Next Article in Journal
Higher Sustainability and Lower Opportunistic Behaviour in Healthcare: A New Framework for Performing Hospital-Based Health Technology Assessment
Next Article in Special Issue
Influence of Sampling Point Discretization on the Regional Variability of Soil Organic Carbon in the Red Soil Region, China
Previous Article in Journal
Monitoring Viability and Sustainability in Healthcare Organizations
Previous Article in Special Issue
Land Use Types and Geomorphic Settings Reflected in Soil Organic Carbon Distribution at the Scale of Watershed
 
 
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Functional Diversity of Soil Microbial Communities in Response to the Application of Cefuroxime and/or Antibiotic-Resistant Pseudomonas putida Strain MC1

1
Department of Microbiology and Virology, School of Pharmacy with the Division of Laboratory Medicine, Medical University of Silesia, Jagiellońska 4, 41-200 Sosnowiec, Poland
2
Department of Microbiology, Faculty of Biology and Environmental Protection, University of Silesia, Jagiellońska 28, 40-032 Katowice, Poland
*
Author to whom correspondence should be addressed.
Sustainability 2018, 10(10), 3549; https://doi.org/10.3390/su10103549
Received: 4 September 2018 / Revised: 25 September 2018 / Accepted: 30 September 2018 / Published: 3 October 2018

Abstract

:
Cefuroxime (XM), the most commonly prescribed antibiotic from the cephalosporin group, may cause changes in the structure of the soil microbial community, and these changes may also be reflected in the alteration of its functionality. Therefore, due to the lack of studies on this topic, the scope of this study was to assess the functional diversity and catabolic activity of the microbial community in soil treated with XM (1 mg/kg and 10 mg/kg soil) using the community-level physiological profile (CLPP) approach during a 90-day experiment. In addition, the effect of antibiotic-resistant Pseudomonas putida strain MC1 (Ps) was also evaluated. The resistance/resilience concept and multifactorial analysis were used to interpret the data. The results showed that the introduction of XM and/or Ps into the soil caused changes in the catabolic activity and functional diversity of the microbial community. A decrease in the values of the CLPP indices (i.e., microbial activity expressed as the average well-color development (AWCD), substrate richness (R), the Shannon-Wiener (H) and evenness (E) indices and the AWCD values for the six carbon substrate groups) for the XM-treated soil was generally detected up to 30 days. In turn, at the same time, the activity measured in the Ps-inoculated soil was higher compared to the control soil. A stimulatory effect of XM at 10 mg/kg (XM10) and XM10+Ps on the utilization pattern of each substrate group was found at the following sampling times (days 60 and 90). The AWCD values for the utilization of amines, amino acids, carbohydrates, carboxylic acids, miscellaneous compounds and polymers for these treatments were found to be up to 2.3-, 3.1-, 2.3-, 13-, 3.4- and 3.3-fold higher compared to the values for the nontreated control, respectively. The resistance of the CLPP indices and the AWCD values for the carbon substrate groups were categorized as follows: E > H > R > AWCD and amino acids = carbohydrates > polymers > amines > miscellaneous > carboxylic acids, respectively. The results suggest a low initial resistance of the soil microbial community to XM and/or Ps, and despite the short-term negative effect, the balance of the soil ecosystem may be disturbed.

1. Introduction

Antibiotics belong to a group of pharmaceuticals that have recently been excessively used, consequently leading to their accumulation in various parts of the environment, including the soil. Studies have shown that the concentration of antibiotics in the soil can range from a few nanograms to even 50 mg/kg of soil [1,2,3,4]. Antibiotics, as organic compounds, can be subjected to various processes in the soil environment, the most important of which are sorption on soil components, as well as transformation and/or degradation [5,6,7,8,9]. Factoring in both various abiotic and biotic factors, which influence the behavior of the antibiotic in the soil, its degradation time (DT50 or half-life) may vary from less than one (e.g., amoxicillin) [10] up to 3466 (e.g., azithromycin) days [11]. In addition to antibiotics, bacterial strains resistant to these compounds may also enter the environment [12,13,14,15]. The consequences of such actions may be the development of antibiotic-resistant microorganisms [16,17,18,19,20]. It has been shown that even very low concentrations of antibiotics can cause changes in the genome of microorganisms and consequently lead to the transfer of resistance genes between individual members of an exposed population of microorganisms. In addition, microorganisms naturally present in the soil can be a source of resistance genes that can be transferred to other microorganisms such as human and animal pathogens [21,22,23,24]. In addition to this effect, antibiotics belonging to different classes may adversely affect the soil microflora, possibly manifesting themselves in the change of their structure [15,25,26,27] and functionality [6,9,28,29,30]. The consequence of these changes may be the disturbance of the soil ecosystem balance. Data on the effect of antibiotics on soil microorganisms are varied and include results related to the impact on individual species and whole populations of microorganisms obtained on the basis of biochemical and genetic indices analyses [9,30,31,32,33].
One of the most frequently used groups of antibiotics are the second-generation cephalosporins, particularly cefuroxime (XM) [34]. The mode of action of this antibiotic involves blocking the synthesis of the bacterial wall and is directed against a wide group of microorganisms [35,36,37]. For example, the consumption of cefuroxime in many European countries reaches as much as 50% of the total consumption of cephalosporins [34,38,39]. The consequence of such a high consumption of XM is its detection in wastewater and surface waters, with the highest concentration reaching 210 μg/L for wastewater from the pharmaceutical industry and hospitals around the world [40,41]. Considering that currently used wastewater treatment systems do not guarantee 100% removal of antibiotics, these compounds may enter the soil with the sewage sludge. However, there are no reports on concentrations of this antibiotic in the soil environment, and studies on its degradation have shown that in aerobic conditions, its elimination reached 42.8–80% in 64 days [42]. Additionally, data on the effect of XM on soil microorganisms are limited. In our previous studies [43], including the determination of the effect of XM and multidrug resistant Pseudomonas putida MC1 on the soil bacterial genetic structure, the antibiotic showed a negative effect at 10 mg/kg in soil on the bacterial population analyzed, resulting in a decrease in its biodiversity after 30, 60 and 90 days from the application of antibiotics. In turn, the effect of strain MC1 on the measured biodiversity indices was not demonstrated. Use of the resistance (RS)/resilience (RL) concept during the 90-day experiment demonstrated the progressively detrimental effect of XM on the genetic structure of the soil bacteria [43]. The changes observed in the diversity of microbial communities under the influence of XM may also be reflected in the altered functions of soil microorganisms, possibly translating into soil processes important for the ecosystem. Therefore, in this study, due to the lack of such studies, we assumed that the application of XM and/or strain MC1 to the soil may result in changes in the functional biodiversity of the soil microorganisms. To obtain this knowledge, the metabolic potential of the soil microbial community analyzed was assessed using the community-level physiological profile (CLPP) approach. In addition, the RS/RL concept and multivariate analysis were used to determine the ability of microorganisms to maintain their activity and functional diversity under the stress conditions caused by XM and/or strain MC1.

2. Materials and Methods

2.1. Experimental Design

The experiment was conducted using soil classified as a sandy loam [43], the parameters of which were determined using the ISO (International Organization for Standardization) standards [44]. The experiment had a random block system and consisted of three replicates for the control soil and soil contaminated with two concentrations of XM (1 mg/kg and 10 mg/kg soil) and/or inoculated with strain MC1. The concentrations of XM reflect the adverse scenarios associated with the entry of large quantities of antibiotics into the soil as a result of the uncontrolled disposal of unused drugs into municipal waste or depositing them in landfills. The experiment used MC1 (accession number in GenBank: MC327770), which was isolated from raw sewage in the presence of cefuroxime [43,45] and identified as Pseudomonas putida based on the analysis of the 16S RNA gene [45] and biochemical properties [46]. In addition, the strain MC1 was characterized by resistance to cefuroxime, clindamycin, erythromycin and vancomycin with MIC values above 256 μg/mL [43]. Strain MC1 was introduced into soil at 1.6 × 107 cells/g soil, and the preparation of its suspension was made with the use of a previously described method [45]. All the treatments were incubated in the dark at 22 ± 1 °C for 90 days. On specific days of the experiment (1, 15, 30, 60 and 90 days), samples were randomly removed to determine the biochemical potential of the soil microbial community. The design of the experiment and the list of analyses performed are shown in Figure 1.

