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Transcriptomic analysis of Staphylococcus equorum KM1031 from the high-salt fermented seafood jeotgal under chloramphenicol, erythromycin and lincomycin stresses


Comprehensive transcriptome analysis of S. equorum strain KM1031 under antibiotic stress

In a previous study, S. equorum strain KM1031 showed resistance to chloramphenicol, erythromycin, lincomycin, and penicillin G based on disk diffusion analysis 10,13. However, in the MIC results, it was determined that strain KM1031 was sensitive to the penicillin G because it did not grow at 1 mg/L of penicillin G. To understand the bacterial response and adaptations during three antibiotic stress excluding penicillin G, RNA was isolated from S. equorum strain KM1031 following antibiotic stress for RNA-Seq analysis. RNA-Seq data were acquired, mapped, and normalized as described in the “Methods” section (Supplementary Tables S1 and S2). A total of 2,336 strain KM1031 genes were categorized using COG analysis. Antibiotic treatment affected the expression of several genes in strain KM1031 (Supplementary Figs. S1 and S2). After mRNA abundance was compared between control and antibiotic-exposed cells, genes showing a log2 (fold-change) greater than 2 or less than − 2 were considered to be DEGs (Supplementary Tables S6 and S7; Supplementary Fig. S1). In strain KM1031 cells exposed to chloramphenicol, erythromycin, and lincomycin stress, 8.3% (183/2,336), 16.0% (354/2,336), and 2.9% (63/2,336) of genes exhibited significant differences in their expression, respectively (Fig. 1B).

Figure 1
figure 1

Classification of differentially expressed genes (DEGs) based on predicted functions. (A) DEG analysis from RNA-Seq data comparing untreated Staphylococcus equorum strain KM1031 with strain KM1031 treated with antibiotics. The x-axis shows log-scaled Trimmed Mean of M-value (TMM) data for strain KM1031, and the y-axis shows log-scaled TMM values for cells treated with chloramphenicol, erythromycin, and lincomycin, respectively. Total gene expression in the two conditions was filtered to identify significantly down- or upregulated genes with the criteria P-value ≤ 0.05 and fold-change ≥ 2. (B) Genes upregulated or downregulated by twofold or more following treatment of the bacterium with antibiotics were grouped into functional categories based on the Clusters of Orthologous Groups database.

Following chloramphenicol treatment, 75 genes were significantly upregulated and 108 genes were significantly downregulated (Supplementary Tables S6 and S7; and Fig. 1). Significant upregulation was observed for genes associated with translation, ribosomal structure, and biogenesis (22.7%; 17/75) as well as genes associated with amino acid transport and metabolism (20.0%; 15/75). By contrast, genes associated with “function unknown” (24.1%; 26/108) and transcription (19.4%; 21/108) were downregulated.

Following erythromycin treatment, 214 genes were significantly upregulated and 140 genes were significantly downregulated. Significant upregulation was observed for genes associated with translation, ribosomal structure, and biogenesis (28.5%; 61/214) as well as genes associated with “function unknown” (11.2%; 24/214). By contrast, genes associated with “function unknown” (29.3%; 41/140) and transcription (14.3%; 20/140) were downregulated.

Following lincomycin treatment, 43 genes were significantly upregulated and 20 genes were significantly downregulated. Significant upregulation was observed for genes associated with “function unknown” (25.6%; 11/43) as well as genes associated with translation, ribosomal structure, and biogenesis (14.0%; 6/43). By contrast, genes associated with “function unknown” (45.0%; 9/20) and transcription (20.0%; 9/20) were downregulated.

Effects of antibiotics on efflux proteins and transporters

Transporter and efflux proteins are required for antibiotics to enter or be expelled from bacteria17,18. Thus, we hypothesized that antibiotics would alter the expression of efflux- and transporter-related genes in S. equorum. DEGs were screened using the keywords “efflux” and “transporter.” Among DEGs following treatment with chloramphenicol, erythromycin and lincomycin, 7.1% (13/183), 6.8% (24/354), and 4.8% (3/63), respectively, were related to transporters and efflux (Fig. 2A; Supplementary Table S8). Chloramphenicol and erythromycin treatment (especially the former) upregulated efflux-related genes and downregulated transporter-related genes. Similar results were observed for lincomycin, although expression changes were less dramatic compared with the other two antibiotics.

