PLOS ONE
RESEARCH ARTICLE
Degradation of antibiotic resistance genes
and mobile gene elements in dairy manure
anerobic digestion
Yi Wang1,2, Pramod K. Pandey ID1*, Sundaram Kuppu1, Richard Pereira1, Sharif Aly ID1,
Ruihong Zhang2
1 Department of Population Health and Reproduction, School of Veterinary Medicine, University of CaliforniaDavis, Davis, California, United States of America, 2 Department of Biological and Agricultural Engineering,
University of California-Davis, Davis, California, United States of America
* pkpandey@ucdavis.edu
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OPEN ACCESS
Citation: Wang Y, Pandey PK, Kuppu S, Pereira R,
Aly S, Zhang R (2021) Degradation of antibiotic
resistance genes and mobile gene elements in
dairy manure anerobic digestion. PLoS ONE 16(8):
e0254836. https://doi.org/10.1371/journal.
pone.0254836
Editor: Abasiofiok M. Ibekwe, USDA-ARS Salinity
Laboratory, UNITED STATES
Received: January 30, 2021
Accepted: July 3, 2021
Published: August 25, 2021
Copyright: © 2021 Wang et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: No special funding was received for this
study.
Competing interests: The authors have declared
that no competing interests exist.
Abstract
Antibiotic resistance genes (ARGs) are emerging contaminants causing serious global
health concern. Interventions to address this concern include improving our understanding of methods for treating waste material of human and animal origin that are known to
harbor ARGs. Anaerobic digestion is a commonly used process for treating dairy manure,
and although effective in reducing ARGs, its mechanism of action is not clear. In this
study, we used three ARGs to conducted a longitudinal bench scale anaerobic digestion
experiment with various temperatures (28, 36, 44, and 52˚C) in triplicate using fresh dairy
manure for 30 days to evaluate the reduction of gene abundance. Three ARGs and two
mobile genetic elements (MGEs) were studied: sulfonamide resistance gene (sulII), tetracycline resistance genes (tetW), macrolide-lincosamide-streptogramin B (MLSB) superfamily resistance genes (ermF), class 1 integrase gene (intI1), and transposase gene
(tnpA). Genes were quantified by real-time quantitative PCR. Results show that the thermophilic anaerobic digestion (52˚C) significantly reduced (p < 0.05) the absolute abundance of sulII (95%), intI1 (95%), tnpA (77%) and 16S rRNA gene (76%) after 30 days of
digestion. A modified Collins–Selleck model was used to fit the decay curve, and results
suggest that the gene reduction during the startup phase of anaerobic digestion (first 5
days) was faster than the later stage, and reductions in the first five days were more than
50% for most genes.
Introduction
Antibiotic resistance is an emerging issue causing serious health concerns. In the United
States, at least 2.8 million antibiotic-resistant infections are reported annually, and more than
35,000 human deaths occur as a result of antibiotic-resistant infections [1]. It has been
reported that the deaths caused by methicillin-resistant Staphylococcus aureus (MRSA) in the
US could be higher than that caused by HIV [2,3]. Antimicrobial drugs are known to be
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important for the selection of antimicrobial resistance in addition to being a shared resource
between human and animal populations [4–7].
Each year, more than 11 million kilograms of antimicrobial drugs are used in food-producing animals, within which 52% are medically important drugs [8]. In 2016, 87.5% of US cattle
feedlots gave antibiotics in feed, water, or by injection to treat, prevent, and control diseases
[9]. More than half of feedlots (55.6%) used medically important antibiotics (i.e., important for
treating human disease) in feed. To prevent, control, or treat respiratory disease, 41.8% of feedlots treated cattle with antibiotics in feed. In addition, 80% of feedlots treated sick animals individually with antibiotics by injection.
Up to 90% of used antibiotics are excreted via urine and feces in the unchanged form as
well as the active metabolites, including tylosin, tetracycline, and sulfonamides [10,11].
When animal excretion is used as fertilizers on agricultural lands and/or directly deposited
on grazing land by livestock, antibiotics and ARGs may be transferred to croplands [12–14].
Excessive use of manure in the field increases the chances of manure borne ARGs in contaminating soil and water resources [12,15]. Some antibiotic resistant bacteria in manure
are human pathogens such as S. Typhimurium and Shiga-toxigenic E. coli [16,17], and
other bacteria in the environment including pathogens may gain ARGs by horizontal gene
transfer (HGT) mechanisms mediated by transposons, integrons, and plasmids [18,19] via
transformation, transduction, and conjugation [20,21]. To that end, several national and
state regulations to limit the use of antibiotics have been made. For example, the U.S. Drug
and Food Administration (FDA)’s directive (Veterinary Feed Directive (VFD) prohibits
subtherapeutic doses in animal feed and/or animal drinking water to promote growth and
improve feed efficiency. In California, Senate Bill No. 27 (2015) requires a prescription
from a California-licensed veterinarian to purchase and use medically important antimicrobial drugs in livestock.