2.2. Determination of the Catabolic Activity of Soil Microorganisms

The biochemical potential of soil microorganisms referred as to CLPP were determined using the Biolog EcoPlateTM system (Biolog Inc., CA, USA). To extract the microbial community, soil suspensions (10 g dry weight of soil in 90 mL sterile 0.85% NaCl solution) were shaken for 1 h, and aliquots of 125 µL were inoculated onto plates and incubated at 22 °C in the dark. The readings were taken at 590 nm after inoculation and at 12 h intervals for 144 h using a BIOLOGTM microplate reader. The absorbance measurements for individual substrates were corrected against the control well, which contained only water, and by subtracting the absorbance of the first reading to eliminate the background absorbance [47].

2.3. Analysis of the Data

The data obtained from the absorbance measurements were used to determine the CLPP indices, i.e., microbial activity expressed as the average well-color development (AWCD), substrate richness (RS), and the Shannon-Wiener (H) and evenness (E) indices. The AWCD values were determined using Equation (1) [48].
AWCD = ∑ODi/31
where ODi is the optical density value from each well.
The RS and H values were calculated using Equations (2) and (3), respectively [48].
H = −∑pi (lnpi)
E = H/Hmax= H/lnRS
where pi is the ratio of the activity on each substrate (ODi) to the sum of the activities on all of the substrates (∑ODi), and RS is the number of substrates metabolized.
To assess the resistance (RS) of the catabolic activity of the soil microorganisms under the stress conditions caused by XM and/or strain MC1 at individual sampling days of the experiment and resilience (RL), indicating the recovery of balance, on day 90 after the application of the XM and/or strain MC1, indices proposed by Orwin and Wardle [49] were used. The RS index was calculated using Equation (4).
RS(t0) = 1 − 2|D0|/(C0 + |D0|)
where D0 is the difference between the control (C0) and the disturbed soil (P0) at the end of the disturbance (t0). The RL index was calculated using Equation (5).
RL(tx) = 2|D0|/(|D0| + |Dx|) − 1
where D0 is as above and Dx is the difference between the nontreated control (Cx) and the exposed soil (Px) at the time point (tx) chosen to measure the resilience.
The data obtained for the CLPP indices (i.e., AWCD, RS, H and E), and the AWCD values for the six carbon substrate groups (i.e., amines, amino acids, carbohydrates, carboxylic acids, miscellaneous and polymers) were analyzed using a three-way analysis of variance (ANOVA) to assess the level of variability (%) related to the factors tested, i.e., the concentration of XM, the presence of MC1 strain and the sampling time [50]. In the case of the data obtained for the RS and RL indices, two-way and one-way ANOVAs were applied to examine the differences among sampling time and differences among the different treatments, respectively. The statistical significance of the differences (p < 0.05) was assessed using the least significant differences (LSD) test. A principal component analysis (PCA) of the data for the CLPPs and the AWCD data for the carbon substrate groups was conducted for all the sampling days and separately for each sampling day. The principal component (PC) scores from the PCA were subjected to a three-way and two-way multivariate analysis of variance (MANOVA) for the first and second PCA sets, respectively. The Statistica 13.0 PL software package was used for all of the statistical analyses performed.

3. Results

3.1. The CLPP Indices

The results of this study showed that the introduction of XM and/or strain MC1 to the soil caused changes in the catabolic activity of the microbial community analyzed, and these changes were demonstrated by a significant decrease (p < 0.05) in the CLPP indices for the XM-treated soil generally up to 30 days. At the same time, the catabolic activity in the soil inoculated only with strain MC1 was significantly higher (p < 0.05) (Figure 2A–D). In general, statistical analysis of the results showed that the values of the CLPP indices measured were influenced by all of the factors analyzed, i.e., strain MC1, the concentration of XM and incubation time, and among them the time effect contributed to the most of the variability observed. In addition, the interactions between these factors had a significant impact (Table 1). The XM and XM+Ps treatments contributed to a significant decrease (p < 0.05) in the AWCD values, and this decrease accounted for 26–68%, 6–31% and 9–22% of the control values on days 1, 15 and 30 of the experiment, respectively (Figure 2A). In turn, in the second part of the experiment, a significant increase in the AWCD values was observed. The XM10 and XM10+Ps-treatments showed approximately three-fold higher AWCD values, when compared to the non-treated control on day 90 (Figure 2A). The study revealed that the substrate richness (RS) value was influenced by the XM treatment, and its decrease was observed on days 1 and 15 compared to the control soil. In turn, an increase in the RS index values for the XM10 and XM10 + Ps treatments within the range of 15–20% and 28–33% was noted on days 60 and 90, respectively, compared to the control soil (Figure 2B). A similar effect of XM and/or Ps was observed in the case of the H index. The XM and XM+Ps treatments contributed to a significant decrease (p < 0.05) in the H index values, and this decrease accounted for 85–95%, 76–94% and 74–88% of the control values on days 1, 15 and 30 of the experiment, respectively (Figure 2C). In turn, in the second part of the experiment, an increase in the H index values for the XM10 and XM10+Ps treatments within the range of 17–21% and 20–23% was noted on days 60 and 90, respectively, compared to the nontreated soil (Figure 2C).

3.2. Carbon Substrate Utilization Pattern

An analysis of the AWCD values showed that XM and/or strain MC1 changed the pattern of carbon substrate group utilization during the experiment (Figure 3). The XM and XM+Ps treatments contributed to a significant decrease (p < 0.05) in the AWCD values for the utilization of all of the carbon substrate groups up to 30 days of the experimental period. At the same time, the catabolic activity in the soil inoculated only with strain MC1 was significantly higher (p < 0.05) (Figure 3A–F). At the following sampling times (days 60 and 90), a stimulatory effect of XM10 and XM10+Ps on the utilization pattern of each substrate group was observed (Figure 3). The AWCD values for the utilization of amines, amino acids, carbohydrates, carboxylic acids, miscellaneous and polymers for these treatments were found to be up to 2.3-, 3.1-, 2.3-, 13-, 3.4- and 3.3-fold higher compared to the value for the nontreated control, respectively (Figure 3). In general, statistical analysis of the results showed that the AWCD values of all the substrate groups were affected by all of the factors analyzed, i.e., strain MC1, the concentration of XM and incubation time, and among them the time effect contributed to the most of the variability observed. In addition, the interactions between these factors had a significant impact (Table 1).

3.3. Principal Component Analysis

The PCA plots obtained for the CLPP indices (Figure 4A) and the carbon substrate groups (Figure 4B) revealed a pattern of variability dependent on all of the factors tested (i.e., bacterial strain, XM dosage and time). The PCA plot generated for all of the sampling days showed that the samples were primarily scattered along the PC 1 axis, and this scattering contributed to 71.1% and 85.0% of the total variability for the CLPP indices and the carbon substrate groups, respectively (Figure 4). The first PCA axis was characterized by a decreasing gradient of all of the CLPP indices (Figure 4A) and carbon substrate groups (Figure 4B). Generally, a significant impact of the XM dosage and/or Ps was evident during the whole experimental period. It was clearly observed that the CLPP profiles and carbon substrate utilization patterns for the soil samples treated with XM and/or Ps were separated from those obtained for the control soil (Figure 4A,B). This observation was also confirmed by analysis of the scores from PC 1 and PC 2 using a three-way MANOVA. However, among the three factors tested, the primary contribution in the observed variability was obtained for time (Table 2).
The PCA plots generated for each sampling day also showed that the samples were primarily scattered along the PC 1 axis, and this scattering contributed to 68.2–89.0% and 63.9–98.4%% of the total variability for the CLPP indices (Figure 5A) and the carbon substrate groups (Figure 5B), respectively. This observation was also confirmed by analysis of the scores from PC 1 and PC 2 using a two-way MANOVA. However, among the two factors tested, the primary contribution for PC 1 (up to 93% and 99% for the CLPP indices and the carbon substrate groups, respectively) in the observed variability was obtained for the dosage of XM (Table 3). The first PCA axis was characterized by an increasing and decreasing gradient of all of the CLPP indices on days 1 and 15 and on days 30, 60 and 90, respectively (Figure 5A). In the case of the carbon substrate groups, the gradient generally decreased, and a significant impact of the XM dosage and/or Ps was evident for each sampling day (Figure 5B).

