## 2 Results
### (p)ppGpp- and DksA- Dependent Cell Functions in Y. enterocolitica  
The gene expression levels of the WT and mutant strains were analyzed using the RSEM software (Version 1.3.1). A total of 3651 genes were detected in WT, accounting for 84.65% of the total gene number in _Y. enterocolitica_ (4313 genes). In addition, 3644 (84.49%), 3434 (79.62%), and 3614 (83.79%) genes were detected in \(\Delta\)_dksA_, \(\Delta\)_relA\(\Delta\)spoT_, and \(\Delta\)_dksA_relA\(\Delta\)spoT_, respectively (Table 1). The DEGs with a \(\mid\)log\({}_{2}\) fold change (FC) \(\mid\) \(>\) 1 and a _p_-adjust \(<\) 0.05 from mutant strains were analyzed. Among the 801 DEGs in \(\Delta\)_dksA_, 387 genes were upregulated and 417 genes were downregulated. A total of 1437 DEGs were observed in \(\Delta\)_relA\(\Delta\)spoT_, including 695 upregulated genes and 742 downregulated genes. Deletion of both _dksA_ and genes involved in (p)ppGpp production resulted in 560 upregulated and 499 downregulated genes. Principal component analysis (PCA) was performed to compare the samples. Three biological replicates of each strain clustered together to form distinct groups from the others, suggesting that major variation arose from differences between each strain (Figure 1A). Correlation analysis also showed that the differences in genes were mainly due to differences among the strains (Figure 1B). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Samples** & **Genetic** & **Ratios** & **Number of** & \multicolumn{2}{c}{**DEGs**} \\ \cline{3-6}  & **Number** & **Ratios** & **DEGs** & **Upregulated** & **Downregulated** \\ \hline WT & 3651 & 84.65\% & 0 & 0 & 0 \\ \(\Delta\)_dksA_ & 3644 & 84.49\% & 801 & 384 & 417 \\ \(\Delta\)_relA_spoT_ & 3434 & 79.62\% & 1437 & 695 & 742 \\ \(\Delta\)_dksA_relA\(\Delta\)spoT_ & 3614 & 83.79\% & 1059 & 560 & 499 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical analysis of DEGs. To investigate the correlation between DksA and (p)ppGpp, a Venn analysis was performed to determine the co-expression and specific expression of DEGs. As shown in Figure 1C, more than 62% of DEGs in \(\Delta\)_dksA_ were regulated by (p)ppGpp, lending support to the claim that DksA acts as a pivotal cofactor of (p)ppGpp in global regulation. Furthermore, functional categories of the DEGs were categorized according to the KEGG database, and the pathways modulated by DksA and (p)ppGpp were similar, including carbon metabolism, amino acid metabolism, and membrane transport (Figure S3). Notably, 17, 14, and 15 cellular processes were significantly (\(p\)-adjust < 0.05) enriched in \(\Delta\)_dksA_, \(\Delta\)_relA\(\Delta\)_spoT_, and \(\Delta\)_dksA\(\Delta\)_relA\(\Delta\)_spoT_, respectively, via the KEGG database (Figure 2). While DksA and (p)ppGpp regulate several similar cellular processes, such as ribosomes, amino acid metabolism, and cell metabolism, some distinct cellular processes are uniquely regulated by (p)ppGpp or DksA, revealing separate regulatory mechanisms. Taken collectively, these bioinformatic findings suggest that DksA and (p)ppGpp modulate comparable genes and cellular processes in _Y. enterocolitica_. Figure 1: Expression analysis between samples. (**A**) Principal component analysis of twelve samples. (**B**) Correlation analysis of twelve samples. (**C**) Venn diagram of overlapping DEGs between \(\Delta\)_dksA_, \(\Delta\)_relA\(\Delta\)_spoT_, and \(\Delta\)_dksA_relA\(\Delta\)_spoT_. To further verify the accuracy of the RNA-Seq results, 19 DEGs from the mutant strains were randomly selected, and their expression levels were measured and compared to those of the WT using RT-qPCR. These genes are involved in biological processes enriched in bioinformatic analysis, such as translation (_rpsN_), flagellar formation (_fliE_ and _flgC_), nutrient import (_gntT_ and _hisJ_), RNA biosynthesis (_trmD_ and _dusB_), and others. The amplified band's uniqueness was confirmed through agarose electrophoresis (Figure S4).