Gut resistomes, microbiota and antibiotic residues in Chinese patients undergoing antibiotic administration and healthy individuals

Journal Pre-proof Gut resistomes, microbiota and antibiotic residues in Chinese patients undergoing antibiotic administration and healthy individuals

Yujing Duan, Zeyou Chen, Lu Tan, Xiaolong Wang, Yingang Xue, Shaopeng Wang, Qing Wang, Ranjit Das, Huai Lin, Jie Hou, Linyun Li, Daqing Mao, Yi Luo PII:

S0048-9697(19)35669-4

DOI:

https://doi.org/10.1016/j.scitotenv.2019.135674

Reference:

STOTEN 135674

To appear in:

Science of the Total Environment

Received date:

4 October 2019

Revised date:

18 November 2019

Accepted date:

19 November 2019

Please cite this article as: Y. Duan, Z. Chen, L. Tan, et al., Gut resistomes, microbiota and antibiotic residues in Chinese patients undergoing antibiotic administration and healthy individuals, Science of the Total Environment (2019), https://doi.org/10.1016/ j.scitotenv.2019.135674

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2019 Published by Elsevier.

Journal Pre-proof

Gut resistomes, microbiota and antibiotic residues in Chinese patients undergoing antibiotic administration and healthy individuals

Yujing Duana,1, Zeyou Chena,1, Lu Tana, Xiaolong Wanga, Yingang Xuec, Shaopeng

oo

f

Wanga, Qing Wanga, Ranjit Dasa, Huai Lin a, Jie Houa, Linyun Lia, Daqing Maob,*, Yi

Ministry of Education Key Laboratory of Pollution Processes and Environmental

e-

a

pr

Luoa,*

Pr

Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China

School of Medicine, Nankai University, Tianjin 300071, China

c

Key Laboratory of Environmental Protection of Water Environment Biological

rn

al

b

Jo u

Monitoring of Jiangsu Province, Changzhou Environmental Monitoring Center, Changzhou 213001, China

1

These authors contributed equally to this work.

*

Corresponding authors.

E-mail addresses: [email protected] (D.-Q. Mao), [email protected] (Y. Luo).

1

Journal Pre-proof

ABSTRACT

Human gut microbiota is an important reservoir of antibiotic resistance genes (ARGs). Although dysbacteriosis after the antibiotic course has been previously observed in the patient guts, a comprehensive comparison of gut resistomes, microbiota and antibiotic residues in healthy individuals and patients undergoing antibiotic

oo

f

administration is little. Using high-throughput qPCR, 16S rRNA gene amplicon sequencing and UPLC-MS/MS, we systematically examined the antibiotic resistome,

pr

gut microbiota, and antibiotic residues in fecal samples from both Chinese healthy

e-

individuals and patients receiving antibiotic therapy. Compared with healthy

Pr

individuals, patients’ guts harbored lower diverse gut resistome and microbiota, but higher concentrations of antibiotics and ARGs. Antibiotic concentration in human

al

guts was positively correlated with ARG total abundance, but was negatively related

rn

to the diversity of both ARGs and bacterial communities, which demonstrated that

Jo u

antibiotic administration could shape the antibiotic resistomes and bacterial communities in the patient guts. Gene cfxA was evaluated as a potential biomarker to distinguish the patients receiving antibiotic therapy from the healthy individuals in China since its wide detection and significant enrichment in the guts of the patients. The detection of some veterinary antibiotics in human guts illustrated the potential transmission of antibiotic from the external environment to human via the food chain. The obtained results could help to better understand the influence of antibiotic therapy in shaping antibiotic reistomes and bacterial communities in Chinese individuals.