3.4. The Resistance (RS)/Resilience (RL) Indices

The changes observed in the catabolic activity of the microbial community were reflected in its resistance to stress factors, i.e., XM and/or Ps (Table 4). In general, the ANOVA revealed that the treatment, time and interaction between the factors tested had a significant impact on the resistance of the CLPP indices and the metabolic activity expressed as the AWCD for the carbon substrate groups (Table 5). The decrease observed in the RS index values for the soil treated with XM or XM+Ps at the beginning of the experiment corresponded to the inhibitory effect, while in the second part of the study (days 60 and 90), they were generally associated with the stimulatory effect of the parameters measured (Table 4). Factoring in the mean values of the RS index for all of the sampling days, the resistance of the CLPP indices and the AWCD values for the carbon substrate groups was categorized as follows: E (0.868) > H (0.830) > R (0.748) > AWCD (0.417) and amino acids = carbohydrates (0.441) > polymers (0.422) > amines (0.398) > miscellaneous (0.360) > carboxylic acids (0.325), respectively. An analysis of the RL index for measured parameters demonstrated differences in its value for individual treatments at the end of the experiment (day 90) (Table 6). In general, the positive values of the RL index were obtained for the XM1-, Ps- and XM1+Ps treated soils in relation to all of the parameters tested. In contrast, the RL index was generally found to be negative in the case of the XM10- and XM10+Ps treated soils for all of the CLPP indices and the AWCD values for carbon substrate groups (Table 6).

4. Discussion

Due to the significant share of microorganisms in maintaining the balance of the soil ecosystem, it is important to determine the impact of various factors on their catabolic potential. This potential may be measured by the Biolog method and EcoPlatesTM that allow for insight into the functional activity of the soil microbial community. However, some authors stated that the effects of contamination can be better evaluated by measuring the activity of some soil enzymes rather than use of Biolog EcoPlatesTM [51]. This conclusion may be due to the fact that the Biolog technique does not take into account the activity of catabolically inactive microorganisms that exist in a dormant state or non-culturable microorganisms. Moreover, mainly fast growing microorganisms are involved in this analysis. Despite some limitations, as other studies have shown, this method can be useful to assess the functional biodiversity of soils contaminated with antibiotics [32,52,53,54]. There are no reports on the effects of XM and/or antibiotic-resistant Pseudomonas putida on soil microbial activity. However, the results of other studies indicate that antibiotics may negatively affect the microbial activity of the soil [9,25,52,55].
The presented study showed that the introduction of XM and/or strain MC1 to the soil caused changes in the catabolic activity of the microbial community analyzed. At both doses of the antibiotic, a decrease in the values of all the CLPP indices measured was generally observed up to 30 days. In turn, at the beginning of the experiment, the catabolic activity in the soil inoculated with the MC1 strain was higher compared to the control soil. In contrast to this effect, at the following measurement times (days 60 and 90), the values of the CLPP indices increased, particularly for the XM-treated soil (10 mg/kg) in combination with strain MC1. A short-term negative effect on the catabolic activity of soil microorganisms has also been noted for other antibiotics. For example, Fang et al. [56] found that AWCD and functional diversity indices decreased significantly up to 35 days after chlortetracycline application. A slight reduction in the value of the H index was observed for soil treated with a gradient of oxytetracycline concentration (1–300 mg/kg in soil). In contrast, a marked decline in functional diversity and AWCD values with increasing concentrations of oxytetracycline was reported by Kong et al. [52]. Antibiotics from the sulfonamide group, such as sulfamethoxazole and sulfamethazine, can also alter the activity of microbial populations; however, they only had short-term detrimental effects [54,57]. Furthermore, sulfadimethoxine or chlortetracycline did not affect the CLPP indices including AWCD, whereas other antibiotics such as monensin contributed to an increase in the value of the H index [53].
An analysis of the AWCD values showed that XM and/or strain MC1 changed the pattern of carbon substrate group utilization during the experiment. The introduction of XM at both dosages contributed to a decrease in the AWCD values for the utilization of all of the carbon substrate groups up to 30 days. In contrast, for soil inoculated only with strain MC1, the AWCD values increased up to 30 days for all the carbon substrate groups. On the following measurement days, i.e., on day 60 and 90, a significant increase in the catabolic activity of the microbial community analyzed for all of the substrate groups was observed in the XM+MC1 treatments. In many studies, changes in the preferential degradation by microorganisms of some of the substrate groups were observed over the course of an experimental period. For example, Xu et al. [27] revealed that sulfadiazine at a higher concentration contributed to a decrease in the utilization rates of four categories of substrates (carboxylic acids, amino acids, carbohydrates, and aromatic acids). Also, Liu et al. [58] observed a short-term decrease in the usage of carbohydrates and miscellaneous by the analyzed microbial community in soil treated with sulfamethoxazole. In contrast, the application of doxycycline generally contributed to a stimulation of the substrate utilization [59].
In this study, it was noted that regardless of whether only XM was used alone or in combination with strain MC1, the antibiotic contributed to environmental stress conditions resulting in changes in the catabolic activity and functional diversity of the microbial community analyzed. The loss of the capability of the soil microbial community to utilize selected carbon substrates at the beginning of the experiment could be associated with the negative effect of XM on specific enzymes produced by microorganisms. Since XM is active against both Gram-positive and Gram-negative bacteria, some microorganisms could have been killed or their metabolic activity inhibited. As a consequence of this phenomenon, catabolic activity and functional diversity significantly decreased at the beginning of the experiment. However, the negative effect of the antibiotic in combination with the MC1 strain observed at the beginning of the experiment was slightly smaller compared to that observed for the soil contaminated with only XM at both dosages. This suggests that part of the negative antibiotic effect was abolished by inoculation with the MC1 strain. This is also confirmed by the results obtained for the soil only inoculated with the MC1 strain. Higher values of the parameters measured suggest that the bacterial strain introduced had the ability to survive in soil and increased the catabolic potential of the microbial community. Since the MC1 strain is resistant to many antibiotics, including cefuroxime, its activity in the soil has not been disturbed by XM and therefore a higher catabolic activity could have been observed for XM+Ps treatments in comparison with XM treatments. In addition, a higher catabolic activity for the Ps treatment could be the result of a larger starting number of microorganisms as compared to the non-inoculated control. Many studies on the degradation of organic contaminants in the soil environment showed synergy between inoculated strains and natural soil microflora, resulting in the accelerated degradation of pollutants [60,61,62]. However, the lack of any effect after the introduction of bacterial strains into the soil was also observed. For example, a study by Cycoń et al. [9] showed that the antibiotic-resistant strain of Citrobacter freundii did not affect the catabolic activity and functional diversity of the soil microorganisms whether it was introduced alone or in combination with vancomycin. This phenomenon may be related to the survivability of the inoculants in the soil environment, which is often a foreign environment for them [46]. Soil is a very complex ecosystem and many biotic and abiotic factors may determine the survival of the inoculants. In addition, there may be competition between the natural soil microflora and the strains of microorganisms introduced. The phenomenon of soil microorganisms producing various inhibitory substances that limit the activity of inoculants is also of great importance [60,63].
After the initial inhibition caused by XM, the values of the CLPP indices, i.e., AWCD, R, H, E and the AWCD values for the six carbon substrate groups increased over the next few sampling days. This effect could be related to the development of the ability of selected microorganisms to degrade the XM introduced into the soil and use it as a source of carbon and energy. This could result in an increase in the number of degrading microorganisms and therefore, an increase in enzyme production. Alternatively, the negative effect of XM could have been masked by the increased activity of other microorganisms capable of surviving in the presence of an antibiotic and/or using compounds released from the cells of killed microorganisms [9,64,65]. The effect observed could also be related to the XM degradation in the soil. Available studies on degradation, however very scarce, have shown that XM belongs to a group of compounds with relatively low stability under soil conditions, as evidenced by the DT50 value at the level of several dozen days [42]. In addition, the loss of the antibacterial properties of XM during the degradation in the soil could also have been of great importance.
The observed changes in the catabolic activity of the microbial community analyzed were reflected in its resistance to stress factors. Evaluation of the resistance of the CLPP indices and the metabolic activity expressed as the AWCD for the carbon substrate groups showed that the RS index was affected by XM and strain MC1. According to the interpretation proposed by Orwin and Wardle [50], the RS and RL indices may have values between −1 and +1. A value of +1 for the RS index shows maximal resistance and that the exposure had no influence, while lower values show stronger effects (less resistance) related to either stimulation or inhibition. In the case of the RL index, a value of +1 at the sampling time shows maximal resilience, while lower values show a slower recovery rate. The results obtained generally suggest a low initial resistance of microorganisms to the introduction of XM and/or strain MC1, but they were resilient in the long term. A similar phenomenon was noted by Cycoń et al. [9] and Baćmaga et al. [66] who studied the effect of another antibiotic, vancomycin, and the pesticide azoxystrobin on the soil metabolic activity, respectively. Our results suggest that the properties of the microbial community are a key factor responsible for maintaining the soil balance. Despite the initial perturbations caused by stress factors, the initial balance may be restored [67,68,69]. However, alternatively, there may be changes in the structure of microorganisms, but their activity related to the specific microbial process may be similar to that obtained from soil not subjected to stress factors [37,69,70].