Although policies that limit the uses of antibiotics in animal-agriculture system have the
potential to control excessive use of antibiotics, the duration of survival and threat of ARGs
being effectively transferred to bacteria in the environment is not understood. Furthermore,
relying on natural degradation of ARGs in the environment over time may not be realistic
given that studies have shown that the rate of resistance reversibility at the community level
may be slow due to evolution, mutations, and genetic co-selection [22,23].
One potential solution for the degradation of ARGs in the environment is anaerobic digestion, which can serve as a step for manure treatment prior to application on crop fields. Previous studies showed anaerobic digestion process could help in reducing ARGs in animal
manure [24–27]. Primarily, anaerobic digestion is used to treat manure and produce biogas, a
source of renewable energy [28,29]. In addition, anaerobic digestion process produces beneficial organic fertilizer, control odor, and reduces pathogen/bacterial levels in animal waste [30–
33]. Compared to mesophilic and ambient-temperature anaerobic process, thermophilic
anaerobic process is known to accelerate the digestion process, pathogen reduction, and biogas
production [26,34]. Both mesophilic and thermophilic digesters are used to treat organic waste
material, however, how an increase in temperature influences ARG removal in dairy manure
is not well understood. In general, the impacts of temperature on ARG removal is uncertain
[25,35–37].
Hence, the goal of this study was to investigate the effectiveness of anaerobic digestion on
neutralizing ARGs in dairy manure. The specific objectives of this research were to: 1) understand the impacts of anaerobic digestion on the reduction of ARGs and MGEs in dairy
manure; 2) determine the impacts of mesophilic and thermophilic temperatures on the reduction of ARGs and MGEs; and 3) fit a model to predict gene reductions in anaerobic digestion
process over time.
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Materials and methods
Batch anaerobic digestion experiment of liquid manure
Fresh manure was collected from the University of California, Davis research dairy facility.
Manure was diluted with DI water (0.5 L water/kg of manure) and filtered with an 850 μm
standard sieve (Fisher Scientific, Hampton, NH). Subsequently, manure was mixed with granules-inocula, as an inoculum to start anaerobic process inside the reactors. The granules were
obtained from a mesophilic UASB (i.e., upflow anaerobic sludge blanket) reactor treating
potato starch waste (Ingredion Incorporated, Richland, WA).
Total solid content (TS) and volatile solid content (VS) of original manure, filtered manure,
granules-inocula, and mixed feedstock in this experiment were measured according to the American Public Health Association Mehtods [38]. Mixed feedstock refers to the mixture of granulesinocula and filtered liquid manure that was fed directly to the flask digester. To set up the batch
anaerobic digestion experiment (Fig 1), twelve 1000 mL volume Erlenmeyer flasks (Corning Life
Sciences, Tewksbury, MA) were filled with 430 mL of liquid manure and 70 g of inocula. Liquid
manure and inocula were stirred well before feeding to each flask so that there was no difference
between each batch. All the bottles were purged with nitrogen for 2 min and then tightly plugged
by a rubber stopper in order to maintain an anaerobic environment. The rubber stopper was
inserted by two 8-gauge dispensing needles. One was inserted into the liquid portion for sampling.
The other needle in the headspace was connected to a Tedlar bag as a biogas outlet. Connections
were sealed by liquid nails (i.e., adhesive) and the seal integrity was tested for each reactor.
The anaerobic digestion batch experiments were performed in four water baths at temperatures 28˚C (moderate condition), 36˚C (mesophilic condition), 44˚C (high mesophilic condition), and 52˚C (thermophilic condition). Each temperature had three digesters. Filtered
manure, granules-inocula, and mixed feedstock were sampled for DNA extraction and gene
quantification. Samples were taken on 0, 1, 2, 3, 5, 7, 10, 14, 18, 22, 26, 30 days, with a total of
12 time points. By connecting a syringe to the liquid inlet, 10 mL digestate at each time point
was sampled. After collection, samples were stored at -20˚C until use.
Quantitative reverse transcription PCR (RT-qPCR)
Total DNA was extracted by DNeasy PowerSoil Kit (Qiagen, Valencia, CA). Two mL of each
sample was centrifuged at 13,000 rpm for 10 min and the pellet was used for DNA extraction.
