Azithromycin and Ciprofloxacin Can Promote Antibiotic Resistance in Biosolids and Biosolids-Amended Soils

ABSTRACT

Spread of biosolids-borne antibiotic resistance is a growing public and environmental health concern. Herein, we conducted incubation experiments involving biosolids, which are byproducts of sewage treatment processes, and biosolids-amended soil. Quantitative reverse transcription-PCR (RT-qPCR) was employed to assess responses of select antibiotic resistance genes (ARGs) and mobile elements to environmentally relevant concentrations of two biosolids-borne antibiotics, azithromycin (AZ) and ciprofloxacin (CIP). Additionally, we examined sequence distribution of gyrA (encoding DNA gyrase; site of action of CIP) to assess potential shifts in genotype. Increasing antibiotic concentrations generally increased the transcriptional activities of qnrS (encoding CIP resistance) and ermB and mefE (encoding AZ resistance). The transcriptional activity of intl1, a marker of class 1 integrons, was unaffected by CIP or AZ concentrations, but biosolids amendment increased intl1 activity in the soil by 4 to 5 times, which persisted throughout incubation. While the dominant gyrA sequences found herein were unrelated to known CIP-resistant genotypes, the increasing CIP concentrations significantly decreased the diversity of genes encoding the DNA gyrase A subunit, suggesting changes in microbial community structures. This study suggests that biosolids harbor transcriptionally active ARGs and mobile elements that could survive and spread in biosolids-amended soils. However, more research is warranted to investigate these trends under field conditions.

IMPORTANCE Although previous studies have indicated that biosolids may be important spreaders of antibiotics and antibiotic resistance genes (ARGs) in environments, the potential activities of ARGs or their responses to environmental parameters have been understudied. This study highlights that certain biosolids-borne antibiotics can induce transcriptional activities of ARGs and mobile genetic elements in biosolids and biosolids-amended soil, even when present at environmentally relevant concentrations. Furthermore, these antibiotics can alter the structure of microbial populations expressing ARGs. Our findings indicate the bioavailability of the antibiotics in biosolids and provide evidence that biosolids can promote the activities and dissemination of ARGs and mobile genes in biosolids and soils that receive contaminated biosolids, thus, underscoring the importance of investigating anthropogenically induced antibiotic resistance in the environment under real-world scenarios.

INTRODUCTION

Extensive use of manmade antibiotics for clinical and agricultural purposes is of concern because it can potentially increase the distribution of antibiotic resistance genes (ARGs) in the environment (1–3). Antibiotics and ARGs can enter natural environments via various point and nonpoint sources, including human and animal wastes. These natural environments can, then, serve as reservoirs and sources of exposure of these antibiotics and ARGs to nontarget organisms (3, 4). Therefore, from an environmental and public health viewpoint, it is important to understand the fate of antibiotics and the factors that may affect the dissemination and activities of ARGs in the environment.

Biosolids, which are derived from domestic sewage treatment facilities, are well-recognized reservoirs of antibiotics and ARGs (5–7). Because of the widespread use of biosolids as soil amendments, there is concern that land application of biosolids can contribute to antibiotic and ARG contamination and spread in the environment (3, 5, 8). Indeed, recent studies have linked land application of biosolids with increased abundance and diversity of ARGs and mobile genetic elements in soil and crops grown on contaminated soils (3, 6, 9–14). Many efforts have been made to identify and quantify antibiotics and ARGs in biosolids (5–8, 15, 16), but little effort has been devoted to understanding the mechanisms determining the responses of ARGs to antibiotics within biosolids and in biosolids-applied environments.

Antibiotics, which usually coexist with ARGs in biosolids, would be a key factor determining the spread of ARGs because they could selectively provide advantages to ARG-hosting populations (17). Thus, investigating dynamics between antibiotics and ARGs is essential in understanding the potential activities and dissemination of ARGs in biosolids and biosolids-applied environments. We assessed the transcriptional activities of ARGs in response to various concentrations of antibiotics because gene transcription is the process by which ARG-hosting microbes express the resistance genes in response to antibiotics, providing the hosts with the capability of surviving and the opportunity to spread ARGs in the environment. We also examined the response of a mobile genetic element to antibiotics because its presence has been related to the distribution of ARGs via a horizontal gene transfer system.

This study focused on analyses of responses of select ARGs and a mobile genetic element to various concentrations of two biosolids-borne antibiotics, ciprofloxacin (CIP) and azithromycin (AZ), which are among the most commonly occurring and high-priority antibiotics in the biosolids-soil system (5, 15, 16). CIP is a quinolone that inhibits DNA replication through inhibition of DNA gyrase (a subclass of type II topoisomerase) and topoisomerase IV (18, 19). AZ is a macrolide that targets protein synthesis in bacteria by inhibiting translation of mRNA by binding with the 50S subunit of the bacterial ribosome (20). Both CIP and AZ are reportedly bacteriostatic in the environment (18, 19, 21).

We employed laboratory incubations wherein a biosolids and a soil were incubated without or with additions of different environmentally relevant concentrations of biosolids-borne CIP or AZ as described previously in Sidhu et al. (22). The environmentally pertinent antibiotic concentrations, in the context of biosolids-borne antibiotics, were based on the USEPA Targeted National Sewage Sludge Survey (15). The concentrations of the target antibiotics in U.S. biosolids typically range between median and average concentrations of 5 to 10.5 mg kg⁻¹ for CIP and 0.25 to 0.83 mg kg⁻¹ for AZ, and the 95th percentile concentrations of environmental relevance are ∼36.1 mg (CIP) and ∼3.2 mg (AZ) per kg biosolids (dry weight [dw] basis) (15). Considering a typical biosolids application rate of 1% (dw dw⁻¹), the antibiotic concentrations in biosolids-amended soils are approximately 100 times lower than those in the biosolids (see section I in the supplemental material for details). At different time intervals during the incubation, subsamples of biosolids and soil were taken and four groups of ARGs (qnr, ermB, mefE, and gyrA) and a mobile genetic element (intl1) were studied.

qnr, including qnrA, qnrB, and qnrS, are well-known CIP resistance genes; their gene product, Qnr, blocks the action of CIP on DNA gyrase and topoisomerase IV (23). ermB and mefE are AZ resistance genes: the former encodes ribosomal methylase that protects cells by modifying the AZ target site on 23S rRNA (24), and the latter produces an AZ efflux system (25, 26). intI1, which encodes the class 1 integron-integrase, was also selected for study because of its role in horizontal gene transfer potential of ARGs (27, 28). The transcriptional activities of qnr, ermB, mefE, and intl1 were measured using quantitative reverse transcription-PCR (RT-qPCR). Also, as some bacteria are protected from CIP via mutations within gyrA (which is the gene that encodes the A subunit of DNA gyrase) (23, 29, 30), sequencing and subsequent sequence analyses of gyrA were conducted to examine potential shifts in the dominant Enterobacterales genotypes of mRNA transcribed from gyrA; order Enterobacterales was targeted because it includes many pathogens of environmental concern and it has been well characterized with regard to fluoroquinolone-associated gyrA mutations (31).