5. Conclusions

In this study, it was noted that regardless of whether XM was used alone or in combination with Pseudomonas putida strain MC1, the antibiotic contributed to environmental stress conditions at the beginning of the study, resulting in changes in the catabolic activity and functional diversity of the soil microbial community. However, it was shown that strain MC1 introduced into the soil was characterized by some properties to compensate for the negative effect caused by the antibiotic at the beginning of the experiment. Higher values of measured parameters obtained for the soil inoculated with solely the bacterial strain suggest that it had the ability to survive in the soil and increased the catabolic potential of the soil microorganisms. The loss of the ability of a microbial community to degrade selected substrates at the beginning of the experiment could be associated with the negative effect of XM on selected microorganisms responsible for the production of specific enzymes. However, after the initial inhibition, the increase in the catabolic activity of soil microorganisms was noted during the following sampling days. This effect could be related to the increase in the number of microorganisms degrading XM and/or masking the effect of XM through the increased activity of other microorganisms using compounds released from the cells of killed microorganisms. In addition, the degradation of XM and its loss of antibacterial properties could also have great significance. In general, the results obtained suggest a low level of resistance of soil microorganisms to XM and/or strain MC1 at the beginning of the exposure time, but the microbial community had the ability to recover its initial catabolic activity over the experimental period.

Author Contributions

Conceptualization, K.O. and M.C.; Methodology, K.O., A.M. and M.C.; Software, M.C.; Validation, K.O., A.M. and M.C.; Formal Analysis, K.O., A.M., J.S.-D. and M.C.; Investigation, K.O., A.M., J.S.-D. and M.C.; Resources, M.C.; Data Curation, K.O., A.M. and M.C.; Writing—Original Draft Preparation, K.O., Z.P.-S. and M.C.; Writing—Review and Editing, K.O., Z.P.-S. and M.C.; Visualisation, K.O. and M.C.; Supervision, M.C.; Project Administration, M.C.; and Funding Acquisition, M.C.

Funding

This research was funded by the National Science Center, Poland (No. DEC-2014/15B/NZ9/04414).