Fig 1. (a) Anaerobic digester setup. (b) Schematic graph of the anaerobic digester.
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The quality and concentration of the DNA were assessed by NanoDrop 1000 spectrophotometer (Thermo Scientific, Wilmington, DE). All extracted DNA samples were stored at -20˚C
before qPCR amplification.
Five types of genes were under specific concern to be investigated in this study: [sulfonamide resistance genes (sulII), tetracycline resistance genes (tetW), macrolide-lincosamidestreptogramin B (MLSB) superfamily (erythromycin is a member) resistance genes (ermF),
and MGEs (intI1, tnpA)], which are important for assessing the persistence and dispersal of
ARGs. Tetracycline, macrolide, and sulfonamide are three of the most commonly used antibiotics for animal treatment. In 2014, 15.5% of US dairy operations fed tetracycline as medicated
milk replacer to preweaned heifers, specifically chlortetracycline (1.6% of operations), Oxytetracycline (4.9% of operations), or combination of neomycin and oxytetracycline (9% of operations) [39]. Furthermore, macrolides were the primary antibiotics to treat respiratory disease
in preweaned heifers (18.2% of operations) and weaned heifers (14.1% of operations), and
about 66% of weaned heifers were treated by sulfonamides as their primary antibiotics for
diarrhea.
Primers designed in previous work were used to amplify targeted ARGs as in Table 1. PCR
products were purified, verified by gel electrophoresis, and ligated into a pCR2.1-TOPO vector
by TOPO TA Cloning Kit (Invitrogen, Carlsbad, CA) according to the manufacturer’s instructions. Ligations were transformed into DH5α competent cells. Plasmids carrying the targeted
ARGs were extracted from cell cultures using QIAprep Spin Miniprep kit (Qiagen, Valencia,
CA) and sequenced to verify the insert of the targeted ARGs. The quality and concentration of
plasmids were assessed by NanoDrop 1000 spectrophotometer (Thermo Scientific, Wilmington, DE). The copy number of genes per μL plasmid DNA was calculated by [40]:
copy of genes=μLDNA ¼
b�c
L�a�1012
ð1Þ
where a is the weight of kb DNA per pmol (1 kb DNA = 0.66 μg/pmol), b the Avogadro’s constant (6.022×1023/mol), L the length of template containing the target gene, c the concentration
of template measured in μg/μL.
Standard curves of DNA standards were constructed for each 96-well qPCR assay, and each
standard curve was generated by 12-fold serial dilutions of plasmid DNA. DNA dilutions that
fell out of the linear region of the standard curve were removed from the analysis. Generally,
the quantitative limit of qPCR was 103 copies/mL digestate. Efficiencies of PCR standards were
Table 1. Synthetic oligonucleotides and temperature regimes used for qPCR reactions.
Primer
Target gene
Sequences (direction 50 –30 )
qPCR annealing temp (˚C)
Amplicon size (bp)
Reference
sulII-FW
sulII
TCCGGTGGAGGCCGGTATCTGG
60
191
[40]
GAGAGCCTGCTATATGCCAGC
55
168
[41]
CGACACAGCTTTGGTTGAAC
55
309
[42]
CTGGATTTCGATCACGGCACG
57
473
[43]
CCGATCACGGAAAGCTCAAG
56
101
[44]
CCTACGGGAGGCAGCAG
56
193
[45]
CGGGAATGCCATCTGCCTTGAG
sulII-RV
tetW-FW
tetW
GGGCGTATCCACAATGTTAAC
tetW-RV
ermF-189f
ermF
GGACCTACCTCATAGACAAG
ermF-497r
HS463a
intI1
ACATGCGTGTAAATCATCGTCG
HS464
tnpA-04F
tnpA-04
GGCTCGCATGACTTCGAATC
tnpA-04R
357F
518R
16S rRNA gene
ATTACCGCGGCTGCTGG
https://doi.org/10.1371/journal.pone.0254836.t001
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Antibiotic resistance genes degradation in anaerobic digestion
calculated by: Efficiency = 10-(1/slope)– 1, which were all between 90% and 110% with r2 � 0.99
for a minimum of six points.
