Skip to main content
Download PDF

Comparative proteomic analysis reveals drug resistance of Staphylococcus xylosus ATCC700404 under tylosin stress

BMC Veterinary Research volume 15, Article number: 224 (2019) Cite this article

Abstract

Background

As a kind of opportunist pathogen, Staphylococcus xylosus (S. xylosus) can cause mastitis. Antibiotics are widely used for treating infected animals and tylosin is a member of such group. Thus, the continuous use of antibiotics in dairy livestock enterprise will go a long way in increasing tylosin resistance. However, the mechanism of tylosin-resistant S. xylosus is not clear. Here, isobaric tag for relative and absolute quantitation (iTRAQ)-based quantitative proteomics methods was used to find resistance-related proteins.

Results

We compared the differential expression of S. xylosus in response to tylosin stress by iTRAQ. A total of 155 proteins (59 up-regulated, 96 down-regulated) with the fold-change of >1.2 or <0.8 (p value ≤0.05) were observed between the S. xylosus treated with 1/2 MIC (0.25 μg/mL) tylosin and the untreated S. xylosus. Bioinformatic analysis revealed that these proteins play important roles in stress-response and transcription. Then, in order to verify the relationship between the above changed proteins and mechanism of tylosin-resistant S. xylosus, we induced the tylosin-resistant S. xylosus, and performed quantitative PCR analysis to verify the changes in the transcription proteins and the stress-response proteins in tylosin-resistant S. xylosus at the mRNA level. The data displayed that ribosomal protein L23 (rplw), thioredoxin(trxA) and Aldehyde dehydrogenase A(aldA-1) are up-regulated in the tylosin-resistant S. xylosus, compared with the tylosin-sensitive strains.

Conclusion

Our findings demonstrate the important of stress-response and transcription in the tylosin resistance of S. xylosus and provide an insight into the prevention of this resistance, which would aid in finding new medicines .

Background

Staphylococcus xylosus (S. xylosus) is one of the leading causal pathogenic agent of subclinical mastitis in dairy cattle. It belongs to the coagulase-negative Staphylococcus (CNS) group. Dairy cow mastitis has resulted [1] in significant economic losses in the dairy industry worldwide [2] and this trend will continue in an exacerbating manner if urgent steps are not taken to control the activities of S. xylosus. Generally, macrolides and lincosamides are common antibiotics to treat infected cows [3]. Among these, tylosin is the most frequently used [4]. When bacteria are under drug seletion pressure for long time, they will evolve into resistance [5]. Thus, the long-term pressure of tylosin may lead to the prevalence of tylosin-tolerant S.xylosus.. Meanwhile, S. xylosus isolated from milk and some types of cheese exhibited multidrug resistance [6]. This has caused a series of public health problems and attracted the attention of veterinarians and dairy farmers. So, it is necessary to clearly understand the mechanisms of antibiotic resistance in order to enahnce the potency of commonly used antibiotics, and it is also helpful to find new target medicine. [7].

In general, classical antibiotic resistance mechanisms include four modes of action [8, 9]. Firstly, hydrolysis or modification leads to inactivation of drugs [8, 9]. Next, the drug target in the bacteria is altered or bypassed [8, 9]. Again, permeability changes of bacteria cell wall limit antimicrobial access. Finally, efflux exclude the antibiotic from the bacteria cell [8, 9]. In addition to the above general antibiotic resistance mechanism, there is another mechanism of acquired antimicrobial resistance, namely oxidative stress protective mechanisms which has been widely researched. Oxidative stress is a state of imbalance in the normal redox condition of cells. It occurs in cells when there is an increased production of ROS in cells without a resultant detoxification by the cell’s antioxidant defense mechanisms. This phenomenon is a major cause of apoptosis in the cells of living organisms [10]. Data from a resent investigation revealed that oxidative stress mediated pathways are responsible for the development of antibiotic resistance in bacteria [11]. Accumulating evidence have also revealed that antibiotics elicit the antibacterial effect through the initiation of oxidative stress in bacterial cells [12]. However, resistant bacteria have been reported to adapt and resist antibiotic-mediated oxidative stress by evolving a series of defensive mechanisms. They do this either through the detoxification of enzyme [13] as well as the detoxification of scavenging free radical substrates, or through their a system of DNA and protein regeneration or they are involved in competing for substrates which favor their survival and growth.

With recent advancement in proteomics, researchers have been to explore the mechanism of antibiotic resistance in bacteria. This include studies to evaluate the differences in the expression of bacterial whole protein in cells grown under different culture media or antibiotics-induced stress conditions. [14]. currently, the application of comparative proteomic methods have given researchers better understanding and insight in elucidating the mechanism behind antibiotic resistance in bacteria. Comparative proteomic methods is used as a tool to elucidate the pathways and network involved in protein regulation [15]. A very good example, is daptomycin, it was reported that LiaH protein is highly induced in cells treated with daptomycin [16]. A more recent report has shown that the level of daptomycin- resistant phenotype expressed by a bacteria is dependent on the of LiaH protein expression [17]. At the same time, quantitative proteomics method was used to monitor some important mechanisms of antibiotic resistance in clinic. [7]. It can be inferred that quantitative proteomic analysis can be used as an effective tool to reveal novel mechanism of antibiotic resistance. [7]. However, variations or alterations in the proteomics profile of S. xylosus against tylosin have not been reported.

This study applied the procedure of iTRAQ labeling-based quantitative proteomics to make comparison among the differentially expressed proteins of S. xylosus ATCC700704 with and without tolysin treatment. Bioinformatics analysis showed that some proteins could relate to transcription and stress-response, and the mechanism of tylosin resistance may be related to oxidative stress and transcription proteins.

In order to identify whether the above proteins were relevant to tylosin-resistance, S. xylosus ATCC700704 was induced under tylosin pressure in vitro. Quantitative PCR (qPCR) was further used to verify the candidate proteins at the mRNA level. To the best of our knowledge, this is the first study to use high-throughput labeling-based mass spectrometry (MS) to demonstrate the mechanism underlying the occurrence of tylosin resistance in S. xylosus. The results will provide an important insight in elucidating the mechanism of tylosin-resistant S. xylosus, and eliminating drug-resistant S. xylosus through screening the inhibitors of resistant proteins.

