 3 h of diffusion of the
fluorescent solutes at 19°C. It was concluded that colonies of L.
lactis LD61 immobilized in the model cheese were porous to all
dextrans from 4 to 155 kDa after this delay of migration, but no
kinetic aspect of diffusion could be assessed.

The objective of the present work was to determine
how diffusion phenomena inside colonies vary depending
on both the properties of solutes and L. lactis strain.
We quantified the diffusion rates of solutes of different
sizes and physico-chemical properties both around and
inside colonies immobilized in a model cheese, for two
Lactococcus lactis strains presenting different surface properties.
Our experimental device was improved by adapting the
time-lapse microscopy method described in Rani et al.
(2005), originally developed to determine the effective
diffusion coefficients of fluorescent tracers into biofilm cell
clusters of Staphylococcus epidermis and their surrounding
solution.

**Materials and Methods**

Bacterial Strains and Growth Conditions

Lactococcus lactis subsp. lactis biovar diacetylactis LD61 was used (collection of the Centre International de Resources Microbiennes-Bactéries d'Intérêt Alimentaire (CIRM-BIA), INRA, Rennes, France) and was routinely grown under static conditions in M17 lactose broth (Difco, Becton Dickinson, Le Pont de Claix, France) at 30°C.

Lactococcus lactis subsp. lactis TIL1230 was kindly given
M-P Chapot-Chartier and obtained from the parental strain
NCDO2110 (Giaouris et al., 2009). This strain was lactose and
protease negative and was then grown under static conditions in
M17 lactose broth supplemented with 0.5% glucose (Sigma) at
30°C. Therefore, to ensure its optimal growth in milk, the milk
cultures of TIL1230 were supplemented with 1% glucose (Sigma) and 0.3% peptone casein (BD).

## Bacterial Surface Characterization
### Cell Surface Hydrophobicity

Net surface charge of the bacteria and the presence of lipophilic compounds affect partitioning between two immiscible liquids (Burgain et al., 2014). The microbial adhesion to solvents (MATS) method was employed for the evaluation of the hydrophobic/hydrophilic character of the cell surface of *L. lactis* strains and for their Lewis acid-base characteristics. On this basis, we selected chloroform, a monopolar and acidic solvent (electron acceptor) and hexadecane, an apolar alkane using the protocol fully described in Giaouris et al. (2009). The values of MATS obtained with the chloroform were regarded as a measure of electron donor/basic characteristics of bacteria. Adhesion ability of the bacteria to the solvent is expressed as a percentage (%) according to the following relation:

$$ \% \text{ adhesion} = \frac{OD_{400}^{\text{initial aqueous phase}} - OD_{400}^{\text{aqueous phase after mixing}}}{OD_{400}^{\text{initial aqueous phase}}} \quad (1) $$

With OD the optical density of the bacterial suspension measured at 400 nm. Each measurement was performed in triplicate and the experiment was repeated twice with independent bacterial cultures.

### Cell Surface Charge

The electrophoretic mobility (EM) was measured to determine the cell surface net charge of the two bacteria according to the protocol described in Boonaert and Rouxhet (2000). EM of the bacteria with the appropriate pH values were measured at room temperature on a Zetameter model (Zeta Sizer Nano Series, Malvern Instruments Ltd, Malvern, UK). Experiments were made twice with independent culture with triplicate measurements. EM was expressed in $10^{-8}$ m²/V.s.

## Preparation of the Model Cheese in Imaging Chambers

A fat-free cheese made from renneted concentrated skim milk was used as model cheese, as previously described in Floury et al. (2013). This non-fat model cheese has the great advantage over traditional cheese technology to be a repeatable and homogeneous cheese matrix. Moulded after renneting, it is coagulated without further syneresis of the gel, thus exhibiting highly reproducible micro- and macro- structural properties. The concentrated milk was inoculated for a final concentration of $10^5$ CFU/ml, and coagulant agent (Maxiren 180; DSM Food Specialities, Seclin, France) was added at a final concentration of 300 µl/l. After homogenization, 400 µl of the mixture was slowly poured into several CoverWell imaging chambers (Sigma-Aldrich, Saint-Quentin Falavier, France) that allow direct observation under the confocal microscope (Floury et al., 2012). The imaging chambers with the model cheese were then vertically incubated at 30°C for 15 h for coagulation and growth of the *L. lactis* LD61. For the *L. lactis* TIL1230, imaging chambers were also vertically incubated, for 8 h at 30°C and then for 15 h at 19°C.

In parallel, the same media were also inoculated in 30 ml-bottles to measure the pH during acidification by *L. lactis*. The pH of the model cheeses were $5.05 \pm 0.06$, and $5.32 \pm 0.10$ for LD61 and TIL1230, respectively, after 15 h of incubation.

