Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Elevation of Fatty Acid Biosynthesis Metabolism Contributes to Zhongshengmycin Resistance in Xanthomonas oryzae

Elevation of Fatty Acid Biosynthesis Metabolism Contributes to Zhongshengmycin Resistance in... antibiotics Article Elevation of Fatty Acid Biosynthesis Metabolism Contributes to Zhongshengmycin Resistance in Xanthomonas oryzae Qiaoxing Wang, Meiyun Lin, Peihua Shen and Yi Guan * Fujian Key Laboratory of Marine Enzyme Engineering, College of Biological Science and Engineering, Fuzhou University, Fuzhou 350116, China; N190820026@fzu.edu.cn (Q.W.); 200827031@fzu.edu.cn (M.L.); 200827045@fzu.edu.cn (P.S.) * Correspondence: gy@fzu.edu.cn Abstract: Xanthomonas oryzae severely impacts the yield and quality of rice. Antibiotics are the most common control measure for this pathogen; however, the overuse of antibiotics in past decades has caused bacterial resistance to these antibiotics. The agricultural context is of particular importance as antibiotic-resistant bacteria are prevalent, but the resistance mechanism largely remains unex- plored. Herein, using gas chromatography–mass spectrometry (GC–MS), we demonstrated that zhongshengmycin-resistant X. oryzae (Xoo-Rzs) and zhongshengmycin-sensitive X. oryzae (Xoo-S) have distinct metabolic profiles. We found that the resistance to zhongshengmycin (ZS) in X. oryzae is related to increased fatty acid biosynthesis. This was demonstrated by measuring the Acetyl-CoA carboxylase (ACC) activity, the expression levels of enzyme genes involved in the fatty acid biosyn- thesis and degradation pathways, and adding exogenous materials, i.e., triclosan and fatty acids. Our work provides a basis for the subsequent control of the production of antibiotic-resistant strains of X. oryzae and the development of coping strategies. Citation: Wang, Q.; Lin, M.; Shen, P.; Keywords: Xanthomonas oryzae; antibiotics; antibiotic-resistant; zhongshengmycin; fatty acid Guan, Y. Elevation of Fatty Acid biosynthesis metabolism Biosynthesis Metabolism Contributes to Zhongshengmycin Resistance in Xanthomonas oryzae. Antibiotics 2021, 10, 1166. https://doi.org/10.3390/ 1. Introduction antibiotics10101166 Rice is one of the most widely cultivated and economically important crops in the world and is related to the survival of half of the world’s population [1]. However, Academic Editor: Marc Maresca the quality and yield of rice are threatened by Xanthomonas oryzae, a Gram-negative bac- terium. X. oryzae is characterized as being short and rod-shaped, with a layer of mucinous Received: 16 August 2021 extracellular polysaccharide on the surface, which makes its colony yellow and shiny [2,3]. Accepted: 22 September 2021 Published: 25 September 2021 Xanthomonas oryzae pv. oryzae and Xanthomonas oryzae pv. oryzicola are two main pathovars of X. oryzae that infect rice leaves through wounds or stomata, and cause rice leaf blight and rice leaf streak [4–6]. These two diseases often occur in rice-growing areas, such as Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in Japan, India, and China, and are explosive and destructive in nature, sometimes leading to published maps and institutional affil- more than a 50% reduction in production or, in extreme cases, a harvestless year [3,7]. iations. In the field of pathogen control, agricultural, biological, and chemical measures are the three main strategies [8,9]. As compared with biological and chemical measures, agricultural control is a more traditional and simpler way to avoid crop diseases, but these methods are often limited by the labor force, planting area, and the climatic environment. Genetic breeding is the most direct measure to control X. oryzae. This involves selecting Copyright: © 2021 by the authors. natural mutations that confer resistance to the infection. Importantly, the selected strains Licensee MDPI, Basel, Switzerland. This article is an open access article can then be further propagated for large-scale production [10]. Nonetheless, this process is distributed under the terms and time-consuming and labor-intensive. To fill this gap, chemical measures, and antibiotics in conditions of the Creative Commons particular, are widely adopted to treat X. oryzae infection. However, the misuse/overuse of Attribution (CC BY) license (https:// antibiotics causes widespread antibiotic resistance, and these bacteria are insensitive to the creativecommons.org/licenses/by/ currently available antibiotics. Recently, antibiotic-resistant X. oryzae strains were identified 4.0/). Antibiotics 2021, 10, 1166. https://doi.org/10.3390/antibiotics10101166 https://www.mdpi.com/journal/antibiotics Antibiotics 2021, 10, 1166 2 of 13 in a number of studies [11–13]. Thus, the mechanisms underlying drug resistance have aroused the interest of researchers. Zhongshengmycin (ZS herein) is a low toxicity aminoglycoside antibiotic produced by Streptomyces lavendulae var. Hainanensis in Hainan, which is widely used as a chemical bac- tericide in the control of phytopathogens. Previous studies showed that ZS can be used to control vegetable bacterial diseases, rice bacterial leaf blight, and fruit tree diseases [14,15]. Additionally, ZS has good herbicidal and fungicidal properties and has been used as a control agent with which to study the activity of many new compounds [16,17]. Currently, ZS is applied in 10 provinces in Southeast Asia for the control of plant bacterial diseases, and economic benefits have been reported [18]. However, as a result of drug resistance, the control effect of ZS on rice bacterial blight has become negligible, and it will take a long time for new effective agents to be developed. Therefore, it is necessary to study the resistance mechanism of X. oryzae to ZS to restore ZS-mediated efficacy. Four main mechanisms of bacterial antibiotics resistance have been revealed: the production of inactivating enzymes, changing the target of antimicrobial agents, activating the efflux pump system, and changing the permeability of the outer membrane [19–21]. Recently, the key role that metabolism plays in bacterial antibiotics resistance has been demonstrated [22–24]. For example, a low abundance of respiratory nitrate reductase is essential for Escherichia coli resistance to aminoglycoside and cephalosporin [25]. The low efficiency of the central carbon and energy metabolism was shown to mediate the resistance of Vibrio alginolyticus to levofloxacin and ceftazidime [26,27]. Drug-resistant bacteria can provide protection for other more vulnerable bacteria through the secondary metabolite indole so as to improve the survival ability of the whole population in stressful environments [28]. The existing research on X. oryzae is mainly focused on the effects that biological agents, nano agents, and gene knockout have on its virulence and con- trol [8,29–31]; however, studies concerning the mechanism of drug resistance of X. oryzae from the perspective of metabolomics are lacking. Metabolomics is a newly developed discipline and has become a research hotspot in re- cent years. Gas chromatography–mass spectrometry (GC–MS) is a useful technical method to collect the metabolite spectrum of samples through full spectrum analyses. Recently, metabolomics assays have been extensively used to analyze the metabolic mechanism of microbial resistance [32,33]. For instance, alanine metabolism promoted the production of reactive oxygen species, while glucose increased the metabolism of glycine, serine, and threonine, which promoted the killing of kanamycin-mediated antibiotic-resistant bacte- ria [34,35]. Fructose, glycine, and serine were the key metabolites promoting the growth and antifungal activity of Penicillium citrinum W1 [36,37]. Therefore, the GC–MS-based metabolomics method is an ideal approach with which to explore the metabolic mechanism of X. oryzae resistance to ZS. In the present study, we compared the metabolomes of zhongshengmycin-resistant strain X. oryzae (Xoo-Rzs) and zhongshengmycin-sensitive strain X. oryzae (Xoo-S) to investigate the metabolic signature of ZS-resistant X. oryzae. Our research not only revealed the metabolomic mechanisms of antibiotics resistance in Xoo-Rzs but also provided a novel method for the control of antibiotics-resistant X. oryzae. 2. Results 2.1. Phenotypes of Xoo-Rzs In order to investigate the resistance mechanism of Xanthomonas oryzae to ZS, we established a ZS-resistant X. oryzae, i.e., Xoo-Rzs, which exhibited 16-fold higher MIC levels than the parental strain, i.e., X. oryzae (Xoo-S) (Figure 1A). The growth rates of Xoo-Rzs were lower than those of Xoo-S during 0–16 h, but the total production showed no significant difference between the two strains after a 28 h culture (Figure 1B), and the growth rates of the two strains at specific timing points were shown in Table S2 (Supplementary Materials). Antibiotics 2021, 10, x FOR PEER REVIEW 3 of 13 Antibiotics 2021, 10, 1166 3 of 13 rates of the two strains at specific timing points were shown in Table S2 (Supplementary Materials). Figure 1. Phenotypes of Xoo-S and Xoo-Rzs. (A) MIC of zhongshengmycin (ZS) in Xoo-S and Xoo- Figure 1. Phenotypes of Xoo-S and Xoo-Rzs. (A) MIC of zhongshengmycin (ZS) in Xoo-S and Xoo- Rzs. Xoo-S and Xoo-Rzs were incubated with PSA broth containing ZS ranging from 0 to 24 µg/mL Rzs. Xoo-S and Xoo-Rzs were incubated with PSA broth containing ZS ranging from 0 to 24 g/mL in 24-well plates at 30 °C for 18 h. (B) Growth curve of Xoo-S and Xoo-Rzs. Xoo-S and Xoo-Rzs were in 24-well plates at 30 C for 18 h. (B) Growth curve of Xoo-S and Xoo-Rzs. Xoo-S and Xoo-Rzs diluted to 1:100 using fresh PSA medium and grown in the indicated periods. The OD600 of the bac- were diluted to 1:100 using fresh PSA medium and grown in the indicated periods. The OD of terial cultures was measured at the indicated time. Note: The results in (B) are displayed as mean ± the bacterial cultures was measured at the indicated time. Note: The results in (B) are displayed as mean SD. At least  SD. At three bio least threelogical re biologicalpeats were per repeats were performed. formed. 2.2. Difference in Metabolic Spectrum between Xoo-Rzs and Xoo-S 2.2. Difference in Metabolic Spectrum between Xoo-Rzs and Xoo-S In order to gain insight into the metabolic alterations in Xoo-Rzs, Xoo-Rzs and Xoo-S In order to gain insight into the metabolic alterations in Xoo-Rzs, Xoo-Rzs and Xoo- were cultured and collected for GC–MS analysis. The bacteria were incubated into 3 mL PSA S we br re oth cultur as a ed seed and cultur collect e fored 24for h at GC–M 30 C, S and analy then sis. Th transferr e bacter ed to ia w 50 e mL re inc PSA ubated for into 3 mL enlarging cultivation. The sampling point was approximately 48 h, until the OD of the PSA broth as a seed culture for 24 h at 30 °C, and then transferred to 50 mL PSA for en- cultures reached 1.0. Five biological replicates for each strain yielded a total of 10 datasets. larging cultivation. The sampling point was approximately 48 h, until the OD600 of the After removing the internal standard, i.e., ribitol, and combining the same compounds, a cultures reached 1.0. Five biological replicates for each strain yielded a total of 10 datasets. total of 70 metabolites were identified (Figure S1A). The categories of the identified metabo- After removing the internal standard, i.e., ribitol, and combining the same compounds, a lites were investigated using the Kyoto Encyclopedia of Genes and Genomes (KEGG). total of 70 metabolites were identified (Figure S1A). The categories of the identified me- Six categories were enriched, including carbohydrate (10.00%), amino acid (20.00%), car- boxylic tabolit acid es were (14.29%), invest lipid igat (17.14%), ed usinnucleotide g the Kyo(1.43%), to Encyclope and others dia o (37.14%) f Genes (Figur and G e S1B). enomes (KEGG). The Mann–Whitney test was used to compare the metabolite abundance between Xoo-Rzs Six categories were enriched, including carbohydrate (10.00%), amino acid (20.00%), car- and Xoo-S. In total, 65 metabolites of differential abundance were identified and displayed boxylic acid (14.29%), lipid (17.14%), nucleotide (1.43%), and others (37.14%) (Figure S1B). as a heat map (Figure 2A). The Z values ranged from 13.65 to 131.50; the abundance of The Mann–Whitney test was used to compare the metabolite abundance between Xoo- 37 metabolites was decreased and the abundance of 28 metabolites was increased in Xoo- Rzs and Xoo-S. In total, 65 metabolites of differential abundance were identified and dis- Rzs, as compared to Xoo-S (Figure 2B). We further analyzed the differential abundances of played as a heat map (Figure 2A). The Z values ranged from −13.65 to 131.50; the abun- metabolites to obtain metabolic categories. They were divided into six categories, including carbohydrate (9.23%), amino acid (16.92%), carboxylic acid (15.38%), lipid (16.92%), nu- dance of 37 metabolites was decreased and the abundance of 28 metabolites was increased cleotide (1.54%), and others (40.00%) (Figure 2C). The upregulation and downregulation of in Xoo-Rzs, as compared to Xoo-S (Figure 2B). We further analyzed the differential abun- each metabolic category is shown in Figure 2D. These results indicate that the metabolism dances of metabolites to obtain metabolic categories. They were divided into six catego- of Xoo-Rzs shifted substantially. ries, including carbohydrate (9.23%), amino acid (16.92%), carboxylic acid (15.38%), lipid (16.92%), nucleotide (1.54%), and others (40.00%) (Figure 2C). The upregulation and downregulation of each metabolic category is shown in Figure 2D. These results indicate that the metabolism of Xoo-Rzs shifted substantially. Antibiotics 2021, 10, 1166 4 of 13 Antibiotics 2021, 10, x FOR PEER REVIEW 4 of 13 Figure 2. Metabolic profiles of Xoo-S and Xoo-Rzs. (A) Heat map of differentially abundant metabolites. Yellow and blue Figure 2. Metabolic profiles of Xoo-S and Xoo-Rzs. (A) Heat map of differentially abundant metabo- indicate an increase and decrease in the metabolites scaled to the mean and SD of the metabolite row, respectively (see lites. Yellow and blue indicate an increase and decrease in the metabolites scaled to the mean and color scale). Five biologic replicates in each group were performed, yielding a total of 10 datasets. (B) Z-score. The data SD of the metabolite row, respectively (see color scale). Five biologic replicates in each group were from Xoo-Rzs are separately scaled to the mean and SD of Xoo-S. Each point represents one metabolite in one biological performed, yielding a total of 10 datasets. (B) Z-score. The data from Xoo-Rzs are separately scaled repeat and is colored by sample types (black: Xoo-S, red: Xoo-Rzs). (C) Percentage of differentially abundant metabolites to the mean and SD of Xoo-S. Each point represents one metabolite in one biological repeat and is