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Interactions between soil properties and tetracycline toxicity affecting to bacterial community growth in agricultural soil
Applied Soil Ecology xxx (xxxx) xxxx
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Interactions between soil properties and tetracycline toxicity affecting to bacterial community growth in agricultural soil V. Santás-Miguela, M. Arias-Estéveza, M. Díaz-Raviñab, M.J. Fernández-Sanjurjoc, ⁎ E. Álvarez-Rodríguezc, A. Núñez-Delgadoc, D. Fernández-Calviñoa, a Área de Edafoloxía e Química Agrícola, Departamento de Bioloxía Vexetal e Ciencia do Solo, Facultade de Ciencias, Universidade de Vigo, As Lagoas, s/n, 32004 Ourense, Spain b Departamento de Bioquímica del Suelo, Instituto de Investigaciones Agrobiológicas de Galicia (IIAG-CSIC), Apartado 12, Avda Vigo s/n, 15780 Santiago de Compostela, Spain c Departamento de Edafoloxía e Química Agrícola, Escola Politécnica Superior de Enxeñaría, Universidade de Santiago de Compostela, Campus Universitario, s/n, 27002 Lugo, Spain
A R T I C LE I N FO
A B S T R A C T
Keywords: Leucine incorporation Microorganisms Antibiotics Veterinary Risk assessment
The effect of tetracycline toxicity on the growth of soil bacterial communities was assessed using 22 agricultural soils with different pH values (between 4.1 and 7.4) and organic carbon concentrations (1.1–10.9%). Samples from all these soil types were spiked with different concentrations of tetracycline, and bacterial community growth was estimated after 1, 8, and 42 days of incubation, by using the 3H leucine incorporation method. Although tetracycline toxicity persisted in the soil during the incubation period, its magnitude gradually decreased with time. Moreover, tetracycline toxicity largely depends on soil characteristics, decreasing as a function of increasing organic matter, clay content, and effective exchangeable cation capacity (eCEC), and decreasing soil pH values. Further, an equation that allows the prediction of the direct effect of tetracycline on the growth of soil bacterial community was developed considering easily determinable parameters as the input; this equation will help achieve quick assessment of potentially hazardous situations.
1. Introduction Veterinary antibiotics (VAs) are important drugs used to treat animal diseases (Sapkota et al., 2008) and improve their growth rate (Li et al., 2011). The most widely consumed VAs in the European Union include tetracyclines (TCs) (33.4%), penicillins (25.5%), and sulfonamides (11.0%) (European Medicines Agency, 2016). Antibiotics administered to livestock are poorly absorbed or degraded in the gut of the animals, resulting in an excretion rate ranging from 30% to 90% of the original compound (Sarmah et al., 2006). These antibiotics reach different environmental compartments via different pathways, with spreading of manure and slurry on soil being the primary source (Hamscher et al., 2002). Once in the soil, VAs undergo a series of physical, chemical, and biological processes. Of these, interaction of antibiotics with soil microorganisms is extremely important because soil microorganisms play a vital role in many soil processes such as organic matter turn-over and nutrient cycling. Soil pollution due to antibiotics can affect soil microbial communities, changing their structure and function (Aminov and Mackie, 2007; Urra et al., 2019), as
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well as their activity (Rousk et al., 2008, 2009; Caban et al., 2018). Tetracyclines constitute a family of broad-spectrum bacteriostatic antibiotics. The favorable antimicrobial properties of these agents and the absence of major adverse side effects have led to their extensive use in therapy for human and animal infections (Chopra and Roberts, 2001). Because of their extensive utilization, most available evidence suggest that tetracycline antibiotics are omnipresent, found across terrestrial and aquatic environments (Hernandez et al., 2007; Liu et al., 2009; Andreu et al., 2009; Li et al., 2011; Chen et al., 2011). Several studies have evaluated the effect of tetracycline antibiotic on soil microorganisms, mainly on soil microbiome (Ma et al., 2019) and resistance (Schmitt et al., 2006; Srinivasan et al., 2008; Wu et al., 2010; Kang et al., 2016; Song et al., 2017). However, less studies focuses on the effects of tetracycline antibiotic on the functions of soil microbes. Thiele-Bruhn (2005) studied the effect of nine pharmaceutical antibiotics on microbial Fe3+ reduction, and observed dose-related inhibition of this process, primarily governed by soil properties, especially organic matter, but also soil pH. Wei et al. (2009) observed significant disturbances in the structure of microbial communities and
Corresponding author. E-mail address: [email protected] (D. Fernández-Calviño).
https://doi.org/10.1016/j.apsoil.2019.103437 Received 29 August 2019; Received in revised form 26 October 2019; Accepted 8 November 2019 0929-1393/ © 2019 Elsevier B.V. All rights reserved.
Please cite this article as: V. Santás-Miguel, et al., Applied Soil Ecology, https://doi.org/10.1016/j.apsoil.2019.103437
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2.3. Experimental design
soil enzymatic activities in the presence of tetracycline. Yang et al. (2010) also observed the effect of tetracycline on bacterial community structure in the form of decreasing bacterial diversity. However, despite its involvement in organic matter turn-over (Rousk and Bååth, 2011), the effect of tetracycline on the growth of soil bacterial communities was not studied until date. Another antibiotic (oxytetracycline) belonging to the same family was previously examined in two different studies, but only one soil type was considered in each study (Rousk et al., 2008, 2009). Moreover, potentially applied information from these studies in relation with soil management is very poor. The aim of this study was to evaluate the effect of tetracycline on the growth of soil bacterial community. We also aimed to study the effect of soil properties on the degree of toxicity exerted by tetracycline on the growth of soil bacterial community. Our initial hypotheses were: 1) tetracycline addition to the soil would inhibit bacterial community growth, 2) the inhibition magnitude will be modulated by general soil characteristics, 3) the tetracycline toxicity on soil bacterial communities' may be predicted by using general soil characteristics. In order to test these hypotheses, twenty-two types of soils, differing in their characteristics (primarily in organic matter content and pH), were selected and spiked with different concentrations of tetracycline, followed by assessment of bacterial community growth at three different incubation times by using leucine incorporation technique. The findings of this study can provide relevant information about the eventual effects of tetracycline, which can spread to different environmental compartments as well as exert damage to bacterial communities, posing subsequent hazards for the environment as a whole.
Dried soil samples were rewetted up to 60–80% of water-holding capacity and incubated at 22 °C for 1 week, to allow adequate time to stabilize soil bacterial community growth after moisture adjustment (Meisner et al., 2013). Then, tetracycline was added, in triplicate, to different soil samples (up to 8 different concentrations to each soil sample), resulting in a total of 528 microcosms. The final tetracycline concentrations considered were 0.00, 0.49, 1.95, 7.81, 31.25, 125, 500, and 2000 mg kg−1 soil. These concentrations were selected in order to obtain dose-response curves to estimate tetracycline toxicity according Fox and Landis (2016). Tetracycline was added to the soil samples using talc powder as a carrier, for equalizing the amount of dry material added to each microcosm, thereby facilitating mixing with soil (Rousk et al., 2008). After spiking with tetracycline, each soil microcosm was incubated at 22 °C in the dark, and bacterial community growth was determined after 1, 8, and 42 days to study the effects of tetracycline on soil bacterial community at different time points (short, medium and long-term).
2.4. Bacterial community growth determination Bacterial community growth was estimated using the leucine incorporation technique (Bååth, 1994; Bååth et al., 2001). Briefly, 1 g of soil (fresh weight) was mixed with 10 mL of distilled water by using a multi-vortex shaker at maximum intensity for 3 min, followed by lowspeed centrifugation at 1000 ×g for 10 min, to generate a bacterial suspension in the supernatant. An aliquot (1.5 mL) of this suspension was transferred to a 2-mL micro-centrifugation tube. Then, 2 μL [3H] Leu (3.7 MBq mL−1 and 0.574 TBq mmol−1; Perkin Elmer, USA) was added, with non-labeled Leu, to each tube, resulting in a final concentration of 275 nM Leu in the bacterial suspension. After incubation for 2 h at 22 °C, bacterial growth was stopped with 75 μL of 100% trichloroacetic acid. Washing was performed as described by Bååth et al. (2001), and radioactivity was determined by scintillation liquid counting (Tri-Carb 2810 TR, PerkinElmer, USA).