Results

Proteomics analysis of the differential expression of S. xylosus against tylosin

We investigated the effects of 1/2 MIC tylosin (0.25 μg/mL) on S. xylosus between the treated (using tylosin) and the untreated bacterial culture with isobaric reagents by quantitative proteomic analysis. And then, we pooled the samples together and then the mixture was fractioned by SCX chromatography, separated by LC and analyzed by MS/MS. Consequently, 1765 proteins were detected (Additional file 1). Based on a fold-change of >1.2 or < 0.8(with p value set at <0.05), 155 proteins significantly displayed differential expression between the S. xylosus treated with tylosin or untreated. 59 proteins were up-regulated, and 96 proteins were down-regulated (Fig. 1a). Detailed information can be found in Additional file 2 We also identified significant changes between two groups by K-means clustering heatmaps (Fig. 1b).

Fig. 1
figure 1

Significantly differential proteins of Staphylococcus xylosus ATCC700404 in 0.25 μg/mL tylosin stress using iTRAQ. a The number of altered proteins. b K-means clustering representation of total 155 DEP profiles. The magnitude of the percentage is represented by a color scale (top right) going from low (green) to high (red)

Full size image

Go annotation and KEGG pathway analyzed the altered proteins in S. xylosus under tyolsin stress

Bioinformatics were used to analyze the functional clusters of changed proteins in S. xylosus under tyolsin stress. Go ontology enrichment was applied to classify proteins in terms of their involvements into three main categories (biological process, molecular function and cellular component) and a specific term was used to describe each protein. Cellular component was mainly occupied by cell, membrane and macromolecular, which described the actions of a gene product at the molecular level (Fig. 2a). According to the biological process classifications, most of the proteins were associated with metabolic and cellular processes (Fig. 2b). Meanwhile, in the molecular function category, proteins related with catalytic activity and binding occupied the largest parts (Fig. 2c).

Fig. 2
figure 2

Go annotation and KEGG pathway of DEPs: Geneontology terms for subcellular laocation distribution. a cellular component (b) biological process (c) molecular function (d) main KEGG pathway

Full size image

In addition, KEGG pathway analysis was applied to better understand the tylosin effects on bacteria. Pathway analysis can provide a comprehensive, systematic and direct understanding of cell biology, resistance mechanisms, and drug mechanisms of action. Generally, 64 KEGG pathways were mapped. Among these, 21 pathways were identified as statistically significant (p < 0.05), including biosynthesis of amino acids, ribosome, carbon metabolism and so on. The detailed information was presented in Fig. 2d. Biosynthesis of amino acids included the greatest number of proteins, followed by the ribosome and other metabolic pathways.

Protein-protein interaction analysis

Protein-protein interaction and network analysis were used to explain the interaction of the 155 significant differentially expressed proteins by the web-based tool STRING. The results showed that 59 proteins connected with each other in the network. Based on the STRING analysis (Fig. 3), a complicated protein-protein interaction network was identified. As shown in Fig. 3, these proteins were mainly from the two clusters, which separately constituted a related network in response to tylosin stress. One cluster was involved in translation related ribosomal subunits including 30S ribosomal protein S2 (rpsB), 30S ribosomal protein S4 (rpsD), 30S ribosomal protein S14 (rpsZ), 50S ribosomal protein L14 (rplN), 50S ribosomal protein L23(rplW), 50S ribosomal protein L19(rplS) and translation initiation factor IF-2(infB). Another cluster was related to stress response including chaperonin (gros), thioredoxin (trxA), aldelyde dehydrgenase (aldA) and catalase (KAT).

Fig. 3
figure 3

String network of significantly differential proteins of Staphylococcus xylosus ATCC700404 in 0.25 μg/mL tylosin stress. Colored lines between the proteins indicate the various types of interaction evidence. Structure which is drawn in the protein nodes indicated the availability of 3D protein structure information

Full size image

Investigation of tylosin-resistant S. xylosus primary ribosome and stress response proteins at the mRNA level

To identify the veracity of proteomics data, real time PCR was applied to detect the mRNA levels of six proteins in the two groups of the S. xylosus treated with 1/2 MIC tylosin (0.25 μg/mL) and untreated at mRNA level. Similar to the proteomics data, we observed an increase in 50S ribosomal protein L23(rplW), translation initiation factor IF-2(infB) and aldelyde dehydrgenase (aldA) mRNA level, whereas mRNA level of thioredoxin (trxA), catalase (KAT) and chaperonin (gros) decreased. In addition, in order to identify the relationship between the ribosome and stress-response with tylosin-resistance, we induced the tylosin-resistant S. xylosus. Then, qPCR was applied to detect the mRNA level of the proteins in tylosin-resistant S. xylosus (Fig. 4). The data showed that rplW, trxA, aldA and gros mRNA in tylosin-resistant S. xylosus was up-regulated, compared to tylosin-sensitive strains. (Fig. 4b). However, the other genes were not changed at the mRNA level.

Fig. 4
figure 4

The string network and qPCR analysis of possible tylosin-resistant proteins. a The string network of primary tylosin-resistant proteins with altered expression. b The mRNA levels of two translation and four stress-response related genes were respectively analyzed by qPCR method in tylosin stress and tylosin-senstive Staphylococcus xylosus ATCC700404 and tylosin-resistant Staphylococcus xylosus ATCC700404. T: tylsoin, S.X.: tylosin-senstive Staphylococcus xylosus ATCC700404, S.X.-R: tylosin-resistant Staphylococcus xylosus ATCC700404

Full size image

Discussion

Antibiotics were usually as the first line of action for veterinarians and farmers to treat infected animals even without investigating the pathogen which is the causative agent of the mastitis [18]. The prolong usage or abuse of antibiotics may lead to bacteria resistance [19, 20]. Thus, a proper understanding of the antibiotic resistance is very important to enhance the potency of most of the currently available antibiotics. And then, this will also aid in the discovery of novel targets [7]. In previous studies, resistant and mutant genes in bacteria were widely found using genomic method. [21], and so on. This method could not properly elucidate how resistant bacteria live and operate in their host systems [22]. This method was limited in scope because the procedure cannot detect biological phenotypes from the pathways of gene biosynthesis. Therefore, this method could not provide a lucid explanation of the process which led to infection [23]. However, several authors have pointed out that proteomics methods may help researchers to identify the existence of target proteins, bacterial phenotype and as well as improve the development of new therapeutic protocols [7, 24]. In the last few years, application of quantitative proteomics methods has become a powerful tool for investigation of antibiotic resistance mechanisms [25]. This is due to the complex nature of the process involved in the identification and quantification of the functional proteins associated with antibiotics treatment in bacteria.