Based on the expected minimal bioaccessibility of the two biosolids-borne antibiotics (32), we hypothesized that the environmentally relevant concentrations of the antibiotics minimally affect the ARGs and intl1.

RESULTS

The laboratory incubations included (i) a biosolids and (ii) an artificial soil composed of sand mixed with 4% (dw dw⁻¹) cattle manure (32, 33). The biosolids intrinsically contained 1 mg CIP and 0.06 mg AZ per kg; these unspiked biosolids samples served as biosolids controls. To examine the effect of antibiotics on ARG transcription, the biosolids were spiked with two additional concentrations of CIP or AZ, that is, 10.5 or 36.1 mg CIP kg⁻¹ (taking the total [spiked plus intrinsic] nominal concentration to 11.5 or 37.1 mg kg⁻¹ in the biosolids, respectively) or 0.83 or 3.2 mg AZ kg⁻¹ (taking the total nominal concentration to 0.89 or 3.26 mg kg⁻¹ in the biosolids, respectively). The concentrations of 10.5 and 0.83 mg kg⁻¹ represent average and 36.1 and 3.2 mg kg⁻¹ represent the 95th percentile concentrations of the two antibiotics in the U.S. biosolids (15). On the other hand, the nominal concentrations of CIP and AZ in the soil were below detection limits (<0.001 mg CIP and 0.0004 mg AZ kg⁻¹); these unamended (and unspiked) soil samples served as soil controls. Samples of the soil were mixed with the various spiked biosolids (1%, dw dw⁻¹), yielding total nominal concentrations of ∼0.12 or 0.37 mg CIP kg⁻¹ or 0.01 or 0.03 mg AZ kg⁻¹ in the biosolids-amended soils.

Transcription of CIP resistance genes.

RT-qPCR estimated the numbers of transcripts related to CIP resistance genes qnrA, qnrB, and qnrS. While no transcription of any of the three genes was observed in the biosolids controls, only qnrS was transcribed in the biosolids samples that were spiked with additional CIP (Fig. 1A). The extent of gene activity was treatment dependent; the greater the CIP concentration, the more qnrS mRNA copies. The greatest number of copies of qnrS transcripts was observed on day 5 of incubation, reaching mean values of 2.0 × 10⁴ and 3.5 × 10⁴ g⁻¹ (dw) in biosolids spiked with 10.5 and 36.1 mg CIP kg⁻¹, respectively. Thereafter, the copies of qnrS transcripts in the two spiked biosolids samples gradually decreased to 3.4 × 10³ and 4.8 × 10³ g⁻¹, respectively, by day 90 but were detectable throughout the incubation.

FIG 1.

The copies of qnrS transcript measured from (A) biosolids and (B) soil incubations. Biosolids were spiked with 10.5 mg CIP kg⁻¹ (CBS I) or 36.1 mg CIP kg⁻¹ (CBS II) or not spiked (CT). Soil was incubated without mixing with biosolids (CT) or after mixing with biosolids CBS I or CBS II at 1% (dw dw⁻¹) rates. Error bars represent standard errors of the mean from 5 replications. The letters (a, b) indicate statistically significant differences in the copies of gene transcripts among treatments for a specific incubation time (i.e., comparison is between same colored bars). An asterisk (*) indicates that the mean copies of gene transcripts were below quantification limit of ∼2 × 10³ copies g⁻¹ (where some replicates were above the quantification limits, the mean cDNA copies were calculated by considering cDNA copy number below quantification levels as equal to half of the quantification limit).

The soil controls did not show transcriptional activities for any of the three qnr genes during the incubation. However, qnrS transcripts were detected for up to 45 days at ≤3 × 10³ g⁻¹ copies in the soil samples amended with the CIP-spiked biosolids, a transcription level 10-fold smaller than that in the biosolids, likely due to 100-times-smaller CIP concentrations in the biosolids-amended soil samples. The qnrS transcripts were quantifiable only in the samples containing 0.37 mg CIP kg⁻¹ (Fig. 1B). The transcripts were dependent on the incubation time and were undetectable by day 90.

Transcription of AZ resistance genes.

The ermB transcript copy numbers in the biosolids controls were detected at ≥1.4 × 10⁴ copies g⁻¹ throughout the incubation (Fig. 2A). The numbers of the transcripts increased with increase in AZ concentrations. On day 5 of incubation, the mean ermB transcripts (copies g⁻¹) were 1.4 × 10⁴ (for biosolids control), 2.1 × 10⁴ (for biosolids spiked with 0.83 mg AZ kg⁻¹), and 3.0 × 10⁴ (for biosolids spiked with 3.2 mg AZ kg⁻¹). The ermB transcript copies remained proportional to AZ concentrations throughout the incubation without showing any significant decrease.

FIG 2.

The copies of ermB and mefE transcripts measured from (A) biosolids and (B) soil incubations. Biosolids were spiked with 0.83 mg AZ kg⁻¹ (ABS I) or 3.2 mg AZ kg⁻¹ (ABS II) or not spiked (CT). Soil was incubated without mixing with biosolids (CT) or after mixing with biosolids ABS I or ABS II at 1% (dw dw⁻¹) rates. Error bars represent standard errors of the mean from 5 replications. The letters (a, b) indicate statistically significant differences in the copies of gene transcripts among treatments for a specific incubation time (i.e., comparison is between same colored bars). An asterisk (*) indicates that the mean copies of gene transcripts were below quantification limit of ∼2 × 10³ copies g⁻¹ (where some replicates were above the quantification limits, the mean cDNA copies were calculated by considering cDNA copy number below quantification levels as equal to half of the quantification limit).

ermB transcripts were not detected in the soil controls but were quantified in biosolids-amended soils containing 0.01 and 0.03 mg AZ kg⁻¹, where mean transcript copies ranged from 2.2 × 10³ g⁻¹ to 3.4 × 10³ g⁻¹ (Fig. 2B). The transcripts remained stable and quantifiable throughout the incubation.

