Changes in soil bacterial community profiles associated with ...

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44 Ryoichi DOI, Pongsak SAHUNALU, Chongrak WACHRINRAT, Sakhan TEEJUNTUK and Katsutoshi SAKURAITable 1. Soil characteristicsVegetativetypeReplicatesMoisture Bulk densitycontent (%) (kg L -1 )pHEC(dS m -1 )Total C(g kg -1 dry soil)Total N(g kg -1 dry soil)C/NExch H(m eq kg -1 dry soil)Exch Al(m eq kg -1 dry soil)SoicolorMPN count (x 10 8 g -1 fresh soil)Dry evergreenforest1225.221.90.851.096.205.8210.646.8335.421.63.832.239.249.693.421.570.000.00Dull yellowbrown4.834.80Bare ground1214.011.81.571.305.055.122.274.067.857.580.840.689.3511.25.502.7714.01.03Reddishbrown3.302.18ANOVA0.0330.1240.0410.1180.0950.1060.4880.4250.3660.066: The MPN counts for the basal medium having no antibiotics are indicated.: The one-way ANOVA was performed hypothesizing vegetative type to be the significant source of variation. The p value for each soil characteristic is indicated.AntibioticAmpicillinChloramphenicolDapsonErythromycinKanamycin sulfateLasalocidNafcillinNalidixic acidNeomycin∙HClNovobiocinPenicillin GSpectinomycin∙2HClStreptomycin sulfateSulfamethoxazoleTetracyclineTrimethoprimBare ground 1Bare ground 2AntibioticAmpicillinChloramphenicolDapsonErythromycinKanamycin sulfateLasalocidNafcillinNadixic acidNeomycin∙HClNovobiocinPenicillin GSpectinomycin∙2HClStreptomycin sulfateSulfamethoxazoleTetracyclineTrimethoprimDry evergreenforest 1Dry evergreenforest 20 0.2 0.4 0.6 0 0.2 0.4 0.6Relative abundance ofresistant bacterial cellsRelative abundance ofresistant bacterial cellsFig. 2. Profiles of the soil samples based on most probable numbers of resistant bacteria to the antibiotics.The error bars indicate standard deviations (n=3). The horizontal axes indicate the relative abundanceof the bacteria resistant to each antibiotic determined by the log-ratio transformation (see text).
Soil bacterial communities reflecting land degradation45AntibioticAmpicillinChloramphenicolDapsonErythromycinKanamycin sulfateLasalocidNafcillinNalidixic acidNeomycin∙HClNovobiocinPenicillin GSpectinomycin∙2HClStreptomycin sulfateSulfamethoxazoleTetracyclineTrimethoprimMixture 1Mixture 2Bare ground 1Bare ground 2AntibioticAmpicillinChloramphenicolDapsonErythromycinKanamycin sulfateLasalocidNafcillinNadixic acidNeomycin∙HClNovobiocinPenicillin GSpectinomycin∙2HClStreptomycin sulfateSulfamethoxazoleTetracyclineTrimethoprimMixture 1Mixture 2Dry evergreenforest 10 0.1 0.2 0 0.1 0.2Relative susceptibilityDry evergreenforest 2Relative susceptibilityFig. 3. Profiles of the soil samples based on susceptibility of the soil bacterial communities to theantibiotics. The error bars indicate standard deviations (n=3). The horizontal axes indicatethe relative susceptibility of the bacterial community to each antibiotic determined by the ratiotransformation (see text).than that from DEF 2.Susceptibility patterns of the bacterial communities also reflected effects of deforestation (Fig. 3). The bacterialcommunity in the BG soil had high susceptibility to novobiocin, penicillin, spectinomycin, streptomycin, sulfamethoxazole,tetracycline and tetracycline-trimethoprim mixture (mixture 2). The DEF bacterial community showed high susceptibility topenicillin, sulfamethoxazole and the two mixtures. The DEF soil samples suggested differences in bacterial communitysusceptibility patterns between them: the DEF 1 sample had high susceptibility to dapson, while the DEF 2 sample tonovobiocin and streptomycin.Fig2,3PCAMethod 1 discriminated between the sample groups as shown in Fig. 4a. The first and second PCs explained more than halfof the variation; 44 and 13%, respectively. The sample groups were separated by the first PC axis. The second PC axis didnot separate the sampling points under each of the vegetative types.Method 2 also discriminated between the soil sample groups (Fig. 4b). The first and second PCs explained 30 and 19%
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