In this study, we applied sample-pooling comparative proteomics method to investigate the differences between S. xylosus treated with 0.25 μg/mL tylosin and untreated. Furthermore, we carried out an in-depth study to monitor the differentially expressed proteins in the two groups to elucidate the resistant mechanism of S. xylosus under tylosin.

From the study, we observed that 155 proteins showed significant differences in their expression profile. This was done in line with already established criteria (FDR<1%, the folds cut off higher than 1.2 or lower than 0.8). From bioinformatics analysis, we observed that proteins such as 30S ribosomal protein S2 (rpsB), 30S ribosomal protein S4 (rpsD), 30S ribosomal protein S14 (rpsZ), 50S ribosomal protein L14 (rplN), 50S ribosomal protein L23 (rplW), 50S ribosomal protein L19 (rplS) as well as translation initiation factor IF-2(infB) are abundantly increased under tylosin stress. Ribosomes, the site of protein synthesis, are a major target for natural and synthetic antibiotics [26]. Among these, macrolide bind to the 50 S subunit [26,27,28]. Recently X-ray crystal structures revealed the molecular details of how macrolide bind to the 50 S subunit. The Staphylococcus aureus crystal structures of 50 S subunit with telithromycin [29,30,31,32,33] revealed that the drug is bound at the pocket which is in the ribosome exit tunnel, forming the typical ketolide (and macrolide) hydrogen bond between its desosamine sugar and A2058. In addition, the macrolides bind to the same site in the 50S subunit of D. radiodurans, at the entrance of the tunnel [26]. In our study, we utilized proteomics techniques to shed light into the mechanism of action of tylosin against S. xylosus. The results showed that 50 S ribosomal proteins were significantly changed, demonstrating that tylosin inhibits the S. xylosus by acting on 50 S subunit. On the other hand, we inferred that the reason for the increasing translation expression can be due to the fact that the 50S ribosome attacks the function of tylosin and the major role that ribosome plays in living cells [34]. The expression of target subunits for survival can be seen as a resistance strategy of S. xylosu s. against antibiotics. In addition, most of antibiotics inhibited the bacterial cellular energy output processes using metabolomics methods. Therefore, the upregulation of ribosomal subunits can be seen as a defensive mechanism which bacteria deploy to protect itself against the activity of tylosin. The observations in our work at the proteome level are consistent with this hypothesis.

Meanwhile, proteins relevant to stress-response, such as chaperonin (gros), thioredoxin (trxA), aldelyde dehydrgenase (aldA) and catalase (KAT) were significantly changed under tylosin stress. Generally, microorganisms induce ROS generation under various types of antibiotics, including aminoglycosides, β-Lactams and fluoroquinolones, and consequently lead to loss of cell viability [35, 36]. The oxidative stress is a state caused by the imbalance of oxidant and antioxidant levels in cells and the inability to maintain normal physiological redox-regulated regulation. [37]. Similarly, earlier reports also affirmed that the generation of ROS is very important in the mediation of toxic products in bacteria cells [38, 39] However, Thai et al. had stated that the bacteria stress response has been thought to enable bacteria survival under various natural stresses, such as heat, cold and toxicants [40]. In Staphylococcus epidermidis, a dysfunctional TCA cycle enables it to resist oxidative stress, leading to the S. epidermidis becoming less susceptible to β-lactam antibiotics [41]. At the samne time, recent study showed that antibiotic resistance include broader bacterial stress responses, such as the SOS DNA stress response, heat shock response and oxidative stress response. These antibiotic-induced responses then contribute functionally to resistance [42]. Hence, the differential expression of stress-response related proteins was a possible mechanism responsible for fighting against the tylosin.

In order to identify the relationship between the translation and stress-response related proteins with tylosin resistance in vitro, the tylosin-resistant S. xylosus was induced. Among the significantly changed proteins, 6 proteins, including 50S ribosomal protein L23 (rplW), translation initiation factor IF-2(infB), chaperonin (gros), thioredoxin (trxA), aldelyde dehydrgenase (aldA) and catalase (KAT) were selected to investigate S. xylosus and tylosin-resistant S. xylosu at the mRNA levels. Because previous studies had shown that these proteins were related to drug resistance [43,44,45,46,47,48].

As it is well known, ribosomal proteins may have been developed to improve rRNA folding and stability and could be relied upon to carry out extra ribosomal functions [49]. Regardless of the primordial sequence of events, there is increasing evidence that ribosomal proteins possess other functions apart from joining in ribosome. Several authors [49,50,51,52,53,54,55,56] have reported over 30 special functions of these group of proteins. Among these, RPL23 (rplW) has been identified as upregulated proteins in multidrug-resistant gastric cancer cell line and it could promote multi-resistant phenotype of gastric cancer cells [43]. In addition, Wang et al. also observed that ribosomal protein L23 mRNA was highly expressed in vincristine-resistance cells [57]. Moreover, in our study, rplW was found to be up-regulated in tylosin-resistant strain. Our result was in accordance with previous studies. Thus the upregulation of rplW is reasonable and it could be possibly connected with tylosin-resistance.

Mutations abolishing formylation of Met-tRNAi confer resistance to actinonin since bypass of the formylation step makes the deformylation step and hence the PDF activity redundant [58]. And mutations in IF2 can partially compensate for the formylation deficiency in actinonin resistant strains with a normal concentration of initiator tRNA [44]. So IF2 played important role in the resistant strains. Previously, specifically engineered mutations in domain IV can improve the activity of IF2 in initiation with non-formylated Met-tRNAi and thus increases the affinity of IF2 for Met-tRNAi [59]. Besides, the new group of IF2 mutant genes may assist in promoting the rate of initiation with Met-tRNAi by increasing the propensity of IF2 to adopt the 50S docking conformation on the 30S ribosomal subunit not only when fMet-tRNAi is present, but also in the presence of Met-tRNAi, deacylated tRNAi and elongator tRNAs. So, the IF2 was chosen to identify the relationship with the tylosin resistance. Unexpectedly, infB(IF2) was not significantly changed in tylosin-resistant strain. According to the above researches, the mutations in IF2 would lead to drug resistance, but not the change of expression in mRNA level. Therefore, the unchanged IF2 in tylosin resistant strain can be understood.