## Fluorescent Dyes and Labeled Solutes

SYTO9™ was added before coagulation of the model cheese to a final concentration of 1.2 µmol/l, in order to dye the bacterial cells and to visualize colonies within the opaque matrix of cheese. SYTO9™ penetrates all bacterial membranes and dies all the bacterial cells, alive, and damaged (Boulos et al., 1999).

Three Rhodamine B isothiocyanate (RITC) conjugated dextrans of 10, 70, and 155 kDa were chosen as model of flexible and neutral polymers of anhydroglucose of different sizes (Table 1), labeled with an extent of labeling from 0.002 to 0.015 mol RITC per mol glucose (Sigma-Aldrich, Saint-Quentin Falavier, France). RITC-dextrans were dissolved to 50 mg/ml in distilled water.

The studied set of solutes was completed with three milk proteins, one random coil milk protein, the $\alpha_{S1}$-casein (INRA, Rennes, France), and two globular dairy proteins, bovine serum albumin (BSA, Sigma-Aldrich, Saint-Quentin Falavier, France) and lactoferrin (LF, Fonterra Boulogne-Billancourt, France). The proteins were labeled with free RITC (Sigma) using the protocol described in Silva et al. (2013). The three solutions were lyophilized and labeling efficiencies were determined by mass spectroscopy. BSA, LF, and $\alpha_{S1}$-casein were mainly mono-labeled with RITC. Finally, the RITC-labeled solutes were either dissolved to 50 mg/ml in water for the dextrans and the lactoferrin, in a permeate solution obtained from the ultrafiltration of skimmed milk for $\alpha_{S1}$-casein, and in a 0.1 M BisTris buffer at pH 6.8 for the BSA.

The solutions of labeled solutes were stored at -20°C, protected from light before and during fluorescence measurements.

Physicochemical properties of the solutes are summarized in Table 1.

<table><caption>TABLE 1 | Physicochemical properties of the fluorescently-labeled solutes.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Dextran</th><th rowspan="2">BSA</th><th rowspan="2">Lactoferrin</th><th rowspan="2">α<sub>S1</sub>-casein</th></tr><tr><th>10</th><th>70</th><th>155</th></tr></thead><tbody><tr><td>Molecular weight (kDa)</td><td>10</td><td>70</td><td>155</td><td>66.4<sup>a</sup></td><td>77<sup>b</sup></td><td>23.6<sup>c</sup></td></tr><tr><td>Isoelectrical point</td><td>-</td><td>-</td><td>-</td><td>≈ 5<sup>a</sup></td><td>8-9<sup>b</sup></td><td>4.94<sup>d</sup></td></tr><tr><td>Hydrodynamic radius (nm)</td><td>2.3<sup>e</sup></td><td>6<sup>e</sup></td><td>8.5<sup>e</sup></td><td>3.65</td><td>2.2<sup>f</sup></td><td>2.9<sup>c</sup></td></tr><tr><td>Flexibility</td><td colspan="3">flexible</td><td colspan="3">rigid</td></tr><tr><td>Hydrophobicity</td><td colspan="3">hydrophilic</td><td colspan="3">hydrophobic</td></tr><tr><td></td><td colspan="3"></td><td colspan="3">amphiphile</td></tr></tbody></table>

<footnote><sup>a</sup>Böhme and Scheler (2007); <sup>b</sup>Bokkhim et al. (2013); <sup>c</sup>Marchin et al. (2007); <sup>d</sup>-values reported by expasy.org; <sup>e</sup>-values reported by Sigma-Aldrich; <sup>f</sup>supplier data online; <sup>g</sup>Chaufer et al. (2000).</footnote>
Experimental Device for Solute Diffusion
Experimental Set-up

After the incubation time necessary for bacterial growth, a concentration gradient of the fluorescently-labeled solutes between the surface of the imaging chamber and the model cheese was triggered in order to induce the diffusion phenomenon. Five microliter of the fluorescently-labeled dextran or protein solution was dropped off at the surface of the coagulated model cheese and left to diffuse for 5 min throughout the surrounding medium, in the dark and in an air-conditioned room at 19°C. Solute diffusion into the model cheeses began as soon as the fluorescent solution was left in contact with the surface of the cheese (t = 0). The fluorescent solutes diffuse into the gel by a plane one-dimensional diffusion mechanism (Figure 1).