Conflicts of Interest

The authors declare no conflict of interest and the funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. Zhou, L.-J.; Ying, G.-G.; Liu, S.; Zhao, J.-L.; Yang, B.; Chen, Z.-F.; Lai, H.-J. Occurrence and fate of eleven classes of antibiotics in two typical wastewater treatment plants in South China. Sci. Total Environ. 2013, 452–453, 365–376. [Google Scholar] [CrossRef] [PubMed]
  2. Hou, J.; Wan, W.; Mao, D.; Wang, C.; Mu, Q.; Qin, S.; Luo, Y. Occurrence and distribution of sulfonamides, tetracyclines, quinolones, macrolides, and nitrofurans in livestock manure and amended soils of Northern China. Environ. Sci. Pollut. Res. 2015, 22, 4545–4554. [Google Scholar] [CrossRef] [PubMed]
  3. Yang, Y.; Owino, A.A.; Gao, Y.; Yan, X.; Xu, C.; Wang, J. Occurrence, composition and risk assessment of antibiotics in soils from Kenya, Africa. Ecotoxicology 2016, 25, 1194–1201. [Google Scholar] [CrossRef] [PubMed]
  4. Pan, M.; Chu, L.M. Leaching behavior of veterinary antibiotics in animal manure-applied soils. Sci. Total Environ. 2017, 579, 466–473. [Google Scholar] [CrossRef] [PubMed]
  5. Kong, W.; Li, C.; Dolhi, J.M.; Li, S.; He, J.; Qiao, M. Characteristics of oxytetracycline sorption and potential bioavailability in soils with various physical-chemical properties. Chemosphere 2012, 87, 542–548. [Google Scholar] [CrossRef] [PubMed]
  6. Cui, H.; Wang, S.-P.; Fu, J.; Zhou, Z.-Q.; Zhang, N.; Guo, L. Influence of ciprofloxacin on microbial community structure and function in soils. Biol. Fertil. Soils 2014, 50, 939–947. [Google Scholar] [CrossRef]
  7. Pan, M.; Chu, L.M. Adsorption and degradation of five selected antibiotics in agricultural soil. Sci. Total Environ. 2016, 545–546, 48–56. [Google Scholar] [CrossRef] [PubMed]
  8. Duan, M.; Li, H.; Gu, J.; Tuo, X.; Sun, W.; Qian, X.; Wang, X. Effects of biochar on reducing the abundance of oxytetracycline, antibiotic resistance genes, and human pathogenic bacteria in soil and lettuce. Environ. Pollut. 2017, 224, 787–795. [Google Scholar] [CrossRef] [PubMed]
  9. Cycoń, M.; Orlewska, K.; Markowicz, A.; Zmijowska, A.; Smoleń-Dzirba, J.; Bratosiewicz-Wasik, J.; Wasik, T.J.; Piotrowska-Seget, Z. Vancomycin and/or multidrug-resistant Citrobacter freundii altered the metabolic pattern of soil microbial community. Front. Microbiol. 2018, 9, 1047. [Google Scholar] [CrossRef] [PubMed]
  10. Braschi, I.; Blasioli, S.; Fellet, C.; Lorenzini, R.; Garelli, A.; Pori, M.; Giacomini, D. Persistence and degradation of new β-lactam antibiotics in the soil and water environment. Chemosphere 2013, 93, 152–159. [Google Scholar] [CrossRef] [PubMed]
  11. Walters, E.; McClellan, K.; Halden, R.U. Occurrence and loss over three years of 72 pharmaceuticals and personal care products from biosolids-soil mixtures in outdoor mesocosms. Water Res. 2010, 44, 6011–6020. [Google Scholar] [CrossRef] [PubMed]
  12. Kay, P.; Blackwell, P.A.; Boxall, A.B.A. Fate of veterinary antibiotics in a macroporous tile drained clay soil. Environ. Toxicol. Chem. 2004, 23, 1136–1143. [Google Scholar] [CrossRef] [PubMed]
  13. Popowska, M.; Rzeczycka, M.; Miernik, A.; Krawczyk-Balska, A.; Walsh, F.; Duffy, B. Influence of soil use on prevalence of tetracycline, streptomycin, and erythromycin resistance and associated resistance genes. Antimicrob. Agents Chemother. 2012, 56, 1434–1443. [Google Scholar] [CrossRef] [PubMed]
  14. DeVries, S.L.; Zhang, P. Antibiotics and the Terrestrial Nitrogen Cycle: A Review. Curr. Pollut. Rep. 2016, 2, 51–67. [Google Scholar] [CrossRef][Green Version]
  15. Orlewska, K.; Piotrowska-Seget, Z.; Bratosiewicz-Wąsik, J.; Cycoń, M. Characterization of bacterial diversity in soil contaminated with the macrolide antibiotic erythromycin and/or inoculated with a multidrug-resistant Raoultella sp. strain using the PCR-DGGE approach. Appl. Soil Ecol. 2018, 126, 57–64. [Google Scholar] [CrossRef]
  16. Binh, C.T.T.; Heuer, H.; Gomes, N.C.M.; Kotzerke, A.; Fulle, M.; Wilke, B.M.; Schloter, M.; Smalla, K. Short-term effects of amoxicillin on bacterial communities in manured soil. FEMS Microbiol. Ecol. 2007, 62, 290–302. [Google Scholar] [CrossRef] [PubMed][Green Version]
  17. Ghosh, S.; LaPara, T.M. The effects of subtherapeutic antibiotic use in farm animals on the proliferation and persistence of antibiotic resistance among soil bacteria. ISME J. 2007, 1, 191–203. [Google Scholar] [CrossRef] [PubMed][Green Version]
  18. Kyselková, M.; Jirout, J.; Chroňáková, A.; Vrchotová, N.; Bradley, R.; Schmitt, H.; Elhottová, D. Cow excrements enhance the occurrence of tetracycline resistance genes in soil regardless of their oxytetracycline content. Chemosphere 2013, 93, 2413–2418. [Google Scholar] [CrossRef] [PubMed]
  19. Ma, J.; Lin, H.; Sun, W.; Wang, Q.; Yu, Q.; Zhao, Y.; Fu, J. Soil microbial systems respond differentially to tetracycline, sulfamonomethoxine, and ciprofloxacin entering soil under pot experimental conditions alone and in combination. Environ. Sci. Pollut. Res. 2014, 21, 7436–7448. [Google Scholar] [CrossRef] [PubMed]
  20. Wepking, C.; Avera, B.; Badgley, B.; Barrett, J.E.; Franklin, J.; Knowlton, K.F.; Ray, P.P.; Smitherman, C.; Strickland, M.S. Exposure to dairy manure leads to greater antibiotic resistance and increased mass-specific respiration in soil microbial communities. Proc. R. Soc. B Biol. Sci. 2017, 284, 20162233. [Google Scholar] [CrossRef] [PubMed][Green Version]
  21. Du, L.; Liu, W. Occurrence, fate, and ecotoxicity of antibiotics in agro-ecosystems. A review. Agron. Sustain. Dev. 2012, 32, 309–327. [Google Scholar] [CrossRef]
  22. Keen, P.L.; Patrick, D.M. Tracking change: A look at the ecological footprint of antibiotics and antimicrobial resistance. Antibiotics 2013, 2, 191–205. [Google Scholar] [CrossRef] [PubMed]
  23. Tang, X.; Lou, C.; Wang, S.; Lu, Y.; Liu, M.; Hashmi, M.Z.; Liang, X.; Li, Z.; Liao, Y.; Qin, W.; et al. Effects of long-term manure applications on the occurrence of antibiotics and antibiotic resistance genes (ARGs) in paddy soils: Evidence from four field experiments in south of China. Soil Biol. Biochem. 2015, 90, 179–187. [Google Scholar] [CrossRef]
  24. Grenni, P.; Ancona, V.; Barra Caracciolo, A. Ecological effects of antibiotics on natural ecosystems: A review. Microchem. J. 2018, 136, 25–39. [Google Scholar] [CrossRef]
  25. Pinna, M.V.; Castaldi, P.; Deiana, P.; Pusino, A.; Garau, G. Sorption behavior of sulfamethazine on unamended and manure-amended soils and short-term impact on soil microbial community. Ecotoxicol. Environ. Saf. 2012, 84, 234–242. [Google Scholar] [CrossRef] [PubMed]
  26. Akimenko, Y.V.; Kazeev, K.S.; Kolesnikov, S.I. Impact assessment of soil contamination with antibiotics (For example, an ordinary chernozem). Am. J. Appl. Sci. 2015, 12, 80–88. [Google Scholar] [CrossRef]
  27. Xu, Y.; Yu, W.; Ma, Q.; Wang, J.; Zhou, H.; Jiang, C. The combined effect of sulfadiazine and copper on soil microbial activity and community structure. Ecotoxicol. Environ. Saf. 2016, 134, 43–52. [Google Scholar] [CrossRef] [PubMed]
  28. Schmitt, H.; Haapakangas, H.; Van Beelen, P. Effects of antibiotics on soil microorganisms: Time and nutrients influence pollution-induced community tolerance. Soil Biol. Biochem. 2005, 37, 1882–1892. [Google Scholar] [CrossRef]
  29. Brandt, K.K.; Sjøholm, O.R.; Krogh, K.A.; Halling-Sørensen, B.; Nybroe, O. Increased pollution-induced bacterial community tolerance to sulfadiazine in soil hotspots amended with artificial root exudates. Environ. Sci. Technol. 2009, 43, 2963–2968. [Google Scholar] [CrossRef] [PubMed]
  30. Liu, B.; Li, Y.; Zhang, X.; Wang, J.; Gao, M. Effects of chlortetracycline on soil microbial communities: Comparisons of enzyme activities to the functional diversity via Biolog EcoPlatesTM. Eur. J. Soil Biol. 2015, 68, 69–76. [Google Scholar] [CrossRef]