in every category. (D) The numbers of differentially abundant metabolites increased and decreased in every category. colored by sample types (black: Xoo-S, red: Xoo-Rzs). (C) Percentage of differentially abundant 2.3. Enrichment of Metabolic Pathways in Xanthomonas Oryzae metabolites in every category. (D) The numbers of differentially abundant metabolites increased and decreased in every category In order to better underst . and the metabolic differences between Xoo-Rzs and Xoo-S, MetaboAnalyst (http://www.metaboanalyst.ca, 11th, Aug, 2021) was used to enrich the 2.3. Enrichment of Metabolic Pathways in Xanthomonas Oryzae pathways of differential metabolites. Fourteen key metabolic pathways were identified: In order to better understand the metabolic differences between Xoo-Rzs and Xoo-S, aminoacyl-tRNA biosynthesis; pyruvate metabolism; alanine, aspartate, and glutamate MetaboAnalyst (http://www.metaboanalyst.ca, accessed on 11 August 2021) was used metabolism; butanoate metabolism; glutathione metabolism; glyoxylate and dicarbox- to enrich the pathways ylate metaboli of dif sm; ni ferential trogen meta metabolites. bolism; gl Fourteen ycolysis/glu key coneogenesis metabolic ; s pathways ulfur metabolism; were identified: aminoacyl-tRNA biosynthesis; pyruvate metabolism; alanine, aspartate, and glutamate metabolism; butanoate metabolism; glutathione metabolism; glyoxylate Antibiotics 2021, 10, 1166 5 of 13 Antibiotics 2021, 10, x FOR PEER REVIEW 5 of 13 and dicarboxylate metabolism; nitrogen metabolism; glycolysis/gluconeogenesis; sulfur metabolism; pantothenate and CoA biosynthesis; phenylalanine metabolism; lysine degra- pantothenate and CoA biosynthesis; phenylalanine metabolism; lysine degradation; phe- dation; phenylalanine, tyrosine, and tryptophon biosynthesis; and the citrate cycle (TCA nylalanine, tyrosine, and tryptophon biosynthesis; and the citrate cycle (TCA cycle) (Fig- cycle) (Figure 3A). The relative abundances of the metabolites in each pathway are shown ure 3A). The relative abundances of the metabolites in each pathway are shown in Figure in Figure 3B. 3B. Figure 3. Pathway analysis. (A) Pathway enrichment of differentially abundant metabolites. (B) In- Figure 3. Pathway analysis. (A) Pathway enrichment of differentially abundant metabolites. tegrative analysis of differentially abundant metabolites in significantly enriched pathways. Yellow (B) Integrative analysis of differentially abundant metabolites in significantly enriched pathways. Yellow and blue indicate the increase and decrease in metabolites, respectively. The numbers show the relative abundance of metabolites in Xoo-S or Xoo-Rzs. Antibiotics 2021, 10, x FOR PEER REVIEW 6 of 13 and blue indicate the increase and decrease in metabolites, respectively. The numbers show the rel- ative abundance of metabolites in Xoo-S or Xoo-Rzs. Antibiotics 2021, 10, 1166 6 of 13 2.4. Biomarkers of ZS Resistance In order to identify the biomarkers of ZS resistance, an orthogonal partial least- square discriminant analysis (OPLS-DA) was used to identify the metabolomic profiles of 2.4. Biomarkers of ZS Resistance Xoo-Rzs and Xoo-S. The component (t [1]) distinguished between Xoo-Rzs and Xoo-S (Fig- In order to identify the biomarkers of ZS resistance, an orthogonal partial least- ure 4A). An S-plot analysis was used to distinguish between the variables. The absolute square discriminant analysis (OPLS-DA) was used to identify the metabolomic profiles values of the covariance p and correlation p (corr) were set to ≥0.05 or ≥0.5, respectively. of Xoo-Rzs and Xoo-S. The component (t [1]) distinguished between Xoo-Rzs and Xoo-S (Figur Accordin e 4A). g t Ano S-plot the S-p analysis lot ana was lys used is, s to even distin met guish abolit between es were the variables. selectedThe as b absolute iomarkers, namely: values of the covariance p and correlation p (corr) were set to 0.05 or 0.5, respectively. acrylamide, monopalmitin, stearic acid, urea, heptadecanoic acid, glycerol, and palmitic According to the S-plot analysis, seven metabolites were selected as biomarkers, namely: acid (Figure 4B, Figure S2). Among these biomarkers, stearic acid and palmitic acid are acrylamide, monopalmitin, stearic acid, urea, heptadecanoic acid, glycerol, and palmitic involved in fatty acid biosynthesis. More importantly, the abundance of palmitic acid was acid (Figure 4B, Figure S2). Among these biomarkers, stearic acid and palmitic acid are mostly significantly upregulated in Xoo-Rzs (Figure 4C). To further demonstrate the ele- involved in fatty acid biosynthesis. More importantly, the abundance of palmitic acid vated fatty acid biosynthesis, we determined the activity of acetyl CoA carboxylase (ACC), was mostly significantly upregulated in Xoo-Rzs (Figure 4C). To further demonstrate the elevated a key en fatty zym acid e invo biosynthesis, lved in fa we tty determined acid biosynt theh activity esis. Th of e ACC acetyl CoA activcarboxylase ity in Xoo-Rzs was sig- (ACC), a key enzyme involved in fatty acid biosynthesis. The ACC activity in Xoo-Rzs nificantly increased as compared to Xoo-S (Figure 4D). We further used qRT-PCR to quan- was significantly increased as compared to Xoo-S (Figure 4D). We further used qRT-PCR to tify the expression of 10 genes related to fatty acid biosynthesis and nine genes related to quantify the expression of 10 genes related to fatty acid biosynthesis and nine genes related fatty acid degradation. Among them, seven genes involved in fatty acid biosynthesis were to fatty acid degradation. Among them, seven genes involved in fatty acid biosynthesis significantly upregulated, whereas five genes involved in fatty acid degradation were sig- were significantly upregulated, whereas five genes involved in fatty acid degradation were nificantly downregulated, indicating that fatty acid biosynthesis was upregulated in Xoo- significantly downregulated, indicating that fatty acid biosynthesis was upregulated in Xoo-Rzs (Figure 4E). Therefore, fatty acid biosynthesis may be enhanced to resist ZS. Rzs (Figure 4E). Therefore, fatty acid biosynthesis may be enhanced to resist ZS. Figure 4. Biomarkers and the altered fatty acid biosynthesis and degradation in Xoo-Rzs. (A) Prin- Figure 4. Biomarkers and the altered fatty acid biosynthesis and degradation in Xoo-Rzs. cipal component analysis of Xoo-S and Xoo-Rzs. Each dot represents one biological repeat in the (A) Principal component analysis of Xoo-S and Xoo-Rzs. Each dot represents one biological re- peat plot. ( in the B) S-plot plot. (B) generated S-plot generated from fr the orthogonal om the orthogonal partial partial lea least-squar st-square dis e discriminant criminaanalysis. nt analysis. Predictive component p [1] and correlation p (corr) [1] differentiate Xoo-S and Xoo-Rzs. Each triangle repre- Antibiotics 2021, 10, x FOR PEER REVIEW 7 of 13 Antibiotics 2021, 10, 1166 7 of 13 sents one differential metabolite, and the biomarkers are highlighted in red. (C) The relative abun- dance of the palmitic acid in Xoo-S and Xoo-Rzs. (D) Activity of Acetyl CoA carboxylase (ACC) in Xoo-S or Predictive Xoo-Rzs. component (E) qRT- p [1]Pand CR for the correlation ex ppression (corr) [1] dif of fer gentiate enes in X fatty oo-S and acid b Xoo-Rzs. iosynthesis an Each triangled degrada- represents one differential metabolite, and the biomarkers are highlighted in red. (C) The relative tion in Xoo-S or Xoo-Rzs. accA/B/C/D: genes encoding ACC; PXO-02711 and PXO-02706: genes en- abundance of the palmitic acid in Xoo-S and Xoo-Rzs. (D) Activity of Acetyl CoA carboxylase coding β-ketoacyl-ACP synthetase; fabD: gene encoding ACP-malonyl-transferase; PXO-02878: (ACC) in Xoo-S or Xoo-Rzs. (E) qRT-PCR for the expression of genes in fatty acid biosynthesis and gene coding for β-ketoacyl-ACP reductase; fabA/Z: genes encoding β-hydroxyacyl-ACP dehydra- degradation in Xoo-S or Xoo-Rzs. accA/B/C/D: genes encoding ACC; PXO-02711 and PXO-02706: tase; PXO-03732: gene encoding fatty acyl-CoA synthetase; PXO-05532, PXO-01706, PXO-01705 and genes encoding b-ketoacyl-ACP synthetase; fabD: gene encoding ACP-malonyl-transferase; PXO- PXO-00100: genes encoding enoyl-CoA hydratase; PXO-01050 and PXO-00103: genes encoding hy- 02878: gene coding for b-ketoacyl-ACP reductase; fabA/Z: genes encoding b-hydroxyacyl-ACP droxyacyl-CoA dehydrogenase; PXO-04228 and PXO-01049: genes encoding β-ketoacyl-CoA thio- dehydratase; PXO-03732: gene encoding fatty acyl-CoA synthetase; PXO-05532, PXO-01706, PXO- lase. Note: Results are displayed as mean ± SD, and significant differences are identified as deter- 01705 and PXO-00100: genes encoding enoyl-CoA hydratase; PXO-01050 and PXO-00103: genes mined by Student’s t-test (* p < 0.05, ** p<0.01). At least three biological repeats were carried out. encoding hydroxyacyl-CoA dehydrogenase; PXO-04228 and PXO-01049: genes encoding b-ketoacyl- CoA thiolase. Note: Results are displayed as mean  SD, and significant differences are identified 2.5. Increased Fatty Acid Biosynthesis in Xoo-Rzs as determined by Student’s t-test (* p < 0.05, ** p < 0.01). At least three biological repeats were carried out. To explore the influence of elevated fatty acid biosynthesis on ZS resistance, we in- hibited biosynthesis with the aim of detecting the ZS-mediated killing efficacy towards 2.5. Increased Fatty Acid Biosynthesis in Xoo-Rzs Xoo-Rzs. When triclosan was used, which is an inhibitor of fatty acid biosynthesis, the ZS- To explore the influence of elevated fatty acid biosynthesis on ZS resistance, we in- hibited biosynthesis with the aim of detecting the ZS-mediated killing efficacy towards mediated killing was increased in a triclosan dose-dependent manner (Figure 5A). Con- Xoo-Rzs. When triclosan was used, which is an inhibitor of fatty acid biosynthesis, the sistently, the qRT-PCR analysis showed that the expression of genes encoding fatty acid ZS-mediated killing was increased in a triclosan dose-dependent manner (Figure 5A). biosynthesis was inhibited. Specifically, among the 10 genes detected, 6 were decreased, Consistently, the qRT-PCR analysis showed that the expression of genes encoding fatty acid 2 were increased, and 2 remained unchanged in the presence of triclosan (Figure 5B). biosynthesis was inhibited. Specifically, among the 10 genes detected, 6 were decreased, 2 were increased, and 2 remained unchanged in the presence of triclosan (Figure 5B). How- However, among the nine genes related to fatty acid degradation, five were increased, one ever, among the nine genes related to fatty acid degradation, five were increased, one was was decreased, and three remained unchanged in the presence of triclosan (Figure 5B). decreased, and three remained unchanged in the presence of triclosan (Figure 5B). These These results indicate that the upregulation of fatty acid biosynthesis and the downregu- results indicate that the upregulation of fatty acid biosynthesis and the downregulation lation of fatty acid degradation is related to resistance to ZS in X. oryzae, and the inhibition of fatty acid degradation is related to resistance to ZS in X. oryzae, and the inhibition of of biosynthesis enhances ZS-mediated killing. biosynthesis enhances ZS-mediated killing. Figure 5. Fatty acid biosynthesis metabolism is upregulated in Xoo-Rzs. (A) Percent survival of Xoo-Rzs in the presence Figure 5. Fatty acid biosynthesis metabolism is upregulated in Xoo-Rzs. (A) Percent survival of Xoo-Rzs in the presence or absence of triclosan. Xoo-Rzs were incubated with ZS (3 µg/mL) plus triclosan (1,2, and 3 µg/mL) in M9 minimal me- or absence of triclosan. Xoo-Rzs were incubated with ZS (3 g/mL) plus triclosan (1, 2, and 3 g/mL) in M9 minimal dium plus NaAc (10 mM), MgSO4 (2 mM), and CaCl2 (0.1 mM) at 30 °C for 15 h. (B) Expression of fatty acid biosynthesis- medium plus NaAc (10 mM), MgSO (2 mM), and CaCl (0.1 mM) at 30 C for 15 h. (B) Expression of fatty acid biosynthesis- 4 2 relatedgenes in relatedgenes Xoo-Rzs in in Xoo-Rzs thein presence the presence or ab or absence sence o of f 1 1.5 .5 µg g/mL /mL triclo triclosan. sa accA/B/C/D: n. accA/B/Cgenes /D: ge encoding nes enco ACC; ding PXO-02711 ACC; PXO-02711 and PXO-0270 and PXO-02706: 6: genes enco genes ding encoding β-ketoacyl-ACP synthetase; b-ketoacyl-ACP synthetase; fabD: gene enco fabD: gene encoding ding ACP- ACP-malonyl-transferase; malonyl-transferase; PXO-02878: PXO-02878: gene coding for b-ketoacyl-ACP reductase; fabA/Z: genes encoding b-hydroxyacyl-ACP dehydratase; PXO-03732: gene gene coding for β-ketoacyl-ACP reductase; fabA/Z: genes encoding β-hydroxyacyl-ACP dehydratase; PXO-03732: gene encoding fatty acyl-CoA synthetase; PXO-05532, PXO-01706, PXO-01705 and PXO-00100: genes encoding enoyl-CoA encoding fatty acyl-CoA synthetase; PXO-05532, PXO-01706, PXO-01705 and PXO-00100: genes encoding enoyl-CoA hy- hydratase; PXO-01050 and PXO-00103: genes encoding hydroxyacyl-CoA dehydrogenase; PXO-04228 and PXO-01049: dratase; PXO-01050 and PXO-00103: genes encoding hydroxyacyl-CoA dehydrogenase; PXO-04228 and PXO-01049: genes genes encoding b-ketoacyl-CoA thiolase. Note: Results are displayed as mean  SD, and significant differences are encoding β-ketoacyl-CoA thiolase. Note: Results are displayed as mean ± SD, and significant differences are identified as identified as determined by Student’s t-test (* p < 0.05, ** p < 0.01). At least three biological repeats were performed. determined by Student’s t-test (* p < 0.05, ** p < 0.01). At least three biological repeats were performed. 2.6. Exogenous Palmitic Acid Promotes the Resistance of Xanthomonas Oryzae to ZS Logically, fatty acid biosynthesis promotion can increase bacterial resistance to ZS. Therefore, exogenous palmitic acid was used to test whether the addition increases the viability of Xoo-Rzs in the presence of ZS. Indeed, the viability of the X. oryzae was higher in the medium with palmitic acid than without the exogenous fatty acid (Figure 6A). Con- sistently, palmitic acid promoted the expression of genes encoding fatty acid biosynthesis and fatty acid degradation. However, the elevated genes encoding fatty acid biosynthesis had a higher expression than the increased genes encoding fatty acid degradation (Figure Antibiotics 2021, 10, 1166 8 of 13 2.6. Exogenous Palmitic Acid Promotes the Resistance of Xanthomonas Oryzae to ZS Logically, fatty acid biosynthesis promotion can increase bacterial resistance to ZS. Therefore, exogenous palmitic acid was used to test whether the addition increases the viability of Xoo-Rzs in the presence of ZS. Indeed, the viability of the X. oryzae was higher in Antibiotics 2021, 10, x FOR PEER REVIEW 8 of 13 the medium with palmitic acid than without the exogenous fatty acid (Figure 6A). Consis- tently, palmitic acid promoted the expression of genes encoding fatty acid biosynthesis and fatty acid degradation. However, the elevated genes encoding fatty acid biosynthesis had a higher expression than the increased genes encoding fatty acid degradation (Figure 6B). 