Time-Lapse Confocal Laser Scanning Microscopy

Model cheese samples were imaged on an inverted NIKON Eclipse-TE2000-C1si microscope allowing confocal laser scanning microscopy (NIKON-France, Champigny sur Marne, France), with an oil-immersion 40 × objective at 512 × 512 pixel resolution. Ten minutes after the deposit of the fluorescent solution containing the diffusing solutes, the first step was to localize a fluorescently (SYTO9™) labeled colony, that had grown both in a focal plane at 10–15 µm depth from the coverslip and at a quite close distance from the surface of the gel, in order to visualize the diffusion front of fluorescence of the RITC-labeled solutes in a reasonable time-scale. SYTO9™ fluorescence was excited with the 488 nm laser and detected between 500 and 530 nm. The second step was to obtain the kinetic of the diffusion process of the RITC labeled solutes thanks to the acquisition of images of the fluorescent front of diffusion around and into the target bacterial colony every 5–15 min for at least 2 h. Fluorescently RITC-labeled dextrans and proteins were excited at 543 nm wavelength, and fluorescence emission was detected between 565 and 615 nm. All experiments were performed at 19°C using a temperature-regulated platform and air-conditioned room and were performed at least in triplicate.

Estimation of Diffusion Coefficients

Images obtained after a time series of acquisition were analyzed using ImageJ software. A line measuring 512 pixels long and 10 pixels wide was drawn through the axial diameter of the colony in

order to quantify the profile of RITC fluorescence intensity (also
named gray-level value) along the direction of the diffusion front
and at each acquisition time. The resulting fluorescence intensity
profiles vs. time were then exported into a single spreadsheet.

According to Crank (1975), in the case of a plane one-dimensional diffusion, induced by an instantaneous source and after a short time *t* of diffusion, the concentration gradient of the investigated solute is given by the following relationship:

$$
C(x, t) = \frac{M}{8\sqrt{\pi Dt}} \exp\left(-\frac{x^2}{4Dt}\right) \quad (2)
$$

where C is the concentration expressed as the amount of diffusing solute per unit area of surface, x is the perpendicular axis to the surface of the model cheese in µm (x = 0 corresponded to the surface), t is the time, D is the diffusion coefficient and M is the surface concentration of the diffusing solute, which corresponds to the total amount of the investigated fluorescently labeled solute in the gel related to the gel surface unit. This concentration M was considered as constant since it was in great excess compared to its concentration inside the cheese and as there was no reaction between the investigated component and the model cheese.

Equation (2) can be linearized:

$$
\ln(C(x)) = \ln\left(\frac{M}{8\sqrt{\pi Dt}}\right) - \frac{x^2}{4Dt} \quad (3)
$$

The diffusion coefficient *D* was estimated from the slope of
the straight line *Ln* (*C*(*x*)) vs. *x*², equal to -1/(4*D*t). The slopes
of the lines inside and outside the colonies were obtained by
performing a linear regression with the best-fit linear trend
function in Microsoft Excel using the least-squares method.
Two effective diffusion coefficients *D*out and *D*in were quantified
from the concentration profiles around the bacterial colony
(*D*out) and inside the bacterial colony (*D*in). Both *D*out and *D*in
were determined from the concentration profiles (gray values
or fluorescence intensity profiles) obtained at a short time of
diffusion, i.e., 30 min after the deposit, of the fluorescent solution
at the surface of the model cheese.

**Statistical Analysis**

One-Way analysis of variance (ANOVA) and Tukey's paired
comparison test were applied to the diffusion coefficient data in
order to determine which mean values were significantly different
from one another at the 95% confidence level using the R software
package (version R i386 3.0.2).

**Results and Discussion**

Surface of Both Lactoccoci Strains Are
Hydrophilic, but TIL1230 is More Electronegative
than LD61 at the pH of the Model Cheese

The first step of the strategy of this study was to determine the
hydrophobic/hydrophilic character, Lewis acid-base interactions,
and electrostatic cell surface properties. The MATS method and
EM measurements gave us information on the potential ability of

<figure><img src="image_75.png" /></figure>
<table><caption>TABLE 2 | Results of the microbial adhesion to solvents (MATS) method*.</caption><thead><tr><th rowspan="2">L. lactis strain</th><th colspan="2">% of adhesion to</th></tr><tr><th>Hexadecane</th><th>Chloroform</th></tr></thead><tbody><tr><td>LD61</td><td>8.6 ± 5.2 (n = 4)</td><td>11.3 ± 9.8 (n = 3)</td></tr><tr><td>TIL1230</td><td>12.8 ± 5.3 (n = 6)</td><td>13.7 ± 2.5 (n = 5)</td></tr></tbody></table>

the two *L. lactis* strains to generate physicochemical interactions between both the cheese matrix and the diffusing solutes.