Figure 2
figure 2

Log2 fold-change values for genes related to (A) efflux and transporters, and (B) salt tolerance, on treatment of S. equorum strain KM1031 with chloramphenicol, erythromycin and lincomycin, respectively. Color code: red: efflux-related genes, blue, transporter-related genes.

Effects of antibiotics on expression of genes related to salt tolerance

Accumulation or release of compatible solutes such as glycine betaine, proline betaine, and carnitine confers salt tolerance by facilitating the response of cells to osmotic pressure19. Interestingly, osmoprotectant-related genes, such as those involved in the synthesis of trehalose, glycine betaine, choline, and proline, were upregulated following chloramphenicol and erythromycin treatment (Fig. 2B; Supplementary Table S8), while lincomycin only slightly affected the expression of a few genes related to salt tolerance. Zhu and Dai20 reported that overexpression of efflux pumps required for salt tolerance led to decreased antibiotic susceptibility. Our results suggest that strain KM1031 express salt tolerance-related genes to counter the effects of antibiotics, especially chloramphenicol and erythromycin.

Responsive genes to three antibiotics based on transcriptomic and comparative genomic analyses

We hypothesized that some genes in S. equorum strain KM031 might be specifically and functionally (i.e., mechanistically) related to chloramphenicol and erythromycin resistance. To identify such genes, we undertook comparative genomic analysis of strains KM1031 (CRERLR), C2014 (CSESLS), and KS1039 (CSESLS).

We plotted Venn diagrams of genes that were significantly differentially expressed in S. equorum strain KM031 in response to chloramphenicol, erythromycin and lincomycin (Fig. 3). Four genes (AWC34_RS06585, AWC34_RS08650, AWC34_RS10220, and AWC34_RS12080) were upregulated by all three antibiotics, while one (AWC34_RS11270) was downregulated by all three antibiotics (Supplementary Tables S6 and S7). These genes were detected in one or more of the complete genome sequences of the antibiotic-sensitive S. equorum strains C2014 and KS1039 based on comparative genomic analysis.

Figure 3
figure 3

Venn diagram of differentially expressed genes (DEGs) of S. equorum strain KM1031 following treatment with chloramphenicol, erythromycin and lincomycin. Overlapping regions represent genes that were differentially expressed in strain KM1031 (compared with untreated cells) on treatment with two or three of the antibiotics. The numbers outside overlapping regions indicate the numbers of significantly differentially expressed genes that were affected by each antibiotic individually.

Interestingly, an antibiotic ABC transporter ATP-binding protein-encoding gene (msr, AWC34_RS11115) was identified among genes specifically upregulated in response to erythromycin. Msr is annotated, among other things, as an erythromycin resistance ATP-binding protein. This gene was suggested to be responsible for erythromycin resistance in a previous genomic study of S. equorum strain KM103113. Reynolds et al. reported that Msr gives rise to erythromycin resistance via an active transport process21. Collectively, these findings strongly suggest that the antibiotic ABC transporter ATP-binding protein-coding gene (AWC34_RS11115) confers erythromycin resistance in strain KM1031. Chloramphenicol- and lincomycin-specific response genes were not identified among DEGs. Therefore, we conclude that most commonly up- and downregulated genes under antibiotic pressure are associated with general environmental responses, and not responses to chloramphenicol, erythromycin, and/or lincomycin specifically.