2

Journal Pre-proof

Keywords: Antibiotic resistome; Gut microbiota; Antibiotic therapy; Chinese patient

1. Introduction

The human gut microbiota has been considered as the important reservoir of antibiotic resistance genes (ARGs) (Salyers et al. 2004, Sommer et al. 2010). Prior studies have demonstrated that the increasing antibiotic resistome in human gut

oo

f

microbiota could be related to the misuse and abuse of antibiotics (Feng et al. 2018,

pr

Hu et al. 2013, Pruden et al. 2006, Salyers et al. 2004). Antibiotics, especially with

e-

strong and broad activity against anaerobes were reported to create the long-lasting adverse effects on the gut microbiota (Jernberg et al. 2007, Jernberg et al. 2010,

Pr

Jernberg et al. 2005). More alarmingly, antibiotic resistant bacteria (ARB) selected by

al

the short-term antibiotic use could persist in the intestine for years. Considering many

rn

intestinal commensal bacteria such as Klebsiella, Acinetobacter, Pseudomonas, and

Jo u

etc. are opportunistic pathogens (I and AE 2009, Ryan and Ray 2004), the acquisition of ARGs by these bacteria after the prescription of antibiotics could cause substantial adverse health effects (Becattini et al. 2016). Meanwhile, the antibiotics induced shifts in human gut microbiota were also found to be involved in some diseases (Allin et al. 2018, Becattini et al. 2016, Jackson et al. 2018, Simon and Gorbach 1984, Zhao 2013). For instance, Duvaller et al. found that the changes of only 10-15 bacterial genera in intestinal flora could lead to the development of multiple human diseases (Duvallet et al. 2017). Consequently, the quantitative measurement of microbial shifts and resistomes in guts of human who involved in the antibiotic therapy might be informed 3

Journal Pre-proof

for the evaluation of the potential health risk associated with antibiotic use (Casén et al. 2015). By investigating the diversity and richness of ARGs in human gut microbiota, prior studies have shown that Chinese population generally harbored more diverse and abundant ARGs than the other countries (Feng et al. 2018, Hu et al. 2013).

f

Meanwhile, in these studies some abundant and representative ARGs in Chinese

oo

people, e.g., tetQ, ermF, and etc., were proposed as the discriminative ARGs to

pr

distinguish the Chinese individuals from the other countries. As such, the

e-

discriminative ARGs between the patients that received antibiotic therapy and healthy

Pr

individuals are likely to provide a clue to reveal the effect of antibiotics on gut microbiota (Angulo et al. 2009, Feng et al. 2018, Goossens et al. 2005, Hu et al. 2013,

al

Xie et al. 2018). Moreover, exploration of microbial resistomes and discriminative

rn

ARGs in gut microbiota of patients undergoing antibiotic therapy might be also a

Jo u

good way to assess the risk scenarios of antibiotic resistance, which would provide potential diagnostics and therapeutic strategies for human disease control and prevention. ARB and ARGs in human feces could enter the environment through the discharge of reclaimed water from wastewater treatment plants (WWTPs) (Karkman et al. 2019, Rizzo et al. 2013). Moreover, agricultural application of sewage sludge as fertilizer in fields could also lead to the migration of human gut resistome to the surrounding environments (Fang et al. 2018). Therefore, exploration of the antibiotic resistomes in human guts is important for evaluating their public health risk on the promotion of antibiotic resistance spread in the receiving environments. 4

Journal Pre-proof

In the present study, we combined high-throughput qPCR and 16S rRNA gene amplicon sequencing to study antibiotic resistomes and bacterial microbiomes in the guts of Chinese healthy individuals and patients. The 134 human fecal samples from different geographic locations of China were collected. The resistomes, microbiota, and antibiotic residues in fecal samples of Chinese patients were systematically

f

analyzed to compare with those of healthy individuals, and a discriminative set of

oo

ARGs that specifically related to the Chinese patients was identified. This study

pr

would help us to better understand the antibiotic resistomes, bacterial microbiota, and

e-

antibiotic residues in both Chinese healthy individuals and antibiotic-administrated

Pr

patients, and the obtained discriminative ARG sets are potentially promising for the

rn

2. Materials and methods

al

clinic to provide a correct antibiotic therapeutic strategy.

