Antibiotics Disturb the Microbiome and Increase the Incidence of

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Antibiotics Disturb the Microbiome and Increase the Incidence of Resistance Genes in the Gut of a Common Soil Collembolan Dong Zhu, Xin-Li An, Qing-Lin Chen, Xiaoru Yang, Peter Christie, Xin Ke, Longhua Wu, and Yong-Guan Zhu Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b04292 • Publication Date (Web): 29 Jan 2018 Downloaded from http://pubs.acs.org on January 31, 2018

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Antibiotics Disturb the Microbiome and Increase the Incidence of

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Resistance Genes in the Gut of a Common Soil Collembolan

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Dong Zhu,†,‡ Xin-Li An,†,‡ Qing-Lin Chen,†,‡ Xiao-Ru Yang, † Peter Christie,

§

Xin Ke,||

5

Long-Hua Wu, § Yong-Guan Zhu, *, †,

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7

Chinese Academy of Sciences, 1799 Jimei Road, Xiamen 361021, China.

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University of the Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, China.

9

§

Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science,



Key Laboratory of Urban Environment and Health, Institute of Urban Environment,

10

Chinese Academy of Sciences, Nanjing 210008, China.

11

||

12

Chinese Academy of Sciences, Shanghai 200032, China.

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14

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

Institute of Plant Physiology and Ecology, Shanghai Institute of Biological Sciences,

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

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ABSTRACT:

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Gut microbiota make an important contribution to host health but the effects of

19

environmental pressures on the gut microbiota of soil fauna are largely uncharacterized. Here,

20

we examine the effects of norfloxacin and oxytetracycline on the gut microbiome of the

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common soil collembolan Folsomia candida and concomitant changes in the incidence of

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antibiotic resistance genes (ARGs) in the gut and in growth of the collembolan. Exposure to 1

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10 mg antibiotics kg-1 for two weeks significantly inhibited the growth of the collembolan

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with roughly a ten-fold decrease in 16S rRNA gene abundance. Antibiotics did alter the

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composition and structure of the collembolan gut microbiome and decreased the diversity of

26

the gut bacteria. A decline in the Firmicutes/Bacteroidetes ratio in the antibiotic-treated

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collembolans may be responsible for the decrease in body weight. Exposure to antibiotics

28

significantly increased the diversity and abundance of ARGs in the collembolan gut. The

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Mantel test and Procrustes analysis both reveal that ARGs and gut microbiota were

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significantly correlated with one another (P < 0.05). These results indicate that antibiotics

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may induce a shift in the gut microbiota of non-target organisms such as soil collembolans

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and thereby affect their growth and enrichment of ARGs.

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TOC ART

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INTRODUCTION

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Soil fauna play a key role in the soil ecosystem, influencing the maintenance of 1-3

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biodiversity, litter decomposition, nutrient cycling and energy transfer

. Collembolans are

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one of the most widespread and important groups of soil microarthropods and are often found

65

in areas of high organic matter content where organic particles and microorganisms are

66

ingested

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extensively in soil ecology, ecogenomics and ecotoxicology5-11. However, to date, only a

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study has described the collembolan gut microbiome using 16S rRNA gene high-throughput

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sequencing 12. Gut microbiota make a vital contribution to many aspects of animal health 13-16,

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including feeding, growth, pathogen defense, energy metabolism, reproduction, immunity

71

and aging. It has been reported that arthropods rely on their gut microbiota to obtain essential

72

nutrient resources

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collembolan gut microbiome.

4, 5

. The collembolan Folsomia candida is a model organism that has been studied

17, 18

. We therefore urgently require more information regarding the

74 75

Soil pollution is a matter of great concern in many agricultural and industrial areas 19 and

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soil fauna are direct witnesses of soil pollution and are often used as indicators of

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environmental pollution20. Antibiotics are major soil contaminants that have recently given

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rise to considerable concern because they (or their residues) usually have long half-lives and

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high biological activity

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evolve as a consequence of competition among bacterial communities, repeated applications

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of large amounts of animal manure to agricultural soils have profoundly increased antibiotic

18, 21

. Although antibiotics are part of the natural soil ecosystem and

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concentrations in the soils

. It is generally thought that the effects of antibiotics on

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animals consist of direct damage to their physiology and disturbance of their gut microbial

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communities

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animal physiology (e.g. growth, reproduction and metabolism)

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microbiota will be exposed to antibiotics when manure containing antibiotics are ingested by

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soil biota. Studies on aquatic and aboveground terrestrial animals have shown that ingestion

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of antibiotics can reduce their gut microbial diversity

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exposure on the gut microbiota of non-target soil fauna remain largely unknown. At the same

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time, it has been demonstrated that anthropogenic arsenic exposure can suppress the

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microbiomes of several taxa of earthworm such as Verminephrobacter symbionts 27. A study

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has also indicated that the viability of the earthworm gut microbiota has some potential in

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reflecting the status of soil pollution 28. These investigations suggest that further studies are

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required to explore the responses of soil animal gut microbial communities to antibiotics.