Results of the MATS method are reported in **Table 2**.

The partitioning of cells between aqueous and hexadecane is a direct measurement of the cell surface hydrophobicity or hydrophilicity. The surface property of a cell can be considered as hydrophilic if its affinity for apolar hexadecane is below 40% (Giaouris et al., 2009). As shown in **Table 2**, the percentage of adherent cells to hexadecane was slightly higher (not significantly) for TIL1230 than for LD61, with values largely inferior to 20% for both *L. lactis* strains, demonstrating a clear hydrophilic character of their surface. The hydrophilic character of bacteria is largely due to the nature of the compounds present on the surface, useful for adhesion (Burgain et al., 2014).

The percentages of bacterial adhesion to the chloroform, an acidic solvent and electron acceptor, were not significantly different between the two *L. lactis* strains, with values also inferior to 20% (Table 2). These results are in agreement with values of adhesion to chloroform obtained on various *L. lactis* strains by Ly et al. (2006) and Giaouris et al. (2009).

The electrophoretic mobilities (EM) of the two *L. lactis* strains at different pH values indicated that the isoelectric points were around pH 2.5 and 4.5 for TIL1230 and LD61 strains, respectively (Figure 2). Between pH 2 and 6, the EM of *L. lactis* TIL1230 drastically decreased by about $4 \times 10^{-8}$, whereas it decreased only $0.4 \times 10^{-8} \text{ m}^2/\text{V.s}$ for LD61. *L. lactis* TIL1230 has a greater EM above pH 3 than LD61. Interestingly, LD61 strain presented EM very close to zero at all pH values tested, revealing very low electronegative cell surface in those conditions. In contrast, *L. lactis* TIL1230 was found to be highly negatively charged at pH between 4 and 6, as previously observed for most of *L. lactis* strains, with same order of magnitude for EM values, ranging from -2 to $-5 \times 10^{-8} \text{ m}^2/\text{V.s}$ (Ly et al., 2006; Habimana et al., 2007; Giaouris et al., 2009). Giaouris et al. (2009) reported for the first time that some lactic acid bacteria possess a very low surface electronegativity around neutral pHs, as observed here for *L. lactis* LD61. This diversity in the global charge of lactococcal cell surface may be linked to the variability of the molecules containing ionized groups in the cell envelope. Three types of ionized groups are considered to determine the surface electrical properties of *L. lactis*: phosphate groups present in teichoic and lipoteichoic acids, and carboxylate and protonated amino groups of proteins (Boonaert and Rouxhet, 2000). It has been previously shown that the expression of the major cell wall-anchored protease was responsible for altering *L. lactis* surface physicochemical properties, shifting the cell envelope from a

hydrophilic surface to an extremely hydrophobic one, going along with an increase of negative charges at the cell surface (Habimana et al., 2007). In the present study, the two strains of *L. lactis* LD61 and TIL1230 were thus supposed to present greater differences in their cell surface properties as TIL1230 does not possess the cell wall protease and LD61 does. Obviously, the global property of the cell surface is multi-causal and then difficult to predict.

Bacterial cell wall properties were shown to affect diffusion particles of nanoparticles inside biofilm matrices of *L. lactis* (Habimana et al., 2011). They measured that the diffusion of 50-nm radius particles of anionic carboxylate-modified fluorescent polystyrene beads was more hindered in biofilm matrix of *L. lactis* which possessed the anchored protease. Based on these results, the present study aimed at comparing the diffusion of solutes of different charge, flexibility, and hydrophobicity, inside immobilized colonies (in cheese) of two different *L. lactis* strains presenting (LD61) or not (TIL1230) the anchored protease in their cell walls.

## Diffusion Coefficients of Dextrans Inside the Colonies Depend on the Solute Size but Not on the Lactococci Strains

Figure 3A shows typical confocal microscopic observations of *L. lactis* colonies, immobilized in the model cheese, and visualized at different times after the deposit of the 70 kDa fluorescently-labeled dextran solution. The images obtained with *L. lactis* TIL1230 and the two other fluorescently-labeled dextrans (10 and 155 kDa) were similar and are thus not shown here. Both lactococci strains grew in this model cheese as perfect spheres with diameters around 30–40 µm, as previously reported in Jeanson et al. (2011) and Floury et al. (2013). Figure 3A also clearly shows the progressive increase of the red

<figure><img src="image_76.png" /><figcaption>FIGURE 2 | Variation of electrophoretic mobility as a function of pH for *Lactococcus lactis* LD61 (circles) and TIL1230 (squares). Cells were harvested in the stationary growth phase. Measurements were made in 1 mM KNO$_3$ solution on two independent sets of data; the bars represent the standard deviations of mean values.</figcaption></figure>