Jo u

2.1. Sample collection and DNA extraction

A total of 500 fecal samples of ethnic Han Chinese volunteers were collected from three distinct geographic locations, i.e., Changzhou of Jiangsu Province, Shenyang of Liaoning Province, and Taibai of Gansu Province during April 2016 to January 2018. Informed consent together with the questionnaire was dispatched to every participant to make sure their understanding of the study purpose, procedures, risks, benefits, and rights. Human research involved in this study were approved by the Human Ethics Committees of Nankai University and the corresponding hospitals. Based on physical and antibiotic use situations of the participants, these peoples could 5

Journal Pre-proof

be classified into the healthy and patient groups, as detailed in the supplemental Table S1. In this study, the adult subjects without disease and no medication experience for nearly six months before the feces collection were termed as ‘healthy individuals’, while the individuals with diseases and receiving antibiotic therapy in one month were termed as ‘patients’. Stool form scale was widely used to indicate the intestinal transit

f

time of stool (Lewis and Heaton 1997). In general, human feces with the scores of

oo

Bristol Stool Scale (BSS) 3 and 4 were the ideal stools for collection and research as

pr

they are easy to defecate while not containing excess liquid (Lacy and Patel 2017).

e-

Finally, 134 of 500 fecal samples with BSS of 3 and 4 were screened (Heaton et al.

Pr

1992, Lewis and Heaton 1997, Riegler and Esposito 2001), in which, 73 samples were from healthy individuals and 61 samples were from patients. All the information

al

associated with the 134 individuals and feces including health condition, disease type,

rn

antibiotics prescription, age, gender, BSS score, water content, etc. was summarized

Jo u

in supplemental Table S1.

Each sample was divided into five aliquots to avoid contamination and all the samples were stored at −80°C for subsequent analyses. The DNA was extracted using the PowerFecalTM DNA Isolation Kit (MoBio Laboratories, Inc., USA) according to the instruction manual (Costea et al. 2017). The quality and quantity of the extracted DNA were detected by Nano-Photometer N60 (Implen, Germany) and Qubit DNA quantification system (Invitrogen, USA). All extracted DNA samples were stored at −20°C for subsequent analyses. The residual fecal samples were lyophilized to quantify the water content and antibiotics concentration. 6

Journal Pre-proof

2.2. High-throughput quantitative PCR

In this study, the high-throughput qPCR assays with 130 primers targeting 122 ARGs, 7 MGEs (mobile genetic elements) and 16S rRNA gene were performed on a Wafergen SmartChip Real-time PCR System (Wafergen Inc., USA). All the primers used in this research were provided in Table S2. The composition of the amplification

oo

f

reaction system and setting conditions of the thermal cycler program were in the Text S1.

pr

A melting curve was generated and the obtained data were further analyzed by

e-

the program of Wafergen SmartChip qPCR software. The threshold cycle (Ct) 31 was

Pr

selected as the detection limit according to the method published by previous research (Ouyang et al. 2015, Wang et al. 2014), and samples with three positive replicates

al

were included. Meanwhile, the results with multiple melting peaks and the

rn

amplification efficiency beyond the range (1.80-2.20) were discarded. Gene copy

formulas:

Jo u

number and the relative abundance of each ARG were calculated using the following

(1) Gene Copy Number=10((31-Ct)/(10/3)) (2) Relative Abundance of ARG=Gene Copy Number of ARG / Gene Copy Number of 16S rRNA Gene

2.3. Absolute quantification of 16S rRNA gene

The absolute quantification of 16S rRNA gene was also conducted using SYBR Green dye-based qPCR in a thermal cycler (CFX96, BioRad Inc., USA). The 7

Journal Pre-proof

composition of the amplification reaction system and setting conditions of the thermal cycler were in Text S2. The non-template control was added as contrast, and amplification was performed in triplicate. To prepare the standard calibration curves with six-point, a plasmid (pTOPO-Blunt Simple) containing the target sequence of 16S rRNA gene clone was serially diluted 10-fold. A significantly high correlation between the results from the SmartChip Real-time PCR Systems and BioRad CFX96

oo

f

was observed (Spearman’s correlation coefficient of ρ = 0.87 and p < 0.01). All the

pr

details were provided in Text S2.

e-

2.4. Bacterial 16S rRNA gene sequencing for gut microbiota

Pr

Considering more direct perturbation on human gut microbiota, fecal samples of

al

21 patients that received oral antibiotic treatment were selected for the 16S rRNA

rn

gene amplicon sequencing (Table S1) (Dethlefsen and Relman 2011, Eckburg et al.