18

. Many previous studies have investigated the effects of antibiotics on soil 23-25

.

And soil animal gut

16, 26

. However, the effects of antibiotic

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In addition, some bacteria can adapt rapidly to antibiotic exposure in the environment via

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antibiotic-resistance mechanisms, and antibiotic resistance genes (ARGs) can transfer within

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and between bacterial communities via numerous mobile genetic elements (MGEs) 18, 29. The

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overuse or misuse of antibiotics has led to a significant increase in the abundance and 30-32

100

amounts of ARGs in the soil environment worldwide

, and this is now a topic of great

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public concern. The gut is an important site for the accumulation of ARGs due to the

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selective pressure of the ingested antibiotics 33, 34. Previous studies confirm that oral exposure

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to antibiotics can increase the diversity and abundance of ARGs in the gut of mice and pigs 35, 5

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animals due to antibiotic exposure and this restricts our understanding of the spread of ARGs

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in soil food webs. In addition, antibiotic biosynthesis genes have recently been found in F.

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candida and these are believed to originate from microorganisms

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microbiome and ARGs of F. candida may contribute to our understanding of gut microbial

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and ARG diversity.

. However, there is virtually no information available on ARG enrichment in the gut of soil

37

. Thus, studying the gut

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The aims of the present study were therefore to confirm the effects of exposure to

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antibiotics on soil collembolan growth and gut microbiota using bacterial 16S rRNA gene

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high-throughput sequencing, to characterize the abundance and diversity of ARGs in the

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collembolan gut via high throughput qPCR (295 primer sets targeting almost all major classes

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of ARGs and 10 MGE marker genes), and to explore the relationship between the

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collembolan gut microbial community and ARGs. The results may help us to understand the

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spread of ARGs in soil food webs.

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MATERIALS AND METHODS

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Test Animals and Antibiotics. The soil collembolan Folsomia candida (“Berlin strain”)

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used originated from Aarhus University in Denmark and has been cultured in our laboratory

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for > six years. The conditions of rearing and synchronization of the animals have been

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described by Zhu et al. (2016)

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individuals were exposed to food spiked with antibiotics.

38

. In the current experiment, synchronized F. candida

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Norfloxacin

(NFC;

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-carboxylicacid;

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10,12,12a-hexahydroxy-6-methyl-1,11-dioxo-monohydrochloride;

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purity, >99%) were selected as the model antibiotics. Both are widely used in human and

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veterinary medicine and are the main residual antibiotics in animal manure. Previous studies

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shown that their concentrations are typically up to several mg kg-1 in contaminated manure39,

132

40

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collembolans. Therefore, in the present study we selected an environmentally relevant

134

concentration (10 mg kg-1) of norfloxacin and oxytetracycline for exposure to the test

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collembolan.

CAS

1-ethyl-6-fluoro-1,4-dihydro-7-(1-piperazinyl)-4-oxo-3-quinoline 70458-96-7,

purity,

>99%)

and

oxytetracycline CAS

(OTC;

2058-46-0,

. When these manure are applied to agricultural soils they are likely ingested by the soil

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Antibiotic Exposure Treatment. Baker’s yeast (Angel Yeast Co., Ltd, Hubei, China)

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was used as the diet. Yeast spiked with each antibiotic (10 mg antibiotic kg-1 dry weight food)

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was obtained by thorough mixing of the yeast with an appropriate volume of antibiotic

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solution. The same volume of deionized water was mixed with the yeast as a control. The

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three mixtures were frozen at -20 oC, freeze-dried, ground and stored at -20 oC prior to use.

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There were three treatments including the control and four replicates of each treatment,

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giving a total of 12 samples. For each replicate, fifty synchronized F. candida individuals

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(18-20 days) were transferred into a Petri dish (inner diameter 90 mm) with a layer (thickness

145

5 mm) of a moist mixture of activated charcoal and plaster of Paris (1:8 w/w).