2. Materials and methods 2.1. Chemicals Tetracycline hydrochloride (CAS. 64-75-5; ≥95% in purity), supplied by Sigma–Aldrich (Steinheim, Germany), was used for performing tetracycline toxicity assessment in soil samples. 2.2. Soil samples and general characterization Twenty-two soil samples were selected (according their pH and organic carbon content values) from two different areas in Galicia (NW Iberian Peninsula), one being a dedicated intensive pastureland (Sarria) and another being dedicated to potato cultivation in rotation with cereal (A Limia). For each soil sample, ten soil sub-samples (0–20 cm depth) were collected with a soil auger and subsequently mixed into a single composite soil sample (~2 kg). Then soils were transported to the laboratory, air dried and stored in polypropylene jars. These soils were previously analyzed for general characteristics, and the results were published in Conde-Cid et al. (2018), where in detailed descriptions of both areas and general soil characterization are included. In this study, dissolved organic carbon (DOC) was analyzed using distilled water as the extraction solution (soil:water ratio, 1:10) and measuring it in a total organic carbon (TOC) analyzer. The general characteristics for these 22 soil samples are listed in Table S1 (Supplementary material). Soil samples presented sand content values ranging from 20% to 70%, silt content values from 12% to 61%, and clay content values from 17% to 34%. Soil pH in water (pHW) ranged from 4.1 to 7.4, and pH in 0.1 M KCl (pHKCl) ranged from 3.7 to 6.6. Total organic carbon (TOC) values ranged from 1.1% to 10.9%, DOC values ranged from 211 to 773 mg kg−1, and total nitrogen ranged from 0.09% to 0.84%. The effective cation exchange capacity (eCEC) ranged from 4.1 to 23.2 cmolc kg−1. In order to perform a deeper analysis of tetracycline direct toxicity, soils were divided in three soil groups: first: similar soil pH (strongly acidic) and different TOC; second: different soil pH and similar TOC; and third: similar soil pH (moderately acidic) and different TOC.
2.5. Data analysis For each soil, bacterial community growth data (Leucine incorporation) were normalized dividing each value by the corresponding one in the control with no tetracycline. The added tetracycline concentration that inhibited 50% of bacterial community growth in each soil microcosm (IC50) was estimated using a logistic model: Y = c/[1 + eb(a-x)], where Y is the extent of Leu incorporation (bacterial community growth) observed at each added tetracycline concentration, x is the logarithmic value of the added tetracycline concentration, a is the value of Log IC50, b is a parameter related to the slope of the inhibition curve, and c is the bacterial growth rate observed in the control sample (antibiotic-free). High values obtained for Log IC50 indicate low tetracycline toxicity, whereas low values of Log IC50 indicate high tetracycline toxicity. Log IC10 was calculated using the following equation fitted for IC50 estimation: Log IC10 = a - (Ln((c/0.9) - 1))/b. The effect of time on the Log IC50 values was statistically studied using a two-way repeated measures ANOVA, while the effect of soil properties on tetracycline toxicity affecting bacterial community growth was studied using Pearson correlation and linear multiple regression analyses. IBM SPSS Statistics 21 software was used for statistical analysis.
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Fig. 1. Relative bacterial community growth as a function of tetracycline (TC) concentration in the soil, after 1, 8 and 42 days of incubation, as observed for 6 soil samples (as example). A, B, C, D, E, and F represent soil sample 1, 2, 5, 8, 18, and 19 (Table S1, Supplementary material).
3. Results
incubation, which was lower compared to that observed at 1 and 8 days, i.e., tetracycline toxicity decreased with the incubation time (the layout of curves move to the right with time). For all soil samples and incubation times, the dose-response curves were generally well described by the logistic model, with R2 values ranging from 0.889 to 0.991 for 1 day of incubation (mean R2 = 0.964), from 0.883 to 0.992 for 8 days of incubation (mean R2 = 0.947), and from 0.708 to 0.998 for 42 days of incubation (mean R2 = 0.915). Log IC50 values for the 22 samples, as observed at 1, 8, and 42 days of incubation, are listed in Table 1. After 1 day of incubation, the Log IC50 values ranged between 1.90 ± 0.14 and 3.83 ± 0.12 (mean = 2.79);
3.1. Bacterial community growth inhibition in response to tetracycline. Effect of dose and incubation time Fig. 1 includes six representative bacterial growth inhibition curves indicating the response to tetracycline antibiotic after 1, 8, and 42 days of incubation. All soil samples (22) yielded sigmoid dose-response curves, i.e., low tetracycline doses did not inhibit bacterial growth, but at high doses, the extent of inhibition increased with the dose. As a general trend, tetracycline toxicity was observed even after 42 days of 3
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Table 1 Toxicity exerted by tetracycline on the growth of soil bacterial community estimated as Log IC10 and Log IC50 values (mean values with the standard error range in brackets) after 1, 8, and 42 days of incubation. R2 values represent the coefficients of determination for model fits used for Log IC50 determination. 1 day
8 days
Log IC50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
2.51 2.70 2.69 2.59 3.02 3.83 2.69 2.70 2.68 2.90 2.84 2.90 2.58 2.37 2.54 1.90 2.33 2.98 3.04 3.50 3.20 2.86
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0.11 0.03 0.08 0.07 0.05 0.12 0.14 0.06 0.08 0.06 0.09 0.09 0.05 0.06 0.09 0.14 0.09 0.10 0.11 0.16 0.08 0.25
Log IC10
R2
Log IC50
1.70 2.21 1.92 1.71 2.48 2.29 1.73 2.20 1.82 2.17 1.76 1.51 1.40 1.45 1.52 0.76 1.20 1.98 2.10 2.27 1.62 0.86
0.963 0.991 0.967 0.984 0.977 0.975 0.924 0.974 0.973 0.974 0.968 0.978 0.990 0.989 0.973 0.967 0.978 0.949 0.934 0.902 0.981 0.889
2.61 2.73 3.06 2.36 3.26 3.33 2.74 2.76 2.48 2.86 2.96 3.02 2.58 2.61 2.74 2.61 2.60 3.12 2.97 3.53 3.89 3.04
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
42 days
0.06 0.11 0.14 0.14 0.08 0.09 0.11 0.07 0.09 0.05 0.10 0.06 0.05 0.05 0.05 0.07 0.12 0.06 0.15 0.19 0.33 0.19
Log IC10
R2
Log IC50
1.86 1.97 2.10 1.71 2.62 2.51 1.92 2.07 1.77 2.06 2.15 2.39 1.73 1.63 2.03 1.52 1.38 2.12 1.38 2.36 1.69 1.48
0.983 0.946 0.883 0.928 0.909 0.922 0.944 0.971 0.973 0.987 0.936 0.970 0.990 0.992 0.985 0.986 0.959 0.977 0.940 0.865 0.889 0.895
2.74 2.86 3.03 2.51 3.32 3.40 2.70 2.97 2.84 2.93 2.99 3.41 2.51 2.62 2.74 2.56 2.41 3.74 3.53 3.05 3.68 2.99
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0.04 0.12 0.13 0.08 0.50 0.11 0.11 0.08 0.02 0.16 0.03 0.07 0.06 0.01 0.10 0.08 0.06 0.33 0.25 0.27 0.50 0.12
Log IC10
R2
2.23 2.56 1.99 1.81 3.27 2.74 1.91 2.19 2.12 2.00 2.43 2.44 1.61 1.67 1.95 1.64 1.47 2.12 2.41 1.87 1.63 1.97
0.987 0.938 0.908 0.977 0.814 0.881 0.947 0.956 0.998 0.875 0.992 0.965 0.988 0.982 0.947 0.979 0.988 0.818 0.780 0.785 0.708 0.920
Table 2 Pearson correlation coefficients between soil characteristics and Log IC50/IC10 values estimated after 1, 8, and 42 days of incubation. pHW Log Log Log Log Log Log
IC50 IC50 IC50 IC10 IC10 IC10
Day Day Day Day Day Day
pHKCl
−0.311 0.063 −0.073 −0.568⁎⁎ −0.465⁎ −0.525⁎
1 8 42 1 8 42
eCEC
−0.286 0.115 −0.037 −0.507⁎ −0.433⁎ −0.508⁎
TOC
Sand ⁎⁎
0.316 0.638⁎⁎ 0.352 −0.195 −0.115 −0.309
0.758 0.621⁎⁎ 0.489⁎ 0.269 0.250 0.300
0.199 −0.057 −0.058 0.441⁎ 0.351 0.290
Silt −0.320 −0.029 −0.058 −0.526⁎ −0.478⁎ −0.464⁎
Clay
DOC ⁎
0.462 0.281 0.386 0.397 0.520⁎ 0.666⁎⁎
0.607⁎⁎ 0.576⁎⁎ 0.327 0.197 0.276 0.149
pHW, pH measured in water; pHKCl, pH measured in 0.1 M KCl; TOC, total organic carbon; eCEC, cation exchange capacity; DOC, dissolved organic carbon. ⁎⁎ P < 0.01. ⁎ P < 0.05.
