# Evolution of Bacterial Persistence to Antibiotics during a 50,000-Generation Experiment in an Antibiotic-Free Environment

^{*}

## Abstract

**:**

## 1. Introduction

^{−8}to 10

^{−2}in natural populations [7], that benefits from a low death rate associated with low metabolic activity. This persistent state allows bacterial cells to cope with a broad range of stresses, albeit being genetically susceptible. Indeed, the size of a population exposed to antibiotics and containing persister cells first quickly decreases, owing to the death of susceptible and metabolically active cells, before experiencing a much slower decrease due to the death of persister quiescent cells. This physiological state is not heritable; hence, if a population regenerated from the persister sub-population is re-exposed to the antibiotic, a similar phenotypic heterogeneity will typically be observed again. This phenomenon allows growth rescue and restart after many stresses, including nutrient depletion, temperature change, acid, oxidative or osmotic challenges, phage infection, and exposure to heavy metals or antibiotics [6,8].

## 2. Results

^{0}to 10

^{7}. For each dilution, we recorded the bacterial culture revival, i.e., the presence/absence of growth after 24 h and, if revived, we quantified the number of persister cells by analyzing the growth curve ($\widehat{\mathrm{initial}\mathrm{OD}}$ approach described hereafter). Absence of growth after 24 h was related to either the absence of persister cells or a number of persister cells too low for their revival and detection. In this case, we set the number of persister cells to zero.

#### 2.1. Validation of the $\widehat{\mathit{initial}\mathit{OD}}$ Approach to Quantify Persister Cells

#### 2.2. Evolution of Persistence in the ‘LTEE-50K’ Analysis

#### 2.2.1. Overall Trends in the Persistence Level to Ampicillin and Ciprofloxacin

#### 2.2.2. Evolution of Persistence after 50,000 Generations of Evolution

#### 2.3. Evolution of Persistence in the ‘Ara−2_S_L’ Analysis

## 3. Discussion

_{2}of each growth curve, which estimates the growth rate and initial population density.

## 4. Materials and Methods

#### 4.1. Strains

`−`2 (Table 1). Indeed, the population Ara

`−`2 experienced an adaptive diversification event, during which two phenotypically distinct ecotypes, called S and L, emerged by generation 6500 and have co-existed ever since [51]. Therefore, we sampled one Ara

`−`2 evolved clone from generations 2000 and 5000 before the emergence of this polymorphism, and one S and L clone at each of the generations, 6500, 11,000, 20,000, and 50,000 (Table 1).

#### 4.2. Measuring the Proportion of Persister Cells

^{0}-to-10

^{7}-fold, and monitored growth in an Infinite M200 microplate reader (Tecan

^{®}, Mennedorf, Switzerland) to quantify the proportion of persister cells for each clone. We recorded the OD

_{600}every 15 min for 24 h which we used as the ‘time’ variable in our analysis (see below). In addition, for each antibiotic-free control tube, we estimated the CFU number to compute the relationship between the number of cells and the time after which regrowth was detected by OD. We performed at least three biological replicates for each clone.

#### 4.3. Rationale of Data Analyses

#### 4.4. Quantification of the Population Size of Persister Cells

_{600}), which is proportional to the initial amount of material (here, the number of persister cells). Hence, the time needed to reach a given OD

_{600}threshold is defined as the Start Growth Time, SGT [50]. This approach, however, assumed that persister cells have both a growth rate and a lag phase that are similar to the cells of the cultures used for the standard curve, albeit they were not exposed to antibiotics (Figure A1e). Comparing the growth rates of each strain in each treatment showed that persister cells actually had a slower growth rate than other cells. For unknown reasons, this differential effect was stronger for ciprofloxacin than ampicillin (Supplementary material, Figure S1). Therefore, we developed an alternative approach based on a statistical model that predicted the ${\mathrm{log}}_{2}$ of the OD

_{600}observed during exponential growth as a function of time (Supplementary material; heuristic selection of the exponential growth phase). These models estimate both the growth rate (slope) and initial OD

_{600}(intercept; hereafter, $\widehat{\mathrm{initial}\mathrm{OD}}$) for each growth curve. This approach can detect tiny variations in initial OD as they increased during exponential growth (for more details, see Appendix A). Using this approach, we obtained an $\widehat{\mathrm{initial}\mathrm{OD}}$ for each growth curve and converted it into cell numbers using standard curves obtained for each strain by quantifying both $\widehat{\mathrm{initial}\mathrm{OD}}$ and CFUs in dilution series (Figure A1d).