<footnote>*The results are expressed as the mean ± one standard deviation of n independent measurements.</footnote>
<figure><img src="image_77.png" /></figure>

**FIGURE 3 | (A)** Example of a time-lapse microscopic observations of a *Lactococcus lactis* LD61 colony immobilized in a model cheese after 15 h growth at a temperature of 30°C during diffusion of a RITC-labeled dextran (here RITC-Dextran 70kDa). *L. lactis* cells are colored in green, RITC-dextran in red. **(B)** Focus on the microscopic observation of the

fluorescence intensity of RITC-dextran inside and around a *L. lactis* LD61 colony immobilized in a model cheese at *t* = 120 min of diffusion and the corresponding fluorescence intensity profile along a 10 pixels wide line vs. position *x*. Black zones in the colony corresponds to the *L. lactis* cells.

fluorescence both along the x-axis and through time, proving that the fluorescent solute progressively moved inward toward a *Lactococcus* colony because of the concentration gradient between the surface and the interior of the model cheese. After 2 h of diffusion (**Figure 3B**), the red color was uniform in all the directions around the colony, meaning that the concentration of the fluorescently-labeled solute had reached a plateau. The diffusing process ended because there was no more concentration gradient in this area. The simple observation of these time series of images also suggests that the hypothesis of unidirectional diffusion of solute is valid. Therefore, image analysis of the intensity profiles of red fluorescence along this x-axis allowed to directly quantifying the diffusive penetration of the solutes as a function of time (**Figure 3B**). Typical fluorescence intensity profiles were obtained (**Figures 4A–C**) at different times (between 20 and 150 min). Only an example of the fluorescence intensity profiles obtained with TIL1230 strain was shown on **Figure 4D** because the profiles were very similar to those obtained with LD61.

From all these fluorescence intensity data as a function of the position and the time of diffusion, we calculated the corresponding relative fluorescence intensity by dividing the fluorescence intensity at each position *x* by the average fluorescence intensity obtained on the region upstream the colony. An example is given on **Figure 4E** for the diffusion of the 70 kDa dextran in a colony of *L. lactis* LD61. The relative fluorescence intensity profiles obtained with the other dextrans and with the other strain were very similar (data not shown). The purpose of this graphical representation of the results was to observe the evolution of the ratio of the fluorescence intensity in the colony vs. outside the colony as a function of time.

As shown on **Figure 4E**, whatever the time of diffusion considered, the fluorescence intensity inside the colony of both *L. lactis* strains drastically dropped compared to the fluorescence intensity in the surrounding cheese matrix, with a quite constant ratio around 0.4–0.6 depending on the x-axis position inside the colony. We previously showed that even the fluorescently-labeled solutes diffuse inside a bacterial colony (Floury et al., 2013), but do not penetrate into the bacterial cells. Furthermore, dextrans are not metabolized by lactococci cells. Thus, the only way to explain this drop of relative fluorescence intensity inside the colony is because on the line of 10-pixel wide (**Figure 3B**), the volume filled with the fluorescent solution is lower inside the colony than outside the colony due to the presence of the bacterial cells (corresponding to black zones with no fluorescence intensity). So even if the experimental fluorescence intensity is different, the real concentration of the fluorescent solute is effectively the same both inside and around the colony. The calculated average value represented by the relative fluorescence intensity profiles was quite stable along the x-axis inside the colonies for the fluorescently-labeled dextran solutes (**Figure 3B**), and this was also true whatever the time of diffusion considered (**Figure 4E**).

The concentration of the diffusing solute cannot be calculated from experimental data. The fluorescent intensity is, however, proportional to the diffusing solute concentration. Then, assuming a one-dimensional Fickian diffusion induced by an instantaneous source, the effective diffusion coefficients both inside the colony and in the surrounding matrix could be estimated from the slope of the linearization of the experimental fluorescence intensity profiles obtained a short time after the beginning of the diffusive process, with $D = -\frac{1}{4.t.slope}$ from Equation (3). A typical linearized curve and the corresponding
<figure><img src="image_78.png" /><figcaption>FIGURE 4 | Profiles of fluorescence intensity during diffusion of RITC-dextran 10 kDa (A), 70 kDa (B), and 155 kDa (C) in colonies of *Lactococcus lactis* LD61 and of RITC-dextran 70 kDa in</figcaption></figure>

TIL1230 (D) as function of diffusion time (from 20 to 120 min). (E)
Typical relative fluorescence intensity profile obtained with RITC-dextran
70 kDa in LD61.

fitted equations obtained both outside and inside the colony are
shown on **Figure 5**. Regression coefficients of the linear models
were generally higher than 0.9 in the surrounding matrix, and
slightly lower inside the colony with values around 0.7 because
of a lower signal to noise ratio inside colonies. The slope of the
line was clearly higher inside than outside the colony (**Figure 5**),
suggesting that the diffusion of solutes was slower inside the
colony. The diffusion of the fluorescent solute was probably
more hindered inside the colony, most likely because of the high
volume filled with the bacterial cells, than in the protein-network
of the surrounding matrix.