With mean transcript copies ranging from 8.7 × 10³ g⁻¹ to 2.2 × 10⁴ g⁻¹, mefE was transcribed in all the biosolids samples, including the biosolids controls, at all the AZ concentrations (Fig. 2A). The copies of mefE transcripts increased relative to the biosolids controls only in the biosolids spiked with 3.2 mg AZ kg⁻¹ on day 5 of incubation. The increased copies, however, decreased to the background levels over time, reaching concentrations similar to those in the biosolids controls and other AZ treatments by day 90.

Like those of ermB, mefE transcripts were not detected in the soil controls and were quantified in biosolids-amended soils containing 0.01 and 0.03 mg AZ kg⁻¹, where mean transcript copies ranged from 2.6 × 10³ g⁻¹ to 2.7 × 10³ g⁻¹. (Fig. 2B). However, unlike in the case of ermB, the transcript copies of mefE depended on the incubation time and were below quantification limit of 2 × 10³ copies g⁻¹ by day 90. The transcription levels of both ermB and mefE were 10-fold smaller than those in the biosolids, likely due to 100-times-smaller AZ concentrations in the biosolids-amended soil samples.

Transcription of intl1.

The biosolids controls harbored intl1 with transcripts ranging between 2.7 × 10⁷ and 5.0 × 10⁷ copies g⁻¹ (Fig. 3A). The transcript copy numbers were unaffected by addition of CIP or AZ throughout the incubation. The gene was also transcribed in the soil controls, where the transcripts (5.8 × 10⁴ to 9.0 × 10⁴ copies g⁻¹) were 2 to 3 orders of magnitude smaller than those in the biosolids (Fig. 3B). Biosolids amendments to the soil increased the intl1 transcript copies by 4 to 5 times, from ≤9.0 × 10⁴ to ≥3.1 × 10⁵ copies g⁻¹, in both CIP and AZ treatments (Fig. 3B), suggesting that biosolids were a major source of intI1 in the amended soil. The gene transcript copy numbers for a particular medium (i.e., biosolids or soil) were statistically the same irrespective of CIP and/or AZ concentrations, indicating that intl1 transcription was not related to the antibiotics added to the biosolids. Also, the intI1 cDNA concentration remained unaffected by the incubation time, suggesting persistence and minimal attenuation of the gene.

FIG 3.

The copies of intl1 transcripts measured from (A) biosolids and (B) soil incubations. Biosolids were spiked with 0.83 mg AZ kg⁻¹ (ABS I), 3.2 mg AZ kg⁻¹ (ABS II), 10.5 mg CIP kg⁻¹ (CBS I), or 36.1 mg CIP kg⁻¹ (CBS II) or not spiked (CT). Soil was incubated without mixing with biosolids (CT) or after mixing with biosolids ABS I, ABS II, CBS I, or CBS II at 1% (dw dw⁻¹) rates. Error bars represent standard errors of the mean from 5 replications. The letters (a, b) indicate statistically significant differences in the copies of gene transcripts among treatments for a specific incubation time (i.e., comparison is between same-colored bars).

Analysis of gyrA sequences.

Herein, we wanted to examine if microbial exposure to the various concentrations of CIP selected for CIP-resistant gyrA sequences or changed structures of the Enterobacterales. To do so, we analyzed the transcript sequences of gyrA obtained through RT-PCR from the biosolids (controls and those spiked with 10.5 and 36.1 mg CIP kg⁻¹) and soil (controls and those amended with biosolids spiked with 10.5 and 36.1 mg CIP kg⁻¹) samples from 0 and 90 days of incubation. To reduce the volume of sequences, this assessment was restricted to Enterobacterales because this class has been well characterized with regard to fluoroquinolone-associated gyrA mutations (31). Initially, a total of 14,048 raw reads were obtained from gyrA amplicons through PacBio sequencing. After filtering and editing processes, a total of 1,949 refined gyrA transcript sequences were used to determine the distribution of and potential selection for gyrA sequences (Table S1).

(i) Composition of Enterobacterales gyrA transcripts. The sequences were assigned to nine operational taxonomic units (OTUs), which were defined by a 5% difference in protein sequences deduced in silico from transcript sequences. All OTUs were affiliated with the classes Enterobacteriaceae and Yersiniaceae within the order Enterobacterales (Fig. 4A). Good’s coverage estimated that ≥99% of OTUs were included from the biosolids and soil samples (Table S1). Addition of CIP (10.5 or 36.1 mg kg⁻¹) significantly changed the composition of Enterobacterales gyrA in the biosolids after 90 days of incubation. GYRA-I represented the majority of OTUs (≥63%) in biosolids controls; however, its proportion decreased to below 5.6% after incubation with the spiked CIP (Fig. 4B). In contrast, the CIP addition significantly enriched OTU GYRA-II from ≤24% to ≥76% and OTU GYRA-III by ∼12%.

FIG 4.

Distribution of gyrA sequences in biosolids and soil samples. (A) Maximum-likelihood tree showing the phylogenetic position of OTUs of DNA gyrase. OTUs detected in this study are denoted with blue while the reference Enterobacteriaceae strains taken from the NCBI database are marked with brown color. (B) The composition of gyrase OTUs detected from biosolids and soil. Error bars represent standard error of the mean from 4 replicates. (C) Principal coordinate analysis (PCoA) plot showing the relationship between gyrA-hosting communities. Abbreviations: S, soil control; BS, biosolids control; CBS I, biosolids spiked with 10.5 mg CIP kg⁻¹; CBS II, biosolids spiked with 36.1 mg CIP kg⁻¹; S+CBS, soil amended with CIP-spiked biosolids.