As technology advances, there are accumulating evidence on antibiotics resistance, thus the focus of scientists have been directed towards the development of drugs that have multiple antibacterial mechanisms, including bacterial cell wall and cellular membrane inhibition as well as the retardation or blockage of pathways involved in DNA and protein synthesis [60], while antibiotics based on inhibition of bacteria thiol-dependent redox system is a new antibiotic strategy. [61]. The thiol-dependent enzyme systems in prokaryotes are classified into two major groups and this classification is based on the presence of thioredoxin (TrxA) and glutathione (GSH) their cells [62]. One of the major distinguishing characteristics of Gram-negative and Gram-positive bacteria has to do with the presence or absence of GSH in their cells. It has been reported that almost all Gram-negative bacteria have both GSH and glutaredoxin, while Gram-positive bacteria is characterized by the presence of glutaredoxin alone [63,64,65]. These two systems are very significant in the survival and growth of a bacteria. They are involved in the synthesis of DNA, enhance cellular defense against oxidative stress, regenerate damaged proteins due to oxidative stress and promote post-translational modifications [62, 66]. This thiol-dependent redox systems in bacteria can serve as a potential therapeutic target in the antibacterial drug design. This was demonstrated in a recent study involving the combination of silver and ebselen as strong inhibitors of both Trx and TrxR to attenuate the occurrence MDR in Gram-negative by significantly increasing antibiotic-mediated ROS generation in treated cells [61]. From our study, the result of the mRNA level of stress-response related gene, trxA showed a significant increase in tylosin-resistant strain, compared with control strain. Thus, the up-regulated expression of trxA could fight against oxidative stress and make the strain survive under tylosin pressure.

On the other hand, when bacteria are under natural stress conditions, the generation of reactive oxygen species (ROS) would lead to DNA damage due to reactions between H2O2 and the intracellular pool of labile iron [67,68,69]. Dwyer et al. gave an explanation for the lethal effect of antibiotics, it was reported that cell death occurs when enough of the lesions are generated to make replication impossible to recover [70]. Meanwhile, some of the lethal effects of aminoglycosides, β-lactams, and fluoroquinolones are due to the generation of ROS [35, 36, 71]. In order to fight against the damage, aldehyde dehydrigenases metabolize aldehydes and thereby mitigate oxidative stress [46]. Aldehyde dehydrogenase (ALDH) enzymes are involved in the resistance of cancer cells to many cytostatic drugs [72,73,74,75]. Meanwhile, a recent study indicated high expression levels of ALDH enzymes in CSCs, suggesting that these molecules can cooperate in the development of drug resistance in cancers [76]. In addition, ALDHs plays important role in cell communication and signaling, regulation of gene expression, metabolic regulations, drug extrusion and drug resistance [77]. Results from this investigation revealed that aldA in the mRNA level was markedly upregulated, when S. xylosus was resistant to tylosin. Thus, S. xylosus may survive in tylosin pressure, via expressing aldelyde dehydrgenase to withstand oxidative stress.

Besides, bacteria can respond to threats by inducing defensive enzymes [78]. For example, overproduction of superoxide dismutase, NADH peroxidase, catalase, MutT, and MutS all reduced the killing rate of E. coli by classical antibiotics [45]. However, KAT (catalase) mRNA level was not significantly changed in tylosin-resistant strain because bacteria can oppose oxidative stress. Perhaps, defensive enzymes are not the only pathway in tylosin resistant strain to survival. In addition, previous study demonstrated that mutants in ROS scavenging enzymes—superoxide dismutase, NADH peroxidase, and catalase—may lead to bacteria resistance [79, 80]. So the mutation of catalase may exist in the tylosin-resistant strain. Finally, increased mRNA levels of chaperones were observed in the tylosin-resistant strain in this study. This is consistent with previous studies, which have found that proteins play an important role in bacterial survival, and proteins tend to unfold and aggregate to fight against stress conditions. [48]. From the foregoing, these results corroborated the findings of previous studies, and confirm that the mechanism of tylosin resistance in S. xylosu is complex, systemic and intricate Conclusion.

We first depicted the differentially expressed proteins of S. xylosus in response to tylosin stress by iTRAQ-based proteomic analysis. We observed that there are several ways against the tylosin stress in S. xylosus, including ribosome and stress-response. Furthermore, we detected six related proteins in tylosin-resistant S. xylosus in mRNA level. The results showed that ribosomal related proteins including 50S ribosomal protein L23 (rplW) and stress response related proteins, thioredoxin (trxA), aldelyde dehydrgenase (aldA), catalase (KAT) and chaperonin (gros) chang significantly. So we can infer that these variations may be responsible for the tylosin resistance. Our data provided a new insight into the mechanism of tylosin resistance in tylosin-resistant S. xylosus and may be valuable in developing new targets in antibiotics resistant strains.

Methods

Cultivation of bacterial strains and determination of minimal inhibitory concentration with tylosin

S. xylosus ATCC700404 was grown in Tryptic Soy Broth (TSB) at 37 °C for 12 h with constant shaking [81]. Then, minimal inhibitory concentration (MIC) assay of tylosin was done as previously reported [81]. Briefly S. xylosus ATCC700404 was grown overnight at 37 °C.The overnight cultures were diluted in sterile physiological saline, which correspond to 1 × 108 colony-forming units/mL. After that, the above cultures were diluted again with TSB till a culture concentration of 1 × 106 colony-forming units/ mL was obtained. Finally, 100 mL of samples were added to a 96-well plate containing serial dilutions of tylosin in culture medium. Control bacterial culture and medium were cultivated in the absence of tylosin. The MIC was defined as the lowest concentration of tylosin to visually inhibit growth. The above assays were repeated 3 times.

iTRAQ analysis

Protein was extracted from S. xylosus treated with 1/2 MIC (0.25 μg/mL) Tylosin and untreated [81]. iTRAQ analysis was conducted at Shanghai Applied Protein Technology Co., Ltd. (APT, Shanghai, China). Bioinformatics analysis was conducted as previously described [81].