6B). These results further support the conclusion that elevated fatty acid biosynthesis en- These results further support the conclusion that elevated fatty acid biosynthesis enhances hances ZS resistance. ZS resistance. Figure 6. Exogenous palmitic acid promotes ZS resistance in Xoo-Rzs. (A) Percent survival of Xoo-Rzs in the presence or Figure 6. Exogenous palmitic acid promotes ZS resistance in Xoo-Rzs. (A) Percent survival of Xoo-Rzs in the presence absence of palmitic acid. Xoo-Rzs were inoculated in M9 minimal medium with ZS (6 µg/mL) plus palmitic acid (0 Mm, or absence of palmitic acid. Xoo-Rzs were inoculated in M9 minimal medium with ZS (6 g/mL) plus palmitic acid 0.2 mM, 0.(3 0 mM, Mm, 0.2 or 0 mM .4 mM) , 0.3 mM, and incubate or 0.4 mM) and d incubated at 30 °C for at 30 6 h. ( C for B6 ) Expressio h. (B) Expression n of fa oftty acid- fatty acid-r related elated genes genes iniXoo-Rzs n Xoo-Rzs in the in the presence or absence of 0.4 mM palmitic acid. accA/B/C/D: genes encoding ACC; PXO-02711 and PXO-02706: presence or absence of 0.4 mM palmitic acid. accA/B/C/D: genes encoding ACC; PXO-02711 and PXO-02706: genes encod- genes encoding b-ketoacyl-ACP synthetase; fabD: gene encoding ACP-malonyl-transferase; PXO-02878: gene coding ing β-ketoacyl-ACP synthetase; fabD: gene encoding ACP-malonyl-transferase; PXO-02878: gene coding for β-ketoacyl- for b-ketoacyl-ACP reductase; fabA/Z: genes encoding b-hydroxyacyl-ACP dehydratase; PXO-03732: gene encoding ACP reductase; fabA/Z: genes encoding β-hydroxyacyl-ACP dehydratase; PXO-03732: gene encoding fatty acyl-CoA syn- fatty acyl-CoA synthetase; PXO-05532, PXO-01706, PXO-01705 and PXO-00100: genes encoding enoyl-CoA hydratase; thetase; PXO-05532, PXO-01706, PXO-01705 and PXO-00100: genes encoding enoyl-CoA hydratase; PXO-01050 and PXO- PXO-01050 and PXO-00103: genes encoding hydroxyacyl-CoA dehydrogenase; PXO-04228 and PXO-01049: genes encoding 00103: genes encoding hydroxyacyl-CoA dehydrogenase; PXO-04228 and PXO-01049: genes encoding β-ketoacyl-CoA thi- b-ketoacyl-CoA thiolase. Note: Results are displayed as mean  SD, and significant differences are identified as determined olase. Note: Results are displayed as mean ± SD, and significant differences are identified as determined by Student’s t- by Student’s t-test (* p < 0.05, ** p < 0.01). At least three biological repeats were performed. test (* p < 0.05, ** p < 0.01). At least three biological repeats were performed. 3. Discussion 3. Discussion The widespread use of antibiotics poses new challenges to agriculture, the environ- ment, and human health. Therefore, it is of vital importance to study antibiotic resistance in The widespread use of antibiotics poses new challenges to agriculture, the environ- bacteria, including agricultural crop pathogens, to ensure food safety and human and eco- ment, and human health. Therefore, it is of vital importance to study antibiotic resistance logical health. Recent studies have shown that bacterial resistance is influenced by bacterial in bacteria, including agricultural crop pathogens, to ensure food safety and human and metabolism, and the metabolome of antibiotic-resistant bacteria can be reprogrammed by ecological health. Recent studies have shown that bacterial resistance is influenced by bac- certain key biomarkers to restore bacterial sensitivity to antibiotics [38,39]. Therefore, it is important to study the metabolic mechanism of bacterial resistance and identify potential terial metabolism, and the metabolome of antibiotic-resistant bacteria can be repro- biomarkers in bacteria. The aim of this study was to explore the metabolic mechanism grammed by certain key biomarkers to restore bacterial sensitivity to antibiotics [38,39]. of Xoo-Rzs. To the best of our knowledge, this is the first study to explore the resistance Therefore, it is important to study the metabolic mechanism of bacterial resistance and mechanism of Xanthomonas oryzae from the perspective of metabolism. identify potential biomarkers in bacteria. The aim of this study was to explore the meta- The present study demonstrates that Xoo-Rzs has a differential metabolome in which bolic mechanism of Xoo-Rzs. To the best of our knowledge, this is the first study to explore the most prominent characteristic is the upregulation of fatty acid biosynthesis. The elevation was evidenced by the enhanced ACC activity and the transcriptional level of the the resistance mechanism of Xanthomonas oryzae from the perspective of metabolism. genes encoding the pathways. This finding is consistent with recent studies that report The present study demonstrates that Xoo-Rzs has a differential metabolome in which that antibiotic-resistant bacteria have an antibiotic-resistant metabolome [40,41]. Therefore, the most prominent characteristic is the upregulation of fatty acid biosynthesis. The ele- metabolomics approaches can display global metabolic modulation and provide interesting vation was evidenced by the enhanced ACC activity and the transcriptional level of the avenues for further investigation. genes encoding the pathways. This finding is consistent with recent studies that report that antibiotic-resistant bacteria have an antibiotic-resistant metabolome [40,41]. There- fore, metabolomics approaches can display global metabolic modulation and provide in- teresting avenues for further investigation. To explore whether enhanced fatty acid biosynthesis is related to ZS resistance, two methods were adopted. Firstly, fatty acid biosynthesis was inhibited by triclosan to inves- tigate whether the ZS-mediated killing of Xoo-Rzs was elevated. Secondly, fatty acid bio- synthesis was promoted by palmitic acid to investigate whether the ZS-mediated killing of Xoo-Rzs was decreased. Our results showed that triclosan potentiated ZS to kill Xoo- Rzs, while palmitic acid inhibited the ZS-mediated killing. The potentiation and inhibition were accompanied by the elevation and decreased expression of genes encoding fatty acid biosynthesis, respectively. The association of elevated fatty acid synthesis and antibiotic resistance has been previously reported [26,27,42]; however, the relationship between in- creased fatty acid biosynthesis and X. oryzae resistance to ZS is a novel finding. Antibiotics 2021, 10, 1166 9 of 13 To explore whether enhanced fatty acid biosynthesis is related to ZS resistance, two methods were adopted. Firstly, fatty acid biosynthesis was inhibited by triclosan to in- vestigate whether the ZS-mediated killing of Xoo-Rzs was elevated. Secondly, fatty acid biosynthesis was promoted by palmitic acid to investigate whether the ZS-mediated killing of Xoo-Rzs was decreased. Our results showed that triclosan potentiated ZS to kill Xoo-Rzs, while palmitic acid inhibited the ZS-mediated killing. The potentiation and inhibition were accompanied by the elevation and decreased expression of genes encoding fatty acid biosynthesis, respectively. The association of elevated fatty acid synthesis and antibiotic resistance has been previously reported [26,27,42]; however, the relationship between increased fatty acid biosynthesis and X. oryzae resistance to ZS is a novel finding. The effect of key metabolites on bacterial sensitivity and resistance to antibiotics has been reported [43–45]. Studies demonstrated that exogenous additives regulate the resistance of drug-resistant bacteria. Six exogenous metabolites reprogram the colicin- resistant metabolome into a colicin-sensitive metabolome, resulting in increased gene expression, enzyme activity, and/or protein abundance, thereby elevating the sensitivity to the drug [46]. Glucose, fructose, and/or alanine transform kanamycin-resistant bacteria into kanamycin-sensitive bacteria to potentiate kanamycin-mediated killing [47]. Exoge- nous glucose restores the sensitivity of Vibrio alginolyticus to gentamicin [48]. Metabolites in the TCA cycle promote Edwardsiella tarda resistance to chloramphenicol [49]. In this study, exogenous palmitic acid promoted the resistance of X. oryzae to ZS, whereas triclosan potentiated ZS to kill Xoo-Rzs. These findings show that elevated fatty acid biosynthesis is required for ZS resistance. Therefore, comprehensive metabolic analyses will be helpful in understanding the mechanisms of antibiotic resistance and identifying an approach with which to control antibiotic-resistant bacteria via metabolic modulation. 4. Materials and Methods 4.1. Strain Characteristics and MIC Measurement The wild-type strain of Xanthomonas oryzae used in this study was a Zhongshengmycin (ZS)-sensitive strain (Xoo-S herein), and Xoo-Rzs refers to the X. oryzae strain selected by antibiotic-stressed subculturing, which harbors high resistance to ZS. PSA medium (peptone 10 g/L, sucrose 10 g/L, sodium glutamate 1 g/L, agar 2%, pH = 7.0) was used for initial culture, and PSA broth (PSA medium without agar) was used for rapid growth to create the growth curves for the strains. For the minimum inhibitory concentration (MIC) measurement, 1% (v/v) Xoo-S and Xoo-Rzs were inoculated into 2 mL PSA broth with different concentrations of ZS (concen- tration gradient of 0, 0.375, 0.75, 1.5, 3, 6, 12, and 24 g/mL) in 24-deep-well plates and cultured at 600 rpm, 30 C. The OD value was measured after 18 h of shaking using the Multiskan Sky microplate reader, and the minimum concentration of ZS that inhibited strain growth was defined as the MIC. 4.2. Sample Preparation and Parameter Setting for the GC–MS Analysis X. oryzae (1%) was inoculated into 50 mL PSA broth and cultured at 30 C, 200 rpm to the value of OD = 1.0. An appropriate amount of X. oryzae culture was collected and centrifuged at 4 C and 10,000 rpm for 5 min. The supernatant was discarded, and the bacterial cells were washed with 0.9% saline three times. Thereafter, 10 mL of saline was added, and a 2-fold volume of frozen methanol (placed at 20 C for more than 24 h) was immediately added to quench the sample for 1 h at 4 C to terminate cell metabolism. Bacterial cells were collected after centrifugation at 12,000 rpm for 5 min at 4 C, followed by resuspension with 500 L frozen chromatographic grade methanol. A total of 10 L ribitol with a concentration of 0.2 mg/mL was used as an internal reference. The cells were lysed with sound waves (60% intensity) for 6 min and centrifuged at 4 C, 12,000 g for 10 min. A total of 500 L supernatant was transferred to a new centrifuge tube and dried with a nitrogen blower. Thereafter, 80 L of methoxyamine hydrochloride (20 mg/mL) was added to the sample, followed by shaking at 200 rpm, 37 C for 30 min to Antibiotics 2021, 10, 1166 10 of 13 expose the protected carbonyl part of the sample. Then, 80 L of N-methyl-n-trimethylsilyl trifluoroacetamide (MSTFA, Sigma-Aldrich, Shanghai, China) was added and reacted at 37 C for 30 min for the acidic protons-derived. Finally, the samples were centrifuged at 4 C for 5 min at 12,000 g and prepared to be processed on the GC–MS. The GC–MS analysis was performed following a two-stage technique as previously described [50,51]. Briefly, the injection port was maintained at 270 C. Then, 1 L derivatized sample was injected into a Rxi-5MS column (30 m  250 m i.d.  0.25 m) using splitless injection, and the analysis was carried out using an GC-GC HRT 4D plus (LECO, St. Joseph, MI, USA). The initial temperature of the GC oven was held at 85 C for 3 min, followed by an increase to 285 C at a rate of 5 C/min, then a rise to 310 C at a rate of 20 C/min where it was held for 7 min. Helium was used as the carrier gas, and flow was kept constant at 1 mL/min. The MS was operated by using EI source, 70 eV EM voltage, and the scanning range was from 35 to 600 amu. 4.3. Technical Tools and Software Used for Data Management The relative abundance of a metabolite was scaled according to the total abundance of all metabolites in the sample. The Z-score analysis scaled each metabolite according to a reference distribution and was calculated based on the mean and SD of the reference. Hierarchical clustering was completed in the R platform with the gplots package using the distance matrix. Principal component analysis (SIMCA-P 12.0) was used to discriminate sample patterns to identify the metabolites in Xoo-Rzs and to minimize the interindividual variation’s influence. SPSS Statistics and Graphpad Prism 7 Project were used to draw the histogram, the scatter plot, and other graphs. In this study, the method of data analysis was multivariate statistical analysis [39,47]. The specific method was performed as previously described [52]. 4.4. Determination of the Activity of ACC X. oryzae (1%) was inoculated into 30 mL PSA broth and cultured at 30 C, 200 rpm for 24 h. An appropriate amount of X. oryzae culture was collected and centrifuged at 4 C and 10,000 rpm for 5 min. The supernatant was discarded and an appropriate amount of extract (in the ratio of 5 million cells to 1 mL of extract) was added. The cells were lysed with sound waves (60% intensity) for 6 min and centrifuged at 4 C, 12,000 g for 10 min, and the supernatant was used as the sample. The acetyl CoA carboxylase (ACC) activity was measured with the Acetyl CoA carboxylase Assay Kit (Suzhou geruisi biotechnology company, Suzhou, China). The protein concentration was determined using a BCA Protein Assay Kit (Dalian meilunbio biotechnology company, Dalian, China). 4.5. Assay of the Gene Expression Levels In Vivo The expression levels of the genes related to fatty acid biosynthesis and degrada- tion metabolism were detected using quantitative real-time PCR (qRT-PCR) following a method described previously [53]. In brief, the bacterial cells were harvested and the total RNA of each sample was isolated with a Biospin Total RNA Extraction Kit (Dobiothec, Fuzhou, China). An amount of 1 mg of total RNA was used for reverse transcription with the TransScript One-Step gDNA Removal and cDNA Synthesis Supermix Kit (TransGen Biotech, Beijing, China). qRT-PCR was performed in 96-well plates with PerfectStart Green qPCR SuperMix (TransGen Biotech, Beijing, China). The primers for each gene were listed in Table S1. Each primer pair was specific, and the relative expression of each gene was determined by the comparative threshold cycle method (2-DDCT method). 