The soil controls were also dominated by OTU GYRA-I prior to the amendment of CIP-spiked biosolids. Herein, too, the soil gyrA assemblage shifted toward a GYRA-II- and GYRA-III-dominated distribution after incubation with CIP-spiked biosolids. This CIP-driven change is illustrated in the PCoA (principal coordinate analysis) plot (Fig. 4C), in which the sequences representative of the samples that received external CIP separated from those in the biosolids or soil controls along axis 1, which explains 96.8% of the variation.

The CIP addition adversely affected the diversity of the gyrA host Enterobacterales in the biosolids and soil incubations. The biosolids controls were predicted to harbor 6 OTUs after 90 days of incubation, whereas 4 and 2 OTUs were predicted for the biosolids spiked with 10.5 and 36.1 mg CIP kg⁻¹, respectively (Table S1). Likewise, Chao1 richness and Shannon’s index suggest that CIP additions to biosolids lowered OTU diversity after 90 days of incubation. Similarly, amending CIP-spiked biosolids to soil decreased the predicted OTU number from 4 to 2 after 90 days incubation, concurrently decreasing Chao1 richness and Shannon’s diversity indices from 3 to 1 and from 0.71 to 0, respectively.

(ii) Sequence distribution of DNA gyrase. Fluoroquinolone resistance is often associated with DNA mutations in codons encoding amino acids glycine at the 81st position (Gly-81), serine at the 83rd position (Ser-83), and aspartic acid at the 87th position (Asp-87) in the quinolone resistance-determining (QRDR) region (31, 34–37). In order to assess gyrA mutations associated with QRDR, we examined the presence of substitutions on the three amino acid sequence positions of the gyrA sequences. Among the three targeted amino acids, Ser-83 was substituted in the largest number of sequences (a total of 17 sequences) to Thr (in 5 seqs), Cys (in 5 seqs), Gly (in 3 seqs), Ala (in 2 seqs), Tyr (in 1 seq), or Ile (in 1 seq) (Table 1). Substitutions at Asp-87 and Gly-81 were found in three sequences in each amino acid. Out of 23 substitutions, 18 substitutions occurred in the sequences detected from biosolids controls (6 cases) or soil controls (12 cases), and the remaining were from CIP-spiked biosolids (4 cases) or soil samples amended with CIP-spiked biosolids (1 case). Among OTUs, GYRA-I exhibited the most substitutions (13 cases), followed by GYRA-III (5 cases).

TABLE 1.

Amino acid (AA) substitutions in DNA gyrase detected in biosolids and soil^a

OTU (total seq. no.)	Gly-81b		Ser-83		Asp-87
	AA (case no.)	Source	AA (case no.)	Source	AA (case no.)	Source
GYRA-I (933)	Asp (2)	S	Cys (4)	S	Asn (2)	S
			Ala (2)	BS
			Gly (1)	CBS
GYRA-II (727)			Thr (1)	S	Gly (1)	CBS
			Tyr (1)	BS
GYRA-III (155)	Ser (1)	S+CBS	Thr (3)	BS (2), S (1)
			Ile (1)	CBS
GYRA-IV (68)			Gly (2)	S
GYRA-V (2)
GYRA-VI (1)
GYRA-VII (1)			Cys (1)	CBS
GYRA-VIII (1)			Thr (1)	BS
GYRA-IX (1)

^aAbbreviations: S, soil control; BS, biosolid control; CBS, CIP-spiked biosolids; S+CBS, soil amended with CIP-spiked biosolids.

^bNumbers correspond to the amino acid positions of Escherichia coli gyrA.

DISCUSSION

Although biosolids are widely recognized sources of antibiotics and ARGs in the environment (3, 5, 6, 8, 10, 11, 38, 39), the mechanisms involved in the distribution of ARGs within biosolids or biosolids-amended soils are largely understudied. An important consideration when judging the potential impacts of ARGs carried by biosolids is whether the genes are actively transcribed. ARG transcription may enable ARG hosts to increase survival rate, outcompete antibiotic-susceptible populations, and promote spread of ARGs (17). This study employed RT-qPCR to assess transcription of common ARGs that confer resistance to CIP and AZ, which are among frequently detected and high-priority biosolids-borne antibiotics (15, 16). We also assessed the transcription of integrase from a class 1 integron (intI1) via RT-qPCR because it is frequently linked to antibiotic resistance genes (27, 28). Additionally, sequencing of gyrA (the target gene for CIP action and the gene commonly involved in conferring resistance to CIP) assessed changes in the distribution of the dominant Enterobacterales genotypes of mRNA transcribed from gyrA.

RT-qPCR results reveal that the qnrS was not transcribed in the biosolids controls even though the biosolids intrinsically contained CIP (1 mg CIP kg⁻¹) and qnrS gene (9 × 10⁵ g⁻¹ copies of DNA) (see Fig. S1 and section II in the supplemental material for details). Similarly, the transcripts of qnr gene were not detected in the soil controls, which contained 8 × 10³ g⁻¹ copies of qnrS DNA (Fig. S1). However, this gene was clearly transcribed when the biosolids were spiked with CIP at the average (10.5 mg kg⁻¹) or the 95th percentile (36.1 mg kg⁻¹) concentrations detected in the U.S. biosolids (15) and when the CIP-spiked biosolids were applied to the soil. These results suggest that CIP concentration, and consequently biosolids-loading rate in soils, is an important factor controlling transcription of this gene. The transcript copy numbers in the spiked biosolids and the biosolids-amended soil samples decreased with incubation time, suggesting that there was a physiochemical change in the CIP added. CIP reportedly strongly sorbs to biosolids and soils, in mostly nonextractable forms, along with low biotic and abiotic degradation (<4%) over several months (22, 32, 40). Herein, the decrease in qnrS transcript copies can be likely attributed to antibiotic attenuation (e.g., formation of bound residues) and diminishing CIP bioaccessibility over time (22, 40). The decrease is consistent with literature reports of dissipation of antibiotic resistance determinants to background levels in other biosolids-amended soils (10, 38). The high sorption of the intrinsic CIP may also explain why qnrS was not expressed in the unspiked biosolids.