Jo u

2005, Sekirov et al. 2008). In addition, samples from 16 healthy individuals randomly selected from all 73 healthy individuals were also chosen to perform the detection of gut microbiota. The 16S rRNA gene amplicon sequencing was carried out using the universal primers to target the V4 hypervariable region. The high-throughput sequencing analysis of bacterial 16S rRNA genes was performed on the Illumina Hiseq 2500 platform (2×250 paired ends) at Biomarker Technologies Corporation, Beijing, China. The sequences have been deposited in the NCBI Sequence Read Archive Database under accession number SRP153274. The additional information was provided in Text S3. 8

Journal Pre-proof

2.5. Quantifications of antibiotics

The same 37 samples used for high-throughput sequencing analysis of bacterial 16S rRNA genes were also selected to perform the antibiotic detection using ultra-high-performance

liquid

chromatography-tandem

mass

spectrometry

(UPLC-MS/MS) (Waters, USA). A total of 17 antibiotics, including 4 tetracyclines, 5

oo

f

sulfonamides, 2 macrolides, 3 quinolones, and 3 beta-lactams were selected for analyses (Table S3). Samples pretreatment accompanied by a solid-phase extraction

pr

were carried out according to the previous research (Hu et al. 2010, Luo et al. 2010,

e-

Luo et al. 2011, Wang et al. 2019). The internal standard simetone was applied for the

Pr

analyses of antibiotics as described previously (Luo et al. 2011). The limit of detection (LOD) and the limit of quantification (LOQ) were calculated as the lowest

al

concentration level corresponding to signal-to-noise (S/N) ratio of 3 and 10. The

rn

detailed recovery, relative standard deviations (RSD), LOD, and LOQ of each

Jo u

antibiotic were provided in Table S4 and Text S4.

2.6. Statistical analyses

We used Microsoft Excel 2016 (Microsoft, USA) to calculate the statistical analyses of relative abundances of ARGs and antibiotic concentrations. The heatmap graphs were generated through a heatmap package of RStudio (R version 3.4.1). The scatter plots and diversity calculation of Shannon indexes were made by PAST 3.16 (Hammer et al. 2001). We performed the Spearman’s correlation coefficient, box plots and the significant difference of Mann-Whitney tests by RStudio (R version 3.4.1) 9

Journal Pre-proof

and OriginPro 2015 (OriginLab, USA). A correlation was considered to be statistically evident if ρ > 0.5 or < -0.5 and p < 0.05. The online version of LEfSe analysis (http://huttenhower.sph.harvard.edu/galaxy) was used to identify the discriminative ARGs. LEfSe is an algorithm for characterizing the differences between two or more biological conditions (Segata et al.

f

2011). Linear discriminant analysis (LDA) in LEfSe analysis could estimate the effect

oo

size of each differentially abundant feature and the non-parametric factorial

pr

Kruskal-Wallis (KW) test is used to detect features with significant differential

e-

abundance. The threshold on the LDA score was set at 3.5, and the alpha value for the

Pr

pairwise Wilcoxon test was 0.05 in the present research. The STAMP software was also used to analyze the discriminative ARGs of the patients as well as healthy

al

individuals (Parks et al. 2014). Graphs of extended error bar indicated the difference

rn

in mean proportion between the two groups and the p values of the discriminative

Jo u

ARGs were subjected to false discovery rate (FDR) correction through the Benjamini–Hochberg method. Network analyses were performed in R environment and network plot was realized on the Gephi platform (Bastian et al. 2009).