Targeted ARGs and 16S rRNA gene qPCR reactions were performed using the AriaMx
Real-time PCR System (Agilent Technologies, Santa Clara, CA) in a 10 μL reaction mixture
(5 μL PowerUp SYBR Green Master Mix [2×] (Life Technology, Carlsbad, CA), 0.5 μL 10 mM
each primer, and 1 μL of template) with a thermal cycling of 2 min at 50˚C for UDG activation
and 2 min at 95˚C for Dual-Lock™ DNA polymerase activation, followed by 40 cycles of 15 s at
95˚C; 15 s at 55˚C-60˚C; 1 min at 72˚C. Each reaction was repeated three times. The average
copy and DNA standard deviation were calculated from three dataset for each reaction.
The absolute copy number of genes, which is called absolute abundance (AA), was quantified by referring to the corresponding DNA standard curve obtained by plotting threshold
cycles versus log-copy number of genes, which represents gene copy numbers per milliliter of
digestate sample. The AA of 16S rRNA gene also represents the total bacterial biomass [46–
49]. Levels of targetedARGs were normalized as the percentage of copy number of a gene/copy
number of 16S rRNA gene for each sample to emphasize the relative abundance (RA) in
manure samples [50–52]. Relative abundance (RA) of genes represents gene absolute abundance normalized to the total number of 16S rRNA genes present in the sample. This normalization provides the proportion of bacterial populations carrying the ARGs/MGEs of interest
[53].
Statistical analysis
Statistical analysis showed gene abundance data followed a log-normal distribution, which was
also reported in other studies [47,49]. Therefore, data were log-transformed before statistical
analysis. Gene AAs of a total of 144 samples were analyzed, representing 12 reactors at 12 time
points. The ermF was excluded for further analysis because the gene concentration in samples
was below the detection limit (103 copies/mL). Two-factor mixed-design ANOVA analysis was
conducted to evaluate the impacts of digestion time and temperature on gene reductions as
measured using AA. Each reactor was treated as a block. Time was regarded as a within-subject
factor, whereas temperature was a between-subject factor. Residuals passed the Brown-Forsythe test for heteroscedasticity and the Anderson-Darling test for normality (p < 0.05). Tukey
test was used for multiple comparisons between Day 0, 5, and 30 for each gene (α = 0.05). Multiplicity adjusted p-value was reported for each comparison.
Pearson’s correlation matrix between gene relative abundance was generated by pooling of
gene abundance data together (144 samples with 12 time points and 12 digesters). The correlation coefficient values range from “0” meaning no correlations to “1” which is a monotone
increasing relationship, and “-1” which is a monotone decreasing relationship.
The decay rate of ARGs was determined by a Collins-Selleck model (Eq 2). Collins-Selleck
model was initially developed to describe the inactivation of coliform microbes in domestic
wastewater by chlorine [54,55]. This model is also used in alternative disinfection methodologies [54]. Previous studies have shown that the decay of ARGs in wastewater solids-amended
soil microcosms [49] and anaerobic digestion of municipal wastewater solids [47] can be
described by a modified Collins–Selleck model [54,55]. The model (Eq 2) is a power function.
� �
N
log10
¼ LCS ½log10 t log10 b�
ð2Þ
N0
In this equation, N is the number of ARG copies at time t, N0 is the initial quantity of
ARGs, ΛCS is the specific lethality coefficient and b is a lag coefficient. Nonlinear regression
curves based on gene concentrations versus time was established. The average of three
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replicates (samples from three digesters) of each treatment (temperature) was used for curve
fitting. Least squares regression was chosen as the fitting method. Residuals analysis passed the
normality test (Anderson-Darling) and homoscedasticity test checked by the Diagnostics tab.
A p-value of less than 0.05 was considered significant. All statistical analyses were conducted
using GraphPad Prism 8.
Results and discussion
Absolute abundance and relative abundance of ARGs and MGEs in
anaerobic digestion
The analysis of gene AA in granules-inocula showed sulII, tetW, ermF, intI1, and tnpA levels
were below the detection limit in inoculum, which indicates mixing granules-inocula did not
add external ARGs and MGEs of concern into the dairy manure. Total solid content (TS) and
volatile solid (VS) mixed feedstocks (filtered liquid manure mixed with granules) were 6.64%
and 5.37%, respectively (S1 Table).
Gene absolute abundance at Day 0, 5, 30 were compared as shown in Fig 2. sulII, intI1 and
16S rRNA gene were significantly reduced (p < 0.001) in anaerobic digestion at all temperatures by Day 30. tnpA was significantly reduced under 52˚C (p < 0.05) by 77% on Day 30. The
reduction in tetW abundance was not significant, and tetW was increased by 12–13% on Day 5
at 44˚C and on Day 30 at 52˚C (S2 Table). Results of ANOVA analysis (S3 Table) showed that
the impacts of time on gene absolute abundance was significant (p � 0.0001). Impacts of temperature and interaction effects of time and temperature on gene sulII, tetW, intI1 and 16S
rRNA gene were significant (p � 0.05). Among all the sources of variation, effect of time
explained most of the total variation for each gene, ranging from 43% (sulII) to 75% (16S
rRNA gene). These results suggest that the impacts of digestion time on reduction of gene AA
was considerable during anaerobic digestion process.