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Antibiotic-spiked and control yeasts (5 mg) were respectively added twice a week, and

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unused old food was removed. Oral exposure of F. candida to the antibiotics took place for 7

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two weeks in a 16:8 h (dark: light, 800 lux) light regime at 20 ± 2 °C. Distilled water was

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supplied twice a week to maintain moist conditions. The number of collembolans was

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counted in each replicate dish at the end of the feeding period and all the collembolans were

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harvested. The guts of thirty of the harvested individuals were dissected. The surplus

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collembolans were starved for 92 hours on moist filter paper to remove the gut contents and

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then freeze-dried. The dry weight of the collembolans was determined using a Mettler Toledo

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XS3DU automatic electronic microbalance (precision ± 1 µg).

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A series of norfloxacin and oxytetracycline solutions were mixed with the yeast to

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produce nominal concentrations of 0 (Control), 0.1, 1, 10, 100 and 1000 mg antibiotics kg-1

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dry weight food. Ten synchronized F. candida (10-12d) were exposed to a series of

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antibiotics-spiked yeast to preform a standard 28-day reproduction test according to ISO

159

guideline 11267 (ISO1999)

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was counted via the number of juvenile.

41

. After exposure for 28 days, the reproduction of collembolan

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DNA Extraction from the Collembolan Gut. The harvested collembolans were killed

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immediately with chloroform to restrain their ingestion and excretion of the gut contents. The

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dead collembolans were immediately introduced into 2% sodium hypochlorite (NaOCl)

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solution for 10 s to prevent interference from the body surface microbiota. The collembolans

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were then rinsed five times with sterilized water. After aseptic treatment of the body surface

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the collembolans were dissected using sterile forceps under sterile conditions, and thirty

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dissected guts were transferred into a 2-mL Eppendorf tube with 0.96 mL sterile phosphate

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buffer solution. The resulting gut samples were stored at −80 °C until DNA isolation. 8

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Total DNA was extracted from the 12 collembolan gut samples using a FastDNA Spin

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Kit for soil (MP Biomedicals, Illkirch, France). The procedure of DNA isolation generally

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followed the manufacturer’s instructions with slight modification as follows. Before the

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bead-beating step, 20 µL proteinase-K (Thermo Fisher Scientific, Waltham, MA) was added

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to the spin column and kept at 55°C for 3 h. The extracted collembolan gut DNA was eluted

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using 50 µL DES solution. The concentration and quality of extracted DNA were checked

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using 1.0% agarose gel electrophoresis and spectrophotometric analysis (Nanodrop ND-1000,

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Thermo Fisher). Finally, the extracted DNA was preserved at -20 oC for further analysis.

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Quantification of Bacterial Abundance using Real-Time Quantitative PCR. The total

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bacterial abundance of the collembolan gut was estimated in triplicate with 16S rRNA gene

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copy number using real-time quantitative PCR (RT-qPCR) with a Light Cycler 480

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used the universal primers 5 ′ - GGGTTGCGCTCGTTGC -3 ′

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ATGGYTGTCGTCAGCTCGTG -3′ to amplify the bacterial 16S rRNA gene. All assays

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were conducted in a 20 µL qPCR reaction system consisting of 10 µL 2× TransStart® Top

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Green qPCR SuperMix (AQ131, Transgen biotech, Beijing, China), 0.8 µL bovine serum

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albumin (BSA, 20 mg mL-1), 0.25 µM each primer, and 2 µL gut DNA as a template. The

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thermal profile of RT-qPCR was as follows: 5 min at 95 °C followed by 40 cycles of 15 s at

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95 °C, 60 s at 60 °C and 20 s at 72 °C. Three non-template reactions were performed as

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negative controls. Triplicate 10-fold serial dilutions of plasmid DNA with target-gene were

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used to obtain a standard curve (amplification efficiency 96−104%, r2 > 0.99). 9

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. We

and 5 ′ -

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High-Throughput Quantitative PCR for Analysis of Antibiotic Resistance Genes. All

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high-throughput quantitative PCR (HT-qPCR) of ARGs were carried out in triplicate using

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the Wafergen SmartChip Real-time PCR system to investigate the abundance and diversity of

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ARGs in collembolan gut samples. We used a total of 296 primer (Table S1) sets to target 285

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ARGs (major classes of ARGs), class 1 intergron, clinical class 1 integron, 8 transposases and

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the 16S rRNA gene in one run. The 100 nL HT-qPCR reaction system per well consisted of

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LightCycler 480 SYBR Green I Master mix, each primer, nuclease-free PCR grade water,

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bovine serum albumin and gut DNA template. The reaction mixture was heated for 10 min at

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95 °C and then 40 cycles of 30s at 95 °C and 30s at 60 °C. The SmartChip qPCR software

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was used to analyze the outcomes. A threshold cycle (CT) of 31 was set up as the detection

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limit. The ARGs were considered to be detected when the ARGs of three replicates were all

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amplified. The normalized copy number of ARGs per cell was calculated using equations

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below 43:

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Relative gene copy number = 10^((31−CT)/(10/3))

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Normalized ARG copy number = (Relative ARG copy number/ Relative 16S rRNA gene

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copy number)*4.1. Because the average number of 16S rRNA gene per bacterial cell is currently estimated

209 210

at 4.1 based on the Ribosomal RNA Operon Copy Number Database 44.