3. Results

3.1. Prevalence of ARGs in the gut of healthy individuals and patients in China

Using high-throughput qPCR assay, 111 unique ARGs and 7 MGEs were detected in the collected fecal samples, and the relative abundance of each ARG normalized to 16S rRNA gene is presented in Fig. 1. According to the abundance and 10

Journal Pre-proof

distribution of ARGs in all samples, detected ARGs could be divided into 4 major clusters (Fig. 1 and Table S5). ARGs in Cluster 1 and Cluster 2 generally had a lower abundance. Genes in Clusters 3 and 4 were detected in most human fecal samples with the relatively higher abundance. Among them, ermF, ermB, tetQ, tetW, tetO-01, and tet32 were detected in all individuals. Regarding antibiotic types, the detected

f

ARGs could confer bacterial resistance to most used antibiotics (Fig. S1 and Fig. S2A,

oo

B). Tetracycline, MLSB (macrolide-lincosamide-streptogramin B), beta-lactam,

pr

aminoglycoside, and multidrug resistance genes were the five most abundant gene

e-

types with the mean relative abundance in all individuals of 0.68, 0.39, 0.43, 0.04, and

Pr

0.09 ARG copy per copy of 16S rRNA, respectively. In general, most detected ARGs in human feces belonged to the three major resistance mechanisms i.e., antibiotic

al

deactivation, cellular protection, and efflux pumps (Fig. S2C, D).

Jo u

rn

3.2. Gut resistome differences between healthy individuals and patients

In the present study, 111 and 92 ARG subtypes were detected in healthy individuals and patients, respectively. The calculated total relative abundance of detected ARGs was significantly higher in the patients than that in the healthy individuals (2.833.51 vs 0.750.43 ARG copy per copy of 16S rRNA) (Fig. 2A). The diversity of ARGs evaluated by Shannon indexes also indicated that the presence of a significant difference (Mann-Whitney test, p < 0.01) between the healthy individuals and the patients (2.340.45 vs 1.750.52 ARG copy per copy of 16S rRNA) (Fig. 2B). As shown in Figure 1, 19 ARGs of blaGES, blaIMP-01, blaPER, blaVIM, catB8, 11

Journal Pre-proof

cmxA, ereB, ermC, marR-01, mdtA, mecA, mphB, msrA-01, qnrA, tet36-01, tetH, tetPB-01, vanC-01, and vanRA-01 detected in healthy individuals were not detectable in patients. Compared to the healthy individuals, the relative abundances of cfxA, ermF, blaTEM, tetQ, sul2, tnpA-01 and IS613 in all patients increased more than 4 folds. The mean relative abundances of ARGs belonging to tetracycline (0.35 vs 1.08),

f

MLSB (0.20 vs 0.63), beta-lactam (0.05 vs 0.89) and multidrug (0.08 vs 0.09)

oo

resistance were significantly different between the healthy individuals and patients as

pr

well (Fig. 2C). In this study, ARGs were further ranked using principal component

e-

analysis (PCA), and the results showed that ARGs from the healthy individuals were

Pr

more inclined to cluster together, while ARGs in the patient guts exhibited an evident scatter (Fig. 2D). However, some overlap between ARGs in healthy and patient

al

samples suggested the existence of some co-occurred ARGs between two groups.

rn

The discriminative ARGs between the patients and the healthy individuals were

Jo u

analyzed using both the LEfSe (Segata et al. 2011) and STAMP (Parks et al. 2014). LEfSe analysis indicated that MGEs, Multidrug, and aminoglycoside were discriminative ARG types, and ermB, tetW, tetO, and tet32 were discriminative ARG subtypes for all healthy individuals. Beta-lactam and cfxA were discriminative ARG type and subtype for the studied patients (Fig. 3A, B). Similarly, the STAMP analysis also verified that cfxA was discriminative ARG for the patients, and ermB, tetO, tet32, and tetW were discriminative ARGs for the healthy people (Table S6, S7). Except for these five discriminative ARGs, STAMP analysis showed other nine ARGs were also significant differences between the healthy individuals and patients (Fig. 3C). 12

Journal Pre-proof

However, the relatively lower abundance for these nine ARGs in both healthy individuals and patients made them impossible as the discriminative ARGs. Interestingly, analyses demonstrated that cfxA gene, a subtype of beta-lactam resistance genes was significantly (Mann-Whitney test, p < 0.01) enriched by 22.6 folds in the patients compared with the healthy individuals (Fig. 3D).