Log IC50 measured
4.0
showed a significant increase (P < 0.05) of Log IC50 with time, i.e. tetracycline toxicity significantly decreased with time. Table 1 also lists the Log IC10 values calculated from each doseresponse curve drawn for the 22 soil samples at three incubation times. After 1 day of incubation, the Log IC10 values ranged between 0.76 and 2.48 (mean = 1.76); after 8 days, the values ranged between 1.38 and 2.62 (mean = 1.93); and after 42 days, the values ranged between 1.47 and 3.27 (mean = 2.09). Two-way repeated measures ANOVA also showed a significant increase (P < 0.05) of Log IC10, i.e. also supporting that tetracycline toxicity decreased with time.
3.5 3.0 2.5 2.0 1.5
1.5
2.0
2.5
3.0
3.5
3.2. Effect of soil characteristics on tetracycline toxicity
4.0
Table 2 lists the Pearson correlation coefficients observed for soil properties and Log IC50 values after 1, 8, and 42 days of incubation. After 1 day of incubation, Log IC50 significantly and positively correlated with TOC, DOC, and clay content, i.e., higher these parameters, lower the effect of tetracycline toxicity on soil bacterial communities. After 8 days of incubation too, Log IC50 positively and significantly correlated with TOC, DOC, and eCEC, but not with clay content. However, after 42 days of incubation, Log IC50 significantly and positively correlated only with TOC. Moreover, the Pearson correlation coefficient decreased with increasing incubation time. Table 2 also lists the Pearson correlation coefficients observed for soil properties and Log IC10 values. This parameter (Log IC10) significantly and negatively correlated with soil pH (pHW and pHKCl) and silt content for all time
Log IC50 esmated Fig. 2. Plotting of Log IC50 values estimated using Eq. (1) versus measured Log IC50 values using the logistic model after 1 incubation day. Continuous line represents a 1:1 relationship, whereas discontinuous lines represent 10% deviation from the 1:1 line.
after 8 days, the values ranged between 2.36 ± 0.14 and 3.89 ± 0.33 (mean = 2.90); and after 42 days, the values ranged between 2.41 ± 0.06 and 3.74 ± 0.33 (mean = 2.98). Log IC50 values were used to evaluate whether tetracycline toxicity showed a statistical decrease with the incubation time. Two-way repeated measures ANOVA
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Fig. 3. Correlations between soil properties and Log IC50 after 1 incubation day for group 1 (similar soil pH (strongly acidic) and different TOC). pHW, pH measured in water; TOC, total organic carbon (%); eCEC, effective cation exchange capacity (cmolc kg-1); DOC, dissolved organic carbon (mg kg-1). Significant correlations (P < 0.05) for r > 0.735.
effect of soil pH on Log IC50, i.e., the effect of tetracycline toxicity on bacterial community growth increased with increasing soil pH. For Log IC10, no significant equation with more than one independent variable was found.
points. In addition to this, after 1 day of incubation, Log IC10 significantly and positively correlated with sand content, whereas after 8 and 42 days of incubation, it significantly and negatively correlated with clay content. To assess direct tetracycline toxicity (after 1 day of incubation), stepwise regression analysis was performed, yielding a significant equation (Eq. (1)), using which the Log IC50 value can be predicted by three parameters: DOC, pHKCl, and eCEC. This equation explained 71% of the variance observed in the Log IC50 values in the samples studied, and indicated good prediction for Log IC50 values (Fig. 2). It also showed the positive effect of DOC and eCEC on Log IC50 values, i.e., the effect of tetracycline toxicity on bacterial community growth decreased with increasing DOC and/or eCEC. The equation also showed a negative
Log IC50 = (3.85 ± 0.36) + (1.15 ± 0.46) × DOC –(0.426 ± 0.082) × pHKCl + (0.054 ± 0.016) × eCEC, expressing DOC in mg g−1; and eCEC in cmol c kg−1 , where R2 = 0.708, F = 18.00, and all the parameters significant (P < 0.05)
(1)
Fig. 3 shows the relationship between Log IC50 and soil variables in 5
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Fig. 4. Correlations between soil properties and Log IC50 after 1 incubation day for group 2 (different soil pH and similar TOC). pHW, pH measured in water; TOC, total organic carbon (%); eCEC, effective cation exchange capacity (cmolc kg-1); DOC, dissolved organic carbon (mg kg-1). Significant correlations (P < 0.05) for r > 0.540.
(pHW) from 4.4 to 7.4 caused a decrease in the Log IC50 values up to 0.78 units. The relationships between the soil variables and Log IC50 values for group 3 (similar soil pH (moderately acidic) and different TOC) are shown in Fig. 5. For this group, Log IC50 significantly and negatively correlated with silt, a variable that caused a decrease of 0.87 units in the Log IC50 values. Log IC10 values did not show any significant correlation with soil properties in case of groups 2 and 3 (Figs. S2 and S3; Supplementary material).
group 1 (similar soil pH (strongly acidic) and different TOC). In this group, Log IC50 significantly and positively correlated with TOC, DOC, eCEC, and clay content. For the 12 soil samples from group 1, variations in the TOC values increased the Log IC50 values up to 1.13 units; DOC, up to 0.98; eCEC, up to 0.82; and clay content, up to 0.80. The Log IC10 values for group 1 showed significant and positive correlation with the same variables as Log IC50, namely, TOC, DOC, eCEC, and clay content (Fig. S1; Supplementary material). For group 1, variations in the TOC values increased the Log IC10 values up to 0.67 units; DOC, up to 0.80; eCEC, up to 0.71; and clay content, up to 0.48 units. Fig. 4 shows the relationship between Log IC50 and soil variables for group 2 (different soil pH and similar TOC); only soil pH significantly (and negatively) correlated with Log IC50. Variation in the soil pH measured in water
4. Discussion Spiking of soil samples with tetracycline clearly repressed bacterial community growth, generating unambiguous dose-response curves. 6
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Fig. 5. Correlation between soil properties and Log IC50 after 1 incubation day for group 3 (similar soil pH (moderately acidic) and different TOC). pHW, pH measured in water; TOC, total organic carbon (%); eCEC, effective cationic exchange capacity (cmolc kg-1); DOC, dissolved organic carbon (mg kg-1). Significant correlations (P < 0.05) for r > 0.540.
These results confirmed the first hypothesis, i.e. despite the high rate of adsorption of tetracycline in soil (Teixidó et al., 2012; FernándezCalviño et al., 2015; Conde-Cid et al., 2019), excess concentrations of this antibiotic when spread on the soil surface pose a high risk for bacterial communities. In this study, tetracycline toxicity, which, in turn, affected, bacterial community growth, persisted even after 42 days of incubation; however, it gradually decreased over time. Recovery of bacterial growth can be attributed to different mechanisms: 1) degradation of tetracycline in soil (Pan and Chu, 2016); 2) aging processes, that is to say increased adsorption with time (Loibner et al., 2006); and/or 3) development of tolerance to tetracycline among the bacterial communities, because soil pollution due to any substance
stands a chance to induce bacterial tolerance to that pollutant (Blanck, 2002). However, it is very difficult to achieve precise discrimination among different mechanisms. In fact, the extent of degradation expected for tetracycline is low, considering the fact that high antibiotic concentrations slow the rate of degradation, and that tetracycline halflife values can go beyond 100 days (Cycoń et al., 2019). Moreover, Song et al. (2017) did not observe any signs of development of tetracycline tolerance in soil samples polluted with this antibiotic, up to a concentration of 100 mg kg−1. The present study showed that soil properties clearly influence the effect of tetracycline toxicity on the growth of soil bacterial communities, confirming the second hypothesis. This may be owing to the
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neutral conditions. Moreover, the potential risk of tetracycline toxicity on the growth of soil bacterial community can be predicted using data corresponding to easily determinable parameters such as dissolved organic carbon, soil pH, and eCEC.