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16S rRNA Gene Amplification, Illumina Sequencing and Bioinformatic Analysis.

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The

gut

DNA

samples

were

amplified

using

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primers

515F:

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GTGCCAGCMGCCGCGG

and

907R

labelled

with

unique

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CCGTCAATCMTTTRAGTTT to target the hypervariable V4-V5 region of the bacterial 16S

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rRNA gene in 50 µL reaction system. The conditions of PCR amplification and recovery of

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purified products were performed as described previously

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spectrophotometer was used to determine the concentration of PCR purified products. The

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sequencing library was constructed via twelve barcoded samples of equal quality and then

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high-throughput sequencing of the library was carried out on the Illumina Hiseq2500

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platform (Novogene, Tianjin, China).

30,

barcodes:

31

. The NanoDrop

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The high-throughput sequencing data were analyzed using Quantitative Insights Into 45

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Microbial Ecology (QIIME, version 1.8.0) following the online instructions

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reads were merged after the adaptor, and primer sequences, ambiguous nucleotides and

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low-quality reads were removed to obtain clean combined reads. In QIIME,

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pick_de_novo_otus.py was used to pick the operational taxonomic units (OTUs), and OTUs

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were identified at 3% sequence difference by UCLUST clustering

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analysis, OTUs with only one sequence (singletons) were discarded. The default method was

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adopted to obtain a representative sequence of the OTU in each cluster. The taxonomy of

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each representative sequence was aligned using PyNAST and assigned via the RDP Classifier

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2.2 based on the Greengenes 13.8 16S rRNA gene database 47, 48. The Fast tree algorithm was

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used to build a phylogenetic tree for downstream analysis

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estimated using metrics observed species (OTU), and it was used to describe bacterial alpha

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diversity. The microbial difference and beta-diversity of different samples were compared 11

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. Before downstream

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. The Shannon index was

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using the Adonis test and principal coordinate analysis (PCoA) based on the unweighted

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Unifrac metric, respectively.

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Statistical Analysis. All data are presented as mean value ± standard error (SE). The R 50

with vegan 2.4−3

51

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version 3.4.1

was used to conduct the Adonis test and calculate the

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Shannon index of the gut microbiota. The Procrustes test was used to describe the correlation

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between ARGs and bacterial communities in R version 3.4.1 with vegan 2.4−3, and a

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heatmap package 52 of R was selected to draw the heatmap. Differences among samples were

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analyzed using single factor analysis of variance (ANOVA), LSD and t-tests using the IBM

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SPSS version 22 statistical software package. The associated OTUs of different samples were

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counted using ANOVA and Bayesian model-based moderated t-tests in R. Significant

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differences were detected at the 0.05 level. The ternary plot was produced using OriginPro

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9.1. In ternary plot, each individual OTU is depicted by one circle. The size of the circle

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reflects the relative abundance of the OTU. The position of each circle is determined by the

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contribution of the indicated compartments to the relative abundance. The associated OTUs

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are based on a Bayes moderated t-test (P < 0.05, FDR-corrected). OriginPro 9.1 and

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Microsoft Excel were used to generate other graphics.

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Accession Numbers. All sequencing data are deposited in the National Center for Biotechnology Information Sequence Read Archive under the accession number SRP116006.

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RESULTS 12

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Change in Collembolan Growth and Gut Bacterial Abundance. The collembolan

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body weights declined significantly in the norfloxacin and oxytetracycline treatments by 16.2

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and 21.6%, respectively, compared to the control (P < 0.05) after 14 days of exposure to the

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antibiotics (Figure 1a). Results of a standard 28-day reproduction test indicated that the

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reproduction of collembolan was reduced after exposure to antibiotics at 10 mg kg-1

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compared to the control, but no significant difference was observed (t-test, P > 0.05) (Figure

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S1). Antibiotic exposure strongly inhibited the abundance of the gut microbiota (P < 0.05)

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and the gut 16S rRNA gene copy number was reduced about 10-fold in the antibiotic

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treatments compared with the control (Figure 1b). No significant differences in body weight

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or gut microbial abundance were observed between norfloxacin and oxytetracycline

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treatments (Figure 1) (P > 0.05).