oo

f

3.3. Gut microbiota differences between healthy individuals and patients

pr

The absolute abundance of gut microbiota was quantified by qPCR of 16S rRNA gene. The absolute abundance of 16S rRNA gene in healthy individuals was

e-

(1.92±1.94)  1013 copies per gram of feces, which was significantly higher than that

Pr

in the patients ((4.03±5.80)  1012 copies per gram of feces, p < 0.01) (Fig. 4A).

al

Using high-throughput 16S rRNA gene sequencing, we also examined the

rn

composition of microbiota in human feces. There was no significant difference in

Jo u

numbers of bacterial OTUs (operational taxonomic units) between healthy individuals and patients (p > 0.05). The Shannon index of bacterial OTUs in healthy individuals was significantly higher than that in patients (Fig. 4B) (p < 0.05), representing the potential intestinal perturbation and imbalance in the patient gut microbiota. Moreover, the bacterial community analysis indicated that Firmicutes, Bacteroidetes, and Proteobacteria were the dominant phyla in both healthy individuals and patients, accounting for 93.7911.85% of total gut bacteria (Fig. 4C, D; Fig. S3A, B). Proteobacteria significantly reduced (p < 0.01) from the proportion of 22.4716.33% in the healthy individuals to 10.8315.83% in the patients. On the contrary, the 13

Journal Pre-proof

proportions of Bacteroidetes were higher in the patients than that in healthy individuals (40.4625.04% vs 25.1118.20%). The genera level of the gut microbiota composition was shown in Fig. 4E and Fig. S3C. The proportion of Bacteroides was 21.5118.10% in healthy individuals, which is lower than that in the patients (33.3224.89%). In contrast, Enterobacter was significantly reduced (p < 0.05) from

f

16.4712.92% in healthy individuals to 8.0914.03% in patients. In this study,

oo

probiotics such as Lactobacillus and Bifidobacterium had higher proportion in healthy

pr

individuals than in patients. Meanwhile, the abundances of some opportunistic

Pr

Helicobacter and Neisseria (Fig. S4).

e-

pathogens were higher in few patients compared with healthy individuals, such as the

al

3.4. Antibiotic residues in the guts of healthy individuals and patients

rn

Antibiotics detected in the fecal samples were divided into five different groups

Jo u

including tetracyclines, sulfonamides, macrolides, quinolones, and beta-lactams (Table S3, Fig. 5A). The detection frequencies of 17 antibiotic compounds were 11.76-76.47% for patients and 0-47.06% for healthy individuals, respectively. Similarly, the total concentration of the detected antibiotics was also much higher in patients than in healthy individuals (242.05±183.69 ng/g vs 26.51±33.48 ng/g). It was observed that the total concentration of the beta-lactam antibiotics (i.e., amoxicillin, ciprofloxacin, and cefalexin) was 34.05-fold higher in the fecal samples of patients than in the healthy individuals, which might be attributed to their large use in patients. Interestingly, four veterinary antibiotics, i.e., chlortetracycline, sulfadimethoxine, 14

Journal Pre-proof

sulfachlorpyridazine, and enrofloxacin were also detected in the studied human guts at the frequencies of 21.62%, 2.70%, 5.41% and 13.51% , indicating that the occurrence of these veterinary antibiotics in the human gut was likely relevant to their uptake from the environmental sources such as drinking water, food chain, etc.

3.5. Potential drivers for observed resistome and microbiome shifts in the human gut

oo

f

Interestingly, the prevalence of some ARGs presented a significantly positive

pr

correlation with the corresponding antibiotic residues, as exemplified by cfxA with

e-

beta-lactams and tetQ with tetracyclines (Fig. 5B, C). Furthermore, the total ARG abundance had a significantly positive correlation with the total concentration of

Pr

detected antibiotics (Fig. 5D). These results collectively demonstrated the potential

al

selection pressures exerted by antibiotic use on the development of gut ARGs.

rn

Besides, we also found the negative correlations between Shannon indexes of ARGs

Jo u

and bacterial OTUs with the total antibiotic concentrations (Fig. 5E, F), which indicated antibiotic could be the reason for the decreased diversities of ARGs and microbiomes in patients. At last, we also analyzed the relationship between Shannon indexes of bacterial OTUs and Shannon indexes of ARGs (Fig. 5G). Significant positive correlation manifested that bacterial community plays an important role in determining antibiotic resistome in the human gut.