In this study, biosolids harbored ermB (1.7 × 10⁷ DNA copies g⁻¹) and mefE (1.9 × 10⁵ DNA copies g⁻¹) (Fig. S1 and section II in the supplemental material), and the transcripts of both genes were detected in the biosolids controls that contained 0.06 mg AZ kg⁻¹. The AZ resistance genes in this study appear to be much more sensitive to AZ than qnrS was to CIP. On the other hand, neither ermB nor mefE was transcribed in the soil controls, which contained undetectable AZ and ∼6 × 10⁴ copies g⁻¹ of ermB and ∼5 × 10³ copies g⁻¹ of mefE DNA (Fig. S1 and section II in the supplemental material) but were transcribed in the biosolids-amended soil samples. The results suggest that transcription of these genes is also dependent on AZ concentration. The elevated concentrations of mefE transcripts in the spiked biosolids gradually decreased to the background levels with time, likely due to sorption of AZ to biosolids/soil or its degradation (22, 32, 41), which reduced AZ bioaccessibility, and thus bioavailability (22), to microbes. The increases in ermB transcription in the antibiotic-treated biosolids and amended soil, however, persisted throughout the incubation, suggesting that the transcription of some resistance genes in biosolids and biosolids-amended soils can stabilize for at least a few months. We also quantified qnrS, ermB, and mefE DNA from select biosolids and soil samples to understand possible processes that led to observed changes in the transcription of CIP and AZ resistance genes (see section II in the supplemental material for details). The DNA analyses data (Fig. S1) suggest that both enrichment in resistant microbes and increased resistance gene transcription could contribute to the biosolids-borne antibiotic resistance.

Class 1 integrons are mobile genetic elements that carry various ARGs, such as those encoding broad-spectrum β-lactamases, resistance to sulfonamides, and aminoglycoside-modifying enzymes. They have been studied extensively in Gram-negative pathogens in animals and the environment (e.g., soils, sediments, and waters) (42). The integrase encoded by intl1 plays a key role in transposition of ARGs into integrons (43, 44). intl1 has been employed as a proxy for the spread of ARGs in the environment because it is (i) reportedly associated with antibiotics resistance genes, (ii) widespread in numerous bacteria, (iii) an indicator of the extent of horizontal gene transfer (HGT) potential, and (iv) susceptible to rapid changes because of HGT and short generation times of bacteria harboring the gene (27, 28, 45, 46). In our biosolids, intl1 had relatively high copy numbers of transcripts, >2.7 × 10⁷ copies g⁻¹, amounting to numbers 2 or 3 orders of magnitude greater than the number of transcripts from CIP and AZ resistance genes. In this study, the intI1 transcripts were independent of target antibiotic concentrations and responded only to biosolids amendment in the soil, where gene transcripts increased by 4 to 5 times after soil was amended with biosolids. Also, intl1 transcripts persisted throughout incubation. Although the factors inducing intl1 transcription are not fully known at this time, the high level of transcription suggests potential for transfer of genes associated with the integron. Our results, consistent with the other literature (e.g., see references 9, 10), suggest that biosolids application can increase mobile genetic elements (MGEs) such as intI1 in biosolids-amended soils. Furthermore, the increased MGEs may remain stable in biosolids and biosolids-amended soils for months.

The potential spread of ARGs and mobile genetic elements through land application of biosolids is well documented (6, 9–11). The present study makes a significant contribution to that literature by demonstrating that biosolids harbor transcriptionally active ARGs and mobile elements and have the potential to transfer those to soil. It should be noted that the transcriptional activity of qnrS exhibited a lifetime in the soil environment that was short compared to the persistence of the genes (i.e., qnrS DNA; see section II in the supplemental material) themselves and that transcription likely depends on the bioavailable concentration of the antibiotic. It is also notable that the increased transcriptional activity of the three genes did not necessarily increase the numbers of copies of corresponding ARGs in the biosolids and soil incubations (Fig. S1). In other words, the copies of ARGs tested were generally stable regardless of the change in transcriptional activity. This finding suggests that ARG transcriptional activity alone would not be enough to promote distribution and increases in numbers of ARGs but rather that increases in the numbers of ARGs would require additional environmental factors. Growth substrates and nutrients, which biosolids offer, are likely to be among the most important factors required to promote the growth of ARG hosts.

One mechanism by which bacteria may be protected from the toxicity of fluoroquinolone antibiotics (e.g., CIP) is through changes in the structure of the target, DNA gyrase (31, 34–37, 47). In Enterobacterales, protective mutations are known to occur at three specific amino acid positions, demarking the quinolone resistance-determining region (QRDR) (i.e., Gly-81, Ser-83, and/or Asp-87) (31). In order to determine if exposure to CIP in our study selected for the previously identified QRDR genotypes, we examined the nucleotide sequences in the DNA gyrase sequences deduced from gyrA transcripts that were obtained from the biosolids and soil incubations with or without CIP addition. Although we observed some shifts in these amino acid sequences (Table 1), the observed changes in proportions of OTUs, referred to as GYRA-I, GYRA-II, GYRA-III, and GYRA-IV, in CIP-treated biosolids and soil samples do not appear to be due to the shifts because we did not detect shifts that were specific to either CIP treatments or controls.

The Enterobacterales gyrA transcript sequences in the spiked biosolids and the biosolids-amended soil samples were dominated by two OTUs, GYRA-II and GYRA-III (Fig. 4). Somewhat surprisingly, only a few strains within these OTUs showed substitutions at the known CIP resistance positions, with a substitution rate of <1% (8 out of 882 strains). Of these eight substitutions, three were obtained from the biosolids controls and two were obtained from the soil controls. Hence, microbial resistance to CIP in our study does not arise from predicted changes in gyrA sequences but from other resistance mechanisms. A likely mechanism, which is supported by the resistance gene analyses data, responsible for the observed shifts is the enrichment of qnrA-bearing bacterial strains. However, it is not known if qnrS is the primary mechanism of resistance in the Enterobacterales.

The gyrA sequencing results show an important aspect of antibiotic impacts on microbial ecology within biosolids and biosolids-amended soils. Increased CIP concentrations reduced diversity of OTUs and greatly enriched some specific OTUs in Enterobacterales assemblages expressing gyrA, illustrating that antibiotics may be a prime determinant of community structures. Since antibiotics could provide selective advantages to antibiotic-resistant microbes, the antibiotic-driven community change may increase dissemination of ARGs. Potential changes in community structure may alter geochemical processes in natural environments (40, 48–50). The role of antibiotic-resistant populations in biogeochemical cycles would be an important topic for further studies in order to assess antibiotics and ARGs from biosolid-applied lands.

