Trends in antimicrobial resistance genes in manure blend pits and

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Environmental Processes

Trends in antimicrobial resistance genes in manure blend pits and long-term storage across dairy farms with comparisons to antimicrobial usage and residual concentrations Jerod J. Hurst, Jason Oliver, Jenna Schueler, Curt A. Gooch, Stephanie Lansing, Emily Crossette, Krista R. Wigginton, Lutgarde Raskin, Diana S Aga, and Lauren M Sassoubre Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b05702 • Publication Date (Web): 01 Feb 2019 Downloaded from http://pubs.acs.org on February 4, 2019

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is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

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Environmental Science & Technology

Antimicrobial resistance genes in dairy manure blend pits & long-term storage

Similarities between Farms & Seasons

Conventional and Organic Farms

ACS Paragon Plus Environment


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Trends in antimicrobial resistance genes in manure blend pits and long-term storage across

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dairy farms with comparisons to antimicrobial usage and residual concentrations

3 4 5 6 7 8

Jerod J. Hurst1, Jason P. Oliver2, Jenna Schueler3, Curt Gooch2, Stephanie Lansing3, Emily Crossette4, Krista Wigginton4, Lutgarde Raskin4, Diana S. Aga1, and Lauren M. Sassoubre5* 1Department

of Chemistry, University at Buffalo, The State University of New York (SUNY), Buffalo, NY 14260

2Department

of Animal Sciences, Cornell University, Ithaca, NY 14850

9 10

3Department

of Environmental Science & Technology, University of Maryland, College Park, MD 20742

11 12

4Department

of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109

13 14

5Department

of Civil, Structural, and Environmental Engineering, University at Buffalo, The State University of New York (SUNY), Buffalo, NY 14260

15 16 17 18 19 20

* Corresponding author: Lauren Sassoubre; email: [email protected] ; telephone: 716-6451810

21 22 23 24 25 26 27 28 29 30 1 ACS Paragon Plus Environment

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Abstract

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The use of antimicrobials by the livestock industry can lead to the release of unmetabolized

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antimicrobials and antimicrobial resistance genes (ARG) into the environment. However, the

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relationship between antimicrobial use, residual antimicrobials, and ARG prevalence within

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manure is not well understood, specifically across temporal and location-based scales. The current

36

study determined ARG abundance in untreated manure blend pits and long-term storage systems

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from 11 conventional and one antimicrobial-free dairy farms in the Northeastern U.S. at six times

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over one-year. Thirteen ARGs corresponding to resistance mechanisms for tetracyclines,

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macrolides-lincosamides, sulfonamides, aminoglycosides, and β-lactams were quantified using a

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Custom qPCR Array or targeted qPCR. ARG abundance differed between locations, suggesting

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farm specific microbial resistomes. ARG abundance also varied temporally. Manure collected

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during the winter contained lower ARG abundances. Overall, normalized ARG concentrations did

43

not correlate to average antimicrobial usage or tetracycline concentrations across farms and

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collection dates. Of the 13 ARGs analyzed, only four genes showed a higher abundance in samples

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from conventional farms and eight ARGs exhibited similar normalized concentrations in the

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conventional and antimicrobial-free farm samples. No clear trends were observed in ARG

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abundance between dairy manure obtained from blend pits and long-term storage collected during

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two drawdown periods (Fall and Spring), although higher ARG abundances were generally

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observed in Spring compared to Fall. This comprehensive study informs future studies needed to

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determine the contributions of ARGs from dairy manure to the environment.

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Introduction

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Livestock industries, including concentrated animal feeding operations (CAFOs), utilize a

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myriad of pharmaceutical compounds for animal treatment. Of these pharmaceutical compounds,

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antimicrobials are the most frequently administered to dairy cattle using up to eight different

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classes of active pharmaceutical ingredients.1 There is growing concern over the extensive use of

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antimicrobials on CAFOs as many microorganisms develop antimicrobial resistance that can

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threaten animal and human health. As of January 2017, a new guidance implemented by the FDA

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restricts the use of antimicrobial compounds that are medically relevant for human health, namely

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sulfonamides and select beta-lactams.2 Though this new guidance limits the use of certain

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antimicrobials, the use of non-medically relevant antimicrobials may still contribute to the

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environmental propagation of antimicrobial resistance.3

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Agricultural practices of utilizing manure that contains residual antimicrobial

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concentrations as soil fertilizer may act as a dissemination route of antimicrobial resistance genes

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(ARGs). Between 20-90% of antimicrobials excreted from livestock animals are excreted in an

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unmetabolized state, with the percentage depending on the antimicrobial class.4 As a result, the

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widely used practice of applying livestock manure to crop fields can spread unmetabolized

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antimicrobial compounds.5 The presence of antimicrobials in the environment, even at low

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concentrations, exerts xenobiotic pressure on the environmental microbial community6 and can

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select for antimicrobial resistance across many species in the terrestrial microbiome.7 Resistance

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genes can be propagated in the environment by mobile genetic elements (MGEs), such as insertion

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sequences and plasmids, increasing the likelihood of bacterial species harboring multiple ARGs.8,

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9

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gene transfer (HGT), which may be exacerbated by antimicrobial use.10 Livestock farms are

Bacterial communities within animal digestive systems can have high occurrences of horizontal


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therefore considered to harbor a reservoir of ARGs that may spread in the environment and

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ultimately impact human health on global scales.11, 12 Livestock farms have been widely studied for the presence of antimicrobials and ARGs12-

77 78

15

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have been suggested to increase the prevalence of select ARGs.6, 16-19 Genes commonly implicated

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in HGT, such as int1, IncQ, and IncPα, have also been shown to accumulate in soil when manure

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is applied as fertilizer, suggesting an increase in HGT between bacterial species.20, 21 One study on

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archived soil from The Netherlands reported a general increase in ARG soil presence from 1940-

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2008.22 Evidence for the persistence and harboring of ARGs in soil due to manure fertilization

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suggests an increased risk of human interaction with ARGs. Although recent research has focused

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on investigating the microbial resistome within and around farms, temporal changes of ARG

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abundance in dairy manure and trends across large farm cohorts are not well understood. This

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information is critical for informing future mitigation strategies in the livestock industry.

and connections between field application of manure and long-term storage treatment of manure

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The current study quantified 13 ARGs in untreated manure blend pits and long-term storage

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collected from 11 conventional and an antimicrobial-free (USDA organic) dairy farms in the

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Northeastern United States at six collection dates spanning a one-year period (September 2016,

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October 2016, December 2016, March 2017, June 2017, and August 2017). While a large number

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of environmental factors can influence the prevalence of ARGs, five main factors were

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investigated in the current study including location, antimicrobial administration, residual

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antimicrobial concentrations, farm type, and long-term storage of untreated manure. The

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objectives were: (1) to identify differences in ARG prevalence across farm location and collection

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time, (2) to compare tetracycline and β-lactam usages on each farm with their corresponding ARG

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abundance, (3) to compare tetracycline concentrations in dairy manure to the tetO resistance gene, 4 ACS Paragon Plus Environment


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(4) to examine ARG prevalence on conventional farms compared to an antimicrobial-free farm,

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and (5) to assess the effect of long-term storage on ARG levels. This comprehensive study

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advances our understanding of spatial and temporal ARG trends and will inform future research

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efforts to assess the efficacy of manure management strategies, the environmental impact of

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manure fertilization practices, and environmental background prevalence of ARGs.

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Materials and Methods

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Manure collection

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Manure was collected at 11 dairy farms (CF1-CF11) from three states (referred to as State

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A, State B, and State C) in the Northeastern U.S. Information regarding farm size, manure

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collection methods, and long-term storage methods are listed in Table S1. Each of the 11 farms

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stored manure, which consisted of urine and feces, along with used bedding and milk wastewater

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in blend pits. Depending on the manure handling system, this untreated manure was either

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transferred directly to long-term storage, or underwent various treatment steps prior to being

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transferred to long-term storage containment.23 Composite manure samples were collected in 18 L

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buckets, homogenized using a paint-mixer shaft impeller powered by a cordless drill, and aliquots

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were transferred to sterile 15 mL centrifuge tubes. Manure samples were transported to the

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University at Buffalo laboratories on ice and frozen at -80oC until antimicrobial and DNA

115

extractions were performed. Untreated manure samples were collected at six time points on each

116

of the 11 farms (September 2016, October 2016, December 2016, March 2017, June 2017, and

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August 2017). Long-term storage samples were collected during seasonal storage emptying,

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known as "drawdown", during the Fall (October 2016) and Spring (June 2016) at six dairy farm

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locations in State A.


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DNA extraction

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DNA extractions were performed on the solid fraction of manure slurry samples. Solids

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were separated by centrifuging each sample at 5,000 x g for 30 minutes prior to extraction. The

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liquid portion was decanted and discarded, while the solid fraction was saved and homogenized

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before weighing a portion for extraction. DNA was extracted from 250 mg of manure solids using

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a Qiagen Power FecalTM kit (Hilden, Germany) following the manufacturer’s protocol and has

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been shown to be effective at extracting DNA from manure samples.24, 25 Extracted DNA (final

127

elution volume of 100 µl) was quantified using a Qubit 3.0TM fluorometer (Life Technologies,

128

Grand Island, NY).

129

ARG screening using Microbial DNA Custom qPCR Arrays

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The screening of nine ARGs was performed using customized Microbial DNA qPCR

131

Arrays (BAID 1901z, Qiagen, Carlsbad, CA) according to the kit instructions. The Custom qPCR

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Array screened for mefA, ermB, ereB, tetA, OXA-2, CTX-M1, VEB-1, aada1, and aaca-6-(lb).

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These genes and their corresponding antimicrobial class or description of resistances are

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summarized in Table S2 and were chosen based on preliminary results from screening a total of

135

86 genes using the Qiagen qPCR Array for Antibiotic Resistance Genes. In addition to the nine

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gene targets, the Custom qPCR Array contains two reference genes used for normalization

137

(referred to as Pan 1 and Pan 3) and a positive control (PPC). The PPC included on the plate acts

138

as a measure of quality assurance for amplification, and the Cq (quantification cycle) value for the

139

PPC should be between 20-22 for each sample. Eight samples can be analyzed on each Custom

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qPCR Array plate (containing 96 wells). The qPCR mixture for each sample consisted of 100 ng

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of DNA template, 140 µl mastermix (provided in the Qiagen Array kit), and molecular grade water

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to achieve a final volume of 280 µl for each sample. Each of the 12 wells was supplied with 20 µl 6 ACS Paragon Plus Environment


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of the qPCR mixture in the Custom qPCR Array (one row). Two no template controls (NTC) were

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run as negative controls using molecular grade water as the sample and using six DNA extraction

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blanks (all with no quantifiable DNA yield) were pooled and run as one extraction blank control.

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Custom Microbial DNA qPCR Array data analysis

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Custom qPCR Array data were evaluated according to the manufacturer’s criteria: (1) the

148

sample was positive for a gene if the Cq was below 34, (2) the detection for a gene was inconclusive

149

if Cq values were between 34-37, and (3) the sample was negative for a gene if the Cq value was

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greater than 37. These guidelines were based on the Cq values obtained with the NTC prepared

151

with molecular grade water, which showed no detections for each of the genes.

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The Cq value for each gene was first normalized to an average of the Cq values for both Pan genes

153

included on the array to give a delta Cq (ΔCq) value for each ARG (Eqn.1).26, 27 This is similar to

154

normalization to the 16S rRNA gene but in log space.

155 156 157

ΔCq = Cq1 – Cq2

(Eq. 1)

Here, the Cq1 represents the value for the respective ARG and Cq2 represents the value for the average of the two Pan reference genes.

158

To compare ARG prevalence at conventional farms relative to the antimicrobial-free farm,

159

the ∆Cq from a sample at the antimicrobial-free farm (ΔCq(antimicrobial-free farm)) (sampled in April

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2017) was subtracted from the ∆Cq for the conventional farm samples (ΔCq(conventional

161

determine a relative quantity, using the following equation:

162

ΔΔCq = ΔCq(conventional farm) – ΔCq(antimicrobial-free farm)

farm))

to

(Eq. 2)


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If the ΔΔCq is positive (greater than zero), the relative ARG abundance at the conventional

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farm was greater than at the antimicrobial-free farm in April 2017. Conversely, if the ΔΔCq is

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negative (less than zero), the relative ARG abundance at the antimicrobial-free farm was greater

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than the conventional farm in April 2017.

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To compare relative ARG abundances between untreated manure and long-term storage

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samples, Eqn. 2 was used with different inputs (ΔΔCq = ΔCq(untreated) – ΔCq(long-term storage)). Two

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∆∆Cq values were obtained for the Fall long-term storage samples, by using the ΔCq from

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September and October 2016 untreated samples, to account for variability between September and

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October samples. For the Spring long-term storage samples, an average ∆∆Cq value was attained

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by averaging three ∆∆Cq values obtained by using the ΔCq from three untreated manure samples

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(December 2016, March 2017, and June 2017) leading up to the Spring long-term storage

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collection date. This averaging method was performed to reflect the temporal variability in

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untreated manure contributing to the long-term storage manure. In addition to studying the effect

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of long-term storage, a direct comparison was made between the Spring and the Fall long-term

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storage samples collected during the drawdown periods. The comparisons between normalized

178

ARG concentrations present in the Spring storage (drawdown) and Fall storage (drawdown), only

179

available for the six dairy farms from State A, were performed by subtracting ∆Cq values of Spring

180

and Fall (ΔΔCq = ΔCq(Spring long-term storage) – ΔCq(Fall long-term storage)).

181

Quantification of int1, sul1, tetO, and OXA-1 genes and the bacterial 16S rRNA gene by targeted

182

qPCR

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The targeted qPCR assays used are based on previously published methods as described

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below. Table 1 shows the primer and probe name, sequence and concentrations,

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annealing/extension temperatures, amplicon size, pooled standard curve qPCR efficiencies, and 8 ACS Paragon Plus Environment


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assay references. Quantification was based on pooled calibration curves, shown previously to be a

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robust quantification method for qPCR.28 The limit of quantification for each of the four targeted

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ARGs was 4000 gene copies/g or 10 gene copies/µl. For the qPCR assay targeting the 16S rRNA

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gene, the standards were made from Enterococcus faecalis (ATCC 29212) genomic DNA,

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accounting for the four rrn operons in this genome. The 16S rRNA gene assay was used to estimate

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the total bacterial concentration in the manure samples and used to normalize the targeted gene

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data.29 In addition to accounting for differences in DNA yields between samples, normalizing by

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16S rRNA gene copies facilitated answering our research questions about location-based,

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temporal, and manure management differences with the assumption that these differences are due

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to bacterial activity (rather than extracellular ARGs). The standards for the other qPCR assays

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were made from synthesized oligonucleotides (IDT, Coralville, IA).

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qPCR was performed using a BioRad CFX96 (BioRad, Hercules, CA) Real-Time qPCR

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instrument using both SYBR and Taqman chemistries, depending on the published assays. qPCRs

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contained either 1x Sso Advanced Universal SYBR mastermix, or 1x Sso Advanced Universal

200

probe mastermix (BioRad, CA), forward and reverse primers (concentrations shown in Table 1),

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BHQ probe for Taqman assays (Table 1; IDT, Coralville, IA), 2 µL of DNA template (diluted

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1:1,000 using molecular grade water for the assay targeting the 16S rRNA gene), and molecular

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grade water for a final qPCR volume of 20 µl.22, 30-32

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Table 1: Targeted qPCR primers, primer and probe concentrations, annealing/extension temperatures,

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amplicon size, and qPCR efficiencies for the following ARGs: sul1(sulfonamides), int1(integrase class 1),

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tetO (tetracycline), OXA-1 (beta-lactams), and 16S rRNA (reference gene). Several assays did not include

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extension steps so just the annealing step is provided.


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Gene

Final

Primer

sul1 FW

Annealing/

Amplicon

Pooled

conc.

Extension

Size (bp)

Efficiency

(nM)

Temp (oC)

200

Sequence (5’- 3’)

CGCACCGGAAACATCGCTGCAC

65 (30 sec),

Ref

(%) 163

90.6

30

72 (30 sec) sul1 RV

200

GAAGTTCCGCCGCAAGGCTCG

-

-

-

30

Int 1

400

GCCTTGATGTTACCCGAGAG

60 (60 sec)

196

91.4

31

400

GATCGGTCGAATGCGTGT

-

-

-

31

200

FAM-

-

-

-

31

LC1 Int 1 LC5 Int1 probe

ATTCCTGGCCGTGGTTCTGGGTTTTBHQ

TetO

200

ACGGARAGTTTATTGTATACC

60 (60 sec)

171

97.8

33

200

TGGCGTATCTATAATGTTGAC

-

-

-

33

400

CACTTACAGGAAACTTGGGGTCG

56 (60 sec),

79

92.4

22

FW TetO RV OXA-1 FW OXA-1

72 (15 sec) 400

AGTGTGTTTAGAATGGTGATC

-

-

-

22

200

FAM-

-

-

-

22

RV OXA-1 probe

ATCAAGCATAAAAGCCAAGAAAAT GC-BHQ

16S FW

200

CGGTGAATACGTTCYCGG

56 (30 sec)

124

85.8

34

16S RV

200

GGWTACCTGTTACGACTT

-

-

-

34

16S

100

FAM-CTTGTACACACCGCCCGTC-

-

-

-

34

probe

BHQ

208 10 ACS Paragon Plus Environment


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Fold change calculation for targeted qPCR assays

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ARG fold changes were calculated to compare ARG prevalence in manure blend pit and

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long-term storage samples using the targeted qPCR data. Fold change values for targeted qPCR

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assays were obtained using the following equation:

213

𝐹𝑜𝑙𝑑 𝐶ℎ𝑎𝑛𝑔𝑒 =

𝑁𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 𝐶𝑜𝑝𝑖𝑒𝑠𝑆𝑡𝑜𝑟𝑎𝑔𝑒 𝑚𝑎𝑛𝑢𝑟𝑒 𝑁𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 𝐶𝑜𝑝𝑖𝑒𝑠𝑈𝑛𝑡𝑟𝑒𝑎𝑡𝑒𝑑 𝑚𝑎𝑛𝑢𝑟𝑒

(Eqn. 3)

214

Where the number of normalized copies for a particular gene (normalized by 16S rRNA

215

gene copies per mL of DNA extract) for storage manure samples were divided by normalized gene

216

copies present in the untreated manure sample(s) contributing to the respective collection date.

217

Fold change values for the Fall long-term storage manure were calculated using both the

218

normalized gene copies for September and October 2016 untreated manure samples (two

219

biological samples, with separate fold change values). The Spring long-term storage manure fold

220

change calculations were obtained by averaging three individual fold change values, calculated

221

using three untreated manure collections (December 2016, March 2016, and June 2017) and one

222

long-term storage manure collection (Spring). The same method (Eqn. 3) was used to compare

223

ARG prevalence for Spring versus Fall long-term storage manure samples (one sample for each

224

collection) with the following inputs:

225 226

𝐹𝑜𝑙𝑑 𝐶ℎ𝑎𝑛𝑔𝑒 =

𝑁𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 𝐶𝑜𝑝𝑖𝑒𝑠 𝑆𝑝𝑟𝑖𝑛𝑔 𝑙𝑜𝑛𝑔 ― 𝑡𝑒𝑟𝑚 𝑠𝑡𝑜𝑟𝑎𝑔𝑒 𝑁𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 𝐶𝑜𝑝𝑖𝑒𝑠 𝐹𝑎𝑙𝑙 𝑙𝑜𝑛𝑔 ― 𝑡𝑒𝑟𝑚 𝑠𝑡𝑜𝑟𝑎𝑔𝑒

(Eqn. 4)

Antimicrobial usage data

227

Daily on-farm antimicrobial usage data were made available for the six farms in State A.

228

Antimicrobial treatments were assembled from written and electronic farm records

229

(DairyComp305, Valley Agricultural Software, Tulare, CA, USA). Based on written farm


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protocols and conversations with the herd managers, the antimicrobials associated with each

231

treatment were identified. Antimicrobial labels were used to ascertain the concentration of the

232

active ingredient(s). Treatment records, dosages, and concentrations were then used to calculate

233

the daily mass use of each active ingredient. As blend pits were not completely mixed or emptied

234

daily, daily usages were summarized for the week prior to sampling, with these weekly usages

235

compared to untreated manure antimicrobial residues and ARG concentrations.

236

Tetracycline and tetracycline transformation product analyses

237

Untreated manure samples were freeze dried (lyophilized) prior to antimicrobial solid-

238

liquid extraction and analysis via liquid chromatography-tandem mass spectrometry (LC-MS/MS)

239

(Agilent 6410 triple quadrupole, Santa Clara, CA) for tetracyclines according to Wallace et al.35

240

One-point standard addition quantification was performed for each sample. Specifically, each

241

sample was split into two 200 µl portions; the unspiked sample was spiked with the internal

242

standard and the spiked sample was spiked with an internal standard mixture and 50 ng/mL of each

243

tetracycline. Total tetracyclines and their transformation products were quantified by LC-MS/MS.

244

Tetracyclines were the only class of antimicrobial compounds to be correlated to ARG

245

concentrations because they were the most abundantly detected class of antimicrobials in the

246

samples.

247

Statistical Analyses

248

To visualize differences amongst locations (farms) and collection times, non-metric

249

multidimensional scaling (nMDS) was performed in XLSTAT using Bray-Curtis dissimilarity

250

matrices obtained for farms in each state (State A, State B, and State C) and temporally (among

251

six collection dates) within the State A farm cohort, to demonstrate temporal trends within one



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state. Dissimilarity matrices were constructed based on ΔCq values for each gene included on the

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Custom qPCR Array. Analysis of similarity (ANOSIM) was performed using PRIMER-7 on Bray-

254

Curtis dissimilarity matrices to test the null hypothesis that the dissimilarity between groups (either

255

between farms or between collection dates) is greater than the similarity within groups. Possible

256

correlations between normalized ARG concentrations and farm antimicrobial usage or detected

257

tetracycline residues (via LC-MS/MS) were obtained using a Spearman correlation, performed in

258

SigmaPlot (version 11.0, SyStat Software Inc.). Average temperature data used to perform a

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Spearman correlation between the normalized ARG abundance (ARG copies/16S rRNA gene

260

copies) to the average monthly temperature in each state during the collection year.36 Heat maps

261

illustrating the ΔΔCq values for each untreated manure sample were created using ‘ggplots’

262

package in R (version 3.4.2).

263

Results and Discussion

264

Antimicrobial resistance genes in untreated manure blend pits

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Out of the 13 ARGs targeted, 12 genes were detected in at least one untreated manure

266

sample and six genes were detected in all 66 manure samples. Detection of ARGs included in the

267

Custom qPCR Array are summarized in Table S3. Detection frequencies for each gene, using

268

either the Custom qPCR Array or targeted qPCR technique, are shown in Figure S1. The results

269

indicate that 10 of the 13 genes studied provided a detection frequency above 90%. The other three

270

genes had detection frequencies of 48% for ereB, 35% for aaca-(6)-lb, and 0% for CTX-M1. CTX-

271

M1 provides resistance to many third generation cephalosporins, where the translated enzyme has

272

a partial preference for cefotaxime (an antimicrobial only used in humans) along with other

273

similarly structured cephalosporins.37, 38 Targeted qPCR analysis for all four ARGs (tetO, sul1,


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OXA-1, and int1) resulted in at least a 97% detection frequency (64 of 66 total samples for int1,

275

66 of 66 for sul1, 66 of 66 for tetO, and 65 of 66 for OXA-1).

276

To assess technical variability in the Custom qPCR Array method, DNA extracted from

277

manure collected from the antimicrobial-free farm in April 2017 was analyzed in triplicate.

278

Variability in Cq values (Table S4) among each of the nine genes included in the array ranged

279

from 0.12-0.51 Cq, or percent relative standard deviation values ranging from 0.42-1.36%

280

indicating reliable reproducibility in amplification and detection. Technical variability in qPCR

281

amplification may arise from other factors such as extraction efficiency and the presence of

282

amplification inhibitors, which were not investigated using the Custom qPCR Array.

283

ARG relative abundance in untreated manure blend pits varied between farm location

284

Location-based differences among relative ARG abundances were observed between farms

285

(Figure 1) within each of the three states studied. Distinct clustering for individual farms in State

286

A (ANOSIM p = 0.014), State B (ANOSIM p = 0.001), and State C (ANOSIM p =0.03) was

287

observed suggesting similarities in ARG assemblages and relative abundance. Although

288

statistically significant clustering was observed within states, a comparison between states did not

289

reveal a statistically significant difference suggesting unique assemblages of ARGs at the farm

290

scale but not between individual states (Figure S2). Differences in antimicrobial administration

291

between farms may contribute to similarities and differences in ARG detection and abundance. In

292

addition to location-based differences, the effects of herd size and manure handling techniques

293

were analyzed. Considering herd size as a potential factor influencing relative ARG abundance,

294

the number of cows (below 1,000 cows and above 1,000 cows) located on dairy farms did not

295

provide distinct or significant differences in clustering (Figure S3, ANOSIM p=0.071).

296

Considering manure handling techniques such as scrape and flush manure collection as another 14 ACS Paragon Plus Environment


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297

potential factor, the ANOSIM was statistically significant (ANOSIM p=0.015) even though

298

distinct clustering between the scrape and flush manure collection systems was not visually

299

observed in the nMDS plot (Figure S4). Other potential factors influencing ARG detection and

300

abundance were not statistically tested in this study. For example, farms in different regions are

301

exposed to different environmental conditions, for example temperature and precipitation, which

302

can influence animal health and microbial communities in manure and soil. Manure management

303

factors such as manure holding time, holding containment methods, and influence of other wastes

304

(such as food) present in the manure pit may also be linked to differences in microbial communities

305

among farms. Differences in animal health, antimicrobial usage, and microbial communities likely

306

contribute to differences in ARG prevalence between farms and warrant future research.

307

To investigate trends between ARGs, correlations were performed to investigate

308

relationships between normalized ARG concentrations. Spearman correlation analysis between

309

normalized (by 16S rRNA gene copies) int1 and OXA-1 genes, shown in Figure S5, reveal a weak

310

but statistically significant correlation (coefficient=0.306, p=0.0129), possibly suggesting similar

311

expression behavior within the resistance-harboring microbiome. Spearman correlation analysis

312

between the normalized (by 16S rRNA gene copies) int1 and sul1 genes (Figure S6) revealed no

313

relationship (coefficient = 0.169, p = 0.174) in untreated manure, which was unexpected given that

314

these genes were previously found to reside on the same plasmid in bacterial species.39, 40


Page 17 of 34


315 316 317 318 319 320 321

Figure 1: Non-metric multidimensional scaling (nMDS) plots showing the Bray-Curtis dissimilarity between relative ARG abundances from the Custom qPCR Array analysis in untreated manure collected from six collection dates. Dissimilarity analyses were grouped by state location of each farm (A) State A (R=0.117, ANOSIM p=0.014) (B) State B (R=0.546, ANOSIM, p=0.001) and (C) State C (R=0.294, ANOSIM, p=0.03). Dashed circles surrounding data points show the relative clustering for each farm within respective states.

322 323

Temporal variation in ARG abundance

324

Variability in the normalized concentration of targeted ARGs by qPCR was observed on

325

temporal scales over a one-year period. nMDS plots for the dissimilarity analysis of ARGs in

326

untreated manure blend pit samples using the Custom qPCR Array data for the State A cohort

327

revealed temporal differences in clustering. The temporal based dissimilarity (stress = 0.100,

328

ANOSIM, p=0.014), shown in Figure 2, revealed a difference in clustering between the winter 16 ACS Paragon Plus Environment


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329

months (December 2016 and March 2017) and the other four collection dates (September 2016,

330

October 2016, June 2017, August 2017) within the State A sample cohort. This temporal difference

331

however was not observed among the 11 farm cohort nMDS analysis, where a statistical difference

332

was observed between each collection month but a distinct difference in clustering of winter

333

months was not present (Figure S7). The difference in temporal behavior between the State A

334

farms and all 11 farms may be reflected in differences in climate conditions, since State A is at a

335

higher latitude than State B and State C and the farms in State A likely experience colder winters

336

with more precipitation and freeze-thaw cycles. Distinct differences in the normalized (by 16S

337

rRNA gene copies) ARG concentrations on temporal scales were observed in targeted qPCR data

338

for OXA-1 and int1 gene analyses (Figures S8 and S9) where at least a two-fold decrease in the

339

normalized int1 and OXA-1 concentrations during the winter months were observed for four and

340

five out of the six State A farms.

341

Decreased relative ARG abundance during the winter months may stem from differences

342

in cellular stress and microbial activity and, in part, provide further evidence for the differences in

343

nMDS clustering observed in the Custom qPCR Array data. Cold temperature stress has been

344

reported to increase the expression of a general stress response gene, rpoS, that has been associated

345

to antimicrobial resistance in E. coli and P. aeruginosa.41-43 Although there may be a general stress

346

response during the winter climate, the data presented do not support the association between the

347

ARGs studied here and the stress response gene due to decreases in ARG abundance. The temporal

348

ARG prevalence values for OXA-1 were observed to have a statistically significant positive

349

correlation to the average temperature during the collection month in State A and State C, while

350

int1 was observed to be positively correlated to average temperature values in State A (Table S5).

351

It can also be possible that a reduction in gene transfer occurs in winter months, partially supported 17 ACS Paragon Plus Environment

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352

by the decreases in normalized int1 concentrations in the current farm cohort. The correlation

353

between the normalized int1 and OXA-1 concentrations (Figure S5) supports the notion that these

354

two genes may be expressed in response to a similar stress or due to their location on a plasmid.

355

Targeted qPCR data are shown for the sul1 and tetO genes, where sul1 was observed to increase

356

in the winter months while tetO gave variable normalized concentrations between farms and

357

collection dates (Figures S10 and S11). Conversely to int1 and OXA-1 temporal behavior, sul1

358

was observed to have statistically significant negative correlation to average temperature for each

359

of the three states, while tetO was observed to have a statistically significant negative correlation

360

to average temperature values for State B (Table S5). The results from this temporal investigation

361

warrant future studies to focus on the characteristic seasonal variability in ARG abundance and

362

prevalence on livestock farms.

363 364 365 366 367 368

Figure 2: Non-parametric multidimensional scaling (MDS) plots showing the Bray-Curtis dissimilarity (R=0.114, ANOSIM p=0.014) for the Custom qPCR Array analysis of untreated manure blend pit samples collected at six NY farms during six collection times (Sept’16, Oct’16, Dec’16, March’17, June’17, and Aug’17), where data points are grouped by collection date. Dashed circles are provided to show relative similarities in clustering among collection dates.



370

Page 20 of 34

Comparison between ARG concentrations and antimicrobial use on farms

371

The normalized concentration of two ARGs, tetO and OXA-1, (normalized by 16S rRNA

372

gene copies) provided no positive correlation to average daily antimicrobial administrative use for

373

tetracycline and beta-lactam antimicrobial classes among the six farms within State A. Spearman

374

correlation analysis was performed on the normalized concentration of tetO and OXA-1 versus the

375

log transformed total use of either β-lactams or tetracyclines for all six State A farms. A correlation

376

analysis between sulfonamide residue concentrations and sul1 abundance was not performed due

377

to the low detection frequency of sulfonamide residues (9%), across each of the nine sulfonamide

378

residues targeted in the LC-MS/MS analysis. (unpublished data) No positive correlation

379

(coefficient = -0.132, p=0.441) was observed between the normalized tetO concentration and the

380

total use of tetracyclines. Spearman correlation between the use of beta-lactam antimicrobials and

381

the normalized OXA-1 concentration also reveal no relationship (correlation = -0.208, p=0.221).

382

These results suggest that the administration of a specific antimicrobial may not directly influence

383

the prevalence of the ARG linked to the corresponding antimicrobial due to the effects of many

384

environmental factors and physicochemical properties of the corresponding antimicrobial.

385

Sulfonamide antimicrobials were utilized infrequently in 2 of the 6 farms and the administration

386

of these was not investigated for the correlation to sul1 prevalence. Two other commonly used

387

classes of antimicrobials (aminocoumarins, macrolides, and lincosamides) were administered on

388

the farms studied but were not assessed for correlation to ARG prevalence because resistance genes

389

to these classes were not analyzed.

390

Although significant correlations between antimicrobial use and corresponding ARG

391

copies were not observed among all six farms located in State A, individual farms show positive

392

trends between the two variables. For example, CF1 and CF6 show similar trends among the use 19 ACS Paragon Plus Environment

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393

of tetracyclines and the normalized (by 16S rRNA gene copies) tetO concentration (Figure S12).

394

A general decrease over the collection months is noticed for both the use of tetracyclines and

395

normalized tetO concentrations among samples from CF1; while a simultaneous increase in

396

tetracycline use and tetO is observed for CF6 at the March collection date with a decrease across

397

the next two collections (June and August). However, temporal trends are not observed for the

398

four other farms in State A (Figure S13 and S14). These results suggest the relationship between

399

antimicrobial use and ARG prevalence may be unique to individual farms.

400

Previous studies focusing on the distinct correlation between antimicrobial use and the

401

subsequent increase in bacterial genetic resistance have only been explored in human clinical

402

environments, where positive correlations were observed between antimicrobial use and changes

403

in ARG prevalence from patient bacterial isolates.44, 45 No such correlations have been published

404

for the comparison within a livestock environment, where complex environmental factors can

405

influence detection and expression of ARGs. Various antimicrobial classes have been shown to

406

structurally transform in the environment. These transformation products may or may not initiate

407

an increase in resistance for a microbial community. In addition to the presence of antimicrobials,

408

other parameters such as biochemical oxygen demand (BOD), available nitrogen and phosphorous,

409

and metal concentrations may contribute to altered levels of ARGs in the environment. Metals

410

have been previously shown to correlate to the presence of ARGs in soil and manure samples.46, 47

411

Further research on full-scale comparisons between antimicrobial use and ARG prevalence present

412

on livestock farms is warranted to decipher the effects of increased antimicrobial administration.

413

Comparison between detection of the tetracycline resistance gene (tetO) and total tetracyclines

414

Normalized tetracycline resistance gene concentrations do not positively correlate to total

415

tetracycline concentrations detected in untreated manure collected from the six farms. One ARG 20 ACS Paragon Plus Environment


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416

associated with tetracycline resistance, tetO, was quantified by targeted qPCR analysis and

417

provided a detection frequency of 100% (66 total samples). Correlating the concentrations of

418

antimicrobials detected to the abundance of ARGs can provide information regarding

419

antimicrobial effects on ARGs, for at low levels of antimicrobial exposure (below a corresponding

420

MIC value), expression of bacterial resistance can be initiated.7 In the present study, no significant

421

correlation was observed between the normalized tetO concentration (normalized by 16S rRNA

422

gene copies) and total concentration of tetracyclines (Spearman correlation = 0.226, p=0.068,

423

Figure S15). This may suggest that the presence of tetracyclines may not directly affect tetO

424

abundance in the untreated manure microbial community.

425

continual external pressure from other contaminants or environmental variables may have an effect

426

on bacterial expression of tetracycline resistance mechanisms and therefore the measured

427

abundance in manure samples. Although not the focus of the current paper, it is interesting to note

428

that no correlation was observed between tetracycline concentrations and tetracycline

429

administration (unpublished data).

However, other factors such as

430

Previous studies have shown positive correlations between the concentration of

431

contaminants (i.e. antimicrobials and metals) and the number of ARG copies (normalized to 16S

432

rRNA copies) in swine manure, beef manure, and manure-amended soil,

433

residual contaminants may lead to an increased presence of the corresponding resistance

434

mechanisms. Limited research exists exploring these relationships in dairy manure. Though the

435

current study did not provide a positive correlation between the residual tetracycline

436

concentrations and normalized tetO concentrations, further experiments involving analysis of

437

other antimicrobials and their corresponding ARGs are necessary to provide for a more accurate

438

assessment of antimicrobial roles in ARG prevalence. The location of tetO on bacterial plasmids

12, 48, 49

suggesting that


Page 23 of 34


439

may also play a role in environmental prevalence, for this gene may be in close proximity to other

440

ARGs that are variably expressed. For instance, previous studies have provided indications of

441

many gene cassettes with multiple ARGs located in the same plasmid location (i.e. int1, sul1, tetG,

442

and aada1).50-53 One particular type of plasmid, IncA/C, has a broad host range, replicating in both

443

Enterobacteriaceae and Pseudomonadaceae bacterial families, while conferring resistance to

444

sulfonamides, chloramphenicol, beta-lactams, and aminoglycosides.54, 55

445

Comparing ARG abundance in untreated manure blend pits from conventional farms and an

446

antimicrobial-free farm

447

Dairy manure collected from conventional farms, which used antimicrobials as

448

therapeutics and prophylactics, did not consistently provide higher ARG prevalence compared to

449

manure collected from an antimicrobial-free farm in State A. Manure from the antimicrobial-free

450

farm showed positive detections of ARGs conferring resistance to the following classes of

451

antimicrobials using both the Custom qPCR Array and targeted qPCR techniques for macrolides

452

(ermB and mefA), tetracyclines (tetA), aminoglycosides (aada1), sulfonamides (sul1) and beta-

453

lactams (OXA-1). Comparisons between ARG detections on the 11 conventional farms and the

454

antimicrobial-free farm were made using the ΔΔCq method (Eqn. 2), as shown in Figure 3. For

455

five of the ARGs (ermB, mefA, tetA, aada1, and aac-6-(lb)), relative abundances were similar

456

based on the Custom qPCR Array. Three ARGs (ereB, OXA-2, and VEB-1) were observed to have

457

ΔΔCq values above 3, where ΔΔCq values above 2 are considered to have a significantly higher

458

prevalence. Similar trends with the significant increase in prevalence for ereB, OXA-2, and VEB-

459

1 were observed within untreated manure samples for State B and State C (Figure S16). This

460

comprehensive sampling across dairy farms to assess the distribution and abundance of ARGs

461

when compared to manure collected from an antimicrobial-free farm resulted in findings consistent 22 ACS Paragon Plus Environment


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462

with previous studies that have shed light on the occurrence of ARGs in manure collected from

463

antimicrobial-free farms56-58 and confirmed that ARGs are ubiquitous in the environmental

464

microbial community.

465 466 467 468 469 470 471

Figure 3: Heat map describing the temporal changes of eight ARGs in untreated manure, when compared to manure collected from an antimicrobial-free dairy farm in State A. CTX-M-1 was excluded in the heat map since it was not detected in any untreated manure. Each color represents the ∆∆Cq associated with each manure sample for a specific gene. The deep red color indicates a larger presence in conventional farm sample compared to the antimicrobial-free farm sample.

472 473

The effect of long-term storage on ARG abundance

474

Long-term storage of dairy manure influences the prevalence of specific genes when

475

compared to untreated manure. Positive and negative detections for ARGs detected in long-term

476

storage samples using the Custom qPCR Array are listed in Table S6, where seven of the nine

477

genes provided detection frequencies above 75% and CTX-M-1 provided no positive detections. 23 ACS Paragon Plus Environment

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478

Comparisons of gene abundance in long-term storage samples and untreated manure reveal distinct

479

differences in ARG occurrence between the two manure conditions. Two ARGs associated with

480

aminoglycosides (aac-(6)-lb and aada1) and three ARGs associated with macrolides (ermB, ereB,

481

and mefA) showed higher relative abundance values in untreated manure (December 2016, March

482

2017, and June 2017) compared to the Spring long-term storage samples (Figure S17). However,

483

the nine ARGs included in the Custom qPCR Array did not show a general trend in gene

484

accumulation after long-term storage, discrediting one possible hypothesis that long-term storage

485

may increase the likelihood of ARG accumulation by an increase in HGT. Comparisons made

486

between untreated and storage manure samples (from September 2016 and October 2016) and the

487

Fall long-term storage manure showed variability in ARG abundance (Table S7). Comparisons

488

between the Fall and Spring long-term storage manure ARG abundance values using the targeted

489

qPCR data were made, which showed large variability along with no significant fold change values

490

(Tables S8 and S9). The large variation in relative ARG abundance comparisons may be

491

exacerbated by factors that can contribute to differences in ARG content such as location of long-

492

term storage pits, storage time (age) of manure in both untreated and long-term storage manure

493

pits, and manure removal techniques utilized (scrape versus flush removal).

494

Relative ARG abundance (∆∆Cq) in manure storage drawdown samples was higher in the

495

Spring storage drawdown manure than in the Fall storage manure (Figure 4). ARGs associated

496

with macrolide resistance (ermB, mefA, and ereB) were 4-fold higher in Spring storage manure

497

samples compared to Fall storage samples (Figure S18). ARGs investigated by targeted qPCR

498

(Figure S19) showed farm specific accumulations in some genes. For instance, CF1 provided a

499

fold change of 2.48 and 8.74 for int1 and tetO, respectively (Table S10). Two farms, CF3 and

500

CF5, had larger occurrences of OXA-1 in Spring long-term storage manure with fold change values 24 ACS Paragon Plus Environment


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501

of 43 and 7.0, respectively. Higher relative ARG abundance in the Spring versus Fall storage

502

samples may be connected to higher antimicrobial usage on the farms during the winter months

503

when bacterial respiratory infections are more common59, 60 or by an increase in microbial activity

504

in the Spring. However, this trend may be farm specific. CF6 consistently provided higher

505

normalized (by 16S rRNA) ARG concentrations in Fall long-term storage manure for int1, OXA-

506

1, sul1, and tetO with fold change values of 0.30, 0.10, 0.37, and 0.12, respectively. Higher

507

normalized ARG concentrations in Fall storage manure was observed for int1 and tetO for CF2

508

with fold change values of 0.35 and 0.38 respectively. Further research is needed to determine if

509

Spring or Fall storage drawdowns may present a higher risk of ARG dissemination into the

510

environment.


Page 27 of 34

512 513 514 515 516


Figure 4: ARGs fold changes from Custom qPCR Array analysis for long-term storage manure samples collected in Fall 2016 and Spring 2017. Bar graphs show the comparison between (A) Fall and Spring at CF2 and (B) Fall and Spring at CF4, where the dashed line represents a ∆∆Cq of ±2 indicating a significant fold change (4 or greater). Positive ∆∆Cq values represent a higher fold change in the Spring storage drawdown manure compared to the Fall.

517 518

This comprehensive study investigated five aspects of antimicrobial resistance in untreated

519

and long-term storage manure that can stimulate further knowledge of farm-scale resistomes.

520

Results show: (1) location and temporal-based variability among untreated blend pit manure across

521

11 conventional dairy farms, (2) normalized ARG concentrations did not correlate to antimicrobial

522

usage administrations in the State A farms, (3) the concentration of tetracycline residues did not

523

correlate to the normalized tetO concentrations present within untreated blend pit manure, (4)

524

ARGs were detected on the antimicrobial-free farm and only specific ARGs associated with

525

macrolide-lincosamide and beta-lactam resistance mechanisms showed higher fold change values

526

on conventional farms compared to the antimicrobial-free farm, and (5) select ARGs were detected

527

with higher fold change values in the Spring storage manure versus the Fall storage, however large

528

variations in ARG abundance and differences in long-term storage manure inputs and storage time

529

require future research. The current study highlights the importance of considering location,

530

temporal, and gene-specific variability. Future research should focus on identifying aspects of

531

manure management practices at conventional farms, which may exacerbate or mitigate ARG

532

prevalence and environmental dissemination. Furthermore, a better understanding for the

533

background levels of ARGs present in antimicrobial-free farms on a temporal scale is needed to

534

accurately decipher the effects of environmental background concentrations and antimicrobial use

535

on the spread of bacterial resistance.

536

Acknowledgements 26 ACS Paragon Plus Environment


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537

The authors would like to thank each of the collaborating dairy farms, for their continued

538

cooperation during the study. This research was financially supported by the National Institute of

539

Food and Agriculture, U.S. Department of Agriculture, under award number 2016-68003-24601.

540

Any opinions, findings, conclusions, or recommendations expressed in this publication are those

541

of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture.

542

Supporting Information

543

The supporting information includes a description of the collaborating farms, information about

544

ARGs used in this study, detection of ARGs by collaborating farm and date, ARG detection

545

frequencies, data from the antimicrobial free manure, nMDS plots by state, farm and number of

546

cows, correlation analyses, ARG data compared to antimicrobial usage, data on ARGs from

547

long-term storage, ARG fold changes, methods for antimicrobial analysis.

548 549


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