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Effects of Antibiotics on Collembolan Gut Bacterial Diversity.

Across all samples,

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453195 non-singleton reads were calculated, minimum 19495 sequences per sample were

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obtained and 4718 OTUs were identified. Actinobacteria (37.93%), Firmicutes (24.42%),

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Proteobacteria (17.22%) and Bacteroidetes (14.57%) were the four predominant phyla in all

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samples of collembolan gut (Figure 2a). The results of high throughput sequencing indicate

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that exposure to norfloxacin and oxytetracycline significantly altered the collembolan gut

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microbial community (Adonis test, P < 0.05). At the phylum level, the collembolans exposed

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to antibiotics had higher abundance of Actinobacteria and Bacteroidetes (P < 0.05) and lower

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abundance of Firmicutes (P < 0.05) in the gut compared to the control. Antibiotics exposure 13

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caused the significant reduction of Firmicutes abundance from 42.51 to 16.38%, and the

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obvious

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Firmicutes/Bacteroidetes (F/B) ratio of the antibiotic treatments was significantly lower than

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that of the control (P < 0.05). Within the phylum Actinobacteria a significant increase in the

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abundance of the family Streptomycetaceae was observed in the antibiotic exposure

285

treatments compared to the control (P < 0.05) (Figure 2a). Exposure to antibiotics led to a

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significantly higher gut abundance of the family Sphingobacteriaceae (P < 0.05) (Figure 2a).

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Compared with the control, the relative abundance of the genus Streptomyces increased

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significantly (by 538 and 348%) in the norfloxacin and oxytetracycline treatments,

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respectively (P < 0.05) (Figure 2b). However, the abundance of 16S reads at the family level

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overall decreased in antibiotic-treated collembolan gut (Figure 2b), a result supported by

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Figure S2 showing that only a few significantly higher OTUs were found in the antibiotic

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treatments. Around 43% of norfloxacin-treated OTUs and 42% of oxytetracycline-treated

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OTUs changed significantly in relative abundance due to antibiotic exposure (Figure S3).

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Compared with the control, pronounced shifts in microbial OTU profiles were observed in

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antibiotic-treated collembolan gut microbiota. Principal coordinates analysis based on

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unweighted unifrac distance also shown a clear separation between the antibiotic treatments

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and the control gut microbiota along with the X axis (Figure 3a). Significant differences were

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observed between intra-treatment distances and inter-treatment distances (Figure 3b) (P
1% families) are

668

shown, and the data presented (mean, n = 4) were log-transformed.

669 670

Figure 3. (a) Principal coordinates analysis (PCoA) of collembolan gut samples using

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unweighted unifrac distances. C, N and O indicate control, norfloxacin and oxytetracycline

672

treatments, respectively. Each point represents a microbial community of one sample. (b)

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Similarity of gut microbiota between samples. Unweighted unifrac distances (mean ± SE)

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between control gut microbiota (C-C), between norfloxacin-treated gut microbiota (N-N),

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between

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norfloxacin-treated gut microbiota (C-N), between control and oxytetracycline-treated gut

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microbiota (C-O) and between norfloxacin-treated and oxytetracycline-treated gut microbiota

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(N-O). Different letters indicate significant differences (p < 0.05) among different distances

oxytetracycline-treated

gut

microbiota

(O-O),

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(LSD test). (c) Change in Shannon index (mean ± SE, n = 4) of the antibiotic-treated

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collembolan gut microbiota. Different letters indicate significant differences (p < 0.05)

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among different treatments (LSD test).

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Figure 4. (a) Detected number per 30 animals and (b) normalized copy number of ARGs and

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mobile genetic elements (MGEs) (mean ± SE, n = 4) in the collembolan gut microbiota

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exposed to antibiotics.

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Figure 5. Heat map describing enrichment of ARGs in the antibiotic-treated collembolan gut

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compared with the control. The data presented (mean, n = 4) were log-transformed.

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Figure 6. Procrustes test showing the significant correlation between ARG profile and

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collembolan gut microbial composition (16S rRNA gene OTU data) on the basis of

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Bray−Curtis dissimilarity metrics (sum of squares M2 = 0.27, r = 0.7965, P = 0.043, 9999

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permutations). The triangles indicated 16S rRNA gene OTU data of collembolan gut

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microbial composition, and the squares indicated experimental treatments.

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