differences in the concentration of free tetracycline available in different soil samples, which further depends on soil properties (Jia et al., 2008). Tetracycline toxicity can also be attributed to the presence of different forms of tetracycline in soils with different pH values (Fig. S4, Supplementary material). Tetracycline has multiple ionizable functional groups, and, therefore, at different pH values, tetracycline may exist as a cation, zwitterion, or a negatively charged ion. This differential speciation of tetracycline might also lead to differential adsorption by soil colloids (Parolo et al., 2008). Moreover, the extent of direct toxicity differs for different tetracycline species. The protonated forms of tetracycline can diffuse through the plasma membrane of bacteria (Yamaguchi et al., 1991) because of the presence of Donnan equilibrium through the outer membrane (Stock et al., 1977), while the diffusion of anionic species is seriously hindered. However, differences in tetracycline toxicity due to speciation were not expected in the current study, because of the fact that for the pH range of the soil samples studied (4.1–7.4), the chief tetracycline species is TC0 (zwitterion) in all cases (Table S2, Supplementary material). Using Log IC50 and Log IC10 values as the toxicity index, soil organic carbon (SOC), DOC, soil texture (sand, silt, and clay), effective eCEC, and soil pH (pHW and pHKCl) were found to possibly affect tetracycline toxicity in soil. These characteristics may influence tetracycline adsorption, thereby indirectly influencing tetracycline toxicity. Pils and Laird (2007) showed that clay and clay-organic matter complexes have good tetracycline adsorption capacity. Also, other studies have showed that the presence of clays might decrease the effect of tetracycline on bacterial growth; however, this depends on the clay type. For example, montmorillonite presents high capacity to diminish the toxic effect on bacterial growth, but kaolinite presents low capacity (Lv et al., 2019). Gu et al. (2007) reported tetracycline adsorption by humic acids, which was strongly pH dependent in nature, showing that adsorption was due to complexation between the cationic/zwitterionic tetracycline species and deprotonated sites in humic acids. Sassman and Lee (2005) studied the adsorption of tetracycline in soils with different characteristics and concluded that it was highly adsorbed in acidic and high-clay-content soils. On the other hand, in similar soil sample as those used in the present study, although the primary characteristic governing tetracycline adsorption was SOC, other factors such as soil pH and eCEC were also relevant (Conde-Cid et al., 2019). Our results are in line with these previous findings, suggesting that certain management practices can aid to prevent damage to soil bacterial communities due to tetracycline pollution, especially those practices leading to increase in soil organic matter, as well as to maintain soil pH at acidic-neutral conditions. Focusing on the applicability of the present work, it helped develop an equation to predict tetracycline toxicity in soils, using Log IC50 values (Eq. (1)). The high quality of the predictive model (Fig. 2) deems the equation a good tool to estimate the risks associated with tetracycline toxicity in other soils, by using easily determinable parameters such as input, specifically DOC, pHKCl, and eCEC. Therefore, the third hypothesis was also confirmed. The equation can be useful to perform an expedited risk assessment of the effects of tetracycline toxicity on soil bacterial communities, in particular in areas where a detailed evaluation is difficult or not possible, thereby aiding decision-making in order to apply appropriate mitigation management practices (Hull et al., 1999).
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This study has been funded by the Spanish Ministry of Economy and Competitiveness through the projects CGL2015-67333-C2-1-R and -2-R (FEDER Funds). David Fernández Calviño holds a Ramón y Cajal contract (RYC-2016-20411), financed by the Spanish Ministry of Economy, Industry and Competitiveness. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.apsoil.2019.103437. References Aminov, R.I., Mackie, R.I., 2007. Evolution and ecology of antibiotic resistance genes. FEMS Microbiol. Lett. 271, 147–161. Andreu, V., Vazguez-Roig, P., Blasco, C., Pico, Y., 2009. Determination of tetracycline residues in soil by pressurized liquid extraction and liquid chromatography tandem mass spectrometry. Anal. Bioanal. Chem. 394, 1329–1339. Bååth, E., 1994. Thymidine and leucine incorporation in soil bacteria with different cell size. Microb. Ecol. 27, 267–278. Bååth, E., Pettersson, M., Söderberg, K.H., 2001. Adaptation of a rapid and economical microcentrifugation method to measure thymidine and leucine incorporation by soil bacteria. Soil Biol. Biochem. 33, 1571–1574. Blanck, H., 2002. A critical review of procedures and approaches used for assessing pollution-induced community tolerance (PICT) in biotic communities. Hum. Ecol. Risk. Assess. 8, 1003–1034. Caban, J.R., Kuppusamy, S., Kim, J.H., Yoon, Y.-E., Kim, S.Y., Lee, Y.B., 2018. Green manure amendment enhances microbial activity and diversity in antibiotic-contaminated soil. Appl. Soil Ecol. 129, 72–76. Chen, G., Zhao, L., Dong, Y., 2011. Oxidative degradation kinetics and products of chlortetracycline by manganese dioxide. J. Hazard. Mater. 193, 128–138. Chopra, I., Roberts, M., 2001. Tetracycline antibiotics: mode of action, applications, molecular biology, and epidemiology of bacterial resistance. Microbiol. Mol. Biol. Rev. 65, 232–260. Conde-Cid, M., Álvarez-Esmorís, C., Paradelo-Núñez, R., Nóvoa-Muñoz, J.C., AriasEstévez, M., Álvarez-Rodríguez, E., Fernández-Sanjurjo, M.J., Núñez-Delgado, A., 2018. Occurrence of tetracyclines and sulfonamides in manures, agricultural soils and crops from different areas in Galicia (NW Spain). J. Clean. Prod. 197, 491–500. Conde-Cid, M., Fernández-Calviño, D., Nóvoa-Muñoz, J.C., Núñez-Delgado, A., Fernández-Sanjurjo, M.J., Arias-Estévez, M., Álvarez-Rodríguez, E., 2019. Experimental data and model prediction of tetracycline adsorption and desorption in agricultural soils. Environ. Res. 177, 108607. Cycoń, M., Mrozik, A., Piotrowska-Seget, Z., 2019. Antibiotics in the soil environmentdegradation and their impact on microbial activity and diversity. Front. Microbiol. 10, 338. European Medicines Agency, 2016. European surveillance of veterinary antimicrobial consumption. In: Sales of Veterinary Antimicrobial Agents in 29 European Countries in 2014, (EMA/61769/2016). Fernández-Calviño, D., Bermúdez-Couso, A., Arias-Estévez, M., Nóvoa-Muñoz, J.C., Fernández-Sanjurjo, M.J., Álvarez-Rodríguez, E., Núñez-Delgado, A., 2015. Kinetics of tetracycline, oxytetracycline, and chlortetracycline adsorption and desorption on two acid soils. Environ. Sci. Pollut. Res. 22, 425–433. Fox, D.R., Landis, W.G., 2016. Don't be fooled-a no-observed-effect concentration is no substitute for a poor concentration-response experiment. Environ. Toxicol. Chem. 35, 2141–2148. Gu, C., Karthikeyan, K.G., Sibley, S.D., Pedersen, J.A., 2007. Complexation of the antibiotic tetracycline with humic acid. Chemosphere 66, 1494–1501. Hamscher, G., Sczesny, S., Höper, H., Nau, H., 2002. Determination of persistent tetracycline residues in soil fertilized with liquid manure by high performance liquid chromatography with electrospray ionization tandem mass spectrometry. Anal. Chem. 74, 1509–1518. Hernandez, F., Sancho, J.V., Ibanez, M., Guerrero, C., 2007. Antibiotic residue determination in environment waters by LC-MS. TrAC Trends Anal. Chem. 26, 466–485. Hull, R.N., Klee, U., Bryant, D., Copeland, T., 1999. Soil microbial communities and ecological risk assessment: risk assessors' perspective. Hum. Ecol. Risk. Assess. 5,
5. Conclusion The effect of tetracycline on the growth of soil bacterial community might be important in view of soils polluted with high concentrations of this antibiotic. The results of this study indicate that although persistent, this effect gradually decreases with time. However, the magnitude of tetracycline toxicity in soil largely depends on soil characteristics, and can be controlled by adopting management practices for polluted soils, which increase soil organic matter and keep soil pH at acidic8
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707–714. Jia, D.A., Zhou, D.M., Wang, Y.J., Zhu, H.W., Chen, J.L., 2008. Adsorption and cosorption of Cu (II) and tetracycline on two soils with different characteristics. Geoderma 146, 224–230. Kang, Y., Hao, Y., Shen, M., Zhao, Q., Li, Q., Hu, J., 2016. Impacts of supplementing chemical fertilizers with organic fertilizers manufactured using pig manure as a substrate on the spread of tetracycline resistance genes in soil. Ecotoxicol. Environ. Saf. 130, 279–288. Li, R., Zhang, Y., Lee, C.C., Liu, L., Huang, Y., 2011. Hydrauphilic interaction chromatography separation mechanisms of tetracyclines on amino-bonded silica column. J. Sep. Sci. 34, 1508–1516. Liu, F., Ying, G.G., Tao, R., Zhao, J.L., Yang, J.F., Zhao, L.F., 2009. Effects of six selected antibiotics on plant growth and soil microbial and enzymatic activities. Environ. Pollut. 157, 1636–1642. Loibner, A., Jensen, J., Ter-Laak, T., Celis, R., Hartnik, T., 2006. Sorption and ageing of soil contamination. In: Ecological Risk Assessment of Contaminated Land-Decision Support for Site Specific Investigations, pp. 19–29. Lv, G., Li, Z., Elliott, L., Schmidt, M.J., MacWilliams, M.P., Zhang, B., 2019. Impact of tetracycline-clay interactions on bacterial growth. J. Hazard. Mater. 370, 91–97. Ma, J., Zhu, D., Chen, Q.-L., Ding, J., Zhu, Y.-G., Sheng, G.D., Qiu, Y.-P., 2019. Exposure to tetracycline perturbs the microbiome of soil oligochaete Enchytraeus crypticus. Sci. Total Environ. 654, 643–650. Meisner, A., Bååth, E., Rousk, J., 2013. Microbial growth responses upon rewetting soil dried for four days or one year. Soil Biol. Biochem. 66, 188–192. Pan, M., Chu, L.M., 2016. Adsorption and degradation of five selected antibiotics in agricultural soil. Sci. Total Environ. 545, 48–56. Parolo, M.E., Savini, M.C., Valles, J.M., Baschini, M.T., Avena, M.J., 2008. Tetracycline adsorption on montmorillonite: pH and ionic strength effects. Appl. Clay Sci. 40, 179–186. Pils, J.R., Laird, D.A., 2007. Sorption of tetracycline and chlortetracycline on K-and Casaturated soil clays, humic substances, and clay−humic complexes. Environ. Sci. Technol. 41, 1928–1933. Rousk, J., Bååth, E., 2011. Growth of saprotrophic fungi and bacteria in soil. FEMS Microbiol. Ecol. 78, 17–30. Rousk, J., Demoling, L.A., Bahr, A., Bååth, E., 2008. Examining the fungal and bacterial niche overlap using selective inhibitors in soil. FEMS Microbiol. Ecol. 63, 350–358. Rousk, J., Demoling, L.A., Bååth, E., 2009. Contrasting short-term antibiotic effects on respiration and bacterial growth compromises the validity of the selective respiratory inhibition technique to distinguish fungi and bacteria. Microb. Ecol. 58, 75–85. Sapkota, A., Sapkota, A.R., Kucharski, M., Burke, J., McKenzie, S., Walker, P., 2008.