The mean effective diffusion coefficients ($D_{\text{eff}}$) of the different
RITC-dextrans were obtained using this modeling approach,
both inside and outside the colonies of the two bacterial strains
(**Figure 6**). $D_{\text{eff}}$ were significantly ($p < 0.05$) lower inside
the colonies than in the surrounding matrix. When $D_{\text{eff}}$ were
plotted against the hydrodynamic radius, linear relationships
($R^2 > 0.8$) were obtained over the molecular weight range of
10–155 kDa, both inside and outside colonies. The statistical
analysis (ANOVA) performed on the estimated values revealed
that the values of $D_{\text{eff}}$ were not significantly different ($p < 0.001$)
between the two bacterial strains, meaning that dextran solutes
<figure><img src="image_79.png" /></figure>

<figure><img src="image_80.png" /></figure>

diffused inside both Lactocci colonies with a similar diffusion coefficient, which depended mainly on size of the solute. The absence of significant difference between both *L. lactis* strains was expected because dextrans are known to be hydrophilic macromolecules and the surface properties of the two different cells were shown to be also both very hydrophilic.

In the present study, we went further than the previous study (Floury et al., 2013) by visualizing the kinetic of the diffusion process inside the colonies. Moreover, we were able to estimate diffusion coefficients of the dextran solutes inside the microbial colonies from image analysis of the data. As shown in Table 3, whatever their size between 10 and 155 kDa and the *Lactococcus*

<table><caption>TABLE 3 | Calculated ratios of effective diffusion coefficients of dextrans obtained inside and outside *L. lactis* colonies ($D_{in}/D_{out}$), of diffusion coefficients of dextrans in water* vs. inside colonies ($D_{w}/D_{in}$), and corresponding tortuosity indices, calculated as square root of $D_{w}/D_{in}$.</caption><thead><tr><th rowspan="3">Dextran<br>MW (kDa)</th><th colspan="2">D<sub>in</sub>/D<sub>out</sub></th><th colspan="2">D<sub>w</sub>*/D<sub>in</sub></th><th colspan="2">Tortuosity index</th></tr><tr><th colspan="6">L. lactis strain</th></tr><tr><th>LD61</th><th>TIL1230</th><th>LD61</th><th>TIL1230</th><th>LD61</th><th>TIL1230</th></tr></thead><tbody><tr><td>10</td><td>0.54</td><td>0.52</td><td>43</td><td>50</td><td>6.6</td><td>7.1</td></tr><tr><td>70</td><td>0.50</td><td>0.40</td><td>20</td><td>32</td><td>4.5</td><td>5.7</td></tr><tr><td>155</td><td>0.40</td><td>0.34</td><td>32</td><td>43</td><td>5.7</td><td>6.5</td></tr></tbody></table>

strain, the effective diffusion coefficients of dextrans obtained inside the colonies were around twice lower than their respective diffusion coefficients in the surrounding cheese matrix around the colonies, and up to 50 times smaller than those in water $D_w$ (calculated from the Stokes-Einstein equation). Silva et al. (2013) also found that effective diffusion coefficient values of fluorescein isothiocyanate (FITC)-dextrans (from 4 to 2000 kDa) in the same model cheese, but not inoculated, were smaller than those in water due to the hindrance of the protein network. However, their values of effective diffusion coefficients were between 4 and 9 times larger than in the present study, depending of the size of the dextran.

The variability observed has two major causes. The first source of variability is the experimental approaches used to estimate diffusion coefficients (Floury et al., 2010). Effective diffusion coefficients estimated thanks to the concentration profiles method in the present method are then difficult to compared to the so called “self-diffusion” coefficients obtained using the FRAP technique in Silva et al. (2013).

The second source of variability is the model cheese. Indeed, even if the model cheese had the same initial composition in both studies, the metabolism of the lactococci inoculated in the present study induced a strong decrease of cheese pH from 6.6 to 5.1–5.3, depending on the *L. lactis* strain, which probably modified to some extent the microstructure of the protein network and then the diffusion behavior of the solutes in both cheeses.