The gyrA sequences obtained from the biosolids and the soil controls were similar despite a 100-fold difference in CIP concentrations. The data suggest either that the intrinsic CIP was minimally bioavailable or that the bacteria were acclimated to the intrinsic antibiotic a priori. Intrinsic CIP and AZ concentrations in most U.S. biosolids are 4 times greater than those in the biosolids utilized in this study (32) and could affect initial acclimation of microbes in biosolids and biosolids-amended soils differently than observed here.

CIP is one of the highly enriched antibiotics in U.S. biosolids (15, 16). Although much of CIP remains biologically unavailable due to physicochemical interactions in the biosolids (22, 32, 40), our study indicates that the fraction of CIP that is bioavailable can lead to the expression of ARGs (e.g., qnrS and gyrA) in biosolids and biosolids-amended soil, changing the community structure of ARG hosts. Understanding the accessibility of biosolids-borne CIP is, therefore, essential to predict how the ARG hosts express ARGs and, consequently, how they are transmitted in biosolids-applied soil environments. This will ultimately enable quantification of the risks from these ARGs and their hosts to humans and the environment.

Conclusions. The present work assessed the potential risks of land-applied biosolids spreading ARGs and mobile genetic elements by assessing transcriptional activities or sequencing transcripts of select genes. Contrary to our hypothesis of minimal impacts on the resistance genes and gyrA genotypes, results from the culture-independent approach suggest that (i) biosolids-borne antibiotics can lead to active transcription of ARGs and transposases associated with class I integrons, (ii) the application of biosolids may carry transcriptionally active ARGs and class I integrons to soil, and (iii) antibiotics can alter the structural aspects of microbial communities in biosolids and biosolids-amended soil. These results verify that biosolids can be an important reservoir of ARGs and mobile elements and that microbes in them can actively express the ARGs in response to biosolids-borne or externally added antibiotics. In addition, the ARGs and antibiotics may influence the microbial ecology of biosolids-applied soil environments. It is important to note that this work involved a single biosolids and soil, antibiotic spiking, and laboratory incubation; therefore, more research is needed to investigate the potential activities and transfer of these ARGs in the field. Notably, several data gaps such as (i) minimal quantification of shifts in antibiotic-resistant bacteria and resistance determinants in various environmental matrices (e.g., soil, groundwater, runoff, plants, atmosphere) because of biosolids application and (ii) limited knowledge of effects of environmental conditions (including soil and biosolids characteristics) on antibiotic resistance determinants, etc., remain. These data gaps must be addressed to assess the full potential of antibiotic resistance development and spread in biosolids and biosolids-amended soils.

MATERIALS AND METHODS

Chemicals, reagents, biosolids, and soil.

³H-CIP (CAS number 85721-33-1, 97.4% radiochemical purity, specific radioactivity of 44.4 GBq mmol⁻¹) and ³H-AZ (CAS number 117772-70-0, 98.4% radiochemical purity, specific radioactivity of 29.6 GBq mmol⁻¹) were custom manufactured by Moravek Biochemicals (Brea, CA). CIP and AZ standards (≥99% pure; pharmaceutical secondary grade) and double deionized and molecular grade water were bought from Sigma-Aldrich (St. Louis, MO).

The study included two media, biosolids and a biosolids-amended artificial soil. The biosolids were an anaerobically digested and air-dried class A (51) material that was acquired from Metropolitan Water Reclamation District of Greater Chicago (MWRDGC). The biosolids were analyzed for the target antibiotics by AXYS Analytical Services Ltd. (BC, Canada) and contained relatively low CIP (1 mg kg⁻¹ dw) and AZ (0.06 mg kg⁻¹ dw). The soil was a sand (golf course topdressing white sand obtained from Vulcan Materials Company, Birmingham, AL) amended with 4% (dw dw⁻¹) cattle manure, which was obtained from University of Florida’s Dairy Research Unit located in Hague, Florida from cattle that were not fed any antibiotic (33); the use of an artificial soil limited experimental variables, and the organic matter added by the manure ensured adequate microbial activity. The nominal CIP and AZ concentrations in the unamended soil (dry weight basis) were below detection limit in our measurement system (<0.001 mg CIP kg⁻¹ and 0.0004 mg AZ kg⁻¹). These nominal concentrations were calculated from antibiotic concentrations reported in the cattle manure (0.01 mg AZ kg⁻¹ and undetectable CIP [i.e., less than the reporting limit of 0.035 mg kg⁻¹]) and the sand (undetectable AZ and CIP) by AXYS Analytical Services Ltd. (BC, Canada). The biosolids-amended soil samples were prepared by adding biosolids to the soil at a common agronomic biosolids application rate of 1% (dw dw⁻¹) or ∼20 Mg ha⁻¹.

Incubation study.

The conditions for soil and biosolids incubations with CIP and AZ were described in our previous study, which described the potential for microbial inhibition by biosolids-borne CIP and AZ (22). Briefly, the biosolids were spiked with environmentally relevant concentrations of CIP or AZ: 10.5 mg CIP or 0.83 mg AZ, or 36.1 mg CIP or 3.2 mg AZ per kg dw. The 10.5 mg CIP and 0.83 mg AZ kg⁻¹ values represent average concentrations of the two antibiotics detected in the U.S. biosolids, and the 36.1 mg CIP and 3.2 mg AZ kg⁻¹ values represent 95th percentile concentrations detected in the U.S. biosolids (15). The total (intrinsic plus spiked) nominal concentrations of the antibiotics in the biosolids were 11.5 mg CIP or 0.89 mg AZ, or 37.1 mg CIP or 3.26 mg AZ kg⁻¹. Spiking enabled the assessment of microbial responses to a range of environmentally relevant concentrations of biosolids-borne CIP and AZ. Biosolids-amended soil was prepared by mixing the antibiotic-spiked biosolids with the soil at an application rate of 1% dw dw⁻¹, a rate typically used in agriculture. The biosolids containing 1.0 mg CIP and 0.06 mg AZ kg⁻¹ served as unspiked biosolids controls. The nominal concentrations in the biosolids-amended soil treatments were ∼100 times smaller than those in the biosolids (0.12 or 0.37 mg CIP kg⁻¹ or 0.01 or 0.03 mg AZ kg⁻¹). The soil samples containing undetectable CIP and AZ served as unamended (and unspiked) soil controls. The spiking success was confirmed by recovery of 98% ± 7% of the spiked ³H compounds (22). As ³H compounds were involved in the spiking, we confirmed negligible effects of tritium radioactivity on the microbes by spiking the biosolids with “low” CIP or AZ concentrations (³H compound only) of 0.01 mg CIP or 0.015 mg AZ kg⁻¹ and including these biosolids, and soil amended with these biosolids (at 1% rate, dw dw⁻¹), in the incubation study. The transcriptional activities of the target antibiotic resistance genes and the mobile element in these samples, spiked with low CIP or AZ, were similar to those in the corresponding biosolids or soil controls (data not shown). The target spiking concentrations in biosolids spiked with greater CIP or AZ concentrations were reached by adding nonlabeled compounds along with the ³H compounds.