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Interactions between soil properties and tetracycline toxicity affecting to bacterial community growth in agricultural soil V. Santás-Miguela, M. Arias-Estéveza, M. Díaz-Raviñab, M.J. Fernández-Sanjurjoc, ⁎ E. Álvarez-Rodríguezc, A. Núñez-Delgadoc, D. Fernández-Calviñoa, a Área de Edafoloxía e Química Agrícola, Departamento de Bioloxía Vexetal e Ciencia do Solo, Facultade de Ciencias, Universidade de Vigo, As Lagoas, s/n, 32004 Ourense, Spain b Departamento de Bioquímica del Suelo, Instituto de Investigaciones Agrobiológicas de Galicia (IIAG-CSIC), Apartado 12, Avda Vigo s/n, 15780 Santiago de Compostela, Spain c Departamento de Edafoloxía e Química Agrícola, Escola Politécnica Superior de Enxeñaría, Universidade de Santiago de Compostela, Campus Universitario, s/n, 27002 Lugo, Spain
A R T I C LE I N FO
A B S T R A C T
Keywords: Leucine incorporation Microorganisms Antibiotics Veterinary Risk assessment
The effect of tetracycline toxicity on the growth of soil bacterial communities was assessed using 22 agricultural soils with different pH values (between 4.1 and 7.4) and organic carbon concentrations (1.1–10.9%). Samples from all these soil types were spiked with different concentrations of tetracycline, and bacterial community growth was estimated after 1, 8, and 42 days of incubation, by using the 3H leucine incorporation method. Although tetracycline toxicity persisted in the soil during the incubation period, its magnitude gradually decreased with time. Moreover, tetracycline toxicity largely depends on soil characteristics, decreasing as a function of increasing organic matter, clay content, and effective exchangeable cation capacity (eCEC), and decreasing soil pH values. Further, an equation that allows the prediction of the direct effect of tetracycline on the growth of soil bacterial community was developed considering easily determinable parameters as the input; this equation will help achieve quick assessment of potentially hazardous situations.
1. Introduction Veterinary antibiotics (VAs) are important drugs used to treat animal diseases (Sapkota et al., 2008) and improve their growth rate (Li et al., 2011). The most widely consumed VAs in the European Union include tetracyclines (TCs) (33.4%), penicillins (25.5%), and sulfonamides (11.0%) (European Medicines Agency, 2016). Antibiotics administered to livestock are poorly absorbed or degraded in the gut of the animals, resulting in an excretion rate ranging from 30% to 90% of the original compound (Sarmah et al., 2006). These antibiotics reach different environmental compartments via different pathways, with spreading of manure and slurry on soil being the primary source (Hamscher et al., 2002). Once in the soil, VAs undergo a series of physical, chemical, and biological processes. Of these, interaction of antibiotics with soil microorganisms is extremely important because soil microorganisms play a vital role in many soil processes such as organic matter turn-over and nutrient cycling. Soil pollution due to antibiotics can affect soil microbial communities, changing their structure and function (Aminov and Mackie, 2007; Urra et al., 2019), as
⁎
well as their activity (Rousk et al., 2008, 2009; Caban et al., 2018). Tetracyclines constitute a family of broad-spectrum bacteriostatic antibiotics. The favorable antimicrobial properties of these agents and the absence of major adverse side effects have led to their extensive use in therapy for human and animal infections (Chopra and Roberts, 2001). Because of their extensive utilization, most available evidence suggest that tetracycline antibiotics are omnipresent, found across terrestrial and aquatic environments (Hernandez et al., 2007; Liu et al., 2009; Andreu et al., 2009; Li et al., 2011; Chen et al., 2011). Several studies have evaluated the effect of tetracycline antibiotic on soil microorganisms, mainly on soil microbiome (Ma et al., 2019) and resistance (Schmitt et al., 2006; Srinivasan et al., 2008; Wu et al., 2010; Kang et al., 2016; Song et al., 2017). However, less studies focuses on the effects of tetracycline antibiotic on the functions of soil microbes. Thiele-Bruhn (2005) studied the effect of nine pharmaceutical antibiotics on microbial Fe3+ reduction, and observed dose-related inhibition of this process, primarily governed by soil properties, especially organic matter, but also soil pH. Wei et al. (2009) observed significant disturbances in the structure of microbial communities and
Corresponding author. E-mail address: [email protected] (D. Fernández-Calviño).
https://doi.org/10.1016/j.apsoil.2019.103437 Received 29 August 2019; Received in revised form 26 October 2019; Accepted 8 November 2019 0929-1393/ © 2019 Elsevier B.V. All rights reserved.
Please cite this article as: V. Santás-Miguel, et al., Applied Soil Ecology, https://doi.org/10.1016/j.apsoil.2019.103437
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2.3. Experimental design
soil enzymatic activities in the presence of tetracycline. Yang et al. (2010) also observed the effect of tetracycline on bacterial community structure in the form of decreasing bacterial diversity. However, despite its involvement in organic matter turn-over (Rousk and Bååth, 2011), the effect of tetracycline on the growth of soil bacterial communities was not studied until date. Another antibiotic (oxytetracycline) belonging to the same family was previously examined in two different studies, but only one soil type was considered in each study (Rousk et al., 2008, 2009). Moreover, potentially applied information from these studies in relation with soil management is very poor. The aim of this study was to evaluate the effect of tetracycline on the growth of soil bacterial community. We also aimed to study the effect of soil properties on the degree of toxicity exerted by tetracycline on the growth of soil bacterial community. Our initial hypotheses were: 1) tetracycline addition to the soil would inhibit bacterial community growth, 2) the inhibition magnitude will be modulated by general soil characteristics, 3) the tetracycline toxicity on soil bacterial communities' may be predicted by using general soil characteristics. In order to test these hypotheses, twenty-two types of soils, differing in their characteristics (primarily in organic matter content and pH), were selected and spiked with different concentrations of tetracycline, followed by assessment of bacterial community growth at three different incubation times by using leucine incorporation technique. The findings of this study can provide relevant information about the eventual effects of tetracycline, which can spread to different environmental compartments as well as exert damage to bacterial communities, posing subsequent hazards for the environment as a whole.
Dried soil samples were rewetted up to 60–80% of water-holding capacity and incubated at 22 °C for 1 week, to allow adequate time to stabilize soil bacterial community growth after moisture adjustment (Meisner et al., 2013). Then, tetracycline was added, in triplicate, to different soil samples (up to 8 different concentrations to each soil sample), resulting in a total of 528 microcosms. The final tetracycline concentrations considered were 0.00, 0.49, 1.95, 7.81, 31.25, 125, 500, and 2000 mg kg−1 soil. These concentrations were selected in order to obtain dose-response curves to estimate tetracycline toxicity according Fox and Landis (2016). Tetracycline was added to the soil samples using talc powder as a carrier, for equalizing the amount of dry material added to each microcosm, thereby facilitating mixing with soil (Rousk et al., 2008). After spiking with tetracycline, each soil microcosm was incubated at 22 °C in the dark, and bacterial community growth was determined after 1, 8, and 42 days to study the effects of tetracycline on soil bacterial community at different time points (short, medium and long-term).