Bioinformation

The sequence data of the differentially expressed proteins were analyzed by AgriGo gene ontology (GO) [23] mapping and annotation. In addition, the Kycto Encyclopedia of Genes and Genomes (KEGG) pathway of altered protein was further categorized utilizing the same resource. Then, the altered proteins were illustrated using K-means clustering in conjunction with a heatmap [82]. Finally, the protein-protein network of the significantly differentially expressed proteins was analyzed by STRING software.

Selection of tylosin-resistant mutants of S. xylosus

Mutants were selected by serial passage in TSB medium containing successively increasing concentration of tylosin [83].Briefly, S. xylosus ATCC700404(final concentration:1 × 106 CFU/mL) in TSB was dispensed in a 96-well microtiter plates containing tylosin at increasing two-fold concentrations ranging from 0.5 to 16 fold which was the MIC determined for the ancestral strain. After 24 h of aerobic incubation at 37 °C, the culture containing the highest antibiotic concentration with detectable growth was used to inoculate another antibiotic dilution panel for the following passage series. The procedure was repeated until growth was obtained at a tylosin concentration of 128 μg/ml, corresponding to a 256-fold increase in antibiotic MIC for the ancestral strain. In addition, the stability of tylosin resistance was tested by serial passage (20 times) in an antibiotic-free medium. Investigation of altered proteins in the tylosin-resistant S. xylosus at the mRNA level was conducted by qPCR.

Total RNA was extracted from the S. xylosus ATCC700404 using an RNA kit according to the manufacturer’s instructions. Then, an equal quantity of total DNA-free RNA from the last sample was reverse-transcribed using Prim-Script TM RT reagent kit with gDNA Eraser. In this study, six proteins were chosen from the altered proteins (Table 1). Meanwhile, 16sRNA was used as an internal control and the primers used for the target genes are listed in Table 2. These reactions were performed for 40 cycles (95 °C for 15 s, 60 °C for 35 s) after initial 30s incubation at 95 °C. The assays were repeated 3 times.

Table 1 Proteins chosen for investigation of altered proteins in tylosin-resistant S. xylosus
Full size table
Table 2 Primers for RT-PCR
Full size table

Statistical analysis

Values were expressed as means ± SDs. The statistical differences among the different groups were compared by 1-way ANOVA, significant means were separated using Tukey’s Honest significant difference and p < 0.05.

Availability of data and materials

The datasets generated and analyzed during the current study are available in the link “https://figshare.com/account/home”.

Abbreviations

ALDH:

Aldehyde dehydrogenase

CNS:

Coagulase-negative Staphylococcus

iTRAQ:

Isobaric tag for relative and absolute quantitation

KEGG:

Kycto Encyclopedia of Genes and Genomes

LC-MS/MS:

Iquid chromatography-tandem mass spectrometry

MS:

Mass spectrometry

qPCR:

Quantitative PCR

S. xylosus :

Staphylococcus xylosus

References

  1. Tremblay YDN, Lamarche D, Chever P, et al. Characterization of the ability of coagulase-negative staphylococci isolated from the milk of Canadian farms to form biofilms. J Dairy Sci. 2013;96(1):234.

    Article  CAS  PubMed  Google Scholar 

  2. Anderson KL, Azizoglu RO. Detection and causes of bovine mastitis with emphasis on Staphylococcus aureus. Encyclopedia of Agriculture & Food Systems; 2014. p. 435–40.

    Google Scholar 

  3. Frey Y, Rodriguez JP, Thomann A, et al. Genetic characterization of antimicrobial resistance in coagulase-negative staphylococci from bovine mastitis milk. J Dairy Sci. 2013;96(4):2247–57.

    Article  CAS  PubMed  Google Scholar 

  4. Entorf M, Feßler AT, Kadlec K, et al. Tylosin susceptibility of staphylococci from bovine mastitis. Vet Microbiol. 2014;171(3–4):368–73.

    Article  CAS  PubMed  Google Scholar 

  5. Tugce O, Aysegul G, Sadik Y, et al. Strength of selection pressure is an important parameter contributing to the complexity of antibiotic resistance evolution. Mol Biol Evol. 2014;31(9):2387.

    Article  CAS  Google Scholar 

  6. M M, J V, R D, et al. Multiresistance of Staphylococcus xylosus and Staphylococcus equorum from Slovak Bryndza cheese. Folia Microbiol. 2014;59(3):223–7.

    Article  CAS  Google Scholar 

  7. Lee CR, Lee JH, Park KS, et al. Quantitative proteomic view associated with resistance to clinically important antibiotics in gram-positive bacteria: a systematic review. Front Microbiol. 2015;6:828.

    PubMed  PubMed Central  Google Scholar 

  8. Blair JM, Webber MA, Baylay AJ, et al. Molecular mechanisms of antibiotic resistance. Nat Rev Microbiol. 2015;13(1):42–51.

    Article  CAS  PubMed  Google Scholar 

  9. Li H, Zhang DF, Lin XM, et al. Outer membrane proteomics of kanamycin-resistant Escherichia coli identified MipA as a novel antibiotic resistance-related protein. FEMS Microbiol Lett. 2015;362(11):fnv074-fnv074.

  10. Halliwell B. Free radicals, antioxidants, and human disease. Curiosity, cause, or consequence? Lancet. 1994;344(8924):721.

    Article  CAS  PubMed  Google Scholar 

  11. Imlay JA. The molecular mechanisms and physiological consequences of oxidative stress: lessons from a model bacterium. Nat Rev Microbiol. 2013;11(7):443–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Jinki Y, Imlay JA, Woojun P. Iron homeostasis affects antibiotic-mediated cell death in Pseudomonas species. J Biol Chem. 2010;285(29):22689–95.