#### 4.5. Estimating and Comparing Persistence

_{10}of the dilution factor, the antibiotic treatment, the clone, and the two second-order interactions with antibiotic treatments, and (ii) as random effects, the ‘replicates’ on the intercept, the three fixed effects of ‘dilution’, ‘treatment’, and their interaction. The intercept of these models estimates the mean ${\mathrm{log}}_{2}\left(\#\mathrm{EqNC}\right)$ in the non-diluted sample, i.e., when the log

_{10}of the dilution is equal to zero.

#### 4.6. Relationship between Persistence and Mutator Phenotype

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A. Estimation of the Equivalent Number of Normal Cells (#EqNC)

_{2}of the OD from which the background OD has been removed. The slope and the intercept of these models estimate the growth rate and log

_{2}of the initial OD minus the background OD, respectively. The estimated $\widehat{\mathrm{initial}\mathrm{OD}}$ is proportional to the number of initial cells and is unaffected by the growth rate.

**Figure A1.**Estimation of the #EqNC by both the Start Growth Time (SGT; [50]) and $\widehat{\mathrm{initial}\mathrm{OD}}$ (our approach). Left and right panels, respectively, refer to the SGT and $\widehat{\mathrm{initial}\mathrm{OD}}$ approaches. Panels (

**a**,

**b**): representative of real data as analyzed by each method; raw data for SGT and log of the OD minus the background (Bg) for $\widehat{\mathrm{initial}\mathrm{OD}}$ (see Supplementary method 1 for background estimation). Panel (

**a**): the SGT approach yields an estimated SGT of 13, using a threshold of 0.105. Panel (

**b**): the linear model gives an $\widehat{\mathrm{initial}\mathrm{OD}-\mathrm{Bg}}={2}^{-16.81}$. Panels (

**c**,

**d**): The standard curve is obtained by quantifying cells by both counting CFUs and analyzing the growth curves, whatever the approach (SGT or $\widehat{\mathrm{initial}\mathrm{OD}}$ ). If growth rates were similar when computing and using the standard curve, both approaches gave similar results. However, as illustrated in panels (

**e**,

**f**), the SGT approach started to be inaccurate when growth rates varied. Panels e and f: Simulated examples showing both the inaccuracy of the SGT approach and the robustness of the $\widehat{\mathrm{initial}\mathrm{OD}}$ approach in the presence of growth rate variation. Panel (

**e**): three theoretical growth curves obtained by assuming growth rates of 1.5, 0.9, and 0.9 cell divisions per hour, for initial ODs of 2

^{−26}, 2

^{−24}, and 2

^{−26}, respectively, for the red, green, and black curves. The SGT approach, based on the OD threshold, would detect more cells in, successively, the red, green, and black curves. However, as illustrated in the inset zoom, this is an artifact induced by growth rate variation. Panel (

**f**): log-transformed OD allows the fit of a linear model that yields an estimate of both the growth rate and initial OD (${\mathrm{log}}_{2}\left(\widehat{\mathrm{initial}\mathrm{OD}\u2013\mathrm{Bg}}\right)$ ).

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**Figure 1.**Persistence to ampicillin vs. ciprofloxacin in evolved clones sampled from each of the 12 LTEE populations.

**Figure 2.**Evolution of persistence to ampicillin (×) and ciprofloxacin (▲) in evolved clones sampled from the 12 LTEE populations. For each antibiotic, we compared the level of persistence of each evolved clone sampled at generation 50,000 to the one in each of the two ancestors, REL606 and REL607, and to be conservative, only the least significant of the two comparisons was kept for each evolved clone. The p-values for each antibiotic are shown below the name of the population (and for each of the S and L ecotypes in population Ara−2, in red and blue, respectively). These values were obtained from the coefficients of the models summarized in Table 2. The 95% confidence intervals are shown. Dark symbols represent the ancestor strains, pink represent the clones from the Ara−1 to Ara−6 populations, and green represent the clones from the Ara+1 to Ara+6 populations. Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.