Fig 2. Absolute abundance of ARGs and MGEs at Day 0, 5, 30 in anaerobic digestion. Values represent the means
based on three replicates. Stars on bars of Day 5 and Day 30 represent their p value when gene AAs were compared
with the corresponded initial value (Day 0). Stars on square brackets showed p value of comparison between Day 5 and
Day 30. Error bars represent standard errors. Symbol and meaning: p � 0.05(� ); p � 0.01(�� ); p � 0.001(��� );
p � 0.0001(���� ).
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At Day 0, tetW was the most abundant among targeted ARGs with AA > 107 copies/mL followed by sulII (>106 copies/mL). The AAs of both intI1 and tnpA were >105 copies/mL. The
AA of 16S rRNA gene were >1010 copies/mL. After a digestion of 30 days, the reduction in
sulII level was significantly higher at 44˚C and 52˚C compared to 28˚C and 36˚C (p < 0.05).
At 44˚C and 52˚C, sulII level was reduced by 95% and 96%, respectively. The intI1 level at
52˚C was reduced by 95%. The highest reduction in tnpA was at 52˚C (reduced by 77%). However, at low temperature, tetW level was reduced by 64% at 28˚C.
The reduction in 16S rRNA gene is shown in Fig 2. After 30 days of anerobic digestion, 16S
rRNA gene was reduced by 76–77%. In general, sulII, intI1, tnpA, and 16S rRNA gene reductions were overall highest at thermophilic temperature (i.e., 52˚C). Similar level of reductions
were obtained at 44˚C for sulII and at 28˚C for intI1 and 16S rRNA. The reduction on gene
AA at 52˚C was in the following order: sulII = intI1 > tnpA = 16S rRNA gene > tetW.
While analyzing the impacts of digestion time, results showed that the levels of sulII and
intI1 AAs on Day 30 were lower than that of Day 5 under all temperature conditions. This
indicates that the longer digestion period will likely to be more effective in reducing genes
compared to less than a week of digestion period. The gene reduction percentages from Day 5
to Day 30 was significant in some cases (sulII at 28˚C and 44˚C, intI1 at 2˚C and 52˚C). In few
genes, for example, the tnpA gene level remained at the same level post 5 days of digestion.
Relative abundance (i.e., RA) of sulII, intI1 were significantly reduced during anaerobic
process (Fig 3). Both 44˚C and 52˚C resulted in reduction of sulII significantly on Day 30
(p < 0.01) (S4 Table). Similarly, RA of intI1 was significantly reduced at 52˚C on Day 30
(p < 0.01), with reductions of log 0.80 and 0.65, respectively. There were no significant
declines in the RAs of tnpA. In few cases, for example, ARG tetW showed relatively higher
level at high mesophilic (44˚C) and thermophilic temperature conditions (52˚C). In general,
RAs of sulII and intI1 were reduced under all temperature conditions at the end of 30 days of
anaerobic digestion. In a previous study, a similar anomaly with regards to tetW and tetO was
reported, where Wallace et al. (2018) [56] investigated the abundance of sulfonamides and tetracyclines resistant ARGs (log (ARG Copies/16S rRNA)) in a full-scale anaerobic digester, and
authors found that sul1 and sul2 copies were reduced significantly (P < 0.001) by 5% and 10%
respectively, however, tetracyclines-related genes (tetW and tetO) concentrations were
unchanged.
Fig 3. Relative abundance of ARGs and MGEs detected at Day 0, 5, 30 during anaerobic digestion. Values
represent the means based on three replicates. Bars represent standard errors. Symbol and meaning: P � 0.05(� );
P � 0.01(�� ); P � 0.001(��� ); P � 0.0001(���� ).
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Considering the existing information with regards to ARGs reductions in anaerobic digestion process, previous research suggested that anaerobic digestion process may have an impact
on reducing ARGs. As an example, Flores Orozco [57] demonstrated that anaerobic digestions
removed more than 90% of cephalosporin-resistance gene cmy-2 in dairy manures in a semicontinuous lab-scale anaerobic digester under mesophilic temperature conditions, with a
hydraulic retention time of 30 days. Zhang, Yang [27] found that among 35 studied ARG subtypes, only 8 subtypes were reduced by > 90.0% under thermophilic temperature conditions.