  31. Chen, W.; Liu, W.; Pan, N.; Jiao, W.; Wang, M. Oxytetracycline on functions and structure of soil microbial community. J. Soil Sci. Plant Nutr. 2013, 13, 967–975. [Google Scholar] [CrossRef]
  32. Ma, T.; Pan, X.; Chen, L.; Liu, W.; Christie, P.; Luo, Y.; Wu, L. Effects of different concentrations and application frequencies of oxytetracycline on soil enzyme activities and microbial community diversity. Eur. J. Soil Biol. 2016, 76, 53–60. [Google Scholar] [CrossRef][Green Version]
  33. Reichel, R.; Michelini, L.; Ghisi, R.; Thiele-Bruhn, S. Soil bacterial community response to sulfadiazine in the soil-root zone. J. Plant Nutr. Soil Sci. 2015, 178, 499–506. [Google Scholar] [CrossRef]
  34. Versporten, A.; Coenen, S.; Adriaenssens, N.; Muller, A.; Minalu, G.; Faes, C.; Vankerckhoven, V.; Aerts, M.; Hens, N.; Molenberghs, G.; et al. European Surveillance of Antimicrobial Consumption (ESAC): Outpatient cephalosporin use in Europe (1997–2009). J. Antimicrob. Chemother. 2011, 66, 25–35. [Google Scholar] [CrossRef]
  35. Ishibiki, K.; Inoue, S.; Suzuki, F.; Okumura, K.; Takeda, K.; Toshimitsu, Y. Investigation of adsorption, metabolism and excretion of cefuroxime axetil in volunteers of gastrectomized patients. Jpn. J. Antibiot. 1990, 43, 337–344. [Google Scholar] [CrossRef] [PubMed]
  36. Cheng, G.; Hu, Y.; Yin, Y.; Yang, X.; Xiang, C.; Wang, B.; Chen, Y.; Yang, F.; Lei, F.; Wu, N.; et al. Functional screening of antibiotic resistance genes from human gut microbiota reveals a novel gene fusion. FEMS Microbiol. Lett. 2012, 336, 11–16. [Google Scholar] [CrossRef] [PubMed][Green Version]
  37. Bhattacharya, D.; Dey, S.; Kadam, S.; Kalal, S.; Jali, S.; Koley, H.; Sinha, R.; Nag, D.; Kholkute, S.D.; Roy, S. Isolation of NDM-1-producing multidrug-resistant Pseudomonas putida from a paediatric case of acute gastroenteritis, India. New Microbes New Infect. 2015, 5, 5–9. [Google Scholar] [CrossRef] [PubMed]
  38. Coenen, S.; Ferech, M.; Dvorakova, K.; Hendrickx, E.; Suetens, C.; Goossens, H. European Surveillance of Antimicrobial Consumption (ESAC): Outpatient cephalosporin use in Europe. J. Antimicrob. Chemother. 2006, 58, 413–417. [Google Scholar] [CrossRef] [PubMed]
  39. Iatrou, E.I.; Stasinakis, A.S.; Thomaidis, N.S. Consumption-based approach for predicting environmental risk in Greece due to the presence of antimicrobials in domestic wastewater. Environ. Sci. Pollut. Res. 2014, 21, 12941–12950. [Google Scholar] [CrossRef] [PubMed]
  40. Oǧuz, M.; Mihçiokur, H. Environmental risk assessment of selected pharmaceuticals in Turkey. Environ. Toxicol. Pharmacol. 2014, 38, 79–83. [Google Scholar] [CrossRef] [PubMed]
  41. Yu, X.; Tang, X.; Zuo, J.; Zhang, M.; Chen, L.; Li, Z. Distribution and persistence of cephalosporins in cephalosporin producing wastewater using SPE and UPLC–MS/MS method. Sci. Total Environ. 2016, 569–570, 23–30. [Google Scholar] [CrossRef] [PubMed]
  42. Material Safety Data Sheet. Available online: http://www.lupinpharmaceuticals.com/pdf/07-08/MSDS%20Cefuroxime%20Axetil%20Tablets.pdf (accessed on 25 September 2018).
  43. Orlewska, K.; Piotrowska-Seget, Z.; Cycon, M. Use of the PCR-DGGE method for the analysis of the bacterial community structure in soil treated with the cephalosporin antibiotic cefuroxime and/or inoculated with a multidrug-resistant Pseudomonas putida strain MC1. Front. Microbiol. 2018, 9, 1387. [Google Scholar] [CrossRef] [PubMed]
  44. Cycoń, M.; Piotrowska-Seget, Z.; Kozdrój, J. Linuron effects on microbiological characteristics of sandy soils as determined in a pot study. Ann. Microbiol. 2010, 60, 439–449. [Google Scholar] [CrossRef]
  45. Cycoń, M.; Borymski, S.; Orlewska, K.; Wąsik, T.J.; Piotrowska-Seget, Z. An analysis of the effects of vancomycin and/or vancomycin-resistant Citrobacter freundii exposure on the microbial community structure in soil. Front. Microbiol. 2016, 7, 1015. [Google Scholar] [CrossRef] [PubMed]
  46. Cycoń, M.; Zmijowska, A.; Piotrowska-Seget, Z. Biodegradation kinetics of 2,4-D by bacterial strains isolated from soil. Cent. Eur. J. Biol. 2011, 6, 188–198. [Google Scholar] [CrossRef][Green Version]
  47. Insam, H. A new set of substrates proposed for community characterization in environmental samples. In Microbial Communities: Functional Versus Structural Approaches; Insam, H., Rangger, A., Eds.; Springer: Berlin, Germany, 1997; pp. 259–260. [Google Scholar]
  48. Garland, J.L. Analysis and interpretation of community-level physiological profiles in microbial ecology. FEMS Microbiol. Ecol. 1997, 24, 289–300. [Google Scholar] [CrossRef]
  49. Orwin, K.H.; Wardle, D.A. New indices for quantifying the resistance and resilience of soil biota to exogenous disturbances. Soil Biol. Biochem. 2004, 36, 1907–1912. [Google Scholar] [CrossRef]
  50. Cycoń, M.; Markowicz, A.; Piotrowska-Seget, Z. Structural and functional diversity of bacterial community in soil treated with the herbicide napropamide estimated by the DGGE, CLPP and r/K-strategy approaches. Appl. Soil Ecol. 2013, 72, 242–250. [Google Scholar] [CrossRef]
  51. Floch, C.; Chevremont, A.-C.; Joanico, K.; Capowiez, Y.; Criquet, S. Indicators of pesticide contamination: Soil enzyme compared to functional diversity of bacterial communities via Biolog® Ecoplates. Eur. J. Soil Biol. 2011, 47, 256–263. [Google Scholar] [CrossRef]
  52. Kong, W.-D.; Zhu, Y.-G.; Fu, B.-J.; Marschner, P.; He, J.-Z. The veterinary antibiotic oxytetracycline and Cu influence functional diversity of the soil microbial community. Environ. Pollut. 2006, 143, 129–137. [Google Scholar] [CrossRef] [PubMed][Green Version]
  53. Toth, J.D.; Feng, Y.; Dou, Z. Veterinary antibiotics at environmentally relevant concentrations inhibit soil iron reduction and nitrification. Soil Biol. Biochem. 2011, 43, 2470–2472. [Google Scholar] [CrossRef]
  54. Pino-Otín, M.R.; Muñiz, S.; Val, J.; Navarro, E. Effects of 18 pharmaceuticals on the physiological diversity of edaphic microorganisms. Sci. Total Environ. 2017, 595, 441–450. [Google Scholar] [CrossRef] [PubMed]
  55. Liu, B.; Li, Y.; Zhang, X.; Wang, J.; Gao, M. Combined effects of chlortetracycline and dissolved organic matter extracted from pig manure on the functional diversity of soil microbial community. Soil Biol. Biochem. 2014, 74, 148–155. [Google Scholar] [CrossRef]
  56. Fang, H.; Han, L.; Cui, Y.; Xue, Y.; Cai, L.; Yu, Y. Changes in soil microbial community structure and function associated with degradation and resistance of carbendazim and chlortetracycline during repeated treatments. Sci. Total Environ. 2016, 572, 1203–1212. [Google Scholar] [CrossRef] [PubMed]
  57. Demoling, L.A.; Bååth, E.; Greve, G.; Wouterse, M.; Schmitt, H. Effects of sulfamethoxazole on soil microbial communities after adding substrate. Soil Biol. Biochem. 2009, 41, 840–848. [Google Scholar] [CrossRef]
  58. Liu, F.; Wu, J.; Ying, G.-G.; Luo, Z.; Feng, H. Changes in functional diversity of soil microbial community with addition of antibiotics sulfamethoxazole and chlortetracycline. Appl. Microbiol. Biotechnol. 2012, 95, 1615–1623. [Google Scholar] [CrossRef] [PubMed]
  59. Wang, J.; Lin, H.; Sun, W.; Xia, Y.; Ma, J.; Fu, J.; Zhang, Z.; Wu, H.; Qian, M. Variations in the fate and biological effects of sulfamethoxazole, norfloxacin and doxycycline in different vegetable-soil systems following manure application. J. Hazard. Mater. 2016, 304, 49–57. [Google Scholar] [CrossRef] [PubMed]
  60. Cycoń, M.; Piotrowska-Seget, Z. Pyrethroid-degrading microorganisms and their potential for the bioremediation of contaminated soils: A review. Front. Microbiol. 2016, 7, 1463. [Google Scholar] [CrossRef] [PubMed]