2.4. Bacterial community growth determination Bacterial community growth was estimated using the leucine incorporation technique (Bååth, 1994; Bååth et al., 2001). Briefly, 1 g of soil (fresh weight) was mixed with 10 mL of distilled water by using a multi-vortex shaker at maximum intensity for 3 min, followed by lowspeed centrifugation at 1000 ×g for 10 min, to generate a bacterial suspension in the supernatant. An aliquot (1.5 mL) of this suspension was transferred to a 2-mL micro-centrifugation tube. Then, 2 μL [3H] Leu (3.7 MBq mL−1 and 0.574 TBq mmol−1; Perkin Elmer, USA) was added, with non-labeled Leu, to each tube, resulting in a final concentration of 275 nM Leu in the bacterial suspension. After incubation for 2 h at 22 °C, bacterial growth was stopped with 75 μL of 100% trichloroacetic acid. Washing was performed as described by Bååth et al. (2001), and radioactivity was determined by scintillation liquid counting (Tri-Carb 2810 TR, PerkinElmer, USA).
2. Materials and methods 2.1. Chemicals Tetracycline hydrochloride (CAS. 64-75-5; ≥95% in purity), supplied by Sigma–Aldrich (Steinheim, Germany), was used for performing tetracycline toxicity assessment in soil samples. 2.2. Soil samples and general characterization Twenty-two soil samples were selected (according their pH and organic carbon content values) from two different areas in Galicia (NW Iberian Peninsula), one being a dedicated intensive pastureland (Sarria) and another being dedicated to potato cultivation in rotation with cereal (A Limia). For each soil sample, ten soil sub-samples (0–20 cm depth) were collected with a soil auger and subsequently mixed into a single composite soil sample (~2 kg). Then soils were transported to the laboratory, air dried and stored in polypropylene jars. These soils were previously analyzed for general characteristics, and the results were published in Conde-Cid et al. (2018), where in detailed descriptions of both areas and general soil characterization are included. In this study, dissolved organic carbon (DOC) was analyzed using distilled water as the extraction solution (soil:water ratio, 1:10) and measuring it in a total organic carbon (TOC) analyzer. The general characteristics for these 22 soil samples are listed in Table S1 (Supplementary material). Soil samples presented sand content values ranging from 20% to 70%, silt content values from 12% to 61%, and clay content values from 17% to 34%. Soil pH in water (pHW) ranged from 4.1 to 7.4, and pH in 0.1 M KCl (pHKCl) ranged from 3.7 to 6.6. Total organic carbon (TOC) values ranged from 1.1% to 10.9%, DOC values ranged from 211 to 773 mg kg−1, and total nitrogen ranged from 0.09% to 0.84%. The effective cation exchange capacity (eCEC) ranged from 4.1 to 23.2 cmolc kg−1. In order to perform a deeper analysis of tetracycline direct toxicity, soils were divided in three soil groups: first: similar soil pH (strongly acidic) and different TOC; second: different soil pH and similar TOC; and third: similar soil pH (moderately acidic) and different TOC.
2.5. Data analysis For each soil, bacterial community growth data (Leucine incorporation) were normalized dividing each value by the corresponding one in the control with no tetracycline. The added tetracycline concentration that inhibited 50% of bacterial community growth in each soil microcosm (IC50) was estimated using a logistic model: Y = c/[1 + eb(a-x)], where Y is the extent of Leu incorporation (bacterial community growth) observed at each added tetracycline concentration, x is the logarithmic value of the added tetracycline concentration, a is the value of Log IC50, b is a parameter related to the slope of the inhibition curve, and c is the bacterial growth rate observed in the control sample (antibiotic-free). High values obtained for Log IC50 indicate low tetracycline toxicity, whereas low values of Log IC50 indicate high tetracycline toxicity. Log IC10 was calculated using the following equation fitted for IC50 estimation: Log IC10 = a - (Ln((c/0.9) - 1))/b. The effect of time on the Log IC50 values was statistically studied using a two-way repeated measures ANOVA, while the effect of soil properties on tetracycline toxicity affecting bacterial community growth was studied using Pearson correlation and linear multiple regression analyses. IBM SPSS Statistics 21 software was used for statistical analysis.
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Fig. 1. Relative bacterial community growth as a function of tetracycline (TC) concentration in the soil, after 1, 8 and 42 days of incubation, as observed for 6 soil samples (as example). A, B, C, D, E, and F represent soil sample 1, 2, 5, 8, 18, and 19 (Table S1, Supplementary material).
3. Results
incubation, which was lower compared to that observed at 1 and 8 days, i.e., tetracycline toxicity decreased with the incubation time (the layout of curves move to the right with time). For all soil samples and incubation times, the dose-response curves were generally well described by the logistic model, with R2 values ranging from 0.889 to 0.991 for 1 day of incubation (mean R2 = 0.964), from 0.883 to 0.992 for 8 days of incubation (mean R2 = 0.947), and from 0.708 to 0.998 for 42 days of incubation (mean R2 = 0.915). Log IC50 values for the 22 samples, as observed at 1, 8, and 42 days of incubation, are listed in Table 1. After 1 day of incubation, the Log IC50 values ranged between 1.90 ± 0.14 and 3.83 ± 0.12 (mean = 2.79);
3.1. Bacterial community growth inhibition in response to tetracycline. Effect of dose and incubation time Fig. 1 includes six representative bacterial growth inhibition curves indicating the response to tetracycline antibiotic after 1, 8, and 42 days of incubation. All soil samples (22) yielded sigmoid dose-response curves, i.e., low tetracycline doses did not inhibit bacterial growth, but at high doses, the extent of inhibition increased with the dose. As a general trend, tetracycline toxicity was observed even after 42 days of 3
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Table 1 Toxicity exerted by tetracycline on the growth of soil bacterial community estimated as Log IC10 and Log IC50 values (mean values with the standard error range in brackets) after 1, 8, and 42 days of incubation. R2 values represent the coefficients of determination for model fits used for Log IC50 determination. 1 day
8 days
Log IC50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
2.51 2.70 2.69 2.59 3.02 3.83 2.69 2.70 2.68 2.90 2.84 2.90 2.58 2.37 2.54 1.90 2.33 2.98 3.04 3.50 3.20 2.86
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0.11 0.03 0.08 0.07 0.05 0.12 0.14 0.06 0.08 0.06 0.09 0.09 0.05 0.06 0.09 0.14 0.09 0.10 0.11 0.16 0.08 0.25
Log IC10
R2
Log IC50
1.70 2.21 1.92 1.71 2.48 2.29 1.73 2.20 1.82 2.17 1.76 1.51 1.40 1.45 1.52 0.76 1.20 1.98 2.10 2.27 1.62 0.86
0.963 0.991 0.967 0.984 0.977 0.975 0.924 0.974 0.973 0.974 0.968 0.978 0.990 0.989 0.973 0.967 0.978 0.949 0.934 0.902 0.981 0.889
2.61 2.73 3.06 2.36 3.26 3.33 2.74 2.76 2.48 2.86 2.96 3.02 2.58 2.61 2.74 2.61 2.60 3.12 2.97 3.53 3.89 3.04
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
42 days
0.06 0.11 0.14 0.14 0.08 0.09 0.11 0.07 0.09 0.05 0.10 0.06 0.05 0.05 0.05 0.07 0.12 0.06 0.15 0.19 0.33 0.19
Log IC10
R2
Log IC50
1.86 1.97 2.10 1.71 2.62 2.51 1.92 2.07 1.77 2.06 2.15 2.39 1.73 1.63 2.03 1.52 1.38 2.12 1.38 2.36 1.69 1.48
0.983 0.946 0.883 0.928 0.909 0.922 0.944 0.971 0.973 0.987 0.936 0.970 0.990 0.992 0.985 0.986 0.959 0.977 0.940 0.865 0.889 0.895
2.74 2.86 3.03 2.51 3.32 3.40 2.70 2.97 2.84 2.93 2.99 3.41 2.51 2.62 2.74 2.56 2.41 3.74 3.53 3.05 3.68 2.99
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0.04 0.12 0.13 0.08 0.50 0.11 0.11 0.08 0.02 0.16 0.03 0.07 0.06 0.01 0.10 0.08 0.06 0.33 0.25 0.27 0.50 0.12
Log IC10
R2
2.23 2.56 1.99 1.81 3.27 2.74 1.91 2.19 2.12 2.00 2.43 2.44 1.61 1.67 1.95 1.64 1.47 2.12 2.41 1.87 1.63 1.97
0.987 0.938 0.908 0.977 0.814 0.881 0.947 0.956 0.998 0.875 0.992 0.965 0.988 0.982 0.947 0.979 0.988 0.818 0.780 0.785 0.708 0.920
Table 2 Pearson correlation coefficients between soil characteristics and Log IC50/IC10 values estimated after 1, 8, and 42 days of incubation. pHW Log Log Log Log Log Log
IC50 IC50 IC50 IC10 IC10 IC10
Day Day Day Day Day Day
pHKCl
−0.311 0.063 −0.073 −0.568⁎⁎ −0.465⁎ −0.525⁎
1 8 42 1 8 42
eCEC
−0.286 0.115 −0.037 −0.507⁎ −0.433⁎ −0.508⁎
TOC
Sand ⁎⁎
0.316 0.638⁎⁎ 0.352 −0.195 −0.115 −0.309
0.758 0.621⁎⁎ 0.489⁎ 0.269 0.250 0.300
0.199 −0.057 −0.058 0.441⁎ 0.351 0.290
Silt −0.320 −0.029 −0.058 −0.526⁎ −0.478⁎ −0.464⁎
Clay
DOC ⁎
0.462 0.281 0.386 0.397 0.520⁎ 0.666⁎⁎
0.607⁎⁎ 0.576⁎⁎ 0.327 0.197 0.276 0.149
pHW, pH measured in water; pHKCl, pH measured in 0.1 M KCl; TOC, total organic carbon; eCEC, cation exchange capacity; DOC, dissolved organic carbon. ⁎⁎ P < 0.01. ⁎ P < 0.05.