A range of other studies, however, suggested that the impact of anaerobic process on gene
reduction could be gene specific [25,58–60]. Howes [58] examined changes of antibiotic resistant genes in lab scale anaerobic digesters at thermophilic temperatures, and found that tetQ
and cfxA concentrations reduced significantly by 70%, whereas mefA concentrations was
increased during 10 days of anaerobic digestion. Similarly, Gao, Gu [61] investigated tetracycline resistance genes and the integrase gene intI1 in thermophilic anaerobic co-digestion
study and reported that all tetracycline resistance genes were reduced except tetW. Authors
reported that tetW encodes ribosomal protection protein, which is relatively more persistent
to thermophilic conditions. Besides, the persistence of tetW in anaerobic digestion may be
explained by a wide variety of microbial hosts. Roberts [62] reported the host range of the
tetW gene includes Gram-positive, Gram-negative, aerobic and anaerobic bacteria, and tetW
gene is also associated with conjugative elements which may contribute to its spread. Many
factors such as the type of ARGs, bacteria community, digestion parameters, and associated
metabolic pathways in the anaerobic digestion process could be attributed to the inconsistent
effect of anaerobic digestion on ARG removal, and it also underscores the need for additional
research [63].
Correlations between ARGs and MGEs
Correlation plot of the four genes in Fig 4 showed that intl1 and tnpA were significantly positive correlated with sulII and tetW (P < 0.01). sulII and tetW were significantly correlated with
intl1, which means if a sample was found to have high level of intl1 and tnpA abundance, the
abundance of sulII and tetW was higher. The degrees of correlations of tetW with intl1 and
tnpA were higher than sulII, indicating tetW abundance was more likely to be positively influenced by intl1 and tnpA abundance than sulII. The correlation coefficient of intl1 and tnpA
was also significant that indicates that intl1 tended to co-exist with tnpA. The correlation coefficient of sulII and tetW was found to be low (-0.01) and not significant (p = 0.89), which indicates that the abundance of these genes was not correlated. Interspecies gene transfer may
have played a role up to an extent to establish linkages between genes. Sørensen, Bailey [64]
suggested horizontal gene transfer (due to gene transfer via MGEs between different bacterial
hosts) and vertical gene transfer (due to the proliferation of bacterial hosts) are two main factors for ARGs abundance. Mobile genetic elements (MGEs), including transposons, plasmids,
and integrons facilitates horizontal gene transfer (HGT) and induce a higher abundance of
ARGs [44]. Mobile integrons usually carry ARGs and mobility is supported by transposons
and plasmids [65]. Huang, Zheng [35] investigated transposase genes and ARGs during anaerobic digestion of swine manure, and transposase genes showed significant correlations with
most ARG types including sulfonamide and tetracycline resistance genes.
Gene reduction curves fit using Collins-Selleck model
Results showed that the modified Collins–Selleck model provided a good fit for estimating the
reductions of ARGs, MGEs, and 16S rRNA genes over time during the anaerobic digestion
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Fig 4. Pearson correlation matrix of relative abundance of ARGs/ MGEs. �� significant at P � 0.01; ��� significant at
P � 0.001; ���� significant at P � 0.0001.
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(Figs 5 and 6) except tetW and tnpA, where r2 value was low. The low r2 value was caused by
increase of the gene abundance over time. The coefficients of model fit are shown in Table 2.
ΛCS was the slope of the transformed log X and Y curve, which described the rate of reduction for each gene. At 36˚C, sulII gene reduction rate was 1–2 times greater than that at 28˚C
and 52˚C. tnpA reduction rate was also higher at temperature 36˚C. For tetW and intI1, however, the reduction rate was found to be greater at 28˚C and 44˚C. 16S rRNA gene reduction
rate was highest at 52˚C.