Twenty-five grams of each biosolids or soil sample was placed in a 300-ml glass Mason jar. Moisture contents of the media were adjusted to their respective field capacities (∼35% for biosolids and ∼10% for soil) by adding double deionized water. The field capacities were estimated from pot water holding capacities of the two solid matrices as described in Pannu et al. (52). The jars were placed in a constant airflow apparatus that continuously supplied the samples with ∼0.5 liters min⁻¹ of humidified air (22). The incubation was conducted at room temperature (∼25°C) for 90 days. Samples were weighed every 3 to 4 days, and double deionized water was added to maintain a moisture content near field capacity. The study was penta-replicated; that is, five mason jars were used per concentration of each antibiotic in each medium (soil or biosolids). Five grams of each incubation unit (i.e., mason jar) was sampled at 0, 5, 45, and 90 days and stored at −80°C until used for nucleic acid extraction.

Isolation and processing of nucleotides.

RNA was isolated from 2.0 g of samples using the RNeasy PowerSoil kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. A DNase MAX kit (Qiagen, Hilden, Germany) was used to remove genomic DNA contamination from the extracted RNA. DNA was also isolated from 0.2 g subsamples using a DNeasy PowerSoil kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. Purity and concentration of the extracted DNA and RNA were evaluated by means of Biophotometer plus (Eppendorf, Hamburg, Germany) and 1.5% (wt vol⁻¹) agarose gel stained with ethidium bromide. A RevertAid first strand cDNA synthesis kit (Thermo Fisher Scientific, Waltham, MA) was utilized for reverse transcription of RNA to cDNA. No contamination of RNA with residual DNA was confirmed by verifying no PCR amplification from RNA for the genes used in this study.

Quantitative real-time PCR.

Estimation of gene (or gene transcript) copies from DNA (or cDNA) was performed using a SYBR green I-based qPCR (or reverse transcription qPCR; RT-qPCR) in a StepOnePlus real-time PCR system (Applied Biosystems, Foster City, CA). The PCR mixture included 10 μl of Maxima SYBR green/ROX qPCR master mix (2×) (Thermo Fisher Scientific, Waltham, MA), 4 μl (each) of 10 μM forward and reverse primers, and 2 μl of DNA (cDNA) template in a final volume of 20 μl. Previously described primers and PCR conditions were employed for quantifying gene and/or transcript copies of qnrA, qnrB, and qnrS (53), mefE and ermB (54), and intI1 (27) (Tables S2 and S3). Briefly, primers used for qnrA were QnrAm-F (AGA GGA TTT CTC ACG CCA GG) and QnrAm-R (TGC CAG GCA CAG ATC TTG AC), for qnrB were QnrBm-F (GGM ATH GAA ATT CGC CAC TG) and QnrBm-R (TTT GCY GYY CGC CAG TCG AA) where M is A or C, H is A, C, or T, and Y is C or T, for qnrS were QnrSm-F (GCA AGT TCA TTG AAC AGG GT) and QnrSm-R (TCT AAA CCG TCG AGT TCG GCG), for ermB were ermB-F (GAA AAG GTA CTC AAC CAA ATA) and ermB-R (AGT AAC GGT ACT TAA ATT GTT TAC), for mefE were mef-F1 (AGT ATC ATT AAT CAC TAG TGC) and mef-R1 (TTC TTC TGG TAC TAA AAG TGG), and for intI1 were intl1-LC1 (GCC TTG ATG TTA CCC GAG AG) and intl1-LC5 (GAT CGG TCG AAT GCG TGT). The specificity and host range of the target genes are detailed in Table S4. All qPCR runs were followed by an image capture step (15 s at 80°C) after the final extension step of each cycle. When the PCR amplification was completed, a melt curve analysis was conducted by increasing the temperature from 60 to 95°C in 0.5°C increments every 10 s. The amplicon quality was also examined on 1.5% (wt vol⁻¹) agarose ethidium bromide-stained gel. All sample DNAs, cDNAs, and standard DNAs/cDNAs were assayed in duplicate.

The potential inhibitory effects of coextracted substances on the qPCR and RT-qPCR amplification of the nucleic acids in the media were tested following the principles described by Henry et al. (55). Briefly, reaction mixtures containing a series of 10-fold dilutions of standard DNAs (with known copy numbers) of select genes were supplemented with 2 μl of nuclease-free H₂O or DNA, RNA, or cDNA extracted from the media. Following this, the gene copy numbers were determined via qPCR or RT-qPCR. The addition of the DNAs, RNAs, or cDNAs did not show apparent inhibition of qPCR or RT-qPCR in this test (Fig. S2 and S3).