**Figure 3.**Evolution of persistence to ampicillin (

**A**) and ciprofloxacin (

**B**) in evolved clones sampled from the Ara−2 population. Dark symbols represent the ancestor strains REL606 and REL607; pink represent the Ara−2 evolved clones sampled before the adaptive diversification event; red and blue represent the evolved clones from the S and L ecotypes, respectively. p-values close to the Ara−2S evolved clones refer to the comparisons to the ancestors, and p-values close to the Ara−2L evolved clones refer to the comparison between the co-existing contemporary S and L evolved clones. The 95% confidence intervals are shown. Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.

Clone | LTEE Population | Generation | Mutator State * | Analyses ** | |
---|---|---|---|---|---|

REL606 | Ancestor (Ara−) | 0 | N | LTEE-50K | Ara−2_S_L |

REL607 | Ancestor (Ara+) | 0 | N | LTEE-50K | Ara−2_S_L |

11330 | Ara−1 | 50,000 | M | LTEE-50K | |

1165A | Ara−2 (BC ***) | 2000 | N | Ara−2_S_L | |

2180A | Ara−2 (BC ***) | 5000 | M | Ara−2_S_L | |

6.5KS1 | Ara−2 (S) | 6500 | M | Ara−2_S_L | |

6.5KL4 | Ara−2 (L) | 6500 | M | Ara−2_S_L | |

11KS1 | Ara−2 (S) | 11,000 | M | Ara−2_S_L | |

11KL1 | Ara−2 (L) | 11,000 | M | Ara−2_S_L | |

20KS1 | Ara−2 (S) | 20,000 | M | Ara−2_S_L | |

20KL1 | Ara−2 (L) | 20,000 | M | Ara−2_S_L | |

13335 | Ara−2 (S) | 50,000 | N | LTEE-50K | Ara−2_S_L |

11333 | Ara−2 (L) | 50,000 | M | LTEE-50K | Ara−2_S_L |

11364 | Ara−3 | 50,000 | M | LTEE-50K | |

11336 | Ara−4 | 50,000 | M | LTEE-50K | |

11339 | Ara−5 | 50,000 | N | LTEE-50K | |

11389 | Ara−6 | 50,000 | N | LTEE-50K | |

11392 | Ara+1 | 50,000 | N | LTEE-50K | |

11342 | Ara+2 | 50,000 | N | LTEE-50K | |

11345 | Ara+3 | 50,000 | M | LTEE-50K | |

11348 | Ara+4 | 50,000 | N | LTEE-50K | |

11367 | Ara+5 | 50,000 | N | LTEE-50K | |

11370 | Ara+6 | 50,000 | M | LTEE-50K |

Variable | df | F-Value | p-Value |
---|---|---|---|

${\mathrm{log}}_{10}$(dilution) | 1, 100.58 | 595.11 | <0.001 |

Clone ID | 23, 59.92 | 48.86 | <0.001 |

Antibiotic | 2, 1342.65 | 144.94 | <0.001 |

$\mathrm{Antibiotic}\times {\mathrm{log}}_{10}$(dilution) | 2, 131.47 | 7.63 | <0.001 |

Antibiotic × clone ID | 44, 399.89 | 9.45 | <0.001 |

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## Share and Cite

**MDPI and ACS Style**

Mathé-Hubert, H.; Amia, R.; Martin, M.; Gaffé, J.; Schneider, D.
Evolution of Bacterial Persistence to Antibiotics during a 50,000-Generation Experiment in an Antibiotic-Free Environment. *Antibiotics* **2022**, *11*, 451.
https://doi.org/10.3390/antibiotics11040451

**AMA Style**

Mathé-Hubert H, Amia R, Martin M, Gaffé J, Schneider D.
Evolution of Bacterial Persistence to Antibiotics during a 50,000-Generation Experiment in an Antibiotic-Free Environment. *Antibiotics*. 2022; 11(4):451.
https://doi.org/10.3390/antibiotics11040451

**Chicago/Turabian Style**

Mathé-Hubert, Hugo, Rafika Amia, Mikaël Martin, Joël Gaffé, and Dominique Schneider.
2022. "Evolution of Bacterial Persistence to Antibiotics during a 50,000-Generation Experiment in an Antibiotic-Free Environment" *Antibiotics* 11, no. 4: 451.
https://doi.org/10.3390/antibiotics11040451