It is important to note that this study uses SYBR green approach for evaluating ARGs. Other
alternative methods such as TaqMan probe provides improved specificity. There are advantages
and disadvantages in both methods (TaqMan and SYBR). While SYBR is easy and cost effective,
specificity is better in TaqMan, however, TaqMan requires specific probe for each target. Considering the important issues such as ARGs, it is important to adapt multiple approaches and testing
methods for robust dataset in order to make the informed decision and reduce the biasness
caused by testing methods. When grouping coefficients by temperatures, there was no significant
difference among the specific lethality coefficient ΛCS under different temperature conditions,
which indicates that the decay rate of each gene might vary with temperature. The lethality coefficient could be gene specific. The intI1, tnpA, and 16S rRNA genes had higher reduction rates (i.e.,
specific lethality coefficient ΛCS compared to sulII and tetW (P < 0.05). In most cases, ARGs/
MGEs declined quickly in the beginning but then the reduction slowed down. Reductions by Day
5 were generally above 50% for all the genes except tetW (Table 2). The quick reduction of targeted ARGs in the beginning phase of anaerobic digestion could be explained by the significant
reduction of aerobic bacteria such as Actinomycetales and Bacilli [66]. The calculations of reductions rates using the modeling tools such as Collins–Selleck can provide insights while comparing
the impacts of various treatment on gene reductions, and help in decision making in terms of
temperature and retention time needed for controlling various genes in digestate.
The use of Collins-Selleck model for understanding the decay of ARGs in landfill leachate
in aerobic and anaerobic conditions [67], land application of sludge bio-drying products [68],
and thermophilic aerobic digestion of sewage sludge [69] are reported and results indicate that
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Fig 5. Modelling fit of sulII, tetW, intI1, and tnpA. Bars represent standard errors. Blue dots represent the mean value of triplicate reactors
under each temperature. Solid red lines represent the best fit of the data to a modified Collins–Selleck model. The model coefficients are shown
in Table 2.
https://doi.org/10.1371/journal.pone.0254836.g005
this model can assist in understanding the reduction rates and determining the impacts of
environmental conditions on ARGs reductions. In a previous study, Burch, Sadowsky [70]
investigated the fate of six ARGs (sul1, tetA, tetW, tetX, ermB, qnrA) and one integrase gene
(intI1) during various treatments conditions (air drying, aerobic digestion, mesophilic/ thermophilic anaerobic digestion, pasteurization, and alkaline stabilization) of wastewater solidsamended soil microcosms. The authors reported that in most cases, ARGs declined quickly in
the initial phase of treatment, and subsequently the reduction rate was reduced. The model
was able to capture the reduction rates under various conditions. Similarly, another study by
Burch, Sadowsky [47] conducted laboratory-scale thermophilic anaerobic digestion experiments at 40, 56, 60, and 63˚C. The authors reported decrease in ARGs (tetW, tetX, qnrA) and
intI1 abundance under all temperature conditions, and reduction rates of genes could be
understood better by Collins-Selleck model than first-order and second-order kinetic models.
Sandberg and LaPara [49] investigated the fate of ARGs (ermB, tetA, tetW, tetX) and class 1
integron (intI1) in swine and dairy manure amended soil and found that Collins–Selleck
model described the decay of ARGs in the soil microcosms well. Other methods such as linear
model fit may underestimate the length of time needed for ARG reduction.
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Antibiotic resistance genes degradation in anaerobic digestion
Fig 6. 16S rRNA gene in anaerobic digestion over time. Bars represent standard errors. Blue dots represent the mean
value of triplicate reactors under each temperature.
https://doi.org/10.1371/journal.pone.0254836.g006
While there are previous studies elaborating the impacts of anaerobic digestion of ARG
reductions [71–75] in a range of feedstocks such as waste water, sludge and dairy manure, the
Table 2. Coefficients for modified Collins–Selleck model by least squares nonlinear regression.
Targeted gene
Temperature
N0
ΛCS
B
R2
Reduction at Day 5
sulII
28˚C
4.62E+06
-0.36
1.12
0.64
41%
36˚C
4.26E+06
-0.50
1.07
0.73
54%
44˚C
3.77E+06
-0.41
0.23
0.72
72%
52˚C
6.30E+06
-0.35
0.02
0.61
85%
28˚C
3.21E+07
-0.49
1.70
0.68
41%
36˚C
3.63E+07
-0.43
2.62
0.56
24%
44˚C
3.04E+07
-0.50
5.67
0.48
-6%
52°C
3.75E+07
0.03
1716.11
0.01
15%
28˚C
1.34E+05
-0.78
1.24
0.94
66%
36˚C
1.78E+05
-0.57
0.53
0.75
72%
44˚C
1.25E+05
-0.77
1.33
0.69
64%
52˚C
2.09E+05
-0.27
0.01
0.38
81%
28°C
4.09E+05
-0.19
0.05
0.17
58%
36˚C
4.00E+05
-0.74
1.94
0.79
50%
44˚C
3.07E+05
-0.57
2.59
0.80
31%
52˚C
5.03E+05
-0.43
0.98
0.75
51%
28˚C
4.13E+10
-0.60
1.14
0.77
59%
36˚C
4.00E+10
-0.54
0.80
0.87
63%
44˚C
3.91E+10
-0.44
0.80
0.67
55%
52˚C
4.70E+10
-0.67
1.55
0.87
54%
tetW
intI1
tnpA
16S rRNA gene
N0 is the initial quantity of genes (copies/mL) at Day 0. ΛCS is the specific lethality coefficient (unitless) and b is a lag coefficient (copies� day/mL). R2 indicates goodness
of fit. Normality of Residuals were checked by Anderson-Darling test. Model also passed homoscedasticity test.