Log IC50 measured
4.0
showed a significant increase (P < 0.05) of Log IC50 with time, i.e. tetracycline toxicity significantly decreased with time. Table 1 also lists the Log IC10 values calculated from each doseresponse curve drawn for the 22 soil samples at three incubation times. After 1 day of incubation, the Log IC10 values ranged between 0.76 and 2.48 (mean = 1.76); after 8 days, the values ranged between 1.38 and 2.62 (mean = 1.93); and after 42 days, the values ranged between 1.47 and 3.27 (mean = 2.09). Two-way repeated measures ANOVA also showed a significant increase (P < 0.05) of Log IC10, i.e. also supporting that tetracycline toxicity decreased with time.
3.5 3.0 2.5 2.0 1.5
1.5
2.0
2.5
3.0
3.5
3.2. Effect of soil characteristics on tetracycline toxicity
4.0
Table 2 lists the Pearson correlation coefficients observed for soil properties and Log IC50 values after 1, 8, and 42 days of incubation. After 1 day of incubation, Log IC50 significantly and positively correlated with TOC, DOC, and clay content, i.e., higher these parameters, lower the effect of tetracycline toxicity on soil bacterial communities. After 8 days of incubation too, Log IC50 positively and significantly correlated with TOC, DOC, and eCEC, but not with clay content. However, after 42 days of incubation, Log IC50 significantly and positively correlated only with TOC. Moreover, the Pearson correlation coefficient decreased with increasing incubation time. Table 2 also lists the Pearson correlation coefficients observed for soil properties and Log IC10 values. This parameter (Log IC10) significantly and negatively correlated with soil pH (pHW and pHKCl) and silt content for all time
Log IC50 esmated Fig. 2. Plotting of Log IC50 values estimated using Eq. (1) versus measured Log IC50 values using the logistic model after 1 incubation day. Continuous line represents a 1:1 relationship, whereas discontinuous lines represent 10% deviation from the 1:1 line.
after 8 days, the values ranged between 2.36 ± 0.14 and 3.89 ± 0.33 (mean = 2.90); and after 42 days, the values ranged between 2.41 ± 0.06 and 3.74 ± 0.33 (mean = 2.98). Log IC50 values were used to evaluate whether tetracycline toxicity showed a statistical decrease with the incubation time. Two-way repeated measures ANOVA
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Fig. 3. Correlations between soil properties and Log IC50 after 1 incubation day for group 1 (similar soil pH (strongly acidic) and different TOC). pHW, pH measured in water; TOC, total organic carbon (%); eCEC, effective cation exchange capacity (cmolc kg-1); DOC, dissolved organic carbon (mg kg-1). Significant correlations (P < 0.05) for r > 0.735.
effect of soil pH on Log IC50, i.e., the effect of tetracycline toxicity on bacterial community growth increased with increasing soil pH. For Log IC10, no significant equation with more than one independent variable was found.
points. In addition to this, after 1 day of incubation, Log IC10 significantly and positively correlated with sand content, whereas after 8 and 42 days of incubation, it significantly and negatively correlated with clay content. To assess direct tetracycline toxicity (after 1 day of incubation), stepwise regression analysis was performed, yielding a significant equation (Eq. (1)), using which the Log IC50 value can be predicted by three parameters: DOC, pHKCl, and eCEC. This equation explained 71% of the variance observed in the Log IC50 values in the samples studied, and indicated good prediction for Log IC50 values (Fig. 2). It also showed the positive effect of DOC and eCEC on Log IC50 values, i.e., the effect of tetracycline toxicity on bacterial community growth decreased with increasing DOC and/or eCEC. The equation also showed a negative
Log IC50 = (3.85 ± 0.36) + (1.15 ± 0.46) × DOC –(0.426 ± 0.082) × pHKCl + (0.054 ± 0.016) × eCEC, expressing DOC in mg g−1; and eCEC in cmol c kg−1 , where R2 = 0.708, F = 18.00, and all the parameters significant (P < 0.05)
(1)
Fig. 3 shows the relationship between Log IC50 and soil variables in 5
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Fig. 4. Correlations between soil properties and Log IC50 after 1 incubation day for group 2 (different soil pH and similar TOC). pHW, pH measured in water; TOC, total organic carbon (%); eCEC, effective cation exchange capacity (cmolc kg-1); DOC, dissolved organic carbon (mg kg-1). Significant correlations (P < 0.05) for r > 0.540.
(pHW) from 4.4 to 7.4 caused a decrease in the Log IC50 values up to 0.78 units. The relationships between the soil variables and Log IC50 values for group 3 (similar soil pH (moderately acidic) and different TOC) are shown in Fig. 5. For this group, Log IC50 significantly and negatively correlated with silt, a variable that caused a decrease of 0.87 units in the Log IC50 values. Log IC10 values did not show any significant correlation with soil properties in case of groups 2 and 3 (Figs. S2 and S3; Supplementary material).
group 1 (similar soil pH (strongly acidic) and different TOC). In this group, Log IC50 significantly and positively correlated with TOC, DOC, eCEC, and clay content. For the 12 soil samples from group 1, variations in the TOC values increased the Log IC50 values up to 1.13 units; DOC, up to 0.98; eCEC, up to 0.82; and clay content, up to 0.80. The Log IC10 values for group 1 showed significant and positive correlation with the same variables as Log IC50, namely, TOC, DOC, eCEC, and clay content (Fig. S1; Supplementary material). For group 1, variations in the TOC values increased the Log IC10 values up to 0.67 units; DOC, up to 0.80; eCEC, up to 0.71; and clay content, up to 0.48 units. Fig. 4 shows the relationship between Log IC50 and soil variables for group 2 (different soil pH and similar TOC); only soil pH significantly (and negatively) correlated with Log IC50. Variation in the soil pH measured in water
4. Discussion Spiking of soil samples with tetracycline clearly repressed bacterial community growth, generating unambiguous dose-response curves. 6
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Fig. 5. Correlation between soil properties and Log IC50 after 1 incubation day for group 3 (similar soil pH (moderately acidic) and different TOC). pHW, pH measured in water; TOC, total organic carbon (%); eCEC, effective cationic exchange capacity (cmolc kg-1); DOC, dissolved organic carbon (mg kg-1). Significant correlations (P < 0.05) for r > 0.540.