In agreement with our results, Guiot et al. (2002) and Thurnheer et al. (2003) observed that the diffusion coefficients of fluorescently-labeled dextrans from 3 to 70 kDa also decreased linearly with hydrodynamic radius in different mono- and poly-species biofilms, and were up to 150 times smaller than those in bulk water. However, the direct comparison of their results with our study is difficult because the microbial cell distribution is rather different in a biofilm and in a food matrix such as cheese. To our knowledge, only two studies are realistically comparable to our system (Rani et al., 2005 and Takenaka et al., 2009). They focused the analysis of the solute diffusion exclusively within identified clusters of microbial cells inside different model oral biofilms. The diffusive penetration of two tracer molecules (rhodamine B and fluorescein, MW~400 Da, chosen as model of antibiotic for their similar size), into staphylococcal cell clusters was directly visualized by confocal

<footnote>*Dw values at 20°C calculated by using the Stokes-Einstein relationship.</footnote>
scanning laser microscopy (Rani et al., 2005). The effective
diffusion coefficients of the two fluorescent tracers were around
10 times lower than the corresponding solute diffusion coefficient
in water. The difference with the present study could be
due to a denser population of cells in our colonies. The
diffusive penetration of fluorescently-labeled dextrans of various
molecular weights (from 3 to 70 kDa) was visualized into three
different species of cell clusters formed by oral bacteria grown
in a flow cell (Takenaka et al., 2009). Like in the present
study, the effective diffusion coefficient of dextrans strongly
decreased with their molecular weights. However, their order of
magnitude was different from our results, with effective diffusion
coefficients only twice smaller than those in water. For Thurnheer
et al. (2003), analysis of diffusion phenomena within biofilms
suggested tortuosity as the most probable factor responsible for
retarded diffusion compared to water. They defined a tortuosity
index, as the square root of D<sub>w</sub>/D<sub>in</sub>, representing an indicator
of solute diffusion through interstitial space between bacterial
cells. A molecule going through a highly convoluted three-
dimensional route in a matrix will be delayed in comparison
with free diffusion in water. As shown in Table 3, the tortuosity
indexes estimated from our experimental data were of the same
order of magnitude regardless of the *L. lactis* strain and the size of

the diffusing dextran solutes. In our conditions, the extracellular
space between the lactococci cells within the colony is filled
with an aqueous phase composed of water, lactose, and minerals
(Floury et al., 2013), thus explaining why the tortuosity index did
not depend on the size of the dextrans.

In conclusion about the diffusion of dextrans within bacterial
colony, we confirmed that macromolecules as large as dextrans
of 155 kDa diffused into lactococci colonies in a model cheese
(Floury et al., 2013). We demonstrated that their diffusion was
similar for two different strains of lactococci whatever the size of
dextrans up to 155 kDa.

Milk Proteins, Such as BSA, Lactoferrin and
αS1-Casein, Do Not Diffuse Inside neither LD61
nor TIL1230 Lactococci Colonies

The typical fluorescence intensity profiles for three fluorescently-labeled proteins, and typical images of the corresponding colonies at the end of the experiments are shown on **Figures 7–9**. Whereas, the fluorescence intensity of the three proteins outside the colonies increased with time, the fluorescence intensity measured inside the colonies was very low all along the duration of the experiments. The increase of the fluorescence intensity throughout time outside the colonies shows that the three

<figure><img src="image_81.png" /><figcaption>FIGURE 7 | Fluorescence profiles of fluorescently-labeled Bovine Serum Albumin (BSA), in colonies of *Lactococcus lactis* LD61 (A) and TIL 1230 (B) at different times from 20 to 120 min after the deposit at the surface of a model cheese and the corresponding microscopic observations of the colony after 120 min of diffusion.</figcaption></figure>
<figure><img src="image_82.png" /><figcaption>FIGURE 8 | Fluorescence profiles of RITC-LF in *Lactococcus lactis* LD61 (A) and TIL1230 (B) from 20 to 150 min and corresponding microscopic observations of the colony after 150 min of diffusion.</figcaption></figure>

labeled-proteins effectively diffused in the surrounding cheese matrix. Whereas, the fluorescence outside the colonies was finally intense, the observation of the micrographs clearly confirmed the intensity profiles by the absence of fluorescence inside the colonies. This means that, surprisingly, none of the three proteins could diffuse inside the bacterial colonies, although their hydrodynamic radii were much smaller than the radius of the largest dextran (Table 1). Concerning the diffusion of proteins in cell clusters, to our knowledge the only published study is from Takenaka et al. (2009). Contrary to our results, they clearly visualized by time-lapse confocal microscopic observations that fluorescently-labeled proteins, even the largest like ConA (MW 104 kDa) and IgG (MW 150 kDa), diffused inside microbial cell clusters that were approximately a few hundred micrometers in diameter and reached the center of these cell clusters in less than 3 min.