https://doi.org/10.1371/journal.pone.0254836.t002
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Antibiotic resistance genes degradation in anaerobic digestion
performance of anaerobic digesters substantially depends on the feedstock, therefore, it is
important to consider existing manure management in animal-agriculture system and the
type of feedstock fed to digesters in order to understand the possible impacts of anaerobic
digestion on ARGs reductions. For example, study by Zhang et al. (2011) [71] is focused on
studying activated sludge, and sludge characteristics differ than flushed manure. Similarly,
research by Ma et al. (2011) [72] studied waste water treatment process of sludge in thermophilic temperature. While the principles of anaerobic digestions process are similar but feedstock characteristics have substantial impacts on anaerobic process, which may be one possible
reason for differences in results among studies. Antunes [75] reported that sulfonamide resistance genes are likely to be correlated with class 1 integron. Article by Sun et al (2016) [73]
studied scrapped dairy manure in moderate, mesophilic, and thermophilic temperature to
understand the dynamics and bacterial communities by q PCR and 16S rRNA and results indicate that anaerobic digestion has a potential to influence ARGs. Temperature can cause selective selection of bacteria carrying a lower number of ARG on their genome plasmids, which
can help in ARGs removal. This study was focused on evaluating the manure of flush system,
which is often used for flushing the manure from confined dairy operations, and the results of
this study may not be transferable to sludge digester systems. Additional studies are needed to
improve the existing knowledge for various types of organic matter fed to anaerobic digesters.
As antibiotic concern is relatively a new issue, existing set of knowledge corresponding to various waste treatment processes and feedstock characteristics yet to be developed robustly.
Future studies, and existing knowledge likely to help in developing improved treatment methods, which can likely to reduce the possible ARGs load from waste material to environment.
Conclusions
This study was focused on evaluating anaerobic digesters impacts on ARGs removal in dairy
manure management system, where flush system was used. Findings of this study based on
three ARGs, and 16S rRNA gene indicates that anaerobic digesters can help reducing ARGs in
dairy manure, however, additional studies are needed to improve the findings, which elaborates further on the effects of feedstock, and temperature. Results showed that after 30-day
thermophilic digestion (52˚C), the absolute abundance of sulII, intI1, tnpA, 16S rRNA gene
was reduced by 76%-95%, however, absolute abundance of tetW was increased by 13%. Reduction of intI1 and 16S rRNA was comparable at moderate and thermophilic conditions, indicating that the impact of thermophilic anaerobic digestion on gene reduction may not necessarily
be greater than lower temperatures. Results of decay curves fitted with modified Collins–Selleck model indicates that reduction of antibiotic resistance genes during startup phase was
greater than the later stages, when dairy manure inside digester was relatively acclimatized.
Supporting information
S1 Table. TS, VS and VS/TS of original manure, filtered manure, granules-inocula, and
mixed feedstock.
(DOCX)
S2 Table. Gene AA reductions on Day 5 and Day 30 compared with Day 0. Numbers are in
percentage.
(DOCX)
S3 Table. Two-factor mixed-design ANOVA for effects of time and temperature on ARGs.
(DOCX)
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Antibiotic resistance genes degradation in anaerobic digestion
S4 Table. Gene RA reductions on Day 5 and Day 30 compared with Day 0. Numbers are in
log scale.
(DOCX)
Acknowledgments
The authors thank the Division of Agriculture and Natural Resources (ANR) and School of
Veterinary Medicine Extension, University of California, Davis.
Author Contributions
Conceptualization: Pramod K. Pandey.
Investigation: Pramod K. Pandey.
Methodology: Sharif Aly.
Supervision: Pramod K. Pandey, Ruihong Zhang.
Visualization: Richard Pereira.
Writing – original draft: Yi Wang.
Writing – review & editing: Pramod K. Pandey, Sundaram Kuppu, Richard Pereira, Sharif
Aly, Ruihong Zhang.
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