4.6. Antibiotic Bactericidal Assay Xoo-Rzs was cultured in PSA broth for 24 h at 30 C. Samples were harvested at 8000 rpm for 3 min centrifugation, and the cells were washed three times with 30 mL sterile saline. The cells were transferred into M9 broth (NaAc 10 mM, MgSO 2 mM, and CaCl 4 2 0.1 mM) containing the indicated concentration of ZS. To test the effect of triclosan on the Antibiotics 2021, 10, 1166 11 of 13 antibiotic resistance of Xoo-Rzs, 3 g/mL ZS and 1–3 g/mL triclosan were added in the M9, while, to test the effect of palmitic acid, the concentration of ZS was 6 g/mL, and the concentrations of palmitic acid were 0.2, 0.3, or 0.4 mM. The OD of the suspension was adjusted to 0.2, and the exogenous reagents, i.e., triclosan or palmitic acid, were added to a final volume of 5 mL. After incubation at 30 C for 6 h, 100 L aliquot samples were collected, serially diluted, and plated (10 L aliquots) on PSA medium, then cultured at 30 C for 48–60 h for the CFU calculation. Only those dilutions generating 20–200 clones per plate were counted. Percent survival was determined by dividing the CFU obtained from a treated sample by the CFU obtained from the control. 5. Conclusions In conclusion, X. oryzae resistance to ZS is related to an increase in fatty acid biosynthe- sis. The use of triclosan to inhibit fatty acid metabolism enhances the sensitivity of X. oryzae to ZS, while exogenous palmitic acid weakens its sensitivity to ZS. Our study showed the importance of fatty acid biosynthesis in Xoo-Rzs resistance to ZS. These results will deepen our understanding of the mechanisms through which antibiotic resistance forms in bacteria from a metabolic perspective. Furthermore, they provide new strategies for rational drug use, which is important for preventing and reducing bacterial antibiotic resistance. Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/antibiotics10101166/s1, Figure S1: Total metabolites detected in Xoo-S and Xoo-Rzs, Figure S2: The relative abundances of the 6 biomarkers. Table S1: Primers for qRT-PCR. Table S2: The specific growth rate of two strains. Author Contributions: Conceptualization, Q.W. and Y.G.; methodology, Q.W.; software, M.L. and P.S.; validation, M.L. and P.S.; formal analysis, Q.W. and Y.G.; investigation, Q.W., M.L. and P.S.; resources, Y.G.; data curation, Q.W., M.L. and P.S.; writing—original draft preparation, Q.W.; writing— review and editing, Y.G.; visualization, Q.W.; supervision, Y.G.; project administration, Y.G.; funding acquisition, Y.G. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the National Natural Science Foundation of China, grant number 32072481. Data Availability Statement: Data is contained within the article. Acknowledgments: We thank the Fujian college association instrumental analysis center of Fuzhou University for technical assistance with the GC–MS. Conflicts of Interest: The authors declare no conflict of interest. References 1. Ke, Y.G.; Wu, M.X.; Zhang, Q.L.; Li, X.H.; Xiao, J.H.; Wang, S.P. Hd3a and OsFD1 negatively regulate rice resistance to Xanthomonas oryzae pv. oryzae and Xanthomonas oryzae pv. oryzicola. Biochem. Bioph. Res. Commun. 2019, 513, 775–780. [CrossRef] [PubMed] 2. Nino-Liu, D.O.; Ronald, P.C.; Bogdanove, A.J. Xanthomonas oryzae pathovars: Model pathogens of a model crop. Mol. Plant Pathol. 2006, 7, 303–324. [CrossRef] [PubMed] 3. Chen, X.L.; Yan, Q.; Li, R.F.; Li, K.H.; Gao, L.J. First report of a new and highly virulent race of Xanthomonas oryzae pv. oryzae, the causal agent of bacterial leaf blight of rice in Guangxi Province, China. Plant Dis. 2016, 100, 1492–1493. [CrossRef] 4. Swings, J.; Vandenmooter, M.; Vauterin, L.; Hoste, B.; Gillis, M.; Mew, T.W.; Kersters, K. Reclassification of the causal agents of bacterial-blight (Xanthomonas campestris pv. oryzae) and bacterial leaf streak (Xanthomonas campestris pv. oryzicola) of rice as pathovars of Xanthomonas Oryzae (Ex Ishiyama 1922) sp nov, nom. rev. Int. J. Syst. Bacteriol. 1990, 40, 309–311. [CrossRef] 5. Gnanamanickam, S.S.; Priyadarisini, V.B.; Narayanan, N.N.; Vasudevan, P.; Kavitha, S. An overview of bacterial blight disease of rice and strategies for its management. Curr. Sci. India 1999, 77, 1435–1444. 6. Yang, P.; Li, F.J.; Huang, S.W.; Luo, M.; Lin, W.; Yuan, G.Q.; Li, Q.Q. Physiological and transcriptional response of Xanthomonas oryzae pv. oryzae to berberine, an emerging chemical control. Phytopathology 2020, 110, 1027–1038. [CrossRef] 7. Mew, T.W.; Alvarez, A.M.; Leach, J.E.; Swings, J. Focus on Bacterial-Blight of Rice. Plant Dis. 1993, 77, 5–12. [CrossRef] 8. Jin, P.F.; Wang, Y.; Tan, Z.; Liu, W.B.; Miao, W.G. Antibacterial activity and rice-induced resistance, mediated by C(15)surfactin A, in controlling rice disease caused by Xanthomonas oryzae pv. oryzae. Pestic Biochem. Phys. 2020, 169, 104669. [CrossRef] 9. Yasmin, S.; Hafeez, F.Y.; Mirza, M.S.; Rasul, M.; Arshad, H.M.I.; Zubair, M.; Iqbal, M. Biocontrol of bacterial leaf blight of rice and profiling of secondary metabolites produced by rhizospheric Pseudomonas aeruginosa BRp3. Front Microbiol. 2017, 8, 1895. [CrossRef] Antibiotics 2021, 10, 1166 12 of 13 10. Ham, J.H.; Melanson, R.A.; Rush, M.C. Burkholderia glumae: Next major pathogen of rice? Mol. Plant Pathol. 2011, 12, 329–339. [CrossRef] 11. Zhu, X.F.; Xu, Y.; Peng, D.; Zhang, Y.; Huang, T.T.; Wang, J.X.; Zhou, M.G. Detection and characterization of bismerthiazol- resistance of Xanthomonas oryzae pv. oryzae. Crop Prot. 2013, 47, 24–29. [CrossRef] 12. Xu, Y.; Zhu, X.F.; Zhou, M.G.; Kuang, J.; Zhang, Y.; Shang, Y.; Wang, J.X. Status of streptomycin resistance development in Xanthomonas oryzae pv. oryzae and Xanthomonas oryzae pv. oryzicola in China and their resistance characters. J. Phytopathol. 2010, 158, 601–608. 13. Yi, C.F.; Chen, J.X.; Hu, D.Y.; Song, B.A. First report about the screening, characterization, and fosmid library construction of Xanthomonas oryzae pv. oryzae strain with resistance to fubianezuofeng. Pestic Biochem. Phys. 2020, 169, 104645. [CrossRef] [PubMed] 14. Wang, Q.P.; Zhang, C.; Long, Y.H.; Wu, X.M.; Su, Y.; Lei, Y.; Ai, Q. Bioactivity and control efficacy of the novel antibiotic tetramycin against Various Kiwifruit diseases. Antibiotics 2021, 10, 289. [CrossRef] 15. Ren, Y.; Li, D.; Jiang, S.; Wang, Y.; Tang, Q.; Huang, H.; Wang, D.; Song, B.; Chen, Z. Integration of transcriptomic and proteomic data reveals the possible action mechanism of the antimicrobial zhongshengmycin against Didymella segeticola, the causal agent of tea leaf spot. Phytopathology 2021. [CrossRef] 16. Wang, B.L.; Shi, Y.X.; Ma, Y.; Liu, X.H.; Li, Y.H.; Song, H.B.; Li, B.J.; Li, Z.-M. Synthesis and biological activity of some novel trifluoromethyl-substituted 1,2,4-triazole and bis(1,2,4-Triazole) mannich bases containing piperazine rings. J. Agr. Food Chem. 2010, 58, 5515–5522. [CrossRef] 17. Wang, B.L.; Shi, Y.X.; Zhang, S.J.; Ma, Y.; Wang, H.X.; Zhang, L.Y.; Wei, W.; Liu, X.-H.; Li, Y.-H.; Li, Z.-M.; et al. Syntheses, biological activities and SAR studies of novel carboxamide compounds containing piperazine and arylsulfonyl moieties. Eur. J. Med. Chem. 2016, 117, 167–178. [CrossRef] 18. Song, Y.; Shi, J.T.; Xiong, Z.Z.; Shentu, X.P.; Yu, X.P. Three antimicrobials alter gut microbial communities and causing different mortality of brown planthopper, Nilaparvata lugens Stal. Pestic Biochem. Phys. 2021, 174, 104806. [CrossRef] 19. Alekshun, M.N.; Levy, S.B. Molecular mechanisms of antibacterial multidrug resistance. Cell 2007, 128, 1037–1050. [CrossRef] 20. Shaw, W.V. Chloramphenicol acetyltransferase: Enzymology and molecular biology. CRC Crit. Rev. Biochem. 1983, 14, 1–46. [CrossRef] 21. Murray, I.A.; Shaw, W.V. O-Acetyltransferases for chloramphenicol and other natural products. Antimicrob. Agents Chemother. 1997, 41, 1–6. [CrossRef] [PubMed] 22. Gabryszewski, S.J.; Wong Fok Lung, T.; Annavajhala, M.K.; Tomlinson, K.L.; Riquelme, S.A.; Khan, I.N.; Noguera, L.P.; Wickersham, M.; Zhao, A.; Mulenos, A.M.; et al. Metabolic adaptation in methicillin-resistant Staphylococcus aureus pneumonia. Am. J. Respir. Cell Mol. Biol. 2019, 61, 185–197. [CrossRef] 23. Wen, X.; Cao, J.; Mi, J.; Huang, J.; Liang, J.; Wang, Y.; Ma, B.; Zou, Y.; Liao, X.; Liang, J.B.; et al. Metabonomics reveals an alleviation of fitness cost in resistant E. coli competing against susceptible E. coli at sub-MIC doxycycline. J. Hazard Mater. 2021, 405, 124215. [CrossRef] [PubMed] 24. Liu, Y.; Yang, K.; Jia, Y.; Shi, J.; Tong, Z.; Wang, Z. Cysteine potentiates bactericidal antibiotics activity against Gram-negative bacterial persisters. Infect. Drug Resist. 2020, 13, 2593–2599. [CrossRef] 25. Ma, Y.; Guo, C.; Li, H.; Peng, X.X. Low abundance of respiratory nitrate reductase is essential for Escherichia coli in resistance to aminoglycoside and cephalosporin. J. Proteom. 2013, 87, 78–88. [CrossRef] [PubMed] 26. Liu, S.R.; Pen, X.X.; Li, H. Metabolic mechanism of ceftazidime resistance in Vibrio alginolyticus. Infect. Drug Resist. 2019, 12, 417–429. [CrossRef] 27. Cheng, Z.X.; Yang, M.J.; Peng, B.; Peng, X.X.; Lin, X.M.; Li, H. The depressed central carbon and energy metabolisms is associated to the acquisition of levofloxacin resistance in Vibrio alginolyticus. J. Proteom. 2018, 181, 83–91. [CrossRef] [PubMed] 28. Lee, H.H.; Molla, M.N.; Cantor, C.R.; Collins, J.J. Bacterial charity work leads to population-wide resistance. Nature 2010, 467, 82–85. [CrossRef] [PubMed] 29. Mishra, S.; Yang, X.D.; Ray, S.; Fraceto, L.F.; Singh, H.B. Antibacterial and biofilm inhibition activity of biofabricated silver nanoparticles against Xanthomonas oryzae pv. oryzae causing blight disease of rice instigates disease suppression. World J. Microb. Biot. 2020, 36, 1–10. [CrossRef] [PubMed] 30. Majumdar, T.D.; Singh, M.; Thapa, M.; Dutta, M.; Mukherjee, A.; Ghosh, C.K. Size-dependent antibacterial activity of copper nanoparticles against Xanthomonas oryzae pv. oryzae—A synthetic and mechanistic approach. Colloid Interfac. Sci. 2019, 32, 100190. [CrossRef] 31. Antar, A.; Lee, M.A.; Yoo, Y.; Cho, M.H.; Lee, S.W. PXO_RS20535, Encoding a novel response regulator, is required for chemotactic motility, biofilm formation, and tolerance to oxidative stress in Xanthomonas oryzae pv. oryzae. Pathogens 2020, 9, 956. [CrossRef] [PubMed] 32. Li, P.P.; Liu, X.J.; Li, H.; Peng, X.X. Downregulation of Na(+)-NQR complex is essential for Vibrio alginolyticus in resistance to balofloxacin. J. Proteom. 2012, 75, 2638–2648. [CrossRef] [PubMed] 33. Yang, J.H.; Bening, S.C.; Collins, J.J. Antibiotic efficacy—Context matters. Curr. Opin. Microbiol. 2017, 39, 73–80. [CrossRef] 34. Ye, J.Z.; Lin, X.M.; Cheng, Z.X.; Su, Y.B.; Li, W.X.; Ali, F.M.; Zheng, J.; Peng, B. Identification and efficacy of glycine, serine and threonine metabolism in potentiating kanamycin-mediated killing of Edwardsiella piscicida. J. Proteom. 2018, 183, 34–44. [CrossRef] Antibiotics 2021, 10, 1166 13 of 13 35. Ye, J.Z.; Su, Y.B.; Lin, X.M.; Lai, S.S.; Li, W.X.; Ali, F.; Zheng, J.; Peng, B. Alanine enhances aminoglycosides-induced ROS production as revealed by proteomic analysis. Front Microbiol. 2018, 9, 29. [CrossRef] 36. Wu, C.W.; Zhao, X.L.; Wu, X.J.; Wen, C.; Li, H.; Chen, X.H.; Peng, X.X. Exogenous glycine and serine promote growth and antifungal activity of Penicillium citrinum W1 from the south-west Indian Ocean. FEMS Microbiol. Lett. 2015, 362, fnv040. [CrossRef] 37. Wu, C.W.; Wu, X.J.; Wen, C.; Peng, B.; Peng, X.X.; Chen, X.H.; Li, H. Fructose promotes growth and antifungal activity of Penicillium citrinum. Protein Cell 2016, 7, 527–532. [CrossRef] 38. Isha, A.; Yusof, N.A.; Shaari, K.; Osman, R.; Abdullah, S.N.A.; Wong, M.Y. Metabolites identification of oil palm roots infected with Ganoderma boninense using GC-MS-based metabolomics. Arab J. Chem. 2020, 13, 6191–6200. [CrossRef] 39. Wang, Z.; Li, M.Y.; Peng, B.; Cheng, Z.X.; Li, H.; Peng, X.X. GC-MS-Based metabolome and metabolite regulation in serum-resistant Streptococcus agalactiae. J. Proteome Res. 2016, 15, 2246–2253. [CrossRef] 40. Chen, X.H.; Zhang, B.W.; Li, H.; Peng, X.X. Myo-inositol improves the host’s ability to eliminate balofloxacin-resistant Escherichia coli. Sci. Rep. 2015, 5, 1–11. [CrossRef] 41. Peng, B.; Su, Y.B.; Li, H.; Han, Y.; Guo, C.; Tian, Y.M.; Peng, X.X. Exogenous alanine and/or glucose plus kanamycin klls antibiotic-resistant bacteria. Cell Metab. 2015, 21, 249–261. [CrossRef] [PubMed] 42. Su, Y.-B.; Kuang, S.-F.; Ye, J.-Z.; Tao, J.-J.; Li, H.; Peng, X.-X.; Peng, B. Enhanced biosynthesis of fatty acids is associated with the acquisition of ciprofloxacin resistance in Edwardsiella tarda. mSystems 2021, 6, e00694-21. [CrossRef] 43. Jiang, M.; Kuang, S.F.; Lai, S.S.; Zhang, S.; Yang, J.; Peng, B.; Peng, X.X.; Chen, Z.G.; Li, H. Na(+)NQR confers aminoglycoside resistance via the regulation of L-alanine netabolism. mBio 2020, 11, e02086-20. [CrossRef] 44. Lin, Y.X.; Li, W.X.; Sun, L.N.; Lin, Z.P.; Jiang, Y.H.; Ling, Y.M.; Lin, X. Comparative metabolomics shows the metabolic profiles fluctuate in multi-drug resistant Escherichia coli strains. J. Proteom. 2019, 207, 103468. [CrossRef] [PubMed] 45. Zhang, S.; Yang, M.J.; Peng, B.; Peng, X.X.; Li, H. Reduced ROS-mediated antibiotic resistance and its reverting by glucose in Vibrio alginolyticus. Environ. Microbiol. 2020, 22, 4367–4380. [CrossRef] 46. Li, L.; Su, Y.B.; Peng, B.; Peng, X.X.; Li, H. Metabolic mechanism of colistin resistance and its reverting in Vibrio alginolyticus. Environ. Microbiol. 2020, 22, 4295–4313. [CrossRef] [PubMed] 47. Allison, K.R.; Brynildsen, M.P.; Collins, J.J. Metabolite-enabled eradication of bacterial persisters by aminoglycosides. Nature 2011, 473, 216–220. [CrossRef] 48. Zhang, S.; Wang, J.; Jiang, M.; Xu, D.; Peng, B.; Peng, X.X.; Li, H. Reduced redox-dependent mechanism and glucose-mediated reversal in gentamicin-resistant Vibrio alginolyticus. Environ. Microbiol. 2019, 21, 4724–4739. [CrossRef] 49. Wang, C.; Dong, X.S.; Yang, Y.Y.; Xu, G.J.; Wu, M.M.; Yan, F.J.; Zhang, L.G.; An, L.; Fu, P.S.; Wang, X.R.; et al. Metabolites in the TCA cycle promote resistance to chloramphenicol of Edwardsiella tarda. J. Proteome Res. 2021, 20, 972–981. [CrossRef] 50. Jiang, M.; Yang, L.F.; Zheng, J.; Chen, Z.G.; Peng, B. Maltose promotes crucian carp survival against Aeromonas sobrialinfection at high temperature. Virulence 2020, 11, 877–888. [CrossRef] 51. Zhao, X.L.; Wu, C.W.; Peng, X.X.; Li, H. Interferon-alpha 2b against microbes through promoting biosynthesis of unsaturated fatty acids. J. Proteome Res. 2014, 13, 4155–4163. [CrossRef] [PubMed] 52. Guan, Y.; Wang, Q.X.; Lv, C.; Wang, D.H.; Ye, X.Y. Fermentation time-dependent pectinase activity is associated with metabolomics variation in Bacillus licheniformis DY2. Process Biochem. 2021, 101, 147–155. [CrossRef] 53. Jiang, M.; Gong, Q.Y.; Lai, S.S.; Cheng, Z.X.; Chen, Z.G.; Zheng, J.; Peng, B. Phenylalanine enhances innate immune response to clear ceftazidime-resistant Vibrio alginolyticus in Danio rerio. Fish Shellfish Immunol. 2019, 84, 912–919. [CrossRef] [PubMed] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Antibiotics Multidisciplinary Digital Publishing Institute

Elevation of Fatty Acid Biosynthesis Metabolism Contributes to Zhongshengmycin Resistance in Xanthomonas oryzae

Wang, Qiaoxing; Lin, Meiyun; Shen, Peihua; Guan, Yi
Antibiotics , Volume 10 (10) – Sep 25, 2021
Download PDF
13 pages

Loading next page...