    Article  CAS  Google Scholar 

  13. D'Costa VM, Mcgrann KM, Hughes DW. Sampling the antibiotic resistome. Science. 2006;311(5759):374–7.

    Article  CAS  PubMed  Google Scholar 

  14. Radhouani H, Pinto L, Poeta P, et al. After genomics, what proteomics tools could help us understand the antimicrobial resistance of Escherichia coli? J Proteome. 2012;75(10):2773–89.

    Article  CAS  Google Scholar 

  15. Kohanski MA, Dwyer DJ, Collins JJ. How antibiotics kill bacteria: from targets to networks. Nat Rev Microbiol. 2010;8(6):423–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Muthaiyan A, Silverman JA, Jayaswal RK, et al. Transcriptional profiling reveals that Daptomycin induces the Staphylococcus aureus Cell Wall stress Stimulon and genes responsive to membrane depolarization. Antimicrob Agents Chemother. 2008;52(3):980–90.

    Article  CAS  PubMed  Google Scholar 

  17. Reyes J, Panesso D, Tran TT, et al. A liaR deletion restores susceptibility to Daptomycin and antimicrobial peptides in multidrug-resistant enterococcus faecalis. J Infect Dis. 2015;211(8):1317.

    Article  CAS  PubMed  Google Scholar 

  18. Saini V, Mcclure JT, Scholl DT, et al. Herd-level association between antimicrobial use and antimicrobial resistance in bovine mastitis Staphylococcus aureus isolates on Canadian dairy farms. J Dairy Sci. 2012;95(4):1921.

    Article  CAS  PubMed  Google Scholar 

  19. Todorović D, Velhner M, Grego E, et al. Molecular characterization of multidrug-resistant Escherichia coli isolates from bovine clinical mastitis and pigs in the Vojvodina Province, Serbia. Microb Drug Resist. 2017;mdr.2017.0016

  20. De AJ, Garch FE, Simjee S, et al. Monitoring of antimicrobial susceptibility of udder pathogens recovered from cases of clinical mastitis in dairy cows across Europe: VetPath results. Vet Microbiol. 2018;213:73–81.

    Article  CAS  Google Scholar 

  21. Descours G, Ginevra C, Jacotin N, et al. Ribosomal Mutations Conferring Macrolide Resistance in Legionella pneumophila. Antimicrob Agents Chemother. 2017;61(3):e02188-16..

  22. Chao TC, Hansmeier N. The current state of microbial proteomics: where we are and where we want to go. Proteomics. 2012;12(4–5):638–50.

    Article  CAS  PubMed  Google Scholar 

  23. Sun L, Chen H, Lin W, et al. Quantitative proteomic analysis of Edwardsiella tarda in response to oxytetracycline stress in biofilm. J Proteome. 2017;150:141–8.

    Article  CAS  Google Scholar 

  24. Brötz-Oesterhelt H, Bandow JE, Labischinski H. Bacterial proteomics and its role in antibacterial drug discovery. Mass Spectrom Rev. 2005;24(4):549–65.

    Article  PubMed  CAS  Google Scholar 

  25. Kaatz GW, Thyagarajan RV, Seo SM. Effect of promoter region mutations and mgrA overexpression on transcription of norA, which encodes a Staphylococcus aureus multidrug efflux transporter. Antimicrob Agents Chemother. 2005;49(1):161–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. F S, R Z, J H, et al. Structural basis for the interaction of antibiotics with the peptidyl transferase Centre in eubacteria. Nature. 2001;413(6858):814–21.

    Article  Google Scholar 

  27. Hansen JL, Ippolito JA, Ban N, et al. The structures of four macrolide antibiotics bound to the large ribosomal subunit. Mol Cell. 2002;10(1):117–28.

    Article  CAS  PubMed  Google Scholar 

  28. Schlünzen F, Harms JM, Franceschi F, et al. Structural basis for the antibiotic activity of Ketolides and Azalides. Structure. 2003;11(3):329–38.

    Article  PubMed  Google Scholar 

  29. Rita B, Joerg H, Frank S, et al. Structural insight into the antibiotic action of telithromycin against resistant mutants. J Bacteriol. 2003;185(14):4276.

    Article  CAS  Google Scholar 

  30. Tu D, Blaha G, Moore PB, Steitz TA. Structures of MLS B K antibiotics bound to mutated large ribosomal subunits provide a structural explanation for resistance. Cell. 2005;121(2):257–70.

    Article  CAS  PubMed  Google Scholar 

  31. Dunkle JA, Liqun X, Mankin AS, Cate JHD. Structures of the Escherichia coli ribosome with antibiotics bound near the peptidyl transferase center explain spectra of drug action. Proc Natl Acad Sci U S A. 2010;107(40):17152–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Bulkley D, Innis CA, Blaha G, Steitz TA. Revisiting the structures of several antibiotics bound to the bacterial ribosome. Proc Natl Acad Sci U S A. 2010;107(40):17158–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Zohar E, Donna M, Miri K, et al. Structural insights into species-specific features of the ribosome from the pathogen Staphylococcus aureus. Proc Natl Acad Sci U S A. 2015;112(43):5805–14.

    Article  CAS  Google Scholar 

  34. Lobritz MA, Belenky P, Porter CB, et al. Antibiotic efficacy is linked to bacterial cellular respiration. Proc Natl Acad Sci U S A. 2015;112(27):8173.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Kohanski MA, Dwyer DJ, Hayete B, et al. A common mechanism of cellular death induced by bactericidal antibiotics. Cell. 2007;130(5):797.

    Article  CAS  PubMed  Google Scholar 

  36. Kohanski MA, Dwyer DJ, Wierzbowski J, et al. Mistranslation of membrane proteins and two-component system activation trigger antibiotic-mediated cell death. Cell. 2008;135(4):679–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Meng JY, Zhang CY, Zhu F, et al. Ultraviolet light-induced oxidative stress: effects on antioxidant response of Helicoverpa armigera adults. J Insect Physiol. 2009;55(6):588–92.

    Article  CAS  PubMed  Google Scholar 

  38. Kim YS, Min J, Hongz HN, et al. Analysis of the stress effects of endocrine disrupting chemicals (EDcs) on Escherichia coli. J Microbiol Biotechnol. 2007;17(8):1390.

