## 4 Materials and Methods
### Protein Abundance Changes and Statistical Analysis  
Statistical quantitative analyses were based on two different generalized linear models depending on the type of quantification method used and data that were generated: either spectral counting for a rough relative quantitative protein quantification or XIC from MS1 ion intensities for more accurate and sensitive relative quantifications of low abundant proteins or small abundant changes. The discrete SC (1) and continuous XIC (2) abundances values were modeled, respectively, as follows, and as described previously [212]. \[ \begin{split}\text{SC}=\upmu+\text{strain}+\text{medium}+\text{ time}+\text{replicate}+\text{strain}\times\text{medium}+\text{strain}\times\text{time}\\ +\text{medium}\times\text{time}+\text{strain}\times\text{medium} \times\text{time}+\epsilon\text{$\sim$}\text{Pois}(\uplambda)\end{split}  \tag{1}\]  
\[ \begin{split}\text{Log2(LFQ)}=\upmu+\text{strain}+\text{medium}+ \text{time}+\text{replicate}+\text{strain}\times\text{medium}\\ +\text{strain}\times\text{time}+\text{medium}\times\text{time}+ \text{strain}\times\text{medium}\times\text{time}+\epsilon\text{$\sim$} \text{N(0,$\sigma$)}.\end{split}  \tag{2}\]Terms represent fixed effect of the different conditions and their interactions for each protein abundance. The residual error (\(\varepsilon\)) follows a Poisson distribution [Pois(\(\lambda\))] or a normal distribution for SC and Log2 (LFQ), respectively. Effects were estimated by maximum likelihood. Statistical significances were calculated using likelihood ratio tests based on the analysis of deviance. \(p\)-values were adjusted using the Benjamini-Hochberg procedure for multiple testing correction. For each protein, first, a significant difference in abundance for the 66 combinations of pairwise comparisons was set at 10 for SC and 1 Log2 fold change for LFQ values. Second, a threshold of 10 significant pairs (minimum number of significant pairwise comparisons expected for a differential protein abundance in at least two conditions) and an adjusted \(p\)-value of 0.05 was used to consider a protein abundance as significantly variable. These statistical analyses were performed with a homemade R script derived from R Studio (version 1.4.17.17) downloaded at [[https://www.rdudio.com/products/rstudio/older-versions/](https://www.rdudio.com/products/rstudio/older-versions/)]([https://www.rdudio.com/products/rstudio/older-versions/](https://www.rdudio.com/products/rstudio/older-versions/)), accessed on 14 November 2022 and using the described models and parameters. A threshold of five significant pairs (minimum number of significant pairwise comparisons expected for a differential protein abundance in at least two conditions) was used to consider a protein abundance as significantly variable. All the statistical analyses were performed by a homemade R script and are provided I Supplementary Table S1. The supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/ijms232314792/s1](https://www.mdpi.com/article/10.3390/ijms232314792/s1)]([https://www.mdpi.com/article/10.3390/ijms232314792/s1](https://www.mdpi.com/article/10.3390/ijms232314792/s1)). L.C. performed all microbial cultivations. S.L. performed digestions, sample preparation for MS analysis, MS analysis, and MS data analysis. C.D. performed statistical analysis of label-free MS quantification data. L.C. performed data analysis in R and prepared all the heatmaps. [PERSON] supervised MS sample preparation and MS data analysis. [PERSON] conceptualized the project, wrote the manuscript, and obtained grants funding. All authors have read and agreed to the published version of the manuscript. The \"Centre National de la Recherche Scientifique\" CNRS ([[http://www.cnrs.fr](http://www.cnrs.fr)]([http://www.cnrs.fr](http://www.cnrs.fr))) contributed to the funding of this work as well as the ANR-17-ASTR-0018 (Innovantibio).