 
/lp/multidisciplinary-digital-publishing-institute/elevation-of-fatty-acid-biosynthesis-metabolism-contributes-to-2Fpkd8xN00

References (53)

Publisher
Multidisciplinary Digital Publishing Institute
Copyright
© 1996-2021 MDPI (Basel, Switzerland) unless otherwise stated Disclaimer The statements, opinions and data contained in the journals are solely those of the individual authors and contributors and not of the publisher and the editor(s). MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Terms and Conditions Privacy Policy
ISSN
2079-6382
DOI
10.3390/antibiotics10101166
Publisher site
See Article on Publisher Site

Abstract

antibiotics Article Elevation of Fatty Acid Biosynthesis Metabolism Contributes to Zhongshengmycin Resistance in Xanthomonas oryzae Qiaoxing Wang, Meiyun Lin, Peihua Shen and Yi Guan * Fujian Key Laboratory of Marine Enzyme Engineering, College of Biological Science and Engineering, Fuzhou University, Fuzhou 350116, China; N190820026@fzu.edu.cn (Q.W.); 200827031@fzu.edu.cn (M.L.); 200827045@fzu.edu.cn (P.S.) * Correspondence: gy@fzu.edu.cn Abstract: Xanthomonas oryzae severely impacts the yield and quality of rice. Antibiotics are the most common control measure for this pathogen; however, the overuse of antibiotics in past decades has caused bacterial resistance to these antibiotics. The agricultural context is of particular importance as antibiotic-resistant bacteria are prevalent, but the resistance mechanism largely remains unex- plored. Herein, using gas chromatography–mass spectrometry (GC–MS), we demonstrated that zhongshengmycin-resistant X. oryzae (Xoo-Rzs) and zhongshengmycin-sensitive X. oryzae (Xoo-S) have distinct metabolic profiles. We found that the resistance to zhongshengmycin (ZS) in X. oryzae is related to increased fatty acid biosynthesis. This was demonstrated by measuring the Acetyl-CoA carboxylase (ACC) activity, the expression levels of enzyme genes involved in the fatty acid biosyn- thesis and degradation pathways, and adding exogenous materials, i.e., triclosan and fatty acids. Our work provides a basis for the subsequent control of the production of antibiotic-resistant strains of X. oryzae and the development of coping strategies. Citation: Wang, Q.; Lin, M.; Shen, P.; Keywords: Xanthomonas oryzae; antibiotics; antibiotic-resistant; zhongshengmycin; fatty acid Guan, Y. Elevation of Fatty Acid biosynthesis metabolism Biosynthesis Metabolism Contributes to Zhongshengmycin Resistance in Xanthomonas oryzae. Antibiotics 2021, 10, 1166. https://doi.org/10.3390/ 1. Introduction antibiotics10101166 Rice is one of the most widely cultivated and economically important crops in the world and is related to the survival of half of the world’s population [1]. However, Academic Editor: Marc Maresca the quality and yield of rice are threatened by Xanthomonas oryzae, a Gram-negative bac- terium. X. oryzae is characterized as being short and rod-shaped, with a layer of mucinous Received: 16 August 2021 extracellular polysaccharide on the surface, which makes its colony yellow and shiny [2,3]. Accepted: 22 September 2021 Published: 25 September 2021 Xanthomonas oryzae pv. oryzae and Xanthomonas oryzae pv. oryzicola are two main pathovars of X. oryzae that infect rice leaves through wounds or stomata, and cause rice leaf blight and rice leaf streak [4–6]. These two diseases often occur in rice-growing areas, such as Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in Japan, India, and China, and are explosive and destructive in nature, sometimes leading to published maps and institutional affil- more than a 50% reduction in production or, in extreme cases, a harvestless year [3,7]. iations. In the field of pathogen control, agricultural, biological, and chemical measures are the three main strategies [8,9]. As compared with biological and chemical measures, agricultural control is a more traditional and simpler way to avoid crop diseases, but these methods are often limited by the labor force, planting area, and the climatic environment. Genetic breeding is the most direct measure to control X. oryzae. This involves selecting Copyright: © 2021 by the authors. natural mutations that confer resistance to the infection. Importantly, the selected strains Licensee MDPI, Basel, Switzerland. This article is an open access article can then be further propagated for large-scale production [10]. Nonetheless, this process is distributed under the terms and time-consuming and labor-intensive. To fill this gap, chemical measures, and antibiotics in conditions of the Creative Commons particular, are widely adopted to treat X. oryzae infection. However, the misuse/overuse of Attribution (CC BY) license (https:// antibiotics causes widespread antibiotic resistance, and these bacteria are insensitive to the creativecommons.org/licenses/by/ currently available antibiotics. Recently, antibiotic-resistant X. oryzae strains were identified 4.0/). Antibiotics 2021, 10, 1166. https://doi.org/10.3390/antibiotics10101166 https://www.mdpi.com/journal/antibiotics Antibiotics 2021, 10, 1166 2 of 13 in a number of studies [11–13]. Thus, the mechanisms underlying drug resistance have aroused the interest of researchers. Zhongshengmycin (ZS herein) is a low toxicity aminoglycoside antibiotic produced by Streptomyces lavendulae var. Hainanensis in Hainan, which is widely used as a chemical bac- tericide in the control of phytopathogens. Previous studies showed that ZS can be used to control vegetable bacterial diseases, rice bacterial leaf blight, and fruit tree diseases [14,15]. Additionally, ZS has good herbicidal and fungicidal properties and has been used as a control agent with which to study the activity of many new compounds [16,17]. Currently, ZS is applied in 10 provinces in Southeast Asia for the control of plant bacterial diseases, and economic benefits have been reported [18]. However, as a result of drug resistance, the control effect of ZS on rice bacterial blight has become negligible, and it will take a long time for new effective agents to be developed. Therefore, it is necessary to study the resistance mechanism of X. oryzae to ZS to restore ZS-mediated efficacy. Four main mechanisms of bacterial antibiotics resistance have been revealed: the production of inactivating enzymes, changing the target of antimicrobial agents, activating the efflux pump system, and changing the permeability of the outer membrane [19–21]. Recently, the key role that metabolism plays in bacterial antibiotics resistance has been demonstrated [22–24]. For example, a low abundance of respiratory nitrate reductase is essential for Escherichia coli resistance to aminoglycoside and cephalosporin [25]. The low efficiency of the central carbon and energy metabolism was shown to mediate the resistance of Vibrio alginolyticus to levofloxacin and ceftazidime [26,27]. Drug-resistant bacteria can provide protection for other more vulnerable bacteria through the secondary metabolite indole so as to improve the survival ability of the whole population in stressful environments [28]. The existing research on X. oryzae is mainly focused on the effects that biological agents, nano agents, and gene knockout have on its virulence and con- trol [8,29–31]; however, studies concerning the mechanism of drug resistance of X. oryzae from the perspective of metabolomics are lacking. Metabolomics is a newly developed discipline and has become a research hotspot in re- cent years. Gas chromatography–mass spectrometry (GC–MS) is a useful technical method to collect the metabolite spectrum of samples through full spectrum analyses. Recently, metabolomics assays have been extensively used to analyze the metabolic mechanism of microbial resistance [32,33]. For instance, alanine metabolism promoted the production of reactive oxygen species, while glucose increased the metabolism of glycine, serine, and threonine, which promoted the killing of kanamycin-mediated antibiotic-resistant bacte- ria [34,35]. Fructose, glycine, and serine were the key metabolites promoting the growth and antifungal activity of Penicillium citrinum W1 [36,37]. Therefore, the GC–MS-based metabolomics method is an ideal approach with which to explore the metabolic mechanism of X. oryzae resistance to ZS. In the present study, we compared the metabolomes of zhongshengmycin-resistant strain X. oryzae (Xoo-Rzs) and zhongshengmycin-sensitive strain X. oryzae (Xoo-S) to investigate the metabolic signature of ZS-resistant X. oryzae. Our research not only revealed the metabolomic mechanisms of antibiotics resistance in Xoo-Rzs but also provided a novel method for the control of antibiotics-resistant X. oryzae. 2. Results 2.1. Phenotypes of Xoo-Rzs In order to investigate the resistance mechanism of Xanthomonas oryzae to ZS, we established a ZS-resistant X. oryzae, i.e., Xoo-Rzs, which exhibited 16-fold higher MIC levels than the parental strain, i.e., X. oryzae (Xoo-S) (Figure 1A). The growth rates of Xoo-Rzs were lower than those of Xoo-S during 0–16 h, but the total production showed no significant difference between the two strains after a 28 h culture (Figure 1B), and the growth rates of the two strains at specific timing points were shown in Table S2 (Supplementary Materials). Antibiotics 2021, 10, x FOR PEER REVIEW 3 of 13 Antibiotics 2021, 10, 1166 3 of 13 rates of the two strains at specific timing points were shown in Table S2 (Supplementary Materials). Figure 1. Phenotypes of Xoo-S and Xoo-Rzs. (A) MIC of zhongshengmycin (ZS) in Xoo-S and Xoo- Figure 1. Phenotypes of Xoo-S and Xoo-Rzs. (A) MIC of zhongshengmycin (ZS) in Xoo-S and Xoo- Rzs. Xoo-S and Xoo-Rzs were incubated with PSA broth containing ZS ranging from 0 to 24 µg/mL Rzs. Xoo-S and Xoo-Rzs were incubated with PSA broth containing ZS ranging from 0 to 24 g/mL in 24-well plates at 30 °C for 18 h. (B) Growth curve of Xoo-S and Xoo-Rzs. Xoo-S and Xoo-Rzs were in 24-well plates at 30 C for 18 h. (B) Growth curve of Xoo-S and Xoo-Rzs. Xoo-S and Xoo-Rzs diluted to 1:100 using fresh PSA medium and grown in the indicated periods. The OD600 of the bac- were diluted to 1:100 using fresh PSA medium and grown in the indicated periods. The OD of terial cultures was measured at the indicated time. Note: The results in (B) are displayed as mean ± the bacterial cultures was measured at the indicated time. Note: The results in (B) are displayed as mean SD. At least  SD. At three bio least threelogical re biologicalpeats were per repeats were performed. formed. 2.2. Difference in Metabolic Spectrum between Xoo-Rzs and Xoo-S 2.2. Difference in Metabolic Spectrum between Xoo-Rzs and Xoo-S In order to gain insight into the metabolic alterations in Xoo-Rzs, Xoo-Rzs and Xoo-S In order to gain insight into the metabolic alterations in Xoo-Rzs, Xoo-Rzs and Xoo- were cultured and collected for GC–MS analysis. The bacteria were incubated into 3 mL PSA S we br re oth cultur as a ed seed and cultur collect e fored 24for h at GC–M 30 C, S and analy then sis. Th transferr e bacter ed to ia w 50 e mL re inc PSA ubated for into 3 mL enlarging cultivation. The sampling point was approximately 48 h, until the OD of the PSA broth as a seed culture for 24 h at 30 °C, and then transferred to 50 mL PSA for en- cultures reached 1.0. Five biological replicates for each strain yielded a total of 10 datasets. larging cultivation. The sampling point was approximately 48 h, until the OD600 of the After removing the internal standard, i.e., ribitol, and combining the same compounds, a cultures reached 1.0. Five biological replicates for each strain yielded a total of 10 datasets. total of 70 metabolites were identified (Figure S1A). The categories of the identified metabo- After removing the internal standard, i.e., ribitol, and combining the same compounds, a lites were investigated using the Kyoto Encyclopedia of Genes and Genomes (KEGG). total of 70 metabolites were identified (Figure S1A). The categories of the identified me- Six categories were enriched, including carbohydrate (10.00%), amino acid (20.00%), car- boxylic tabolit acid es were (14.29%), invest lipid igat (17.14%), ed usinnucleotide g the Kyo(1.43%), to Encyclope and others dia o (37.14%) f Genes (Figur and G e S1B). enomes (KEGG). The Mann–Whitney test was used to compare the metabolite abundance between Xoo-Rzs Six categories were enriched, including carbohydrate (10.00%), amino acid (20.00%), car- and Xoo-S. In total, 65 metabolites of differential abundance were identified and displayed boxylic acid (14.29%), lipid (17.14%), nucleotide (1.43%), and others (37.14%) (Figure S1B). as a heat map (Figure 2A). The Z values ranged from 13.65 to 131.50; the abundance of The Mann–Whitney test was used to compare the metabolite abundance between Xoo- 37 metabolites was decreased and the abundance of 28 metabolites was increased in Xoo- Rzs and Xoo-S. In total, 65 metabolites of differential abundance were identified and dis- Rzs, as compared to Xoo-S (Figure 2B). We further analyzed the differential abundances of played as a heat map (Figure 2A). The Z values ranged from −13.65 to 131.50; the abun- metabolites to obtain metabolic categories. They were divided into six categories, including carbohydrate (9.23%), amino acid (16.92%), carboxylic acid (15.38%), lipid (16.92%), nu- dance of 37 metabolites was decreased and the abundance of 28 metabolites was increased cleotide (1.54%), and others (40.00%) (Figure 2C). The upregulation and downregulation of in Xoo-Rzs, as compared to Xoo-S (Figure 2B). We further analyzed the differential abun- each metabolic category is shown in Figure 2D. These results indicate that the metabolism dances of metabolites to obtain metabolic categories. They were divided into six catego- of Xoo-Rzs shifted substantially. ries, including carbohydrate (9.23%), amino acid (16.92%), carboxylic acid (15.38%), lipid (16.92%), nucleotide (1.54%), and others (40.00%) (Figure 2C). The upregulation and downregulation of each metabolic category is shown in Figure 2D. These results indicate that the metabolism of Xoo-Rzs shifted substantially. Antibiotics 2021, 10, 1166 4 of 13 Antibiotics 2021, 10, x FOR PEER REVIEW 4 of 13 Figure 2. Metabolic profiles of Xoo-S and Xoo-Rzs. (A) Heat map of differentially abundant metabolites. Yellow and blue Figure 2. Metabolic profiles of Xoo-S and Xoo-Rzs. (A) Heat map of differentially abundant metabo- indicate an increase and decrease in the metabolites scaled to the mean and SD of the metabolite row, respectively (see lites. Yellow and blue indicate an increase and decrease in the metabolites scaled to the mean and color scale). Five biologic replicates in each group were performed, yielding a total of 10 datasets. (B) Z-score. The data SD of the metabolite row, respectively (see color scale). Five biologic replicates in each group were from Xoo-Rzs are separately scaled to the mean and SD of Xoo-S. Each point represents one metabolite in one biological