We hypothesized that antibiotic exposure might increase the expression of genes that are specifically related to antibiotic resistance (i.e., that encode proteins that are involved in the molecular-level resistance of the bacteria to the drug). Thus, we took the set of genes that were upregulated in S. equorum strain KM031 (not DEGs) in response to any of the antibiotics and subtracted genes detected in the two CSESLS strains. This left 65 strain KM1031-specific genes (Table 1). The msr (AWC34_RS11115) gene was among them. In a previous study, we suggested that an antibiotic biosynthesis monooxygenase-encoding gene (abm AWC34_RS01805) and a lincosamide nucleotydyltransferase-encoding gene (lnuA, AWC34_RS13300) might confer resistance to chloramphenicol and lincomycin, respectively13. These genes were also among the KM1031-specific genes and abm and lnuA were slightly upregulated by exposure to chloramphenicol and lincomycin, respectively (Table 1). The lincomycin-resistance phenotype of lnuA in strain KM1031 has already been reported22. These results imply that abm may confer resistance to chloramphenicol, although abm was not significantly upregulated by chloramphenicol treatment.

Table 1 Expression of S. equorum KM1031-specific genes (identified by comparative genomic analysis) following treatment with chloramphenicol, erythromycin or lincomycin.

To investigate the effect of the abm and msr genes on chloramphenicol and erythromycin resistance, the genes AWC34_RS01805 and AWC34_RS11115 were PCR amplified and then cloned into the pYJ335 and pCL55 vectors, respectively. The resulting plasmids were designated pYJ335-abm for the gene AWC34_RS01805 and pCL55-msr for the gene AWC34_RS11115. E. coli transformants harboring pYJ335-abm and pCL55-msr grew under chloramphenicol and erythromycin pressure, respectively (Fig. 4). Collectively, these results suggested that chloramphenicol and erythromycin treatment modified the expression of the abm and msr genes in S. equorum strain KM031, and that the gene products encoded by these genes contributed to the phenotypic resistance of E. coli cells to these antibiotics.

Figure 4
figure 4

Effect of overexpression of S. equorum strain KM1031 genes abm and msr on resistance of Escherichia coli to chloramphenicol and erythromycin, respectively.

Effects of antibiotics on two-component systems

Although we identified specific genes that may be responsible for the observed antibiotic resistance of S. equorum strain KM031, the transcriptional regulators of specific antibiotic resistance gene expression remained unclear. Two-component systems (TCSs) are the most common systems for bacterial signal transduction in response to environmental signals such as antibiotics and salts. TCS signaling is involved in bacterial resistance to antibiotics. Several TCSs have been detected in S. equorum genomes13. However, most TCS genes were not markedly up- or down-regulated in our experiments, except the WalKR TCS (Table 2). Although walKR genes were not significantly differentially expressed, expression of these genes increased following treatment with chloramphenicol, erythromycin, and lincomycin. The WalKR TCS regulates genes responsible for cell wall metabolism and homeostasis, as well as genes involved in stress responses, virulence, and biofilm formation23,24,25,26. Although the WalR consensus binding site (5′-TGTWAH N5 TGTWAH-3′)27 was not identified upstream of abm, msr or lnuA, we hypothesize that the WalKR TCS might be related to expression of these three antibiotic-specific responsive genes.

Table 2 Effects of antibiotics on expression of genes related to two-component systems.

Validation of RNA-Seq data by qRT-PCR

qRT-PCR was used to validate the S. equorum strain KM031 transcriptional profiles obtained by RNA-Seq analysis. As shown in Fig. 5, the expression patterns for each gene (abm, msr, and lnuA) were similar by qRT-PCR and RNA-Seq. Expression of the abm, msr and lnuA genes increased following exposure to chloramphenicol, erythromycin, and lincomycin, respectively. In addition, expression of walKR genes was increased following exposure the three antibiotics, although not significantly (Supplementary Fig. S3). Thus, our RNA-Seq data were confirmed by qRT-PCR.

Figure 5
figure 5

Validation of RNA sequencing data by quantitative real-time PCR (qRT-PCR). Genes related to resistance to chloramphenicol, erythromycin, and lincomycin were selected for validation under different antibiotic pressures. Data are expressed as log2 fold-changes in gene expression between control and antibiotic-treated samples. In qRT-PCR, 16S rRNA gene expression was used for normalization of target gene expression.


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