High concentrations of NaCl affect morphology
In the control group, the morphology of S. aureus ZS01 was spherical, the surface of cell was smooth without damage or wrinkles, and the size was relatively neat (Fig. 1A). With the increase of salt concentration, there was no significant change in cell morphology in the 10% NaCl treatment group, but the number of cells was significantly lower than that in the control group (Fig. 1B). In the 20% NaCl treatment group, the cell membrane ruptured obviously, the cell contents overflowed, and some cells showed shrinkage (Fig. 1C).
From the above results, it can be seen that the morphological changes of S. aureus ZS01 in the 20% NaCl treatment group are more obvious than in the 10% NaCl treatment group. High salinity stress can change the permeability of cell membrane and rupture the cell membrane, leading to cell death.
High concentrations of NaCl affect biofilm formation
The distribution of the bacteria and extracellular polymeric substances (EPS) was observed from 3D views of CLSM images (Fig. 2A, 2B, and 2C). FITC-ConA can bind to cell wall polysaccharides to emit green fluorescence, and PI can penetrate bacterial cells and bind to DNA to emit red light. EPS as the main component of biofilm emits green fluorescence; bacterial DNA emits red fluorescence. With the increase of NaCl concentration, the thickness of biofilms increased and then decreased, and the difference between adjacent groups was significant (Fig. 2D). The results show that in the concentration range of less than 10% NaCl, high concentration of NaCl contributes to the formation of biofilms. When the concentration of NaCl is higher than 10%, NaCl has an inhibitory effect on the formation of biofilm.
Transcriptomic profiling through RNA-Seq
Compared with the control group, 248 DEGs (121 upregulated and 127 downregulated) and 891 DEGs (365 upregulated and 526 downregulated) were identified in the 10% and 20% NaCl groups, respectively (Fig. 3A and Fig. 3B). Compared with the 10% NaCl group, 1063 DEGs (399 upregulated and 664 downregulated) were identified in the 20% NaCl group (Fig. 3C). Furthermore, the number of downregulated genes was higher than that of the upregulated genes in the three groups. Comparing Fig. 3A and Fig. 3B, it was found that as the salt concentration increased, more genes were mobilized to participate in the process of high salt stress.
Hierarchical cluster analysis of the DEGs was conducted using the HemI 1.0 software (Deng et al., 2014). Four expression change patterns were displayed among these DEGs with increasing concentrations of NaCl (Fig. 4). The four patterns are Pattern I (increase/increase); Pattern II (increase/decrease); Pattern III (decrease/decrease) and Pattern IV (decrease/increase) (Table S2). The numbers of DEGs that showed each pattern were 2, 80, 43, and 19, respectively. As a result, these DEGs can be considered as the candidate target genes for the direct regulation of salt stress. In addition, cluster analysis displayed that these genes affected by salt stress were abundant in some pathways related to membrane transport, redox process, metabolism, transcription factor activity, kinase activity, phosphatase activity, and stress response.
The predicted five KEGG pathways were statistically significant in the transcriptomic profiling (Table S3). For two comparison groups (0% NaCl vs 20% NaCl and 10% NaCl vs 20% NaCl), DEGs were enriched in ribosome pathways (45 genes and 51 genes). For the third comparison group (0% NaCl vs 10% NaCl), DEGs was enriched in glycolysis/gluconeogenesis metabolism (12 genes), pyruvate metabolism (12 genes), and glucagon signaling pathway (4 genes). After transcriptome enrichment analysis, we observed that the genes could be matched to the KEGG database that was mainly concentrated in energy metabolism, carbon and nitrogen metabolism.
Verification by qRT-PCR
To assess the reliability of our RNA-Seq, 10 DEGs were quantified using qRT-PCR. As shown in Fig. 5, the trend in qRT-PCR expression was in agreement with the RNA-seq expression profile. The results showed similar patterns of mRNA abundance in RNA-seq analysis and qRT-PCR. Therefore, RNA-seq results can reflect the expression of S. aureus transcriptome under high salt stress. Transcriptome data can be used for the analysis.
Metabolomic profiling through GC-MS
In total, 76 endogenous metabolites were identified in S. aureus ZS01. Then, the concentrations of 76 metabolites in these three groups were calculated based on the internal standard peak area (Table S4). Principal component analysis (PCA) is an overall presentation of the distribution of the original data for the samples. As shown in Fig. 6A, the main components of the metabolites were located in different quadrants, indicating clear discrimination among the intracellular metabolome in the three groups. To identify the metabolites affected by salt stress, orthogonal partial least squares discriminant analysis (OPLS-DA) was employed on the metabolic proﬁles. A total of 15 differential metabolites (DMs) were screened under VIP > 1 with p < 0.05 as standard. The 0% NaCl vs 10% NaCl group screened a total of ten DMs (aminobutanoic acid, glycolic acid, D-erythrofuranose, sebacic acid, xylitol, D-threitol, n-hexadecanoic acid, myo-inositol, heptacosane, and undecanedioic acid), the 10% NaCl vs 20% NaCl group screened a total of six DMs (L-proline, phosphoric acid, butanedioic acid, D-arabinose, D-mannitol, and n-hexadecanoic acid), and the 0% NaCl vs 20% NaCl group screened a total of six DMs (aminobutanoic acid, L-proline, phosphoric acid, butanedioic acid, D-arabinose, and xylitol) (Table S5).
We subjected the DMs to the pathway analysis to get the overall view of their contributions. The metabolic pathways related to salt stress responses are shown in Fig. 6B, 6C, and 6D. These pathways were mainly involved in inositol phosphate metabolism, sulfur metabolism, and TCA cycle (Table S6). Therefore, the results suggest that initiating defense (sulfur metabolism), signal response (inositol phosphate metabolism), and energy regulation (TCA cycle) are the key response pathways for S. aureus ZS01 to salt stress.
Integrated analysis of transcriptomeand metabolome
We combine transcriptome and metabolome data to gain insight into the regulatory network of S. aureus ZS01 under salt stress conditions. The transcriptome finally identified 81 related genes, and performed Spearman correlation analysis with 15 DMs. Taking a p ≤ 0.05 as the threshold, paired regulatory relationships were plotted (Fig. 7). A total of 80 nodes that were connected in the network with 236 edges are displayed in the visualization of Cytoscape. According to the edge greater than ten genes (antB, fnbA, gale, hisD, hisG, lysC, mnhD, proP, sdrC_D_E and serA) for salinity stress response were obtained. Furthermore, all of them were present in Pattern III (decrease/decrease) expression change. Overall, the integrated multiomics analysis identified hub genes that were potentially linked to salt stress. They deserve further attention and in-depth functional study and validation for applications.