The copy numbers of the target gene or gene transcripts in sample DNAs or cDNAs were calculated using a standard curve that was constructed using standard DNA plasmids as previously described (56). Briefly, standard DNA plasmids were made by cloning the target gene fragment amplified from the biosolids control. Serial dilutions of the DNA plasmids over 6 orders of magnitude (2 × 10³ to 2 × 10⁸ copies in reaction volume) were run during each qPCR to generate the standard curves. Each standard curve was made by plotting the relative fluorescence units at a cycle threshold (C_T) value against the logarithm of copy number of the standard plasmid DNA. The PCR efficiency (E) was calculated from the slope of the standard curve by using the formula E = 10^−1/slope − 1. In this study, all the standards amplified with mean PCR efficiencies between 94.9 and 99.5% (Table S3). The gene or gene transcript copy numbers were determined per gram of biosolids or soil (dry weight). The quantification limits corresponded to the lowest calibration standard containing ∼80 copies of each target gene, which amounts to ∼2,000 copies g⁻¹ medium for each of the quantified genes or gene transcripts. In a preliminary test using 10-fold serial dilution of standard DNAs, the PCR was found to successfully amplify 10 to 25 copies of the target genes.

The effects of an antibiotic on each gene or gene transcript were assessed across antibiotic concentrations by comparing the relative copy numbers of a gene or gene transcript quantified from a particular medium at a particular time. The differences between the copy numbers were determined using statistical software R (https://www.r-project.org/) at a significance level (α) of 0.05. Shapiro Wilk test was employed to check normality, and Levene’s test was employed to check homogeneity of variance. Data passing both the tests were assumed to follow normal distribution and were analyzed by analysis of variance (ANOVA) (and post hoc Tukey’s honestly significant difference test [HSD]), whereas nonnormal data were analyzed using the Kruskal-Wallis test.

Sequencing and analysis of gyrA transcripts.

gyrA was amplified from the cDNA via PCR (T100 thermal cycler, Bio-Rad, Hercules, CA) prior to sequencing. Primers gyrA6 (CGA CCT TGC GAG AGA AAT) and qyrA631R (GTT CCA TCA GCC CTT CAA) were used for the amplification. The primers and conditions utilized to amplify gyrA are detailed in Weigel et al. (31) (Tables S2 and S3). The amplicon included quinolone-resistant QRDR, which are often associated with fluoroquinolone resistance due to mutations in DNA gyrase (31, 36, 37). The amplicons were visualized on 1.5% (wt vol⁻¹) agarose gel stained with ethidium bromide. The gyrA amplicon fragments (∼626 bp) were removed from the agarose gel using sharp, clean scalpels and purified using a QIAquick gel extraction kit (Qiagen, Hilden, Germany). The purified amplicons were analyzed on Qubit (Thermo Fisher Scientific, Waltham, MA) and Tapestation 4200 (Agilent, Santa Clara, CA) for integrity and purity confirmations. PacBio sequencing of the amplicons was conducted by the NextGen DNA Sequencing Core Laboratory located in the Interdisciplinary Center for Biotechnology Research (ICBR) at University of Florida. The sequencing was conducted only for 4 replicates, sampled on days 0 and 90 of the incubation, of the biosolids and soil controls, biosolids that were spiked with 10.5 or 36.1 mg CIP kg⁻¹, and the soil samples that were amended with biosolids spiked with 10.5 or 36.1 mg CIP kg⁻¹. The sequencing was conducted following Pacific Biosciences protocols (https://www.pacb.com/support/documentation/). Briefly, the amplicons were ligated with SMRTbell adaptors containing specific barcodes. Equimolar concentrations of the ligated amplicons were pooled and sequenced using the P6-C4 chemistry on a PacBio RS II SMRT DNA sequencing system. Two SMRT cells were run and data were captured using 3-h movies.

The raw reads generated by PacBio RSII sequencer from multiple insert-size libraries were analyzed with RS-ReadsOfinsert.1 of SMRT Portal (version 2.3, Pacific Biosciences). The generated consensus reads were filtered by a quality value of 95 (Phred-like score) and length (>200 bp) and split by barcodes. The bacterial gyrA (111 sequences) retrieved from NCBI RefSeq (www.ncbi.nlm.nih.gov/refseq/) were used as reference sequences for gene mapping. All debarcoded consensus sequences were individually mapped to the reference sequences by using Burrows-Wheeler Aligner (BWA/0.7.17) software (57). The SAMTOOLs, FASTX-TOOLKIT, and R-based scripts developed in-house at ICBR were used to pick uniquely mapped reads for analyzing the gene. The gene transcription levels in different species of bacteria were assessed by counting the number of reads mapped to the selected genes.

The gyrA sequences were aligned using ClustalX 2.0 (58) and then reexamined in BioEdit (v.7.1.3) (59). The qualified sequences were translated in silico into corresponding amino acid sequences (gryA sequences) in BioEdit to group them into OTUs and to assign their phylogenetic affiliation. The OTUs were determined in mothur v.1.32.1 (60), with 5% cutoff of dissimilarity in the amino acid sequences. The phylogeny of OTUs was determined by their affiliation patterns on a maximum-likelihood (ML) tree constructed using the representative OTUs and reference sequences retrieved from the NCBI database in MEGA version 5.2.1 (61) with the Whelan and Goldman model (as a substitution model), nearest-neighbor interchange (as a tree inference option), and the bootstrap method for testing the phylogeny (1,000 resamplings). The initial tree(s) for the heuristic search was obtained automatically by applying the neighbor-joining and BIONJ algorithms to a matrix of pairwise distances estimated using the JTT model and then selecting the topology with the superior log-likelihood value. The mothur program was also used to estimate the diversity of OTUs and the coverage of OTUs sampled within each sample and to conduct principal coordinate analysis (PCoA) based on a UniFrac weighted distance matrix (62).

Data availability.

The GenBank accession numbers for the gyrA sequences used for determining the composition of Enterobacterales and QRDR genotype are MN454864 to MN456812. The raw reads from PacBio sequencing were also deposited in the NCBI Sequence Read Archive (accession numbers SRR14410558 to SRR14410589) under the project name PRJNA726536. Sample descriptions are as follows. B1 to B4, biosolids controls from day 0 of the incubation. B5 to B8, soil controls from day 0. B9 to B12, biosolids controls from day 90. B13 to B16, soil controls from day 90. B17 to B20, biosolids, from day 90, spiked with 10.5 mg CIP kg⁻¹. B21 to B24, biosolids-amended soil, from day 90, containing 0.12 mg CIP kg⁻¹. B25 to B28, biosolids, from day 90, spiked with 36.1 mg CIP kg⁻¹. B29 to B32, biosolids-amended soil, from day 90, containing 0.37 mg CIP kg⁻¹.