performed, yielding a total of 10 datasets. (B) Z-score. The data from Xoo-Rzs are separately scaled repeat and is colored by sample types (black: Xoo-S, red: Xoo-Rzs). (C) Percentage of differentially abundant metabolites to the mean and SD of Xoo-S. Each point represents one metabolite in one biological repeat and is in every category. (D) The numbers of differentially abundant metabolites increased and decreased in every category. colored by sample types (black: Xoo-S, red: Xoo-Rzs). (C) Percentage of differentially abundant 2.3. Enrichment of Metabolic Pathways in Xanthomonas Oryzae metabolites in every category. (D) The numbers of differentially abundant metabolites increased and decreased in every category In order to better underst . and the metabolic differences between Xoo-Rzs and Xoo-S, MetaboAnalyst (http://www.metaboanalyst.ca, 11th, Aug, 2021) was used to enrich the 2.3. Enrichment of Metabolic Pathways in Xanthomonas Oryzae pathways of differential metabolites. Fourteen key metabolic pathways were identified: In order to better understand the metabolic differences between Xoo-Rzs and Xoo-S, aminoacyl-tRNA biosynthesis; pyruvate metabolism; alanine, aspartate, and glutamate MetaboAnalyst (http://www.metaboanalyst.ca, accessed on 11 August 2021) was used metabolism; butanoate metabolism; glutathione metabolism; glyoxylate and dicarbox- to enrich the pathways ylate metaboli of dif sm; ni ferential trogen meta metabolites. bolism; gl Fourteen ycolysis/glu key coneogenesis metabolic ; s pathways ulfur metabolism; were identified: aminoacyl-tRNA biosynthesis; pyruvate metabolism; alanine, aspartate, and glutamate metabolism; butanoate metabolism; glutathione metabolism; glyoxylate Antibiotics 2021, 10, 1166 5 of 13 Antibiotics 2021, 10, x FOR PEER REVIEW 5 of 13 and dicarboxylate metabolism; nitrogen metabolism; glycolysis/gluconeogenesis; sulfur metabolism; pantothenate and CoA biosynthesis; phenylalanine metabolism; lysine degra- pantothenate and CoA biosynthesis; phenylalanine metabolism; lysine degradation; phe- dation; phenylalanine, tyrosine, and tryptophon biosynthesis; and the citrate cycle (TCA nylalanine, tyrosine, and tryptophon biosynthesis; and the citrate cycle (TCA cycle) (Fig- cycle) (Figure 3A). The relative abundances of the metabolites in each pathway are shown ure 3A). The relative abundances of the metabolites in each pathway are shown in Figure in Figure 3B. 3B. Figure 3. Pathway analysis. (A) Pathway enrichment of differentially abundant metabolites. (B) In- Figure 3. Pathway analysis. (A) Pathway enrichment of differentially abundant metabolites. tegrative analysis of differentially abundant metabolites in significantly enriched pathways. Yellow (B) Integrative analysis of differentially abundant metabolites in significantly enriched pathways. Yellow and blue indicate the increase and decrease in metabolites, respectively. The numbers show the relative abundance of metabolites in Xoo-S or Xoo-Rzs. Antibiotics 2021, 10, x FOR PEER REVIEW 6 of 13 and blue indicate the increase and decrease in metabolites, respectively. The numbers show the rel- ative abundance of metabolites in Xoo-S or Xoo-Rzs. Antibiotics 2021, 10, 1166 6 of 13 2.4. Biomarkers of ZS Resistance In order to identify the biomarkers of ZS resistance, an orthogonal partial least- square discriminant analysis (OPLS-DA) was used to identify the metabolomic profiles of 2.4. Biomarkers of ZS Resistance Xoo-Rzs and Xoo-S. The component (t [1]) distinguished between Xoo-Rzs and Xoo-S (Fig- In order to identify the biomarkers of ZS resistance, an orthogonal partial least- ure 4A). An S-plot analysis was used to distinguish between the variables. The absolute square discriminant analysis (OPLS-DA) was used to identify the metabolomic profiles values of the covariance p and correlation p (corr) were set to ≥0.05 or ≥0.5, respectively. of Xoo-Rzs and Xoo-S. The component (t [1]) distinguished between Xoo-Rzs and Xoo-S (Figur Accordin e 4A). g t Ano S-plot the S-p analysis lot ana was lys used is, s to even distin met guish abolit between es were the variables. selectedThe as b absolute iomarkers, namely: values of the covariance p and correlation p (corr) were set to 0.05 or 0.5, respectively. acrylamide, monopalmitin, stearic acid, urea, heptadecanoic acid, glycerol, and palmitic According to the S-plot analysis, seven metabolites were selected as biomarkers, namely: acid (Figure 4B, Figure S2). Among these biomarkers, stearic acid and palmitic acid are acrylamide, monopalmitin, stearic acid, urea, heptadecanoic acid, glycerol, and palmitic involved in fatty acid biosynthesis. More importantly, the abundance of palmitic acid was acid (Figure 4B, Figure S2). Among these biomarkers, stearic acid and palmitic acid are mostly significantly upregulated in Xoo-Rzs (Figure 4C). To further demonstrate the ele- involved in fatty acid biosynthesis. More importantly, the abundance of palmitic acid vated fatty acid biosynthesis, we determined the activity of acetyl CoA carboxylase (ACC), was mostly significantly upregulated in Xoo-Rzs (Figure 4C). To further demonstrate the elevated a key en fatty zym acid e invo biosynthesis, lved in fa we tty determined acid biosynt theh activity esis. Th of e ACC acetyl CoA activcarboxylase ity in Xoo-Rzs was sig- (ACC), a key enzyme involved in fatty acid biosynthesis. The ACC activity in Xoo-Rzs nificantly increased as compared to Xoo-S (Figure 4D). We further used qRT-PCR to quan- was significantly increased as compared to Xoo-S (Figure 4D). We further used qRT-PCR to tify the expression of 10 genes related to fatty acid biosynthesis and nine genes related to quantify the expression of 10 genes related to fatty acid biosynthesis and nine genes related fatty acid degradation. Among them, seven genes involved in fatty acid biosynthesis were to fatty acid degradation. Among them, seven genes involved in fatty acid biosynthesis significantly upregulated, whereas five genes involved in fatty acid degradation were sig- were significantly upregulated, whereas five genes involved in fatty acid degradation were nificantly downregulated, indicating that fatty acid biosynthesis was upregulated in Xoo- significantly downregulated, indicating that fatty acid biosynthesis was upregulated in Xoo-Rzs (Figure 4E). Therefore, fatty acid biosynthesis may be enhanced to resist ZS. Rzs (Figure 4E). Therefore, fatty acid biosynthesis may be enhanced to resist ZS. Figure 4. Biomarkers and the altered fatty acid biosynthesis and degradation in Xoo-Rzs. (A) Prin- Figure 4. Biomarkers and the altered fatty acid biosynthesis and degradation in Xoo-Rzs. cipal component analysis of Xoo-S and Xoo-Rzs. Each dot represents one biological repeat in the (A) Principal component analysis of Xoo-S and Xoo-Rzs. Each dot represents one biological re- peat plot. ( in the B) S-plot plot. (B) generated S-plot generated from fr the orthogonal om the orthogonal partial partial lea least-squar st-square dis e discriminant criminaanalysis. nt analysis. Predictive component p [1] and correlation p (corr) [1] differentiate Xoo-S and Xoo-Rzs. Each triangle repre- Antibiotics 2021, 10, x FOR PEER REVIEW 7 of 13 Antibiotics 2021, 10, 1166 7 of 13 sents one differential metabolite, and the biomarkers are highlighted in red. (C) The relative abun- dance of the palmitic acid in Xoo-S and Xoo-Rzs. (D) Activity of Acetyl CoA carboxylase (ACC) in Xoo-S or Predictive Xoo-Rzs. component (E) qRT- p [1]Pand CR for the correlation ex ppression (corr) [1] dif of fer gentiate enes in X fatty oo-S and acid b Xoo-Rzs. iosynthesis an Each triangled degrada- represents one differential metabolite, and the biomarkers are highlighted in red. (C) The relative tion in Xoo-S or Xoo-Rzs. accA/B/C/D: genes encoding ACC; PXO-02711 and PXO-02706: genes en- abundance of the palmitic acid in Xoo-S and Xoo-Rzs. (D) Activity of Acetyl CoA carboxylase coding β-ketoacyl-ACP synthetase; fabD: gene encoding ACP-malonyl-transferase; PXO-02878: (ACC) in Xoo-S or Xoo-Rzs. (E) qRT-PCR for the expression of genes in fatty acid biosynthesis and gene coding for β-ketoacyl-ACP reductase; fabA/Z: genes encoding β-hydroxyacyl-ACP dehydra- degradation in Xoo-S or Xoo-Rzs. accA/B/C/D: genes encoding ACC; PXO-02711 and PXO-02706: tase; PXO-03732: gene encoding fatty acyl-CoA synthetase; PXO-05532, PXO-01706, PXO-01705 and genes encoding b-ketoacyl-ACP synthetase; fabD: gene encoding ACP-malonyl-transferase; PXO- PXO-00100: genes encoding enoyl-CoA hydratase; PXO-01050 and PXO-00103: genes encoding hy- 02878: gene coding for b-ketoacyl-ACP reductase; fabA/Z: genes encoding b-hydroxyacyl-ACP droxyacyl-CoA dehydrogenase; PXO-04228 and PXO-01049: genes encoding β-ketoacyl-CoA thio- dehydratase; PXO-03732: gene encoding fatty acyl-CoA synthetase; PXO-05532, PXO-01706, PXO- lase. Note: Results are displayed as mean ± SD, and significant differences are identified as deter- 01705 and PXO-00100: genes encoding enoyl-CoA hydratase; PXO-01050 and PXO-00103: genes mined by Student’s t-test (* p < 0.05, ** p<0.01). At least three biological repeats were carried out. encoding hydroxyacyl-CoA dehydrogenase; PXO-04228 and PXO-01049: genes encoding b-ketoacyl- CoA thiolase. Note: Results are displayed as mean  SD, and significant differences are identified 2.5. Increased Fatty Acid Biosynthesis in Xoo-Rzs as determined by Student’s t-test (* p < 0.05, ** p < 0.01). At least three biological repeats were carried out. To explore the influence of elevated fatty acid biosynthesis on ZS resistance, we in- hibited biosynthesis with the aim of detecting the ZS-mediated killing efficacy towards 2.5. Increased Fatty Acid Biosynthesis in Xoo-Rzs Xoo-Rzs. When triclosan was used, which is an inhibitor of fatty acid biosynthesis, the ZS- To explore the influence of elevated fatty acid biosynthesis on ZS resistance, we in- hibited biosynthesis with the aim of detecting the ZS-mediated killing efficacy towards mediated killing was increased in a triclosan dose-dependent manner (Figure 5A). Con- Xoo-Rzs. When triclosan was used, which is an inhibitor of fatty acid biosynthesis, the sistently, the qRT-PCR analysis showed that the expression of genes encoding fatty acid ZS-mediated killing was increased in a triclosan dose-dependent manner (Figure 5A). biosynthesis was inhibited. Specifically, among the 10 genes detected, 6 were decreased, Consistently, the qRT-PCR analysis showed that the expression of genes encoding fatty acid 2 were increased, and 2 remained unchanged in the presence of triclosan (Figure 5B). biosynthesis was inhibited. Specifically, among the 10 genes detected, 6 were decreased, 2 were increased, and 2 remained unchanged in the presence of triclosan (Figure 5B). How- However, among the nine genes related to fatty acid degradation, five were increased, one ever, among the nine genes related to fatty acid degradation, five were increased, one was was decreased, and three remained unchanged in the presence of triclosan (Figure 5B). decreased, and three remained unchanged in the presence of triclosan (Figure 5B). These These results indicate that the upregulation of fatty acid biosynthesis and the downregu- results indicate that the upregulation of fatty acid biosynthesis and the downregulation lation of fatty acid degradation is related to resistance to ZS in X. oryzae, and the inhibition of fatty acid degradation is related to resistance to ZS in X. oryzae, and the inhibition of of biosynthesis enhances ZS-mediated killing. biosynthesis enhances ZS-mediated killing. Figure 5. Fatty acid biosynthesis metabolism is upregulated in Xoo-Rzs. (A) Percent survival of Xoo-Rzs in the presence Figure 5. Fatty acid biosynthesis metabolism is upregulated in Xoo-Rzs. (A) Percent survival of Xoo-Rzs in the presence or absence of triclosan. Xoo-Rzs were incubated with ZS (3 µg/mL) plus triclosan (1,2, and 3 µg/mL) in M9 minimal me- or absence of triclosan. Xoo-Rzs were incubated with ZS (3 g/mL) plus triclosan (1, 2, and 3 g/mL) in M9 minimal dium plus NaAc (10 mM), MgSO4 (2 mM), and CaCl2 (0.1 mM) at 30 °C for 15 h. (B) Expression of fatty acid biosynthesis- medium plus NaAc (10 mM), MgSO (2 mM), and CaCl (0.1 mM) at 30 C for 15 h. (B) Expression of fatty acid biosynthesis- 4 2 relatedgenes in relatedgenes Xoo-Rzs in in Xoo-Rzs thein presence the presence or ab or absence sence o of f 1 1.5 .5 µg g/mL /mL triclo triclosan. sa accA/B/C/D: n. accA/B/Cgenes /D: ge encoding nes enco ACC; ding PXO-02711 ACC; PXO-02711 and PXO-0270 and PXO-02706: 6: genes enco genes ding encoding β-ketoacyl-ACP synthetase; b-ketoacyl-ACP synthetase; fabD: gene enco fabD: gene encoding ding ACP- ACP-malonyl-transferase; malonyl-transferase; PXO-02878: PXO-02878: gene coding for b-ketoacyl-ACP reductase; fabA/Z: genes encoding b-hydroxyacyl-ACP dehydratase; PXO-03732: gene gene coding for β-ketoacyl-ACP reductase; fabA/Z: genes encoding β-hydroxyacyl-ACP dehydratase; PXO-03732: gene encoding fatty acyl-CoA synthetase; PXO-05532, PXO-01706, PXO-01705 and PXO-00100: genes encoding enoyl-CoA encoding fatty acyl-CoA synthetase; PXO-05532, PXO-01706, PXO-01705 and PXO-00100: genes encoding enoyl-CoA hy- hydratase; PXO-01050 and PXO-00103: genes encoding hydroxyacyl-CoA dehydrogenase; PXO-04228 and PXO-01049: dratase; PXO-01050 and PXO-00103: genes encoding hydroxyacyl-CoA dehydrogenase; PXO-04228 and PXO-01049: genes genes encoding b-ketoacyl-CoA thiolase. Note: Results are displayed as mean  SD, and significant differences are encoding β-ketoacyl-CoA thiolase. Note: Results are displayed as mean ± SD, and significant differences are identified as identified as determined by Student’s t-test (* p < 0.05, ** p < 0.01). At least three biological repeats were performed. determined by Student’s t-test (* p < 0.05, ** p < 0.01). At least three biological repeats were performed. 2.6. Exogenous Palmitic Acid Promotes the Resistance of Xanthomonas Oryzae to ZS Logically, fatty acid biosynthesis promotion can increase bacterial resistance to ZS. Therefore, exogenous palmitic acid was used to test whether the addition increases the viability of Xoo-Rzs in the presence of ZS. Indeed, the viability of the X. oryzae was higher in the medium with palmitic acid than without the exogenous fatty acid (Figure 6A). Con- sistently, palmitic acid promoted the expression of genes encoding fatty acid biosynthesis and fatty acid degradation. However, the elevated genes encoding fatty acid biosynthesis had a higher expression than the increased genes encoding fatty acid degradation (Figure Antibiotics 2021, 10, 1166 8 of 13 2.6. Exogenous Palmitic Acid Promotes the Resistance of Xanthomonas Oryzae to ZS Logically, fatty acid biosynthesis promotion can increase bacterial resistance to ZS. Therefore, exogenous palmitic acid was used to test whether the addition increases the viability of Xoo-Rzs in the presence of ZS. Indeed, the viability of the X. oryzae was higher in Antibiotics 2021, 10, x FOR PEER REVIEW 8 of 13 the medium with palmitic acid than without the exogenous fatty acid (Figure 6A). Consis- tently, palmitic acid promoted the expression of genes encoding fatty acid biosynthesis and fatty acid degradation. However, the elevated genes encoding fatty acid biosynthesis had a higher expression than the increased genes encoding fatty acid degradation (Figure 6B). 