    CAS  PubMed  Google Scholar 

  39. Karunakaran U, Park KG. A systematic review of oxidative stress and safety of antioxidants in diabetes: focus on islets and their defense. Diabetes Metab J. 2013;37(2):106.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Thai VC, Lim TK, Le KP, et al. iTRAQ-based proteome analysis of fluoroquinolone-resistant Staphylococcus aureus. J Glob Antimicrob Resist. 2016.

  41. Thomas VC, Kinkead LC, Janssen A, Schaeffer CR, Woods KM, et al. A dysfunctional tricarboxylic acid cycle enhances fitness of Staphylococcus epidermidis during beta-lactam stress. Mbio. 2013;4(4):01307-14.

  42. Nandakumar M, Nathan C, Rhee KY. Isocitrate lyase mediates broad antibiotic tolerance in mycobacterium tuberculosis. Nat Commun. 2014;5(5):4306.

    Article  CAS  PubMed  Google Scholar 

  43. Shi Y, Zhai H, Wang X, et al. Ribosomal proteins S13 and L23 promote multidrug resistance in gastric cancer cells by suppressing drug-induced apoptosis. Exp Cell Res. 2004;296(2):337.

    Article  CAS  PubMed  Google Scholar 

  44. Zorzet A, Pavlov MY, Nilsson AI, et al. Error-prone initiation factor 2 mutations reduce the fitness cost of antibiotic resistance. Mol Microbiol. 2010;75(5):1299–313.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Holmgren A. Thioredoxin and glutaredoxin systems. J Biol Chem. 1989;264(24):13963–6.

    CAS  PubMed  Google Scholar 

  46. Perozich J, Nicholas H, et al. Relationships within the aldehyde dehydrogenase extended family. Protein Sci. 1999;8(1):137–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Dwyer DJ, Belenky PA, Yang JH, et al. Antibiotics induce redox-related physiological alterations as part of their lethality. Proc Natl Acad Sci U S A. 2014;111(20):2100–9.

    Article  CAS  Google Scholar 

  48. Frees D, Chastanet A, Qazi S, et al. Clp ATPases are required for stress tolerance, intracellular replication and biofilm formation in Staphylococcus aureus. Mol Microbiol. 2004;54(5):1445–62.

    Article  CAS  PubMed  Google Scholar 

  49. Wool IG. Extraribosomal functions of ribosomal proteins. Trends Biochem Sci. 1996;21(5):164.

    Article  CAS  PubMed  Google Scholar 

  50. Ganger DR, Hamilton PD, Klos DJ, et al. Differential expression of metallopanstimulin/S27 ribosomal protein in hepatic regeneration and neoplasia. Cancer Detect Prev. 2001;25(3):231–6.

    CAS  PubMed  Google Scholar 

  51. Grillari J, Hohenwarter O, Grabherr RM, et al. Subtractive hybridization of mRNA from early passage and senescent endothelial cells. Exp Gerontol. 2000;35(2):187–97.

    Article  CAS  PubMed  Google Scholar 

  52. Loging W, Reisman D. Elevated expression of ribosomal protein genes L37, RPP-1, and S2 in the presence of mutant p53. Cancer Epidemiol Biomark Prev. 1999;8(11):1011–6.

    CAS  Google Scholar 

  53. Naora H, Takai I, Adachi M, et al. Altered cellular responses by varying expression of a ribosomal protein gene: sequential coordination of enhancement and suppression of ribosomal protein S3a gene expression induces apoptosis. J Cell Biol. 1998;141(3):741–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Vogelstein B, Kinzler KW, Zhang L, et al. Gene expression profiles in normal and cancer cells. Science. 1997;276(5316):1268-1272.

    Article  CAS  PubMed  Google Scholar 

  55. Neumann F, Krawinkel U. Constitutive expression of human ribosomal protein L7 arrests the cell cycle in G1 and induces apoptosis in Jurkat T-lymphoma cells. Exp Cell Res. 1997;230(2):252.

    Article  CAS  PubMed  Google Scholar 

  56. Chan YL, Diaz JJ, Denoroy L, et al. The primary structure of rat ribosomal protein L10: relationship to a Jun-binding protein and to a putative Wilms' tumor suppressor. Biochem Biophys Res Commun. 1996;225(3):952.

    Article  CAS  PubMed  Google Scholar 

  57. Ross DD. Novel mechanisms of drug resistance in leukemia. Leukemia. 2000;14(3):467.

    Article  CAS  PubMed  Google Scholar 

  58. Apfel CM, Locher H, Evers S, et al. Peptide deformylase as an antibacterial drug target: target validation and resistance development. Antimicrob Agents Chemother. 2001;45(4):1058–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Steiner-Mosonyi M, Creuzenet C, Keates RA, et al. The Pseudomonas aeruginosa initiation factor IF-2 is responsible for formylation-independent protein initiation in P. aeruginosa. J Biol Chem. 2004;279(50):52262–9.

    Article  CAS  PubMed  Google Scholar 

  60. Walsh C. Where will new antibiotics come from? Nat Rev Microbiol. 2003;1(1):65–70.

    Article  CAS  PubMed  Google Scholar 

  61. Zou L, Lu J, Wang J, et al. Synergistic antibacterial effect of silver and ebselen against multidrug-resistant gram-negative bacterial infections. Embo Mol Med. 2017;9(8):1165.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Lillig CH, Holmgren A. Thioredoxin and related molecules--from biology to health and disease. Antioxid Redox Signal. 2006;9(1):25.

    Article  Google Scholar 

  63. Martin JL. Thioredoxin —a fold for all reasons. Structure. 1995;3(3):245–50.

    Article  CAS  PubMed  Google Scholar 

  64. Wilkinson LJ, White RJ, Chipman JK. Silver and nanoparticles of silver in wound dressings: a review of efficacy and safety. J Wound Care. 2011;20(11):543–9.