These results highlighted that the size of the diffusing solute was not the sole factor conditioning its ability to enter inside colonies of *L. lactis* immobilized in cheese. Other physicochemical factors such as flexibility, charge and/or hydrophobicity of the solute can also be of involved, by generating bacteria-solute interactions of different nature, especially for diffusing solutes such as milk proteins. Moreover, the impact of these factors can also depend on the surface properties of the strain (Habimana et al., 2011).

When diffusing through a non-inoculated model cheese, the rigid, and globally negatively charged BSA protein was hindered more than dextrans with a similar hydrodynamic radius, because of the existence of solute-matrix interactions (Silva et al., 2013). In the same way, deBeer et al. (1997) and Takenaka et al. (2009) reported that diffusion coefficients of solutes in different kinds of cell clusters were conditioned on the network structure in the interstitial space, but also mainly depended on the size and the charge of the diffusing solute. Unlike dextrans that are flexible, neutral, and hydrophilic polymers, proteins possess different shapes, hydrophobicity, and charges (Table 1). Moreover, even if the surface of the cells of the two *L. lactis* strains were shown to be both hydrophilic, *L. lactis* TIL1230 was more electronegative than LD61 at the pH of the model cheeses. Therefore, we could have expected the surface properties of bacterial cells to influence the ability of solutes to diffuse or not inside the colony by generating either repulsive or attractive interactions like electrostatic forces, depending on the charge of the solute (Burgain et al., 2014). The pH of the model cheeses were around 5.1 and 5.3 for LD61 and TIL1230, respectively. Therefore, according to the isoelectric point of the proteins (Table 1), the net charges of BSA and α-51 casein were slightly negative, whereas LF was globally positively charged in both cheese matrices. Electrostatic repulsions could have then occurred between the outer bacterial cells of the colony and the negatively charged solutes, preventing their diffusion
<figure><img src="image_83.png" /><figcaption>FIGURE 9 | Fluorescence profiles of RITC-αS1-casein in Lactococcus lactis LD61 (A) and TIL1230 (B) from 20 to 180 min and corresponding microscopic observations of the colony after 180 min of diffusion.</figcaption></figure>

inside colony. However, it is largely known that cheese is a medium presenting a high ionic strength (around 100 mM). In that case, the Debye length is very small (around 2 nm) and then the energy barrier due to repulsive forces is very low, meaning that the electrostatic contribution is strongly suppressed (Burgain et al., 2011). Interactions of electrostatic nature were not explaining the non-ability of the three milk proteins to penetrate inside L. lactis colonies immobilized in cheese. Other kind of repulsive forces such as hydrophobic interactions can be involved, especially between the hydrophobic BSA and LF proteins and the hydrophilic surfaces of the cells of both L. lactis strains. However, for the amphiphilic α-S1 casein, there was no reason for repulsions of hydrophobic origin with the bacterial surfaces.

We were finally not able to explain why α-S1 casein proteins could not diffuse inside the lactococci colonies. However, it is well-known that milk caseins, especially the α-S1 casein, are hydrolyzed by the cell wall proteases of L. lactis in cheese and/or other proteases present in the cheese. We can thus hypothesize that some of the generated peptides can diffuse inside colonies, and are further metabolized into smaller peptides and amino acids by all the cells located inside the colonies, as strongly suggested by the results obtained in the same model cheese by Le Boucher et al. (personal communication).

## Conclusion

Effective diffusion coefficients of dextran macromolecules were quantified for the first time inside colonies of two different L. lactis strains immobilized in a model cheese. We clearly showed that the diffusion behavior of macromolecules through bacterial colonies immobilized in a model cheese not only depends on the size of the diffusing solutes, but also and mainly on their physicochemical properties. Whereas, a flexible and neutral hydrophilic polymer such as a dextran can diffuse inside colonies whatever its size, none of the three proteins investigated in this study could penetrate inside lactococci colonies. These original results remain unexplained because both the surface of the two bacterial strains and the three diffusing proteins presented various physicochemical properties, from rigid to flexible shapes, and from negatively to neutral and positively charged. Our results finally show that the choice of the fluorescently-labeled molecule as a model of diffusing solute is crucial.

## Acknowledgments

This study was part of the CheeseOmic Project co-funded by the regions Bretagne and Pays-de-la-Loire and supported by Bretagne Biotechnologie Alimentaire (BBA) association.
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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2015 Floury, El Mourdi, Silva, Lortal, Thierry and Jeanson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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