6B). These results further support the conclusion that elevated fatty acid biosynthesis en- These results further support the conclusion that elevated fatty acid biosynthesis enhances hances ZS resistance. ZS resistance. Figure 6. Exogenous palmitic acid promotes ZS resistance in Xoo-Rzs. (A) Percent survival of Xoo-Rzs in the presence or Figure 6. Exogenous palmitic acid promotes ZS resistance in Xoo-Rzs. (A) Percent survival of Xoo-Rzs in the presence absence of palmitic acid. Xoo-Rzs were inoculated in M9 minimal medium with ZS (6 µg/mL) plus palmitic acid (0 Mm, or absence of palmitic acid. Xoo-Rzs were inoculated in M9 minimal medium with ZS (6 g/mL) plus palmitic acid 0.2 mM, 0.(3 0 mM, Mm, 0.2 or 0 mM .4 mM) , 0.3 mM, and incubate or 0.4 mM) and d incubated at 30 °C for at 30 6 h. ( C for B6 ) Expressio h. (B) Expression n of fa oftty acid- fatty acid-r related elated genes genes iniXoo-Rzs n Xoo-Rzs in the in the presence or absence of 0.4 mM palmitic acid. accA/B/C/D: genes encoding ACC; PXO-02711 and PXO-02706: presence or absence of 0.4 mM palmitic acid. accA/B/C/D: genes encoding ACC; PXO-02711 and PXO-02706: genes encod- genes encoding b-ketoacyl-ACP synthetase; fabD: gene encoding ACP-malonyl-transferase; PXO-02878: gene coding ing β-ketoacyl-ACP synthetase; fabD: gene encoding ACP-malonyl-transferase; PXO-02878: gene coding for β-ketoacyl- for b-ketoacyl-ACP reductase; fabA/Z: genes encoding b-hydroxyacyl-ACP dehydratase; PXO-03732: gene encoding ACP reductase; fabA/Z: genes encoding β-hydroxyacyl-ACP dehydratase; PXO-03732: gene encoding fatty acyl-CoA syn- fatty acyl-CoA synthetase; PXO-05532, PXO-01706, PXO-01705 and PXO-00100: genes encoding enoyl-CoA hydratase; thetase; PXO-05532, PXO-01706, PXO-01705 and PXO-00100: genes encoding enoyl-CoA hydratase; PXO-01050 and PXO- PXO-01050 and PXO-00103: genes encoding hydroxyacyl-CoA dehydrogenase; PXO-04228 and PXO-01049: genes encoding 00103: genes encoding hydroxyacyl-CoA dehydrogenase; PXO-04228 and PXO-01049: genes encoding β-ketoacyl-CoA thi- b-ketoacyl-CoA thiolase. Note: Results are displayed as mean  SD, and significant differences are identified as determined olase. Note: Results are displayed as mean ± SD, and significant differences are identified as determined by Student’s t- by Student’s t-test (* p < 0.05, ** p < 0.01). At least three biological repeats were performed. test (* p < 0.05, ** p < 0.01). At least three biological repeats were performed. 3. Discussion 3. Discussion The widespread use of antibiotics poses new challenges to agriculture, the environ- ment, and human health. Therefore, it is of vital importance to study antibiotic resistance in The widespread use of antibiotics poses new challenges to agriculture, the environ- bacteria, including agricultural crop pathogens, to ensure food safety and human and eco- ment, and human health. Therefore, it is of vital importance to study antibiotic resistance logical health. Recent studies have shown that bacterial resistance is influenced by bacterial in bacteria, including agricultural crop pathogens, to ensure food safety and human and metabolism, and the metabolome of antibiotic-resistant bacteria can be reprogrammed by ecological health. Recent studies have shown that bacterial resistance is influenced by bac- certain key biomarkers to restore bacterial sensitivity to antibiotics [38,39]. Therefore, it is important to study the metabolic mechanism of bacterial resistance and identify potential terial metabolism, and the metabolome of antibiotic-resistant bacteria can be repro- biomarkers in bacteria. The aim of this study was to explore the metabolic mechanism grammed by certain key biomarkers to restore bacterial sensitivity to antibiotics [38,39]. of Xoo-Rzs. To the best of our knowledge, this is the first study to explore the resistance Therefore, it is important to study the metabolic mechanism of bacterial resistance and mechanism of Xanthomonas oryzae from the perspective of metabolism. identify potential biomarkers in bacteria. The aim of this study was to explore the meta- The present study demonstrates that Xoo-Rzs has a differential metabolome in which bolic mechanism of Xoo-Rzs. To the best of our knowledge, this is the first study to explore the most prominent characteristic is the upregulation of fatty acid biosynthesis. The elevation was evidenced by the enhanced ACC activity and the transcriptional level of the the resistance mechanism of Xanthomonas oryzae from the perspective of metabolism. genes encoding the pathways. This finding is consistent with recent studies that report The present study demonstrates that Xoo-Rzs has a differential metabolome in which that antibiotic-resistant bacteria have an antibiotic-resistant metabolome [40,41]. Therefore, the most prominent characteristic is the upregulation of fatty acid biosynthesis. The ele- metabolomics approaches can display global metabolic modulation and provide interesting vation was evidenced by the enhanced ACC activity and the transcriptional level of the avenues for further investigation. genes encoding the pathways. This finding is consistent with recent studies that report that antibiotic-resistant bacteria have an antibiotic-resistant metabolome [40,41]. There- fore, metabolomics approaches can display global metabolic modulation and provide in- teresting avenues for further investigation. To explore whether enhanced fatty acid biosynthesis is related to ZS resistance, two methods were adopted. Firstly, fatty acid biosynthesis was inhibited by triclosan to inves- tigate whether the ZS-mediated killing of Xoo-Rzs was elevated. Secondly, fatty acid bio- synthesis was promoted by palmitic acid to investigate whether the ZS-mediated killing of Xoo-Rzs was decreased. Our results showed that triclosan potentiated ZS to kill Xoo- Rzs, while palmitic acid inhibited the ZS-mediated killing. The potentiation and inhibition were accompanied by the elevation and decreased expression of genes encoding fatty acid biosynthesis, respectively. The association of elevated fatty acid synthesis and antibiotic resistance has been previously reported [26,27,42]; however, the relationship between in- creased fatty acid biosynthesis and X. oryzae resistance to ZS is a novel finding. Antibiotics 2021, 10, 1166 9 of 13 To explore whether enhanced fatty acid biosynthesis is related to ZS resistance, two methods were adopted. Firstly, fatty acid biosynthesis was inhibited by triclosan to in- vestigate whether the ZS-mediated killing of Xoo-Rzs was elevated. Secondly, fatty acid biosynthesis was promoted by palmitic acid to investigate whether the ZS-mediated killing of Xoo-Rzs was decreased. Our results showed that triclosan potentiated ZS to kill Xoo-Rzs, while palmitic acid inhibited the ZS-mediated killing. The potentiation and inhibition were accompanied by the elevation and decreased expression of genes encoding fatty acid biosynthesis, respectively. The association of elevated fatty acid synthesis and antibiotic resistance has been previously reported [26,27,42]; however, the relationship between increased fatty acid biosynthesis and X. oryzae resistance to ZS is a novel finding. The effect of key metabolites on bacterial sensitivity and resistance to antibiotics has been reported [43–45]. Studies demonstrated that exogenous additives regulate the resistance of drug-resistant bacteria. Six exogenous metabolites reprogram the colicin- resistant metabolome into a colicin-sensitive metabolome, resulting in increased gene expression, enzyme activity, and/or protein abundance, thereby elevating the sensitivity to the drug [46]. Glucose, fructose, and/or alanine transform kanamycin-resistant bacteria into kanamycin-sensitive bacteria to potentiate kanamycin-mediated killing [47]. Exoge- nous glucose restores the sensitivity of Vibrio alginolyticus to gentamicin [48]. Metabolites in the TCA cycle promote Edwardsiella tarda resistance to chloramphenicol [49]. In this study, exogenous palmitic acid promoted the resistance of X. oryzae to ZS, whereas triclosan potentiated ZS to kill Xoo-Rzs. These findings show that elevated fatty acid biosynthesis is required for ZS resistance. Therefore, comprehensive metabolic analyses will be helpful in understanding the mechanisms of antibiotic resistance and identifying an approach with which to control antibiotic-resistant bacteria via metabolic modulation. 4. Materials and Methods 4.1. Strain Characteristics and MIC Measurement The wild-type strain of Xanthomonas oryzae used in this study was a Zhongshengmycin (ZS)-sensitive strain (Xoo-S herein), and Xoo-Rzs refers to the X. oryzae strain selected by antibiotic-stressed subculturing, which harbors high resistance to ZS. PSA medium (peptone 10 g/L, sucrose 10 g/L, sodium glutamate 1 g/L, agar 2%, pH = 7.0) was used for initial culture, and PSA broth (PSA medium without agar) was used for rapid growth to create the growth curves for the strains. For the minimum inhibitory concentration (MIC) measurement, 1% (v/v) Xoo-S and Xoo-Rzs were inoculated into 2 mL PSA broth with different concentrations of ZS (concen- tration gradient of 0, 0.375, 0.75, 1.5, 3, 6, 12, and 24 g/mL) in 24-deep-well plates and cultured at 600 rpm, 30 C. The OD value was measured after 18 h of shaking using the Multiskan Sky microplate reader, and the minimum concentration of ZS that inhibited strain growth was defined as the MIC. 4.2. Sample Preparation and Parameter Setting for the GC–MS Analysis X. oryzae (1%) was inoculated into 50 mL PSA broth and cultured at 30 C, 200 rpm to the value of OD = 1.0. An appropriate amount of X. oryzae culture was collected and centrifuged at 4 C and 10,000 rpm for 5 min. The supernatant was discarded, and the bacterial cells were washed with 0.9% saline three times. Thereafter, 10 mL of saline was added, and a 2-fold volume of frozen methanol (placed at 20 C for more than 24 h) was immediately added to quench the sample for 1 h at 4 C to terminate cell metabolism. Bacterial cells were collected after centrifugation at 12,000 rpm for 5 min at 4 C, followed by resuspension with 500 L frozen chromatographic grade methanol. A total of 10 L ribitol with a concentration of 0.2 mg/mL was used as an internal reference. The cells were lysed with sound waves (60% intensity) for 6 min and centrifuged at 4 C, 12,000 g for 10 min. A total of 500 L supernatant was transferred to a new centrifuge tube and dried with a nitrogen blower. Thereafter, 80 L of methoxyamine hydrochloride (20 mg/mL) was added to the sample, followed by shaking at 200 rpm, 37 C for 30 min to Antibiotics 2021, 10, 1166 10 of 13 expose the protected carbonyl part of the sample. Then, 80 L of N-methyl-n-trimethylsilyl trifluoroacetamide (MSTFA, Sigma-Aldrich, Shanghai, China) was added and reacted at 37 C for 30 min for the acidic protons-derived. Finally, the samples were centrifuged at 4 C for 5 min at 12,000 g and prepared to be processed on the GC–MS. The GC–MS analysis was performed following a two-stage technique as previously described [50,51]. Briefly, the injection port was maintained at 270 C. Then, 1 L derivatized sample was injected into a Rxi-5MS column (30 m  250 m i.d.  0.25 m) using splitless injection, and the analysis was carried out using an GC-GC HRT 4D plus (LECO, St. Joseph, MI, USA). The initial temperature of the GC oven was held at 85 C for 3 min, followed by an increase to 285 C at a rate of 5 C/min, then a rise to 310 C at a rate of 20 C/min where it was held for 7 min. Helium was used as the carrier gas, and flow was kept constant at 1 mL/min. The MS was operated by using EI source, 70 eV EM voltage, and the scanning range was from 35 to 600 amu. 4.3. Technical Tools and Software Used for Data Management The relative abundance of a metabolite was scaled according to the total abundance of all metabolites in the sample. The Z-score analysis scaled each metabolite according to a reference distribution and was calculated based on the mean and SD of the reference. Hierarchical clustering was completed in the R platform with the gplots package using the distance matrix. Principal component analysis (SIMCA-P 12.0) was used to discriminate sample patterns to identify the metabolites in Xoo-Rzs and to minimize the interindividual variation’s influence. SPSS Statistics and Graphpad Prism 7 Project were used to draw the histogram, the scatter plot, and other graphs. In this study, the method of data analysis was multivariate statistical analysis [39,47]. The specific method was performed as previously described [52]. 4.4. Determination of the Activity of ACC X. oryzae (1%) was inoculated into 30 mL PSA broth and cultured at 30 C, 200 rpm for 24 h. An appropriate amount of X. oryzae culture was collected and centrifuged at 4 C and 10,000 rpm for 5 min. The supernatant was discarded and an appropriate amount of extract (in the ratio of 5 million cells to 1 mL of extract) was added. The cells were lysed with sound waves (60% intensity) for 6 min and centrifuged at 4 C, 12,000 g for 10 min, and the supernatant was used as the sample. The acetyl CoA carboxylase (ACC) activity was measured with the Acetyl CoA carboxylase Assay Kit (Suzhou geruisi biotechnology company, Suzhou, China). The protein concentration was determined using a BCA Protein Assay Kit (Dalian meilunbio biotechnology company, Dalian, China). 4.5. Assay of the Gene Expression Levels In Vivo The expression levels of the genes related to fatty acid biosynthesis and degrada- tion metabolism were detected using quantitative real-time PCR (qRT-PCR) following a method described previously [53]. In brief, the bacterial cells were harvested and the total RNA of each sample was isolated with a Biospin Total RNA Extraction Kit (Dobiothec, Fuzhou, China). An amount of 1 mg of total RNA was used for reverse transcription with the TransScript One-Step gDNA Removal and cDNA Synthesis Supermix Kit (TransGen Biotech, Beijing, China). qRT-PCR was performed in 96-well plates with PerfectStart Green qPCR SuperMix (TransGen Biotech, Beijing, China). The primers for each gene were listed in Table S1. Each primer pair was specific, and the relative expression of each gene was determined by the comparative threshold cycle method (2-DDCT method). 