  61. Hirth, N.; Topp, E.; Dörfler, U.; Stupperich, E.; Munch, J.C.; Schroll, R. An effective bioremediation approach for enhanced microbial degradation of the veterinary antibiotic sulfamethazine in an agricultural soil. Chem. Biol. Technol. Agric. 2016, 3, 29. [Google Scholar] [CrossRef][Green Version]
  62. Zhang, H.; Zhou, Y.; Huang, Y.; Wu, L.; Liu, X.; Luo, Y. Residues and risks of veterinary antibiotics in protected vegetable soils following application of different manures. Chemosphere 2016, 152, 229–237. [Google Scholar] [CrossRef] [PubMed][Green Version]
  63. Cycoń, M.; Mrozik, A.; Piotrowska-Seget, Z. Bioaugmentation as a strategy for the remediation of pesticide-polluted soil: A review. Chemosphere 2017, 172, 52–71. [Google Scholar] [CrossRef] [PubMed]
  64. Ding, G.C.; Radl, V.; Schloter-Hai, B.; Jechalke, S.; Heuer, H.; Smalla, K.; Schloter, M. Dynamics of soil bacterial communities in response to repeated application of manure containing sulfadiazine. PLoS ONE 2014, 9, e92958. [Google Scholar] [CrossRef] [PubMed]
  65. Chessa, L.; Pusino, A.; Garau, G.; Mangia, N.P.; Pinna, M.V. Soil microbial response to tetracycline in two different soils amended with cow manure. Environ. Sci. Pollut. Res. 2016, 23, 5807–5817. [Google Scholar] [CrossRef] [PubMed]
  66. Baćmaga, M.; Kucharski, J.; Wyszkowska, J. Microbial and enzymatic activity of soil contaminated with azoxystrobin. Environ. Monit. Assess. 2015, 187, 615. [Google Scholar] [CrossRef] [PubMed]
  67. Allison, S.D.; Martiny, J.B.H. Resistance, resilience, and redundancy in microbial communities. Proc. Natl. Acad. Sci. USA 2008, 105, 11512–11519. [Google Scholar] [CrossRef] [PubMed][Green Version]
  68. Song, H.-S.; Renslow, R.S.; Fredrickson, J.K.; Lindemann, S.R. Integrating ecological and engineering concepts of resilience in microbial communities. Front. Microbiol. 2015, 6, 1298. [Google Scholar] [CrossRef] [PubMed]
  69. Schaeffer, A.; Amelung, W.; Hollert, H.; Kaestner, M.; Kandeler, E.; Kruse, J.; Miltner, A.; Ottermanns, R.; Pagel, H.; Peth, S.; et al. The impact of chemical pollution on the resilience of soils under multiple stresses: A conceptual framework for future research. Sci. Total Environ. 2016, 568, 1076–1085. [Google Scholar] [CrossRef] [PubMed]
  70. Ludwig, M.; Wilmes, P.; Schrader, S. Measuring soil sustainability via soil resilience. Sci. Total Environ. 2018, 626, 1484–1493. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Experimental design and analyses performed. R1–3 = replicates. T1–6 = treatments. CLPP = community-level physiological profile. AWCD = average well-color development. RS = resistance. RL = resilience. LSD = least significant differences. ANOVA = analysis of variance. MANOVA = multivariate analysis of variance. PCA = principal component analysis.
Figure 1. Experimental design and analyses performed. R1–3 = replicates. T1–6 = treatments. CLPP = community-level physiological profile. AWCD = average well-color development. RS = resistance. RL = resilience. LSD = least significant differences. ANOVA = analysis of variance. MANOVA = multivariate analysis of variance. PCA = principal component analysis.
Sustainability 10 03549 g001
Figure 2. Effect of XM and/or strain MC1 on the values of the CLPP indices, i.e., AWCD (A), Rs (B), H (C) and E (D). The data presented are the means of three replicates with standard deviations. Significant differences (LSD test, p < 0.05) between treatments at the same sampling time and between sampling times within the same treatments are denoted with different lower and uppercase letters, respectively. The treatment abbreviations are explained in Figure 1.
Figure 2. Effect of XM and/or strain MC1 on the values of the CLPP indices, i.e., AWCD (A), Rs (B), H (C) and E (D). The data presented are the means of three replicates with standard deviations. Significant differences (LSD test, p < 0.05) between treatments at the same sampling time and between sampling times within the same treatments are denoted with different lower and uppercase letters, respectively. The treatment abbreviations are explained in Figure 1.
Sustainability 10 03549 g002
Figure 3. Effect of XM and/or strain MC1 on the AWCD values of carbon substrate groups, i.e., amines (A), amino acids (B), carbohydrates (C), carboxylic acids (D), miscellaneous (E) and polymers (F). The data presented are the means of three replicates with standard deviations. Significant differences (LSD test, p < 0.05) between treatments at the same sampling time and between sampling times within the same treatments are denoted with different lower and uppercase letters, respectively. The treatment abbreviations are explained in Figure 1.
Figure 3. Effect of XM and/or strain MC1 on the AWCD values of carbon substrate groups, i.e., amines (A), amino acids (B), carbohydrates (C), carboxylic acids (D), miscellaneous (E) and polymers (F). The data presented are the means of three replicates with standard deviations. Significant differences (LSD test, p < 0.05) between treatments at the same sampling time and between sampling times within the same treatments are denoted with different lower and uppercase letters, respectively. The treatment abbreviations are explained in Figure 1.
Sustainability 10 03549 g003
Figure 4. The PCA plots for the CLPP indices (A) and carbon substrate groups (B) generated for all the days of the experiment. The treatment abbreviations are explained in Figure 1.
Figure 4. The PCA plots for the CLPP indices (A) and carbon substrate groups (B) generated for all the days of the experiment. The treatment abbreviations are explained in Figure 1.
Sustainability 10 03549 g004
Figure 5. The PCA plots for the CLPP indices (A) and carbon substrate groups (B) generated for each sampling day of the experiment. The treatment abbreviations are explained in Figure 1.
Figure 5. The PCA plots for the CLPP indices (A) and carbon substrate groups (B) generated for each sampling day of the experiment. The treatment abbreviations are explained in Figure 1.
Sustainability 10 03549 g005
Table 1. Results of the three-way ANOVA for the CLPP indices and carbon substrate groups.
Table 1. Results of the three-way ANOVA for the CLPP indices and carbon substrate groups.
SVAWCDRHEAmines
VEpVEpVEpVEpVEp
S<10.004 **<10.4032<0.001 ***3<0.001 ***1<0.001 ***
C11<0.001 ***2<0.001 ***5<0.001 ***8<0.001 ***6<0.001 ***
T31<0.001 ***38<0.001 ***40<0.001 ***25<0.001 ***41<0.001 ***
S × C<10.019 *<10.586<10.063<10.210<1<0.001 ***
S × T<1<0.001 ***3<0.001 ***10.001 **40.005 **1<0.001 ***
C × T55<0.001 ***47<0.001 ***46<0.001 ***36<0.001 ***46<0.001 ***
S × C × T<10.86620.029 *20.002 **9<0.0012<0.001 ***
SVAmino acidsCarbohydratesCarboxylic acidsMiscellaneousPolymers
VEpVEpVEpVEpVEp
S<10.084<1<0.001 ***<1<0.001 ***<10.002 **<10.013 *
C8<0.001 ***9<0.001 ***6<0.001 ***18<0.001 ***11<0.001 ***
T41<0.001 ***53<0.001 ***44<0.001 ***9<0.001 ***26<0.001 ***
S × C<1<0.001 ***<10.077<10.025 *<1<0.001 ***<10.001 **
S × T2<0.001 ***1<0.001 ***<1<0.001 ***<10.0801<0.001 ***
C × T47<0.001 ***36<0.001 ***47<0.001 ***72<0.001 ***58<0.001 ***
S × C × T<1<0.001 ***<10.005 **1<0.001 ***<1<0.001 ***10.002 **
SV: source of variation, S: strain, C: concentration, T: time, AWCD: average well-color development, R: substrate richness, H: Shannon-Wiener index, E: evenness index, VE: variance explained (%). Asterisks represent the significance level (* p < 0.05, ** p < 0.01 and *** p < 0.001).
Table 2. Results of the three-way MANOVA for the PC 1 and PC 2 based on the CLPP indices and carbon substrate groups.
Table 2. Results of the three-way MANOVA for the PC 1 and PC 2 based on the CLPP indices and carbon substrate groups.
SVCLPP IndicesCarbon Substrate Groups
PC 1PC 2PC 1PC 2
VEpVEpVEpVEp
S1<0.001 ***10.072<10.003 **<1<0.001 ***
C8<0.001 ***40.005 **11<0.001 ***2<0.001 ***