    Article  CAS  PubMed  Google Scholar 

  65. Lu J, Holmgren A. The thioredoxin antioxidant system. Free Radic Biol Med. 2014;66(8):75–87.

    Article  CAS  PubMed  Google Scholar 

  66. And DR, Beckwith J. Roles of thiol-redox pathways in Bacteria. Annu Rev Microbiol. 2001;55(1):21–48.

    Article  Google Scholar 

  67. Farr SB, D'Ari R, Touati D. Oxygen-Dependent Mutagenesis in Escherichia coli Lacking Superoxide Dismutase. Proc Natl Acad Sci U S A. 1986;83(21):8268.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Touati D, Jacques M, Tardat B, et al. Lethal oxidative damage and mutagenesis are generated by iron in delta fur mutants of Escherichia coli: protective role of superoxide dismutase. J Bacteriol. 1995;177(9):2305–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Keyer K, Imlay JA. Superoxide accelerates DNA damage by elevating free-iron levels. Proc Natl Acad Sci U S A. 1996;93(24):13635–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Dwyer DJ, Collins JJ, Walker GC. Unraveling the physiological complexities of antibiotic lethality. Annu Rev Pharmacol Toxicol. 2015;55(1):313–32.

    Article  CAS  PubMed  Google Scholar 

  71. Foti JJ, Devadoss B, Winkler JA, et al. Oxidation of the guanine nucleotide Pool underlies cell death by bactericidal antibiotics. Science. 2012;336(6079):315–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Hilton J. Role of aldehyde dehydrogenase in cyclophosphamide-resistant L1210 leukemia. Cancer Res. 1984;44(11):5156–60.

    CAS  PubMed  Google Scholar 

  73. Sun QL, Sha HF, Yang XH, et al. Comparative proteomic analysis of paclitaxel sensitive A549 lung adenocarcinoma cell line and its resistant counterpart A549-Taxol. J Cancer Res Clin Oncol. 2011;137(3):521–32.

    Article  CAS  PubMed  Google Scholar 

  74. Tomonori T, Koji M, Kenzo S, et al. Association of breast cancer stem cells identified by aldehyde dehydrogenase 1 expression with resistance to sequential paclitaxel and epirubicin-based chemotherapy for breast cancers. Clin Cancer Res. 2009;15(12):4234–41.

    Article  CAS  Google Scholar 

  75. Croker AK, Allan AL. Inhibition of aldehyde dehydrogenase (ALDH) activity reduces chemotherapy and radiation resistance of stem-like ALDHhiCD44+ human breast cancer cells. Breast Cancer Res Treat. 2012;133(1):75–87.

    Article  CAS  PubMed  Google Scholar 

  76. RadosAw J, Karolina W, Maciej Z. The role of aldehyde dehydrogenase (ALDH) in cancer drug resistance. Biomed Pharmacother. 2013;67(7):669–80.

    Article  CAS  Google Scholar 

  77. Vasiliou V, Thompson DC, Smith C, et al. Aldehyde dehydrogenases: from eye crystallins to metabolic disease and cancer stem cells. Chem Biol Interact. 2013;202(1–3):2–10.

    Article  CAS  PubMed  Google Scholar 

  78. Imlay JA. Diagnosing oxidative stress in bacteria: not as easy as you might think. Curr Opin Microbiol. 2015;24:124–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Liu Y, Imlay JA. Cell death from antibiotics without the involvement of reactive oxygen species. Science. 2013;339(6124):1210.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Ezraty B, Vergnes A, Banzhaf M, et al. Fe-S cluster biosynthesis controls uptake of aminoglycosides in a ROS-less death pathway. Science. 2013;340(6140):1583.

    Article  CAS  PubMed  Google Scholar 

  81. Xu CG, Yang YB, Zhou YH, et al. Comparative proteomic analysis provides insight into the key proteins as possible targets involved in aspirin inhibiting biofilm formation of Staphylococcus xylosus. Front Pharmacol. 2017;8:543.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Z S, C Z, W L, et al. Microarray expression profile analysis of long non-coding RNAs in umbilical cord plasma reveals their potential role in gestational diabetes-induced macrosomia. Cell Physiol Biochem. 2015;36(2):542–54.

    Article  CAS  Google Scholar 

  83. Kim S, Lieberman TD, Kishony R. Alternating antibiotic treatments constrain evolutionary paths to multidrug resistance. Proc Natl Acad Sci U S A. 2014;111(40):14494–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors are grateful to all members of the lab of veterinary medicine for their insightful and thought provoking discussions.

Funding

Our appreciation goes to the earmarked fund for China Agriculture Research System-35 and National Key Research and Development Program of China (2018YFD0500300) for sponsoring the project.

Author information

Authors and Affiliations

  1. College of Veterinary Medicine, Northeast Agricultural University, 600 Road Changjiang, Xiangfang, Harbin, Heilongjiang, 150030, People’s Republic of China

    Xin Liu, Jinpeng Wang, Mo Chen, Ruixiang Che, Wenya Ding, Fei Yu, Yonghui Zhou, Wenqiang Cui, Xing Xiaoxu, Bello-Onaghise God’spower & Yanhua Li

Authors
  1. Xin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinpeng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mo Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ruixiang Che
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wenya Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Fei Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yonghui Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Wenqiang Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xing Xiaoxu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Bello-Onaghise God’spower
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Yanhua Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YL conceived and designed the project; XL, JW, MC, YF and XX performed the experiments; YZ, RC and WC interpreted the data; XL wrote the manuscript; GB and WD modified the language. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yanhua Li.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Additional files

Additional file 1:

Proteins of S. xylosus in response to tylosin treatment by quantitative proteomic method. Comparison between S. xylosus ATCC700404 and the proteins of S. xylosus treated with 0.25 μg/mL tylosin analyzed by iTRAQ. 1765 proteins were detected. (XLSX 207 kb)

Additional file 2:

Altered proteins of S. xylosus in response to tylosin treatment by quantitative proteomic method. Comparison between S. xylosus ATCC700404 and the proteins of S. xylosus treated with 0.25 μg/mL tylosin analyzed by iTRAQ. Based on a fold-change of >1.2 or < 0.8(P-value<0.05), 155 proteins significantly displayed differential expression. (XLSX 2612 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, X., Wang, J., Chen, M. et al. Comparative proteomic analysis reveals drug resistance of Staphylococcus xylosus ATCC700404 under tylosin stress. BMC Vet Res 15, 224 (2019). https://doi.org/10.1186/s12917-019-1959-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12917-019-1959-9

Keywords

BMC Veterinary Research

ISSN: 1746-6148

Contact us