4.6. Antibiotic Bactericidal Assay Xoo-Rzs was cultured in PSA broth for 24 h at 30 C. Samples were harvested at 8000 rpm for 3 min centrifugation, and the cells were washed three times with 30 mL sterile saline. The cells were transferred into M9 broth (NaAc 10 mM, MgSO 2 mM, and CaCl 4 2 0.1 mM) containing the indicated concentration of ZS. To test the effect of triclosan on the Antibiotics 2021, 10, 1166 11 of 13 antibiotic resistance of Xoo-Rzs, 3 g/mL ZS and 1–3 g/mL triclosan were added in the M9, while, to test the effect of palmitic acid, the concentration of ZS was 6 g/mL, and the concentrations of palmitic acid were 0.2, 0.3, or 0.4 mM. The OD of the suspension was adjusted to 0.2, and the exogenous reagents, i.e., triclosan or palmitic acid, were added to a final volume of 5 mL. After incubation at 30 C for 6 h, 100 L aliquot samples were collected, serially diluted, and plated (10 L aliquots) on PSA medium, then cultured at 30 C for 48–60 h for the CFU calculation. Only those dilutions generating 20–200 clones per plate were counted. Percent survival was determined by dividing the CFU obtained from a treated sample by the CFU obtained from the control. 5. Conclusions In conclusion, X. oryzae resistance to ZS is related to an increase in fatty acid biosynthe- sis. The use of triclosan to inhibit fatty acid metabolism enhances the sensitivity of X. oryzae to ZS, while exogenous palmitic acid weakens its sensitivity to ZS. Our study showed the importance of fatty acid biosynthesis in Xoo-Rzs resistance to ZS. These results will deepen our understanding of the mechanisms through which antibiotic resistance forms in bacteria from a metabolic perspective. Furthermore, they provide new strategies for rational drug use, which is important for preventing and reducing bacterial antibiotic resistance. Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/antibiotics10101166/s1, Figure S1: Total metabolites detected in Xoo-S and Xoo-Rzs, Figure S2: The relative abundances of the 6 biomarkers. Table S1: Primers for qRT-PCR. Table S2: The specific growth rate of two strains. Author Contributions: Conceptualization, Q.W. and Y.G.; methodology, Q.W.; software, M.L. and P.S.; validation, M.L. and P.S.; formal analysis, Q.W. and Y.G.; investigation, Q.W., M.L. and P.S.; resources, Y.G.; data curation, Q.W., M.L. and P.S.; writing—original draft preparation, Q.W.; writing— review and editing, Y.G.; visualization, Q.W.; supervision, Y.G.; project administration, Y.G.; funding acquisition, Y.G. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the National Natural Science Foundation of China, grant number 32072481. Data Availability Statement: Data is contained within the article. Acknowledgments: We thank the Fujian college association instrumental analysis center of Fuzhou University for technical assistance with the GC–MS. Conflicts of Interest: The authors declare no conflict of interest. References 1. Ke, Y.G.; Wu, M.X.; Zhang, Q.L.; Li, X.H.; Xiao, J.H.; Wang, S.P. Hd3a and OsFD1 negatively regulate rice resistance to Xanthomonas oryzae pv. oryzae and Xanthomonas oryzae pv. oryzicola. Biochem. Bioph. Res. Commun. 2019, 513, 775–780. [CrossRef] [PubMed] 2. Nino-Liu, D.O.; Ronald, P.C.; Bogdanove, A.J. Xanthomonas oryzae pathovars: Model pathogens of a model crop. Mol. Plant Pathol. 2006, 7, 303–324. [CrossRef] [PubMed] 3. Chen, X.L.; Yan, Q.; Li, R.F.; Li, K.H.; Gao, L.J. First report of a new and highly virulent race of Xanthomonas oryzae pv. oryzae, the causal agent of bacterial leaf blight of rice in Guangxi Province, China. Plant Dis. 2016, 100, 1492–1493. [CrossRef] 4. Swings, J.; Vandenmooter, M.; Vauterin, L.; Hoste, B.; Gillis, M.; Mew, T.W.; Kersters, K. Reclassification of the causal agents of bacterial-blight (Xanthomonas campestris pv. oryzae) and bacterial leaf streak (Xanthomonas campestris pv. oryzicola) of rice as pathovars of Xanthomonas Oryzae (Ex Ishiyama 1922) sp nov, nom. rev. Int. J. Syst. Bacteriol. 1990, 40, 309–311. [CrossRef] 5. Gnanamanickam, S.S.; Priyadarisini, V.B.; Narayanan, N.N.; Vasudevan, P.; Kavitha, S. An overview of bacterial blight disease of rice and strategies for its management. Curr. Sci. India 1999, 77, 1435–1444. 6. Yang, P.; Li, F.J.; Huang, S.W.; Luo, M.; Lin, W.; Yuan, G.Q.; Li, Q.Q. Physiological and transcriptional response of Xanthomonas oryzae pv. oryzae to berberine, an emerging chemical control. Phytopathology 2020, 110, 1027–1038. [CrossRef] 7. Mew, T.W.; Alvarez, A.M.; Leach, J.E.; Swings, J. Focus on Bacterial-Blight of Rice. Plant Dis. 1993, 77, 5–12. [CrossRef] 8. Jin, P.F.; Wang, Y.; Tan, Z.; Liu, W.B.; Miao, W.G. Antibacterial activity and rice-induced resistance, mediated by C(15)surfactin A, in controlling rice disease caused by Xanthomonas oryzae pv. oryzae. Pestic Biochem. Phys. 2020, 169, 104669. [CrossRef] 9. Yasmin, S.; Hafeez, F.Y.; Mirza, M.S.; Rasul, M.; Arshad, H.M.I.; Zubair, M.; Iqbal, M. Biocontrol of bacterial leaf blight of rice and profiling of secondary metabolites produced by rhizospheric Pseudomonas aeruginosa BRp3. Front Microbiol. 2017, 8, 1895. [CrossRef] Antibiotics 2021, 10, 1166 12 of 13 10. Ham, J.H.; Melanson, R.A.; Rush, M.C. Burkholderia glumae: Next major pathogen of rice? Mol. Plant Pathol. 2011, 12, 329–339. [CrossRef] 11. Zhu, X.F.; Xu, Y.; Peng, D.; Zhang, Y.; Huang, T.T.; Wang, J.X.; Zhou, M.G. Detection and characterization of bismerthiazol- resistance of Xanthomonas oryzae pv. oryzae. Crop Prot. 2013, 47, 24–29. [CrossRef] 12. Xu, Y.; Zhu, X.F.; Zhou, M.G.; Kuang, J.; Zhang, Y.; Shang, Y.; Wang, J.X. Status of streptomycin resistance development in Xanthomonas oryzae pv. oryzae and Xanthomonas oryzae pv. oryzicola in China and their resistance characters. J. Phytopathol. 2010, 158, 601–608. 13. Yi, C.F.; Chen, J.X.; Hu, D.Y.; Song, B.A. First report about the screening, characterization, and fosmid library construction of Xanthomonas oryzae pv. oryzae strain with resistance to fubianezuofeng. Pestic Biochem. Phys. 2020, 169, 104645. [CrossRef] [PubMed] 14. Wang, Q.P.; Zhang, C.; Long, Y.H.; Wu, X.M.; Su, Y.; Lei, Y.; Ai, Q. Bioactivity and control efficacy of the novel antibiotic tetramycin against Various Kiwifruit diseases. Antibiotics 2021, 10, 289. [CrossRef] 15. Ren, Y.; Li, D.; Jiang, S.; Wang, Y.; Tang, Q.; Huang, H.; Wang, D.; Song, B.; Chen, Z. Integration of transcriptomic and proteomic data reveals the possible action mechanism of the antimicrobial zhongshengmycin against Didymella segeticola, the causal agent of tea leaf spot. Phytopathology 2021. [CrossRef] 16. Wang, B.L.; Shi, Y.X.; Ma, Y.; Liu, X.H.; Li, Y.H.; Song, H.B.; Li, B.J.; Li, Z.-M. Synthesis and biological activity of some novel trifluoromethyl-substituted 1,2,4-triazole and bis(1,2,4-Triazole) mannich bases containing piperazine rings. J. Agr. Food Chem. 2010, 58, 5515–5522. [CrossRef] 17. Wang, B.L.; Shi, Y.X.; Zhang, S.J.; Ma, Y.; Wang, H.X.; Zhang, L.Y.; Wei, W.; Liu, X.-H.; Li, Y.-H.; Li, Z.-M.; et al. Syntheses, biological activities and SAR studies of novel carboxamide compounds containing piperazine and arylsulfonyl moieties. Eur. J. Med. Chem. 2016, 117, 167–178. [CrossRef] 18. Song, Y.; Shi, J.T.; Xiong, Z.Z.; Shentu, X.P.; Yu, X.P. Three antimicrobials alter gut microbial communities and causing different mortality of brown planthopper, Nilaparvata lugens Stal. Pestic Biochem. Phys. 2021, 174, 104806. [CrossRef] 19. Alekshun, M.N.; Levy, S.B. Molecular mechanisms of antibacterial multidrug resistance. Cell 2007, 128, 1037–1050. [CrossRef] 20. Shaw, W.V. Chloramphenicol acetyltransferase: Enzymology and molecular biology. CRC Crit. Rev. Biochem. 1983, 14, 1–46. [CrossRef] 21. Murray, I.A.; Shaw, W.V. O-Acetyltransferases for chloramphenicol and other natural products. Antimicrob. Agents Chemother. 1997, 41, 1–6. [CrossRef] [PubMed] 22. Gabryszewski, S.J.; Wong Fok Lung, T.; Annavajhala, M.K.; Tomlinson, K.L.; Riquelme, S.A.; Khan, I.N.; Noguera, L.P.; Wickersham, M.; Zhao, A.; Mulenos, A.M.; et al. Metabolic adaptation in methicillin-resistant Staphylococcus aureus pneumonia. Am. J. Respir. Cell Mol. Biol. 2019, 61, 185–197. [CrossRef] 23. Wen, X.; Cao, J.; Mi, J.; Huang, J.; Liang, J.; Wang, Y.; Ma, B.; Zou, Y.; Liao, X.; Liang, J.B.; et al. Metabonomics reveals an alleviation of fitness cost in resistant E. coli competing against susceptible E. coli at sub-MIC doxycycline. J. Hazard Mater. 2021, 405, 124215. [CrossRef] [PubMed] 24. Liu, Y.; Yang, K.; Jia, Y.; Shi, J.; Tong, Z.; Wang, Z. Cysteine potentiates bactericidal antibiotics activity against Gram-negative bacterial persisters. Infect. Drug Resist. 2020, 13, 2593–2599. [CrossRef] 25. Ma, Y.; Guo, C.; Li, H.; Peng, X.X. Low abundance of respiratory nitrate reductase is essential for Escherichia coli in resistance to aminoglycoside and cephalosporin. J. Proteom. 2013, 87, 78–88. [CrossRef] [PubMed] 26. Liu, S.R.; Pen, X.X.; Li, H. Metabolic mechanism of ceftazidime resistance in Vibrio alginolyticus. Infect. Drug Resist. 2019, 12, 417–429. [CrossRef] 27. Cheng, Z.X.; Yang, M.J.; Peng, B.; Peng, X.X.; Lin, X.M.; Li, H. The depressed central carbon and energy metabolisms is associated to the acquisition of levofloxacin resistance in Vibrio alginolyticus. J. Proteom. 2018, 181, 83–91. [CrossRef] [PubMed] 28. Lee, H.H.; Molla, M.N.; Cantor, C.R.; Collins, J.J. Bacterial charity work leads to population-wide resistance. Nature 2010, 467, 82–85. [CrossRef] [PubMed] 29. Mishra, S.; Yang, X.D.; Ray, S.; Fraceto, L.F.; Singh, H.B. Antibacterial and biofilm inhibition activity of biofabricated silver nanoparticles against Xanthomonas oryzae pv. oryzae causing blight disease of rice instigates disease suppression. World J. Microb. Biot. 2020, 36, 1–10. [CrossRef] [PubMed] 30. Majumdar, T.D.; Singh, M.; Thapa, M.; Dutta, M.; Mukherjee, A.; Ghosh, C.K. Size-dependent antibacterial activity of copper nanoparticles against Xanthomonas oryzae pv. oryzae—A synthetic and mechanistic approach. Colloid Interfac. Sci. 2019, 32, 100190. [CrossRef] 31. Antar, A.; Lee, M.A.; Yoo, Y.; Cho, M.H.; Lee, S.W. PXO_RS20535, Encoding a novel response regulator, is required for chemotactic motility, biofilm formation, and tolerance to oxidative stress in Xanthomonas oryzae pv. oryzae. Pathogens 2020, 9, 956. [CrossRef] [PubMed] 32. Li, P.P.; Liu, X.J.; Li, H.; Peng, X.X. Downregulation of Na(+)-NQR complex is essential for Vibrio alginolyticus in resistance to balofloxacin. J. Proteom. 2012, 75, 2638–2648. [CrossRef] [PubMed] 33. Yang, J.H.; Bening, S.C.; Collins, J.J. Antibiotic efficacy—Context matters. Curr. Opin. Microbiol. 2017, 39, 73–80. [CrossRef] 34. Ye, J.Z.; Lin, X.M.; Cheng, Z.X.; Su, Y.B.; Li, W.X.; Ali, F.M.; Zheng, J.; Peng, B. Identification and efficacy of glycine, serine and threonine metabolism in potentiating kanamycin-mediated killing of Edwardsiella piscicida. J. Proteom. 2018, 183, 34–44. [CrossRef] Antibiotics 2021, 10, 1166 13 of 13 35. Ye, J.Z.; Su, Y.B.; Lin, X.M.; Lai, S.S.; Li, W.X.; Ali, F.; Zheng, J.; Peng, B. Alanine enhances aminoglycosides-induced ROS production as revealed by proteomic analysis. Front Microbiol. 2018, 9, 29. [CrossRef] 36. Wu, C.W.; Zhao, X.L.; Wu, X.J.; Wen, C.; Li, H.; Chen, X.H.; Peng, X.X. Exogenous glycine and serine promote growth and antifungal activity of Penicillium citrinum W1 from the south-west Indian Ocean. FEMS Microbiol. Lett. 2015, 362, fnv040. [CrossRef] 37. Wu, C.W.; Wu, X.J.; Wen, C.; Peng, B.; Peng, X.X.; Chen, X.H.; Li, H. Fructose promotes growth and antifungal activity of Penicillium citrinum. Protein Cell 2016, 7, 527–532. [CrossRef] 38. Isha, A.; Yusof, N.A.; Shaari, K.; Osman, R.; Abdullah, S.N.A.; Wong, M.Y. Metabolites identification of oil palm roots infected with Ganoderma boninense using GC-MS-based metabolomics. Arab J. Chem. 2020, 13, 6191–6200. [CrossRef] 39. Wang, Z.; Li, M.Y.; Peng, B.; Cheng, Z.X.; Li, H.; Peng, X.X. GC-MS-Based metabolome and metabolite regulation in serum-resistant Streptococcus agalactiae. J. Proteome Res. 2016, 15, 2246–2253. [CrossRef] 40. Chen, X.H.; Zhang, B.W.; Li, H.; Peng, X.X. Myo-inositol improves the host’s ability to eliminate balofloxacin-resistant Escherichia coli. Sci. Rep. 2015, 5, 1–11. [CrossRef] 41. Peng, B.; Su, Y.B.; Li, H.; Han, Y.; Guo, C.; Tian, Y.M.; Peng, X.X. Exogenous alanine and/or glucose plus kanamycin klls antibiotic-resistant bacteria. Cell Metab. 2015, 21, 249–261. [CrossRef] [PubMed] 42. Su, Y.-B.; Kuang, S.-F.; Ye, J.-Z.; Tao, J.-J.; Li, H.; Peng, X.-X.; Peng, B. Enhanced biosynthesis of fatty acids is associated with the acquisition of ciprofloxacin resistance in Edwardsiella tarda. mSystems 2021, 6, e00694-21. [CrossRef] 43. Jiang, M.; Kuang, S.F.; Lai, S.S.; Zhang, S.; Yang, J.; Peng, B.; Peng, X.X.; Chen, Z.G.; Li, H. Na(+)NQR confers aminoglycoside resistance via the regulation of L-alanine netabolism. mBio 2020, 11, e02086-20. [CrossRef] 44. Lin, Y.X.; Li, W.X.; Sun, L.N.; Lin, Z.P.; Jiang, Y.H.; Ling, Y.M.; Lin, X. Comparative metabolomics shows the metabolic profiles fluctuate in multi-drug resistant Escherichia coli strains. J. Proteom. 2019, 207, 103468. [CrossRef] [PubMed] 45. Zhang, S.; Yang, M.J.; Peng, B.; Peng, X.X.; Li, H. Reduced ROS-mediated antibiotic resistance and its reverting by glucose in Vibrio alginolyticus. Environ. Microbiol. 2020, 22, 4367–4380. [CrossRef] 46. Li, L.; Su, Y.B.; Peng, B.; Peng, X.X.; Li, H. Metabolic mechanism of colistin resistance and its reverting in Vibrio alginolyticus. Environ. Microbiol. 2020, 22, 4295–4313. [CrossRef] [PubMed] 47. Allison, K.R.; Brynildsen, M.P.; Collins, J.J. Metabolite-enabled eradication of bacterial persisters by aminoglycosides. Nature 2011, 473, 216–220. [CrossRef] 48. Zhang, S.; Wang, J.; Jiang, M.; Xu, D.; Peng, B.; Peng, X.X.; Li, H. Reduced redox-dependent mechanism and glucose-mediated reversal in gentamicin-resistant Vibrio alginolyticus. Environ. Microbiol. 2019, 21, 4724–4739. [CrossRef] 49. Wang, C.; Dong, X.S.; Yang, Y.Y.; Xu, G.J.; Wu, M.M.; Yan, F.J.; Zhang, L.G.; An, L.; Fu, P.S.; Wang, X.R.; et al. Metabolites in the TCA cycle promote resistance to chloramphenicol of Edwardsiella tarda. J. Proteome Res. 2021, 20, 972–981. [CrossRef] 50. Jiang, M.; Yang, L.F.; Zheng, J.; Chen, Z.G.; Peng, B. Maltose promotes crucian carp survival against Aeromonas sobrialinfection at high temperature. Virulence 2020, 11, 877–888. [CrossRef] 51. Zhao, X.L.; Wu, C.W.; Peng, X.X.; Li, H. Interferon-alpha 2b against microbes through promoting biosynthesis of unsaturated fatty acids. J. Proteome Res. 2014, 13, 4155–4163. [CrossRef] [PubMed] 52. Guan, Y.; Wang, Q.X.; Lv, C.; Wang, D.H.; Ye, X.Y. Fermentation time-dependent pectinase activity is associated with metabolomics variation in Bacillus licheniformis DY2. Process Biochem. 2021, 101, 147–155. [CrossRef] 53. Jiang, M.; Gong, Q.Y.; Lai, S.S.; Cheng, Z.X.; Chen, Z.G.; Zheng, J.; Peng, B. Phenylalanine enhances innate immune response to clear ceftazidime-resistant Vibrio alginolyticus in Danio rerio. Fish Shellfish Immunol. 2019, 84, 912–919. [CrossRef] [PubMed]

Journal

Antibiotics – Multidisciplinary Digital Publishing Institute

Published: Sep 25, 2021

Keywords: Xanthomonas oryzae; antibiotics; antibiotic-resistant; zhongshengmycin; fatty acid biosynthesis metabolism

There are no references for this article.