T35<0.001 ***25<0.001 ***34<0.001 ***46<0.001 ***
S × C<10.26710.456<10.045 *1<0.001 ***
S × T10.003 **70.001 **1<0.001 ***4<0.001 ***
C × T50<0.001 ***32<0.001 ***54<0.001 ***41<0.001 ***
S × C × T10.005 **100.002 **<10.003 **4<0.001 ***
SV: source of variation, S: strain, C: concentration, T: time, VE: variance explained (%). Asterisks represent the significance level (* p < 0.05, ** p < 0.01 and *** p < 0.001).
Table 3. Results of the two-way MANOVA for the PC 1 and PC 2 based on the CLPP indices and carbon substrate groups.
Table 3. Results of the two-way MANOVA for the PC 1 and PC 2 based on the CLPP indices and carbon substrate groups.
DaySVCLPP IndicesCarbon Substrate Groups
PC 1PC 2PC 1PC 2
VEpVEpVEpVEp
1S8<0.001 ***<10.75820.032 *<10.942
C86<0.001 ***100.37994<0.001 ***450.002 **
S × C30.014 *350.051<10.955310.007 **
15S50.009 **<10.8493<0.001 ***40<0.001 ***
C86<0.001 ***40.72596<0.001 ***33<0.001 ***
S × C40.057260.154<10.07922<0.001 ***
30S11<0.001 ***270.007 **<10.50070.033 *
C81<0.001 ***390.007 **98<0.001 ***33<0.001 ***
S × C30.06630.56910.19447<0.001 ***
60S<10.59240.20110.438130.027 *
C92<0.001 ***<10.95985<0.001 ***470.002 **
S × C<10.90766<0.001 ***30.214160.051
90S<10.33290.114<10.51459<0.001 ***
C93<0.001 ***460.009 **99<0.001 ***10.210
S × C10.33460.435<10.78036<0.001 ***
SV: source of variation, S: strain, C: concentration, VE: variance explained (%). Asterisks represent the significance level (* p < 0.05, ** p < 0.01 and *** p < 0.001).
Table 4. The resistance (RS) index for the CLPP indices and carbon substrate groups.
Table 4. The resistance (RS) index for the CLPP indices and carbon substrate groups.
ParameterDayTreatment x ¯  
XM1XM10PsXM1+PsXM10+Ps
AWCD10.374 Bc0.196 Ad0.731 Ba0.571 Bb0.229 Bd0.420 A
150.085 Dc0.045 Bc0.759 Ba0.184 Cb0.100 Cc0.235 B
300.161 Cb0.086 Bbc0.969 Aa0.146 Cb0.080 Cc0.288 B
600.927 Aa0.244 Ad0.956 Aa0.842 Ab0.427 Ac0.679 A
900.924 Aab−0.252 Cc0.966 Aa0.899 Ab−0.221 Dc0.463 A
Substrate richness (R)10.721 Db0.402 Cd0.816 BCa0.743 Db0.564 Bc0.649 C
150.745C Db0.626 Bc0.862 Ba0.919 ABa0.626 Bc0.755 ABD
300.952 Aa0.783 Ac0.868 Bb0.809 Cbc0.932 Aa0.869 A
600.844 Bb0.686 Bc0.787 Cb0.966 Aa0.573 Bd0.771 AB
900.795 BCc0.432 Cd0.958 Aa0.893 Bb0.394 Cd0.694 CB
Shannon-Wiener index (H)10.838 Cb0.711 Ae0.796 Cc0.925 Ba0.752 Ad0.804 A
150.804 Cc0.691 ABd0.955 ABa0.868 Cb0.801 Ac0.824 A
300.822 Cbc0.662 Bd0.960 Aa0.848 Cb0.786 Ac0.816 A
600.925 Bab0.723 Ac0.978 Aa0.979 Aa0.697 Bc0.860 A
900.964 Aa0.689 ABc0.918 Bb0.991 Aa0.656 Cc0.844 A
Evenness (E)10.936 Aa0.920 Aa0.564 Bb0.955 Aa0.922 Aa0.859 A
150.897 Aa0.823 ABa0.934 Aa0.895 Aa0.961 Aa0.902 A
300.834 Aab0.623 BCb0.911 Aa0.912 Aa0.804 Aab0.817 A
600.909 Aa0.551 Cb0.937 Aa0.981 Aa0.834 Aa0.842 A
900.963 Aa0.886 Aa0.920 Aa0.964 Aa0.866 Aa0.920 A
AWCD—amines10.414 Ba0.323 Ab−0.167 Cc0.335 Dab0.351 Bab0.251 B
150.137 Cc0.051 Ccd0.660 Ba0.532 Cb0.047 Cd0.285 B
300.264 Cb0.147 Bc0.927 Aa0.353 Db0.108 Cc0.360 B
600.789 Ab0.270 Ae0.961 Aa0.668 Bc0.461 Ad0.630 A
900.774 Ab−0.113 Dc0.952 Aa0.863 Aab−0.166 Dc0.462 AB
AWCD—amino acids10.582 Bb0.519 Ab0.892 Ba0.521 Bb0.382 Bc0.579 AB
150.079 Cd0.024 Cd0.673 Ca0.354 Cb0.153 Cc0.257 CD
300.024 Cb0.020 Cb0.597 Da0.020 Db0.014 Db0.135 D
600.879 Aa0.350 Bb0.968 Aa0.942 Aa0.939 Aa0.816 A
900.868 Aa−0.311 Db0.953 ABa0.914 Aa−0.303 Eb0.424 BC
AWCD—carbohydrates10.514 Bab0.126 Bd0.611 Ca0.477 Bb0.299 Bc0.406 BC
150.099 Cc0.030 Bc0.755 Ba0.272 Cb0.246 Bb0.280 BC
300.067 Cb0.035 Bb0.818 Ba0.039 Db0.015 Cb0.195 C
600.757 Ab0.963 Aa0.655 Cb0.920 Aa0.980 Aa0.855 A
900.762 Ab−0.115 Cc0.932 Aa0.933 Aa−0.166 Dc0.469 B
AWCD—carboxylic acids10.748 Bb0.057 Abc0.947 Aa0.709 Bb0.119 Ac0.516 A
150.086 Cb0.028 Bb0.728 Ba0.042 Db0.018 BCb0.180 B
300.165 Cb0.130 Abc0.747 Ba0.094 Dbc0.069 Abc0.241 AB
600.733 Ba−0.396 Dc0.500 Cb0.575 Cb−0.653 Dd0.152 B
900.891 Aa−0.067 Cb0.950 Aa0.952 Aa−0.040 Cb0.537 A
AWCD—miscellaneous10.339 Ba bc0.160 Abc0.436 Ca0.415 BC ab0.212 AB bc0.312 AB
150.057 Cb0.045 Bb0.819 Aa0.037 Db0.031 Bb0.198 B
300.541 Ba0.203 ABb0.715 ABa0.329 Cb0.266 Ab0.411 AB
600.773 Aab0.255 Ac0.606 BCb0.884 Aa0.244 Ac0.552 A
900.964 Aa−0.396 Cc0.851 Aa0.576 Bb−0.362 Cc0.326 AB
AWCD—polymers10.346 Db0.208 Bc0.959 Aa0.254 Dc0.233 Bc0.400 B
150.121 Ec0.105 Cc0.670 Ca0.226 Db0.068 Cc0.238 B
300.576 Cb0.213 Be0.648 Ca0.347 Ccd0.280 Bde0.413 B
600.842 Aa0.337 Ad0.774 Bab0.731 Bb0.609 Ac0.659 A
900.748 Bb−0.367 Dd0.939 Ba0.901 Aa−0.210 Dc0.402 B
The data presented are the means of three replicates. Significant differences (LSD test, p < 0.05) between treatments at the same sampling time and between sampling times within the same treatments are marked with different lower and uppercase letters, respectively. The treatment abbreviations are explained in Figure 1.
Table 5. Results of the two-way ANOVA for the resistance (RS) indices.
Table 5. Results of the two-way ANOVA for the resistance (RS) indices.
SV/ParameterAWCDRHEAmines
VEpVEpVEpVEpVEp
Tr58<0.01 **50<0.01 **73<0.001 ***130.019 *37<0.01 **
T16<0.01 **19<0.01 **3<0.001 ***50.30615<0.01 **
Tr × T26<0.01 **28<0.01 **21<0.001 ***300.06046<0.01 **
SV/ParameterAmino acidsCarbohydratesCarboxylic acidsMiscellaneousPolymers
VEpVEpVEpVEpVEp
Tr36<0.01 **26<0.01 **60<0.01 **47<0.001 ***51<0.01 **
T34<0.01 **35<0.01 **14<0.01 **10<0.001 ***15<0.01 **
Tr × T30<0.01 **37<0.01 **24<0.01 **36<0.001 ***33<0.01 **
SV: source of variation, Tr: treatment, T: time, AWCD: average well-color development, R: substrate richness, H: Shannon-Wiener index, E: evenness index, VE: variance explained (%). Asterisks represent the significance level (* p < 0.05, ** p < 0.01 and *** p < 0.001).
Table 6. Values of the resilience (RL) index for measured parameters obtained at the end of the experiment.
Table 6. Values of the resilience (RL) index for measured parameters obtained at the end of the experiment.
ParameterTreatment x ¯  
XM1XM10PsXM1 + PsXM10 + Ps
AWCD0.750 a−0.206 b0.321 ab0.690 a−0.226 b0.266
Substrate richness (RS)0.302b c0.160 cd0.711 a0.538 ab−0.095 d0.323
Shannon−Wiener index (H)0.678 a−0.011 b0.478 c0.793 a−0.158 b0.356
Evenness (E)0.344 ab−0.246 b0.476 a0.053 ab−0.249 b0.076
AWCD—amines 0.242 c−0.660 c0.938 a0.537 b−0.705 c0.070
AWCD—amino acids −0.278 a−0.959 b−0.354 ab0.078 a−0.945 b−0.491
AWCD—carbohydrates −0.189 b−0.693 c0.283 ab0.456 a−0.797 c−0.188
AWCD—carboxylic acids 0.115 b−0.442 c−0.354 c0.521 a−0.471 c−0.126
AWCD—miscellaneous 0.960 a−0.247 d0.699 b0.495 c−0.260 d0.329
AWCD—polymers 0.682 a−0.354 b0.021 b0.898 a−0.222 b0.205
The data presented are the means of three replicates. Significant differences (LSD test, p < 0.05) between the values of each parameter are marked with different letters. The treatment abbreviations are explained in Figure 1.

Share and Cite

MDPI and ACS Style

Orlewska, K.; Markowicz, A.; Piotrowska-Seget, Z.; Smoleń-Dzirba, J.; Cycoń, M. Functional Diversity of Soil Microbial Communities in Response to the Application of Cefuroxime and/or Antibiotic-Resistant Pseudomonas putida Strain MC1. Sustainability 2018, 10, 3549. https://doi.org/10.3390/su10103549

AMA Style

Orlewska K, Markowicz A, Piotrowska-Seget Z, Smoleń-Dzirba J, Cycoń M. Functional Diversity of Soil Microbial Communities in Response to the Application of Cefuroxime and/or Antibiotic-Resistant Pseudomonas putida Strain MC1. Sustainability. 2018; 10(10):3549. https://doi.org/10.3390/su10103549

Chicago/Turabian Style

Orlewska, Kamila, Anna Markowicz, Zofia Piotrowska-Seget, Joanna Smoleń-Dzirba, and Mariusz Cycoń. 2018. "Functional Diversity of Soil Microbial Communities in Response to the Application of Cefuroxime and/or Antibiotic-Resistant Pseudomonas putida Strain MC1" Sustainability 10, no. 10: 3549. https://doi.org/10.3390/su10103549

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop