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Ecotoxicology and Human Environmental Health

Trophic Transfer of Antibiotic Resistance Genes in a Soil Detritus Food Chain Dong Zhu, Qian Xiang, Xiaoru Yang, Xin Ke, Patrick O'Connor, and Yong-Guan Zhu Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.9b00214 • Publication Date (Web): 04 Jun 2019 Downloaded from http://pubs.acs.org on June 7, 2019

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Environmental Science & Technology

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Trophic Transfer of Antibiotic Resistance Genes in a Soil Detritus Food

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Chain

3 4

Dong Zhu,†,‡ Qian Xiang,‡,§ Xiao-Ru Yang, † Xin Ke,|| Patrick O'Connor,⊥ Yong-

5

Guan Zhu, *, †,§

6 7

†

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Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen

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361021, China.

Key Laboratory of Urban Environment and Health, Institute of Urban

10

‡

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100049, China.

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§

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Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085,

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China.

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||

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Sciences, Chinese Academy of Sciences, Shanghai 200032, China.

17

⊥

18

5005, Australia.

University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing

State Key Laboratory of Urban and Regional Ecology, Research Center for

Institute of Plant Physiology and Ecology, Shanghai Institute of Biological

Centre for Global Food and Resources, University of Adelaide, Adelaide,

19 20 21 22 1

ACS Paragon Plus Environment


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ABSTRACT: The presence and spread of antibiotic resistance genes (ARGs)

24

are causing substantial global public concern, however, the dispersal of ARGs

25

in the food chain is poorly understood. Here, we experimented with a soil

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collembolan (Folsomia candida)-predatory mite (Hypoaspis aculeifer) model

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food chain to study trophic transfer of ARGs in a manure-contaminated soil

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ecosystem. Our results showed that manure amendment of soil could

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significantly increase ARGs in the soil collembolan microbiome. With the ARGs

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in the prey collembolan microbiome increasing, an increase in ARGs in the

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predatory mite microbiome was also observed, especially for three high

32

abundant ARGs (blaSHV, fosX and aph6ia). Three unique ARGs were

33

transferred into the microbiome of the predatory mite from manure amended

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soil via the prey collembolan (aac(6')-lb(akaaacA4), yidY_mdtL and tolC).

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Manure amendment altered the composition and structure and reduced the

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diversity of the microbiomes of the prey collembolan and the predatory mite.

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We further reveal that bacterial communities and mobile genetic elements were

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two important drivers for the trophic transfer of ARGs, not just for ARGs

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distribution in the samples. These findings suggest that the importance of food

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chain transmission of ARGs for the dispersal of resistance genes in soil

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ecosystems may be underestimated.

42 43 44 2


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TOC ART

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INTRODUCTION

57

Antibiotic resistance has only been claimed to be ancient, while not proven

58 59

1, 2.

60

increased the abundance of antibiotic resistance genes (ARGs) in human-

61

associated environments at an unprecedented rate

62

ARGs in medically relevant strains is causing the global health crisis, because

63

the increase in antibiotic-resistant pathogens is making it more difficult to treat

64

infectious diseases

65

abundance of environmental ARGs (e.g. in the air 7, phyllosphere 8, water 9, soil

66

10,

67

through the environment. For instance, the transfer of antibiotic resistant

68

plasmids from chickens to humans was identified in a very early study

69

Recently, many studies have also illustrated the spread of ARGs from the soil

70

to the phyllosphere of vegetables

71

arable land

72

resistance from each other via horizontal gene transfer (HGT)

73

number of ARGs have also been detected in the gut microbiome of individual

74

animal species, e.g. collembolan

75

house fly

76

transportation systems for ARGs 22, 23. However, knowledge of the transmission

77

of ARGs within natural food webs and ecosystems remains limited. And the

Recent the overuse of antibiotics in medicine and animal husbandry has

animal gut

11).

14.

21,

5, 6.

3, 4.

The appearance of

Numerous studies have identified the number and

These studies indicate that ARGs are continuing to spread

13

12.

and from wastewater treatment plants to

Additionally, bacteria have been shown to acquire antibiotic

3, 17, 18,

honey bee

11,

fish

19,

15, 16.

A large

baboon

20

and

which suggests that these animals may act as reservoirs and

4


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78

movement of ARGs may be substantially influenced by trophic relationships

79

within food webs.

80 81

Numerous studies have shown that manure can enhance the abundance and

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diversity of ARGs and contribute to the dissemination of ARGs within affected

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environments (e.g. soil 24, rhizosphere 25 and phyllosphere 26). In a recent study,

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a relationship was observed between manure addition to soil and the ARGs in

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the soil collembolan microbiome 3, suggesting that manure may influence the

86

ARG suite in the microbiomes of soil fauna more generally. Manure entering

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soil ecosystems is preferentially ingested by soil fauna (e.g. collembolans,

88

enchytraeids and earthworms) 27, 28, potentially leading to an increase in ARGs

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in the microbiomes of that fauna. Because it has been identified that manure

90

can carry a large number of ARGs and antibiotics

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manure additions may exert a selection pressure on environmental bacteria 4, 6

92

and therefore on soil food webs.

10, 28,

depending on source,

93 94

Collembolans and predatory mites are the two most abundant soil

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microarthropods in natural ecosystems, occupying key trophic positions in the

96

soil food web and playing an important role in soil ecological processes (e.g.

97

decomposition of litter and carbon-nitrogen cycling) 30-33. Various foods can be

98

ingested by soil collembolans such as manure, litter, bacteria and fungi

99

and collembolans are important prey for predatory mites 5


31, 36.

34, 35,

A large number


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of microorganisms including pathogenic microbiota routinely colonize soil

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collembolans and predatory mites

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ARGs in the microbiome of soil collembolans from field soils 3, and that oral

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antibiotic exposure can significantly enhance the abundance and diversity of

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ARGs in the gut microbiome of collembolans 17. Mite predation on collembolans

105

may result in trophic transfer of ARGs enhancing the chance that the

106

pathogenic microbiota of predatory mites may obtain antibiotic resistance

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genes

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experiment has yet revealed the abundance and diversity of ARGs in the

109

microbiome of predatory mites and any effects of manure on trophic transfer of

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ARGs within the soil food chain. The term resistome indicates all antibiotic

111

resistance in our study.

3.

37-40.

Previous studies have shown diverse

However, soil fauna resistome is poorly understood, and no

112 113

In the present study, we used high-throughput quantitative PCR (HT-qPCR)

114

to screen for potential resistance determinants. Our aims were to 1)

115

characterize the composition of ARGs and mobile genetic elements (MGEs) in

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the microbiomes of the prey collembolan and predatory mite, 2) reveal the

117

effects of manure on ARG and MGE shifts in the prey collembolan and

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predatory mite microbiomes, 3) identify any trophic transfer of ARGs, 4)

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investigate the change in microbiome due to the addition of manure using next

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generation sequencing of 16S rRNA genes, and 5) explore the relationship

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between the bacterial community and the MGE and the ARG profile in the soil 6


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detritus food chain. We hypothesized that (1) manure can enhance the

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abundance and diversity of ARGs in the microbiome of prey collembolans when

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ARGs and antibiotics are abundant in the manure10, and (2) because

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collembolans act as a reservoir and transportation system for ARGs in soil

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ecosystems3, ARGs can be transferred from manure to the microbiome of a

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predatory mite via collembolan predation.

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MATERIALS AND METHODS

130 131

Manure, Soil and Animals. Swine manure commonly applied to the arable

132

land was purchased from a Bio-fertilizer co., LTD (Zhejiang, China). We

133

collected soils (sandy loam) of 0-20 cm depth from an arable land site (29º 48′N,

134

121º 23′E; Zhejiang, China) after harvest of a Brassica chinensis crop. After the

135

collected soil was air dried in the shade, we carefully removed root pieces, large

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stones and other allogenic materials. Then the manure and soil were sieved (2

137

mm). We divided the soil into two parts. One part was mixed with the manure

138

(5 g manure per kg dry soil based on local agronomic practice) to obtain

139

manure-amended soil (Manured soil), the second part was not amended with

140

manure to be used as a control (Control soil). The water content of the soils

141

was adjusted to 60% of field capacity (17 g water per 100 g soil), and the soils

142

were pre-incubated for two weeks before use. The physicochemical properties

143

and basic characteristics of the ARG and bacterial communities of the manure 7



144

and soil are summarized in Tables S1, S2 and S3 and Figures S1, S2, S3 and

145

S4.

146 147

The model soil collembolan (Folsomia candida) and predatory mite

148

(Hypoaspis aculeifer) (originally obtained from Aarhus University, Denmark)

149

were used in our study to construct a model soil detritus food chain. We have

150

reared these species for more than seven years, and they are maintained on

151

Petri dishes covered with a layer of a mixture of activated carbon and moist

152

plaster of Paris (1:9 w/w). More details on culturing and synchronizing of these

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animals is described in previous studies 30, 31. The 10-12 day-old collembolans

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and 33-35 day-old female predatory mites were used in the current study.

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Experimental Design. Our study included two treatments (Control soil and

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Manured soil), and each treatment included three replicates. The exposure of

158

collembolans to the manure was conducted in the soil system. 300 age-

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synchronized collembolans (10-12 days) were transferred into plastic

160

containers (9 cm high, inner diameter 6 cm) with 60 g soil. Soil water was

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replenished twice a week with ultrapure water. After two weeks of incubation,

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all collembolans in each container were extracted by water floating. Based on

163

the body size of the collembolan, we isolated the introduced collembolans from

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any juveniles produced. Then, 250 introduced collembolans were placed on 9

165

cm Petri dishes with a layer (thickness: 0.5 cm) of the mixture of activated 8


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carbon and moist plaster of Paris (1:9 w/w), and the surplus collembolans were

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used to extract DNA. Ten synchronized predatory mites (33-35 days) were

168

added into each Petri dish and incubated for two weeks. In this step, the

169

predatory mite was not directly exposed to the manure-amended soil, but was

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cultured in the cleaned petri dish by adding the prey collembolan with an altered

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resistome, which ensured that the change in the predatory mite resistome was

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due to direct feeding on the prey collembolan with an altered resistome. After

173

two weeks, we identified the introduced predatory mites according to their body

174

size and used them to isolate DNA. In addition, we determined the mortality of

175

the collembolan and mite during the study and found that the manure has no

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significant impact on the mortality of the collembolan and mite.

177 178

DNA Extraction. Three collembolans or predatory mites were used to isolate

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DNA from each sample. Before DNA extraction, we used 0.5% sodium

180

hypochlorite to wash collembolans and predatory mites three times, and then

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sterile water was used to wash them five times at 4 °C. We used a DNeasy

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Blood and Tissue Kit (QIAGEN, Hilden, Germany) to extract DNA of

183

collembolans and predatory mites. In brief, a micro-electric tissue homogenizer

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was used to homogenize collembolans and predatory mites in sterile 1.5-ml

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centrifuge tubes. After homogenate, we added 180 μL tissue lysis buffer and

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20 μL proteinase K (> 600 mAU mL -1, QIAGEN, Hilden, Germany) into each

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centrifuge tube, which was vortexed for 2 min. The centrifuge tube was 9



188

incubated at 56 °C in a water bath lasting 10 hours. Other extraction processes

189

followed the kit manufacturer’s instructions. Finally, we used 100 μL elution

190

buffer to elute the isolated collembolan and predatory mite DNA, and then the

191

DNA obtained was kept at -20 °C until further use.

192 193

High-Throughput Quantitative PCR. We used a total of 296 primer sets

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(Table S4) to determine ARG profiles in the microbiome of the prey collembolan

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and the predatory mite. All major classes of antibiotic resistance genes (285),

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10 genes of MGEs and one 16S rRNA gene were included in these primer sets.

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The Wafergen SmartChip Real-time PCR system was selected to conduct the

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high-throughput quantitative PCR (HT-qPCR). Each sample was detected three

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times (three technical replicates), and the sterile water was also determined as

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the negative control, which ensured the validly of result. A standard mixed

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plasmid containing multiple target genes was used as the positive control. The

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system and condition of the HT-qPCR were consistent with previous studies 3,

203

17.

204

Inc.; Takara Bio.) to analyze the data obtained. Only when the results of three

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technical replicates were all positive, amplification efficiency was between 1.8

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and 2.2 and r2 was > 0.99, the data from amplification was adopted.

207

Considering the PCR bias, an amplification efficiency calibration formula was

208

used to minimize the error before further data analysis. More details of the

209

formula are described in a previous study 3. The threshold cycle was set at 31

We used the SmartChip qPCR software (V 2.7.0.1, WaferGen Biosystems,

10


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18.

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as a detection limit for amplification

We used a normalized copy number of

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ARGs per bacterial cell to represent the abundance of ARGs detected in

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samples, which was calculated based on previous studies

213

because the normalized abundance can minimize the error produced by the

214

differences of 16S rRNA gene abundance in different types of samples 3.

3, 8.

This was used

215 216

DNA Amplification, Library Preparation, Sequencing and Bioinformatics

217

Analysis. The amplicons of the V4 region of bacterial 16S rRNA genes were

218

generated by using the primer 515F (GTGCCAGCMGCCGCGG) and 806R

219

(GGACTACNVGGGTWTCTAA) with unique barcodes. The system and

220

condition of amplification and purification of PCR products were consistent with

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a previous study

222

determine the concentration of PCR purified products in all samples. We pooled

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24 samples of equal DNA content to a library, which was sequenced at the

224

MiSeq 300 instrument with illumina MiSeq Kit v2 (Read length: 2 x 300 bp; Meiji

225

biological medicine co., LTD, Shanghai, China). All high-throughout sequence

226

data are deposited in the NCBI Sequence Read Archive under the accession

227

number SRP116006.

41.

The Qubit 3.0 fluorimeter (Invitrogen) was used to

228

The Quantitative Insights Into Microbial Ecology (QIIME, version 1.9.1) was

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selected to perform the high-throughput 16S sequencing data analysis based

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on the online instructions

231

removed primer sequences and filtered the reads via Phred quality scores to

42.

In short, first, we merged paired-end reads,

11



232

obtain clean reads of samples. Then, the chimeras were removed by using

233

usearch quality filter in the QIIME, and the operational taxonomic units (OTUs)

234

were clustered based on the 3% sequence difference by de novo OTU picking

235

43.

236

sequence of each cluster. We used the Greengenes 13.8 reference database

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to align the representative sequences via the PyNAST and to assign taxonomic

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status of OTUs by the RDP Classifier 2.2 44-46. The Greengenes lanemask was

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selected to filter the alignment to remove these positions that are not needed in

240

building phylogenetic trees. A phylogenetic tree was built by using the FastTree

241

algorithm 47 for further downstream analysis. The bacterial alpha diversity was

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presented using the Shannon index, which was calculated from the relative

243

abundance of OTUs. We used the Adonis test to reveal the difference in

244

bacterial community patterns (significant level: 0.05) and principal coordinate

245

analysis (PCoA) to show the distribution of the bacterial communities of

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different samples based on the Bray−Curtis distance.

We discarded singletons, and OTUs were represented by the most abundant

247 248

Statistical Processing. The data of ARGs and bacterial communities was

249

presented via mean ± standard error (SE), which was calculated by using the

250

Microsoft Excel. Significant differences between treatments were compared

251

using t-test in the SPSS v20.0. The significance level was set at 0.05.

252

Bonferroni-corrected was applied in the multiple test. The R software with

253

version 3.4.3 was used in this study. The heatmap was used to display the 12


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normalized abundance of ARGs in all samples, which was produced by the

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pheatmap package within R 48. The Gephi with version V 0.9.2 was selected to

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perform the bipartite network analysis, showing the shared and unique ARGs

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between the microbiomes of soil, prey collembolan and predatory mite 3. We

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used the online version of Venny 2.1 to produce the Venn diagram

259

heatmap of the bacterial community at the genus level was drawn in Microsoft

260

Excel. We used the EcoSimR NulModels for Ecology within R to calculate the

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checkerboard scores (C-scores) of the assembly of the shared ARGs among

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all sample types in the model soil food chain 41, 49. The proportion of ARG pairs

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was measured in the C-score. We used 5000 random permutations of the same

264

data set to generate the score distribution of a simulated metric. By comparing

265

with the score distribution, we could test the rule of ARGs community assembly

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in the model soil food chain. The null hypothesis indicated random ARGs

267

assortment. The vegan 2.3-1 package within R was used to conduct the

268

Procrustes, Adonis, Anosim and Mantel tests and partial redundancy and non-

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metric multidimensional scaling (NMDS) analysis 3. We used the ggalluvial

270

package within R to draw the alluvial diagram of bacterial composition at the

271

phylum level 50. Other graphics were produced by OriginPro 9.1.

18.

The

272 273

RESULTS

274 275

Characterization of antibiotic resistance genes. A total of 58 ARGs and 5 13



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MGEs were detected in the microbiomes of all prey collembolan and predatory

277

mite samples. According to the antibiotic resistance of ARGs, the detected

278

ARGs can be divided into eight categories (aminoglycoside, beta-lactamase,

279

macrolide-lincosamide-streptogramin

280

sulfonamide, tetracycline, vancomycin). The addition of swine manure

281

significantly increased the number of ARGs detected in the microbiome of the

282

prey collembolan (t-test, t = 6.69, df = 4, P = 0.003) and predatory mite (t-test,

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t = 10.25, df = 4, P < 0.001) compared to those in the control, respectively

284

(Figure 1a). The normalized abundance of ARGs in the microbiomes of the prey

285

collembolan (t-test, t = 9.64, df = 4, P < 0.001) and predatory mite (t-test, t =

286

4.85, df = 4, P = 0.008) in the manured soil treatment were approximately 3.6

287

and 5.2 times higher than those in the control (Figure 1b), respectively. At the

288

same time, the addition of manure also significantly increased the number and

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abundance of ARGs in the soil microbiome (Figure S1; t-test, t < 3.61, df = 4, P

290

< 0.05). Compared with the control, higher abundant MGEs were observed in

291

the microbiomes of collembolan and predatory mite in the manured soil

292

treatment (Table S3; t-test, t < 3.35, df = 4, P < 0.05). With the ARGs in the

293

prey collembolan microbiome increasing, an increase of ARGs in the predatory

294

mite microbiome was also observed. The normalized abundance of most ARGs

295

was enhanced in the manure-treated prey collembolan or predatory mite

296

compared to those in the control, as shown in the heatmap, especially for

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aminoglycoside, beta-lactamase, multidrug and others (Figure 1c). Notably, we

B

(MLSB),

14


multidrug,

others,

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identified three abundant ARGs (blaCTX-M, blaSHV and aph6ia), and their

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normalized abundance was significantly increased in the microbiomes of the

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prey collembolan and predatory mite in the manured soil treatment (t-test, t =

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4.12, df = 4, P < 0.05). The ARG distributions of the same treatments are

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clustered and different from other treatments according to the non-metric

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multidimensional scaling analysis (stress = 0.086; Figure S5). Along with the

304

NMDS1 axis, the ARGs profiles of samples in the manured soil treatment were

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separated from the samples in the control. In the dimension 2 (NMDS2),

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collembolan ARGs profiles were clustered separately from the predatory mite.

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The Anosim and Adonis tests further indicate that the distribution patterns of

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ARGs from different treatments are significantly different (R = 0.821, P = 0.001;

309

Table S5).

310 311

Shared and Unique Resistome between soil, prey collembolan and

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predatory mite. The bipartite network analysis reveals that 22 ARGs are

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shared among soil, prey collembolan and predatory mite (Figure 2), which

314

include eight categories. The number of multidrug ARGs (7) was the highest

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among the shared genes. The blaSHV, fosX and aph6ia had the highest

316

normalized abundance among shared ARGs. With the abundance of blaSHV,

317

fosX and aph6ia in the microbiome of prey collembolan increasing, their

318

abundance also significantly increased in the predatory mite (t-test, t < 3.26, df

319

= 4, P < 0.05). Overall, the shared genes occupied 29.7% of all ARGs detected. 15



320

Similar to microbial community analysis, we further used the C-score to assess

321

the rule of the shared ARGs community assembly. In this analysis, the null

322

hypothesis indicated random ARGs assortment. Our result that the C-score of

323

ARGs is not included in the score distribution of a simulated metric rejected the

324

null hypothesis, indicating that the assembly of shared ARGs is not random

325

assortment in the model soil food chain (Figure S6). Six shared ARGs (acrR,

326

vanYD, mepA, blaZ, sul2 and rarD) were identified between prey collembolan

327

and predatory mite (Figure 2a), and 10 shared ARGs were found between the

328

soil and prey collembolan (Figure 2e). The most unique ARGs (16) were

329

observed in the soil microbiome (Figure 2d). Compared with the predatory mite

330

(43), more ARGs are found in the microbiome of the prey collembolan (58).

331

Moreover, it is the number of different ARGs that is different in their microbiome.

332

Only four ARGs (vanRA, tetR, cmr and sdeB) were unique to the predatory mite

333

(Figure 2b).

334 335

Venn diagrams reveal three unique ARGs (aac(6')-lb(akaaacA4),

336

yidY_mdtL and tolC) were transferred into the microbiome of the predatory mite

337

from swine manure via the soil-prey collembolan-predatory mite food chain

338

(Figure 3). 38 ARGs were shared between the soil and manure, and 14 ARGs

339

were only shared between the manure and the soil amended with manure.

340

Seven of those 14 ARGs were detected in the microbiome of the prey

341

collembolan in the manured soil treatment, but not observed in the prey 16


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collembolan in the control. In the manured soil, a high number of ARGs pop up

343

that are neither found in the control soil, nor in the supplemented manure

344

(Figure 3). The addition of manure increased the number of unique ARGs in the

345

microbiome of the soil, prey collembolans and predatory mites compared to the

346

control.

347 348

Composition and diversity of the bacterial community. We identified

349

315875 high-quality sequences from all samples of the prey collembolan and

350

predatory mite, and at least 18164 reads were obtained in each sample. Overall,

351

10593 OTUs were clustered at 97% similarity across all prey collembolan and

352

predatory mite samples. The prey collembolan includes 9195 OTUs, and 3631

353

OTUs were identified in the microbiome of the predatory mite. 61.5% of all

354

OTUs of the predatory mite are shared with the prey collembolan. The phyla

355

Proteobacteria and Actinobacteria are the two dominant bacterial taxa in the

356

microbiomes of the prey collembolan (occupying 60.4 ± 10.1% and 14.5 ± 3.2%)

357

and the predatory mite (occupying 46.3 ± 6.3% and 30.8 ± 6.5%), respectively

358

(Figure S7). The read abundance of Proteobacteria was significantly enhanced

359

in the microbiome of the prey collembolan in the manured soil treatment

360

compared to that in the control, by 110% (t-test, t = 5.68, df = 4, P = 0.005), but

361

no significant change was observed in the predatory mite. The addition of swine

362

manure significantly reduced the read abundance of Firmicutes and

363

Acidobacteria in the microbiome of the prey collembolan, by 61% and 93%, 17



364

respectively (t-test, t < 3.58, df = 4, P < 0.05). Although there is no significant

365

difference, the read abundance of Actinobacteria, Firmicutes and Acidobacteria

366

were reduced in the predatory mite in the manured soil treatment. At the genus

367

level, Wolbachia (20.9 ± 7.4%) was the dominant microorganism in the

368

microbiome of the prey collembolan, and the predatory mite microbiome was

369

dominated by Tsukamurella (20.1 ± 7.4%) (Figure 4). The endosymbionts

370

Wolbachia were strikingly overrepresented in the collembolan after manure

371

treatment, by 574% (t-test, t = 5.22, df = 4, P = 0.006). Most of the other genera

372

seemed to be more randomly distributed between treatments and hosts. For

373

example, Tsukamurella might be relatively less abundant in collembolan

374

samples (0.06 ± 0.05%) in the manured soil treatment compared to the control

375

(4.97 ± 2.01%) (t-test, t = 4.23, df = 4, P = 0.013), but in the predatory mite,

376

there was no clear pattern (t-test, t = 0.70, df = 4, P = 0.525) since they had a

377

very low relative abundance in a sample (mite+3). The prey collembolan and

378

predatory mite have more similar microbiomes when they come from the same

379

soil treatment. High read abundance of genera were shared between all

380

samples.

381 382

A significant difference was observed between the distribution patterns of the

383

microbiomes of soil, manure and fauna (Figure S8; Adonis test, P < 0.001).

384

Principal coordinates analysis shows that the bacterial community patterns of

385

the prey collembolan in the control soil treatment are clustered and separated 18


Page 18 of 53

Page 19 of 53


386

from those in the manured soil treatment along the PCo1 axis (explaining 28.5%

387

of the variation; Figure 5). The Adonis test further indicates that the distribution

388

patterns of bacterial communities of the prey collembolan and predatory mite

389

show a significant difference between different manure treatments (P < 0.01;

390

Table S5). In addition, the addition of manure also significantly altered the

391

bacterial community composition of the soil (Figure S4; Adonis test, P < 0.05).

392

The addition of swine manure significantly reduced the Shannon index of

393

diversity in the prey collembolan and predatory mite microbiome compared to

394

that in the control, by 62.6% and 33.1%, respectively (Figure 5b; t-test, t < 4.24,

395

df = 4, P < 0.05).

396 397

Relationship between the antibiotic resistome and bacterial community

398

and MGEs. A significant correlation between matrixes of ARG profiles and

399

bacterial communities was observed in all samples using the mantel test

400

(Figure 6; R = 0.503, P = 0.001). Procrustes analysis further indicates that the

401

distribution patterns of ARGs and the bacterial community are clustered by type

402

of sample and treatment, and ARG profiles are significantly related with the

403

composition of the bacterial community (Figure 6; M2 = 0.127, P = 0.0001).

404

Partial redundancy analysis also reveals that 90.36% of total ARGs variation

405

can be explained by using the data from the bacterial community and MGEs

406

(Figure 6b). The bacterial community contributes 50.63% of total variation, and

407

the MGEs explain 10.66% of total variation in the ARG profiles shift. Linear 19



408

regression also shows a significant positive relationship between the

409

abundance of MGEs and ARGs (R2 = 0.815, P < 0.001; Figure S9).

410 411

DISCUSSION

412 413

In this study, although we only performed three replication due to the complexity

414

of soil food chain operations, low coefficient of variation in each treatment

415

ensured the validity of the experimental results (Figures 1 and 5). Moreover,

416

significant statistical results also confirmed solidly trophic transfer of ARGs in

417

our model food chain. The collembolan-predatory mite food chain used in our

418

study can be commonly found in the real soil ecosystem, and the collembolan

419

and mite occupy central trophic levels in real soil food webs. In addition, the

420

food chain system was likely unstable, when it was composed of the animal

421

recently collected from the field. To obtain reliable and repeatable results, the

422

continuously reared organisms are a good choice to construct a model food

423

chain. In this study, we focused on the unsolved question that whether ARGs

424

can be transferred in the soil food chain, and the model food chain could satisfy

425

our need. At the same time, the exposure of collembolans to the manure was

426

conducted in the soil system, and the ratio of collembolan and predatory mite

427

was based on the real soil food web. These all ensured that our laboratory study

428

could reflect the real soil food web to some extent and confirm the aim of our

429

study. 20


Page 20 of 53

Page 21 of 53


430 431

In the present study, putative resistance genes were investigated by using HT-

432

qPCR, and not all genes detected had been proven to provide resistance 1.

433

Many of the investigated genes are inherent to sensitive bacteria where they

434

do not provide resistance, and the rate of false positive calling of actual

435

resistance genes should always be considered very high with these types of

436

studies. However, these facts do not affect our ability to answer the questions

437

of our study. Our results showed that manure amendment of soil could increase

438

the number and abundance of ARGs in the soil collembolan microbiome. With

439

the ARGs in the prey collembolan microbiome increasing, an increase in ARGs

440

in the predatory mite microbiome was also observed. We further identified that

441

three unique ARGs were transferred into the microbiome of the predatory mite

442

from manure amended soil via the prey collembolan. These results all approved

443

our hypotheses, and more attention should be focused on trophic transfer of

444

ARGs in the soil food web in the further study.

445 446

Manure enhances ARGs. As we hypothesized, addition of manure

447

significantly increases the number and abundance of ARGs in the microbiome

448

of the soil collembolan, suggesting that the manure may make an important

449

contribution to the occurrence and spread of ARGs in the soil fauna microbiome.

450

Three possible reasons are given to explain the result. (1) Our results indicate

451

that the manure used contained abundant ARGs (Table S2). These ARGs may 21



452

directly enter the microbiome of the soil collembolan by ingestion. (2) Multiple

453

antibiotics were also detected in the used manure (Table S1). This suggests

454

that collembolans feeding on the manure will be exposed to multiple antibiotics

455

which may increase the ARGs in the microbiome of the collembolan. Because

456

previous studies have shown that the concentration of antibiotics detected in

457

our test can cause an increase in ARGs in natural environments 4, 51. (3) Manure

458

addition significantly increased the number and abundance of ARGs in the soil

459

microbiome (Figure S1), which might contribute indirectly to the increase in

460

ARGs in the microbiome of the collembolan by the exchange of microbiota

461

carrying ARGs between soil and collembolan 23. Since collembolans are usually

462

colonized with many pathogenic microbiota 39, 40, 52, the increase in ARGs in the

463

collembolan microbiome may enhance the occurrence of resistant pathogenic

464

microbiota in soil ecosystems. In our study, more aminoglycoside, beta-

465

lactamase, multidrug and others genes (Not classified according to their

466

antibiotic resistance) are observed in the manure-treated collembolan

467

microbiome compared to the control, indicating that these genes are more

468

readily transferred to the collembolan microbiome from the manure. With the

469

activity of the collembolan, genes may be more easily dispersed in soil

470

ecosystems. Considering that these genes are commonly found in ARG-

471

contaminated soils

10, 53

472

veterinary medicine

54

473

focus may be needed to understand the spread of these ARGs by soil fauna in

and there is a huge demand in the human and

for the antibiotics they are resistant to, therefore, more

22


Page 22 of 53

Page 23 of 53

474


soil ecosystems.

475

In the present study, low abundance of beta-lactam resistance genes were

476

observed in the microbiome of collembolan, which suggested beta-lactam

477

biosynthetic gene might be not over-represented in the collembolan. Because

478

collembolans have recently been shown to include antibiotic biosynthetic gene

479

clusters in their genome 55, 56. More specifically, beta-lactam biosynthetic gene

480

clusters were shown in the genome of F. candida and actively transcribed in

481

the gut

482

that they are produced to interfere with the microbiome and the occurrence of

483

ARGs.

484

Trophic transfer of ARGs. In our study, the trophic transfer of ARGs is

485

identified for the first time in the microbiomes throughout the soil collembolan-

486

predatory mite food chain, indicating that the dispersal of ARGs in soil food

487

webs is occurring, and can be enhanced by application of manure. This is easy

488

to understand, because after soil collembolans are consumed by predators,

489

their microbiota usually remain in the gut of the predator for some time

490

part of the microbiota with these ARGs may remain and horizontal transfer of

491

ARGs from one bacterium to another may occur in the gut of the predator 16, 58,

492

which will contribute to trophic transfer of ARGs in the food chain. The transfer

493

of ARGs may have two opposite effects on the host animal. On the one hand,

494

when ARGs are transferred into pathogens, they may be harmful for the host

495

animal due to increasing the resistance of pathogens. On the other hand,

56.

The functions of these gene products remain elusive, but it is likely

23


57.

A


496

animal-beneficial bacteria carrying these ARGs may enhance the host

497

resistance and prevent their loss in polluted agricultural landscapes, such as

498

where manure is applied. However, to our knowledge, so far no study has

499

explored the effects of ARGs on the health of collembolan or mites.

500

Approximately 29.7% of all detected ARGs were shared between the soil, prey

501

collembolan and predatory mite (Figure 2), suggesting that these genes have

502

a potential chance of transfer into the prey collembolan-predatory mite food

503

chain from soils. Multidrug genes account for 32% of these genes, which is not

504

surprising. Because a previous study has indicated that multidrug efflux pumps

505

are native to all bacteria – even the one sensitive to antibiotics 1. Therefore,

506

their presence did not indicate the presence of resistant bacteria. To evaluate

507

the risk of ARGs for soil ecosystem health, further study may need making the

508

efforts to check for actual resistant bacteria. Our results showed that the

509

blaSHV (beta-lactamase), fosX (others) and aph6ia (aminoglycoside) had the

510

highest normalized abundance among these shared ARGs, and their trophic

511

transfer occurred in the model soil food chain. This suggested that these ARGs

512

may cause a greater risk for soil ecosystem health compared to the dispersal

513

of low abundant resistance genes. We further find that the assembly of these

514

shared genes is not neutral, suggesting that the soil food chain may influence

515

selection in the transfer of ARGs. Selection may be related to the unique niches

516

of different soil fauna, because different niches are generally inhabited by

517

different microorganisms 41, and ARGs are usually associated with microbes 3, 24


Page 24 of 53

Page 25 of 53


518

59, 60.

519

neither found in the control soil, nor in the supplemented manure. These ARGs

520

may mainly originate from soil, because the addition of manure altered soil

521

microbial community and might produce a pressure for soil bacterial community

522

due to commonly occurring toxic compounds (e.g. antibiotics and heavy metals).

523

Of course, the detection limit might also be a reason, because manure might

524

enhance the abundance of “native” ARGs which might be not detected due to

525

low abundance before the addition of manure. In the present study, three

526

unique ARGs are identified, which are transferred into the microbiome of the

527

predatory mite from the manure via the prey collembolan. This is consistent

528

with our hypothesis, and the soil collembolan-predatory mite food chain may

529

act as a reservoir and transportation system for ARGs in soil ecosystems. Thus,

530

food chain transmission of resistance genes is a potentially important pathway

531

for the dispersal of ARGs, especially in ARGs-contaminated soil ecosystems.

532

These results also suggested that when the collembolan and mite are preyed

533

by other animals (e.g. spider and bird), the ARGs may also transfer to animals

534

of a second trophic level. With the collembolan and mite moving, these ARGs

535

may also be transferred into plants. By consuming the plant and direct or

536

indirect contacting with these animals, these ARGs may have implications for

537

human health. In this study, many unique ARGs were observed in microbiomes

538

of soil, prey collembolan and predatory mite. The ARGs unique to soil might be

539

genes naturally inherent to soil bacteria that cannot live in the studied

Noteworthily, in the manured soil, a high number of ARGs pop up that are

25



Page 26 of 53

540

mite/collembolan microbiomes. Likewise, the ARGs unique to mites and

541

collembolans might be part of the core genome of bacteria that obligately (or

542

facultatively) live in the microbiomes of these animals.

543 544

Change in bacterial community. In our study, Proteobacteria and

545

Actinobacteria were two dominant phyla in the microbiome of collembolan F.

546

candida, which was different from a previous study

547

Gammaproteobacteria were confirmed in the core microbiota of collembolan.

548

This may be because the different of cultivation environment leads to this

549

difference. Our collembolans were obtained from the soil system amended with

550

the manure, and the previous study was conducted in the plate system. Similar

551

results have also been observed in our previous study 52. Our results show that

552

addition of manure significantly alters the composition and structure of the

553

bacterial communities of the prey collembolan and predatory mite. Because

554

bacteria are an important part of the feeding ecology of the Collembola, and the

555

addition of manure changed the diet of the soil fauna by changing the bacterial

556

community. In addition, in our study, microbial communities show a significant

557

difference between soils in the control and manured treatments. Diet and

558

environmental factors all have important effects on changes in animal

559

microbiomes 41, 61, 62. The change in the predatory mite microbiome may be due

560

to the microbiome of the prey collembolan shift. Because, for the predatory mite,

561

the difference between different treatments was mainly from prey collembolans 26


55

that Firmicutes and

Page 27 of 53


562

offered. The diversity of the bacterial community in the prey collembolan and

563

predatory mite food chain is significantly reduced with the addition of manure.

564

Collembolans were likely attracted to the manure as a food source which

565

reduced the diversity of food types ingested by the collembolans, and the

566

diversity of food has a relationship with the diversity of the gut microbiota

567

Moreover, the swine manure has different physicochemical properties than the

568

soil or contains some compounds toxic to the soil-inherent bacteria, thereby

569

reducing the microbial diversity, which is also an important reason for the

570

change of collembolan microbiome. Our results also showed that the

571

endosymbionts Wolbachia were strikingly overrepresented in the collembolan

572

after manure treatment. It is well established that Wolbachia has an important

573

contribution to the fitness and reproduction of arthropod

574

manure might affect the health of collembolan by altering the abundance of

575

Wolbachia.

64.

63.

Therefore, the

576 577

Contribution of the bacterial community and MGEs to the change in ARGs.

578

A significant correlation between bacterial communities and ARG profiles is

579

observed in our study, and bacterial communities can explain 50.63% of all

580

ARGs variation. This implies that bacterial communities are a dominant driver

581

of the change in ARGs in the microbiomes throughout the soil collembolan-

582

predatory mite food chain. Numerous previous studies also support this

583

assumption

3, 8.

MGEs are usually known to have an important contribution to 27



16, 58.

Page 28 of 53

584

the horizontal transfer of ARGs between bacteria

Our results reveal that

585

high abundance of MGEs was detected in the microbiomes of the prey

586

collembolan and predatory mite in the manured soil treatment, and the relative

587

abundance of MGEs was significantly correlated with the abundance of ARGs.

588

The pRDA analysis also shows that 10.66 % of all ARGs variation can be

589

explained by the MGEs, which is higher than that of previous studies in the soil

590

65

591

ARGs shift in the prey collembolan and predatory mite. Therefore, bacterial

592

communities and MGEs are two important drivers of trophic transfer of ARGs

593

in the microbiomes throughout the soil collembolan-predatory mite food chain.

and phyllosphere 8. This indicates that MGEs play an important role in the

594 595

In summary, the trophic transfer of ARGs in the microbiomes of the prey

596

collembolan and predatory mite has been observed in our study. The addition

597

of manure to soil significantly increased the abundance and diversity of ARGs

598

in the microbiome of the prey collembolan. The 22 ARGs identified have the

599

potential to migrate in microbial communities throughout the soil food chain,

600

and the assembly of these genes is not random. We also found that three

601

unique ARGs can be transferred from manure into the microbiome of a

602

predatory mite via collembolan predation. Manure can alter the composition and

603

structure of the bacterial community and reduce the diversity in the

604

microbiomes of the prey collembolan and predatory mite. Bacterial

605

communities and MGEs are two important drivers of the trophic transfer of 28


Page 29 of 53


606

ARGs in microbial communities throughout the soil collembolan-predatory mite

607

food chain. These findings contribute to our knowledge of trophic transfer of

608

ARGs in the soil detritus food chain and will direct our attention to the spread

609

and dissemination of resistance genes in the soil food web.

610 611

ASSOCIATED CONTENT

612

Supporting Information

613

Tables

614

Table S1 The basic properties (n = 3; Mean ± SD) of the swine manure and soil

615

used in the experiment.

616

Table S2 The normalized abundance (n = 3; Mean ± SE) of antibiotic resistance

617

genes in the swine manure used.

618

Table S3 The normalized abundance (n = 3; Mean ± SE) of mobile genetic

619

elements (MGEs) in the microbiomes of collembolan, predatory mite, soil and

620

manure.

621

Table S4 Information on the 296 genes detected in the gene chip.

622

Table S5 Analysis of the Adonis test revealing the difference of antibiotic

623

resistance genes (ARGs) and bacterial community patterns between different

624

samples.

625

Figures

626

Figure S1. The characterization of antibiotic resistance genes (ARGs) in the soil

627

microbiome. 29



628

Figure S2. The heatmap revealing the relative abundance of every antibiotic

629

resistance gene (ARG) in all samples.

630

Figure S3. Composition and abundance of the microbiomes of manure and soil.

631

Figure S4. (a) Principal coordinates analysis (PCoA) depicting the distribution

632

pattern of soil bacterial communities based on the Bray-Curtis distance for

633

OTUs. (b) The Shannon index (n = 3; Mean ± SE) of soil bacterial communities

634

at a sequencing depth of 18164.

635

Figure S5. Nonmetric multidimensional scaling analysis depicting the

636

differences in ARG profiles between different samples in the model soil food

637

chain.

638

Figure S6. The assembly of the shared ARGs in the model soil food chain.

639

Figure S7. Alluvial diagram showing the composition (n = 3; Mean) of the

640

microbiomes, at the phylum level, of the prey collembolan and predatory mite.

641

Figure S8. Principal coordinates analysis (PCoA) depicting the distribution

642

pattern of bacterial communities based on the Bray-Curtis distance for OTUs in

643

all samples.

644

Figure S9. Linear regression between relative abundance of mobile genetic

645

elements (MGEs) and antibiotic resistance genes (ARGs).

646

Total:

647

Number of tables: 5

648

Number of figures: 9

649

Number of pages: 46 30


Page 30 of 53

Page 31 of 53


650 651

AUTHOR INFORMATION

652

Corresponding Author

653

* E-mail address: [email protected]. Tel: +86 592- 6190997; Fax: +86 592-

654

6190977.

655

Notes

656

The authors declare no competing financial interest.

657 658

ACKNOWLEDGMENTS

659 660

This work was funded by the National Natural Science Foundation of China

661

(41571130063), the Strategic Priority Research Program of the Chinese

662

Academy of Sciences (XDB15020302 and XDB15020402) and the National

663

Key Research and Development Program of the China-International

664

collaborative project from the Ministry of Science and Technology (Grant No.

665

2017YFE0107300).

666 667

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906 907 908 909 910 911 912 913 42


Page 42 of 53

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914

Figure legends

915

Figure 1. The characterization of antibiotic resistance genes (ARGs) in the

916

microbiome of a model soil food chain. The detected number (a) and

917

normalized abundance (b) of ARGs (n = 3; Mean ± SE) in the microbiome of

918

the prey collembolan and predatory mite. A t-test was used to compare the

919

difference within species between control soil and manured soil treatments

920

(Significant level: 0.05). (c) The heatmap revealed the normalized abundance

921

of every ARG in all samples. Based on the antibiotics they resist, we classified

922

ARGs into eight categories (aminoglycoside, beta-lactamase, macrolide-

923

lincosamide-streptogramin

924

tetracycline, vancomycin). Coll-, prey collembolan in the control soil treatment;

925

Coll+, prey collembolan in the manured soil treatment; Mite-, predatory mite in

926

the control soil treatment; Mite+, predatory mite in the manured soil treatment.

927

The red asterisk to the left of the black line indicates a significant difference

928

between collembolans of the control soil treatment and the manured soil

929

treatment. The red asterisk to the right of the black line indicates a significant

930

difference between predatory mites of the control soil treatment and the

931

manured soil treatment. The “*” indicates P < 0.05, and the “***” indicates P
2% of the highest abundance of reads in all used samples) detected 44


Page 44 of 53

Page 45 of 53


958

in the microbiome of a model soil food chain. If the name of the genus was not

959

available, higher possible taxonomic classification was used. Coll-, prey

960

collembolan in the control soil treatment; Coll+, prey collembolan in the

961

manured soil treatment; Mite-, predatory mite in the control soil treatment; Mite+,

962

predatory mite in the manured soil treatment.

963 964

Figure 5. (a) Principal coordinates analysis (PCoA) depicting the distribution

965

pattern of bacterial communities of the prey collembolan and predatory mite

966

based on the Bray-Curtis distance of OTU in a model soil food chain.

967

Interpretation of the variation is listed in parentheses by the PCoA axes. Coll-,

968

prey collembolan in the control soil treatment; Coll+, prey collembolan in the

969

manured soil treatment; Mite-, predatory mite in the control soil treatment; Mite+,

970

predatory mite in the manured soil treatment. (b) The Shannon index (n = 3;

971

Mean ± SE) of bacterial communities of the prey collembolan and predatory

972

mite at a sequencing depth of 18164. A t-test was used to compare the

973

difference between control soil and manured soil treatments for the same soil

974

animal species (Significant level: 0.05). The “***” indicates P < 0.001, and “**”

975

indicates P < 0.01.

976 977

Figure 6. (a) Procrustes analysis depicting the significant correlation between

978

antibiotic resistance gene (ARG) profiles and composition of the bacterial

979

community (the relative abundance of 16S rRNA gene OTU data) based on 45



980

Bray−Curtis dissimilarity metrics (sum of squares M2 = 0.127, P = 0.0001, 9999

981

permutations). The triangles represent the composition of the bacterial

982

community, the circles indicate ARG profiles, and the colors of squares indicate

983

different samples. Coll-, prey collembolan in the control soil treatment; Coll+,

984

prey collembolan in the manured soil treatment; Mite-, predatory mite in the

985

control soil treatment; Mite+, predatory mite in the manured soil treatment. The

986

relationship between ARGs and bacterial communities is also determined by

987

the Mantel test using the Bray−Curtis distance. (b) Partial redundancy analysis

988

(pRDA) revealing the contribution of bacterial communities (BC; genus level)

989

and mobile genetic elements (MGEs) to the change in ARGs in all samples.

46


Page 46 of 53

Page 47 of 53

c

Figure 1

Environmental Science & Technology Aminoglycoside

* * * * *

Control soil Manured soil

* * Beta_Lactamase

* * *

MLSB Normalized abundance

* * Multidrug

*

Control soil Manured soil

Others Sulfonamide Tetracycline

Vancomycin


* *


Figure 2

Page 48 of 53

c

b

d Shared ARGs

Shared ARGs

a

g e

Shared ARGs

Shared ARGs

f ACS Paragon Plus Environment

Page 49 of 53


Figure 2 Control soil Soil

2

Manured soil

28

13

14

20

25

8

8

7

1

23

2

3 14

33

Predatory mite

Prey Collembolan

3

4

Manure

3


0

3 2



Page 50 of 53

Page 51 of 53


Figure 4 Genus

Tsukamurella Wolbachia Ralstonia Pseudomonas Ochrobactrum Stenotrophomonas Sphingomonas Streptomyces Bacillus Chryseobacterium Delftia Clostridium Elizabethkingia Burkholderia Mycobacterium (f) Streptomycetaceae (f) Nocardioidaceae Acinetobacter (f) Nocardioidaceae (o) iii1-15 (f) Isosphaeraceae (f) Gaiellaceae Paenibacillus Candidatus Nitrososphaera Brevibacillus

Prey collembolan 0.16

Predatory mite

4.88

7.03

3.01

0.00

0.03

13.38

7.04

23.63 24.89 51.45

0.21

6.81

1.29

8.07

39.40 25.57 44.03

0.36

0.41

0.00

22.26

0.51

0.54

0.22

0.40

39.10

1.66

0.22

1.93

0.83

6.00

3.92

4.03

2.53

1.78

1.78

4.25

14.74

0.58

2.09

3.74

2.69

38.32

8.55

1.77

0.78

0.55

0.63

0.85

3.37

26.02

0.13

0.73

0.05

6.04

1.83

0.03

0.09

0.07

0.18

0.23

0.01

0.24

0.64

0.03

0.00

0.07

0.12

19.41

5.04

4.99

2.85

15.66 4.41

1.97

6.84

13.97 15.96

1.36

2.25

12.81

1.84

0.88

1.53

15.58 1.39

0.36

0.51

0.58

0.68

1.46

1.10

0.38

4.32

4.67

4.12

3.21

1.14

1.04

3.41

2.90

3.64

9.95

8.34

1.06

0.20

0.29

0.19

0.11

0.03

0.03

1.11

0.20

0.88

0.72

0.16

8.04

1.52

2.02

1.03

0.61

0.74

1.06

7.35

7.46

4.73

0.59

0.79

2.81

0.37

0.44

0.34

0.12

0.05

0.02

6.88

0.67

0.47

0.05

0.09

0.07

0.00

0.00

0.01

0.00

0.00

0.00

0.00

5.59

0.00

0.00

0.22

0.00

0.91

1.67

0.59

0.00

0.01

4.83

0.01

0.49

4.79

0.00

0.00

0.89

0.50

0.87

0.47

0.00

0.00

0.04

2.02

4.04

1.75

0.26

0.94

0.00

0.79

0.64

0.68

4.00

1.07

0.88

0.76

0.60

1.38

0.27

0.06

1.49

0.85

1.28

0.57

0.01

0.01

0.99

2.42

3.84

3.99

0.05

0.25

2.78

1.19

1.03

0.85

2.31

1.21

1.07

1.16

0.62

1.63

1.02

3.47

1.63

0.77

0.97

1.02

0.00

0.04

0.78

1.36

3.39

1.21

0.07

0.18

2.31

1.77

2.51

2.90

0.09

0.12

0.27

0.42

0.54

1.40

0.07

0.37

1.38

0.59

0.53

0.39

0.00

0.00

0.26

2.77

0.00

1.10

0.03

0.05

0.23

2.10

2.63

2.31

0.05

0.08

0.17

1.47

0.00

0.52

0.00

0.09

0.33

1.00

0.99

1.16

2.57

0.58

0.28

1.15

0.76

0.71

0.07

0.18

0.70

2.21

1.64

1.14

0.02

0.00

0.51

0.37

0.22

0.15

0.01

0.01

0.00

0.45 0.38 0.38 2.12 0.38 0.12 Coll-1 Coll-2 Coll-3 Coll+1 Coll+2 Coll+3

% Read Abundance

0.06

0.06

0.39 0.40 0.69 0.11 0.11 1.34 Mite-1 Mite-2 Mite-3 Mite+1 Mite+2 Mite+3

0


1

60


Figure 5 a

b


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Figure 6 b

a


Trophic Transfer of Antibiotic Resistance Genes in ... - ACS Publications

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