These results confirmed the first hypothesis, i.e. despite the high rate of adsorption of tetracycline in soil (Teixidó et al., 2012; FernándezCalviño et al., 2015; Conde-Cid et al., 2019), excess concentrations of this antibiotic when spread on the soil surface pose a high risk for bacterial communities. In this study, tetracycline toxicity, which, in turn, affected, bacterial community growth, persisted even after 42 days of incubation; however, it gradually decreased over time. Recovery of bacterial growth can be attributed to different mechanisms: 1) degradation of tetracycline in soil (Pan and Chu, 2016); 2) aging processes, that is to say increased adsorption with time (Loibner et al., 2006); and/or 3) development of tolerance to tetracycline among the bacterial communities, because soil pollution due to any substance
stands a chance to induce bacterial tolerance to that pollutant (Blanck, 2002). However, it is very difficult to achieve precise discrimination among different mechanisms. In fact, the extent of degradation expected for tetracycline is low, considering the fact that high antibiotic concentrations slow the rate of degradation, and that tetracycline halflife values can go beyond 100 days (Cycoń et al., 2019). Moreover, Song et al. (2017) did not observe any signs of development of tetracycline tolerance in soil samples polluted with this antibiotic, up to a concentration of 100 mg kg−1. The present study showed that soil properties clearly influence the effect of tetracycline toxicity on the growth of soil bacterial communities, confirming the second hypothesis. This may be owing to the
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neutral conditions. Moreover, the potential risk of tetracycline toxicity on the growth of soil bacterial community can be predicted using data corresponding to easily determinable parameters such as dissolved organic carbon, soil pH, and eCEC.
differences in the concentration of free tetracycline available in different soil samples, which further depends on soil properties (Jia et al., 2008). Tetracycline toxicity can also be attributed to the presence of different forms of tetracycline in soils with different pH values (Fig. S4, Supplementary material). Tetracycline has multiple ionizable functional groups, and, therefore, at different pH values, tetracycline may exist as a cation, zwitterion, or a negatively charged ion. This differential speciation of tetracycline might also lead to differential adsorption by soil colloids (Parolo et al., 2008). Moreover, the extent of direct toxicity differs for different tetracycline species. The protonated forms of tetracycline can diffuse through the plasma membrane of bacteria (Yamaguchi et al., 1991) because of the presence of Donnan equilibrium through the outer membrane (Stock et al., 1977), while the diffusion of anionic species is seriously hindered. However, differences in tetracycline toxicity due to speciation were not expected in the current study, because of the fact that for the pH range of the soil samples studied (4.1–7.4), the chief tetracycline species is TC0 (zwitterion) in all cases (Table S2, Supplementary material). Using Log IC50 and Log IC10 values as the toxicity index, soil organic carbon (SOC), DOC, soil texture (sand, silt, and clay), effective eCEC, and soil pH (pHW and pHKCl) were found to possibly affect tetracycline toxicity in soil. These characteristics may influence tetracycline adsorption, thereby indirectly influencing tetracycline toxicity. Pils and Laird (2007) showed that clay and clay-organic matter complexes have good tetracycline adsorption capacity. Also, other studies have showed that the presence of clays might decrease the effect of tetracycline on bacterial growth; however, this depends on the clay type. For example, montmorillonite presents high capacity to diminish the toxic effect on bacterial growth, but kaolinite presents low capacity (Lv et al., 2019). Gu et al. (2007) reported tetracycline adsorption by humic acids, which was strongly pH dependent in nature, showing that adsorption was due to complexation between the cationic/zwitterionic tetracycline species and deprotonated sites in humic acids. Sassman and Lee (2005) studied the adsorption of tetracycline in soils with different characteristics and concluded that it was highly adsorbed in acidic and high-clay-content soils. On the other hand, in similar soil sample as those used in the present study, although the primary characteristic governing tetracycline adsorption was SOC, other factors such as soil pH and eCEC were also relevant (Conde-Cid et al., 2019). Our results are in line with these previous findings, suggesting that certain management practices can aid to prevent damage to soil bacterial communities due to tetracycline pollution, especially those practices leading to increase in soil organic matter, as well as to maintain soil pH at acidic-neutral conditions. Focusing on the applicability of the present work, it helped develop an equation to predict tetracycline toxicity in soils, using Log IC50 values (Eq. (1)). The high quality of the predictive model (Fig. 2) deems the equation a good tool to estimate the risks associated with tetracycline toxicity in other soils, by using easily determinable parameters such as input, specifically DOC, pHKCl, and eCEC. Therefore, the third hypothesis was also confirmed. The equation can be useful to perform an expedited risk assessment of the effects of tetracycline toxicity on soil bacterial communities, in particular in areas where a detailed evaluation is difficult or not possible, thereby aiding decision-making in order to apply appropriate mitigation management practices (Hull et al., 1999).
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This study has been funded by the Spanish Ministry of Economy and Competitiveness through the projects CGL2015-67333-C2-1-R and -2-R (FEDER Funds). David Fernández Calviño holds a Ramón y Cajal contract (RYC-2016-20411), financed by the Spanish Ministry of Economy, Industry and Competitiveness. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.apsoil.2019.103437. References Aminov, R.I., Mackie, R.I., 2007. Evolution and ecology of antibiotic resistance genes. FEMS Microbiol. Lett. 271, 147–161. Andreu, V., Vazguez-Roig, P., Blasco, C., Pico, Y., 2009. Determination of tetracycline residues in soil by pressurized liquid extraction and liquid chromatography tandem mass spectrometry. Anal. Bioanal. Chem. 394, 1329–1339. Bååth, E., 1994. Thymidine and leucine incorporation in soil bacteria with different cell size. Microb. Ecol. 27, 267–278. Bååth, E., Pettersson, M., Söderberg, K.H., 2001. Adaptation of a rapid and economical microcentrifugation method to measure thymidine and leucine incorporation by soil bacteria. Soil Biol. Biochem. 33, 1571–1574. Blanck, H., 2002. A critical review of procedures and approaches used for assessing pollution-induced community tolerance (PICT) in biotic communities. Hum. Ecol. Risk. Assess. 8, 1003–1034. Caban, J.R., Kuppusamy, S., Kim, J.H., Yoon, Y.-E., Kim, S.Y., Lee, Y.B., 2018. Green manure amendment enhances microbial activity and diversity in antibiotic-contaminated soil. Appl. Soil Ecol. 129, 72–76. Chen, G., Zhao, L., Dong, Y., 2011. Oxidative degradation kinetics and products of chlortetracycline by manganese dioxide. J. Hazard. Mater. 193, 128–138. Chopra, I., Roberts, M., 2001. Tetracycline antibiotics: mode of action, applications, molecular biology, and epidemiology of bacterial resistance. Microbiol. Mol. Biol. Rev. 65, 232–260. Conde-Cid, M., Álvarez-Esmorís, C., Paradelo-Núñez, R., Nóvoa-Muñoz, J.C., AriasEstévez, M., Álvarez-Rodríguez, E., Fernández-Sanjurjo, M.J., Núñez-Delgado, A., 2018. Occurrence of tetracyclines and sulfonamides in manures, agricultural soils and crops from different areas in Galicia (NW Spain). J. Clean. Prod. 197, 491–500. Conde-Cid, M., Fernández-Calviño, D., Nóvoa-Muñoz, J.C., Núñez-Delgado, A., Fernández-Sanjurjo, M.J., Arias-Estévez, M., Álvarez-Rodríguez, E., 2019. Experimental data and model prediction of tetracycline adsorption and desorption in agricultural soils. Environ. Res. 177, 108607. Cycoń, M., Mrozik, A., Piotrowska-Seget, Z., 2019. Antibiotics in the soil environmentdegradation and their impact on microbial activity and diversity. Front. Microbiol. 10, 338. European Medicines Agency, 2016. European surveillance of veterinary antimicrobial consumption. In: Sales of Veterinary Antimicrobial Agents in 29 European Countries in 2014, (EMA/61769/2016). Fernández-Calviño, D., Bermúdez-Couso, A., Arias-Estévez, M., Nóvoa-Muñoz, J.C., Fernández-Sanjurjo, M.J., Álvarez-Rodríguez, E., Núñez-Delgado, A., 2015. Kinetics of tetracycline, oxytetracycline, and chlortetracycline adsorption and desorption on two acid soils. Environ. Sci. Pollut. Res. 22, 425–433. Fox, D.R., Landis, W.G., 2016. Don't be fooled-a no-observed-effect concentration is no substitute for a poor concentration-response experiment. Environ. Toxicol. Chem. 35, 2141–2148. Gu, C., Karthikeyan, K.G., Sibley, S.D., Pedersen, J.A., 2007. Complexation of the antibiotic tetracycline with humic acid. Chemosphere 66, 1494–1501. Hamscher, G., Sczesny, S., Höper, H., Nau, H., 2002. Determination of persistent tetracycline residues in soil fertilized with liquid manure by high performance liquid chromatography with electrospray ionization tandem mass spectrometry. Anal. Chem. 74, 1509–1518. Hernandez, F., Sancho, J.V., Ibanez, M., Guerrero, C., 2007. Antibiotic residue determination in environment waters by LC-MS. TrAC Trends Anal. Chem. 26, 466–485. Hull, R.N., Klee, U., Bryant, D., Copeland, T., 1999. Soil microbial communities and ecological risk assessment: risk assessors' perspective. Hum. Ecol. Risk. Assess. 5,
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