3.6. The potential hosts of ARGs indicated by co-occurrence analyses

It was indicated that nonrandom co-occurrence patterns revealed by network 15

Journal Pre-proof

analysis could be used to track the potential hosts of ARGs in human feces and environments (Li et al. 2015). In this study, 98 ARGs, 7 MGEs, and 15 bacterial phyla were screened to perform the network analyses according to the strong and significant Spearman’s rank correlations (ρ > 0.6, p < 0.01) (Fig. 6, Fig. S5, and Table S8). There were 108 nodes and 320 edges in the network. 12 bacterial phyla were speculated to

f

be the possible hosts of 37 ARGs based on the co-occurrence results. As shown in Fig.

oo

6, Bacteroidetes was a potential host of tetracycline resistance gene tetQ, whereas

pr

Proteobacteria was found to be the host of multidrug resistance genes of ampC-02,

e-

tolC-01, acrA-01, acrA-02, acrF, acrB-01, and yceE/mdtG-01. Actinobacteria was

Pr

the potential host of tetracycline resistance genes of tetW and tetT, MLSB resistance gene of ermB and ermX, and multidrug resistance gene of mexF. Regarding to genera

al

level, Bacteroides mainly carried tetQ, and Pseudomonas might harbor the

rn

beta-lactam resistance gene blaTEM, FCA resistance gene catA1 and aminoglycoside

Jo u

resistance gene aac(6')-Ib(aka aacA4)-01. Enterobacter was predicted to be the host of multidrug resistance genes of acrA-01, acrA-02, acrB-01, acrF, yceE/mdtG-01 and tolC-01 and beta-lactam resistance gene of ampC-02. Escherichia-Shigella could harbor beta-lactam resistance genes of blaSHV-01, blaSHV-02, and ampC-02 and multidrug resistance genes of tolC-01, tolC-02, acrA-01, acrA-02, and acrF.

4. Discussion

4.1 Antibiotic resistomes in the guts of healthy individuals and patients receiving antibiotic administration 16

Journal Pre-proof

Due to limited labor and resource availability, the sample size in each location was relatively small in this study, which hinders the possibility to explore geographical influence on gut resistome and microbiota. Therefore, we combined all samples in three sites together, which allows to comprehensively decipher the difference between healthy individuals and patients receiving antibiotics. As an important reservoir of

f

ARGs, we found significant differences in resistomes between healthy individuals and

oo

patients. Compared with the healthy individuals, the total relative abundance of ARGs

pr

was significantly higher in the patients, while the diversity of ARGs was lower in

e-

patients. Tetracycline, MLSB, beta-lactam, aminoglycoside, and multidrug were five

Pr

most abundant ARG types. In the present research, tetracycline, MLSB and beta-lactam resistance genes all had higher mean relative abundance in patients than

al

healthy individuals (Fig. 2C). The mean relative abundance of tetQ increased more

rn

than 4 folds in patients than in healthy individuals. Similar results were also found by

Jo u

Buelow et al. (Buelow et al. 2017), who observed that tetQ was more abundant among intensive care patients than healthy individuals. Tetracycline resistance genes were the most abundant ARGs in all the groups of individuals, which was consistent with the previous observations that the tet genes were highly detected ARGs across the Europeans, Canadians, Japanese and Chinese individuals (Feng et al. 2018, Hu et al. 2013). Although tetracyclines were less used in human due to their side effects (Feng et al. 2018, Sánchez et al. 2004), plenty of host bacteria of tetracycline resistance genes in human gut microbiota could be the reason for the high abundance of tetracycline resistance ARGs. For instance, tetW gene is 17

Journal Pre-proof

widespread among anaerobic commensal gut bacteria (Scott et al. 2000), and tetQ gene provides resistance to tetracyclines in Bacteroidetes (Buelow et al. 2017). Tetracycline resistance genes also owned other functions that are not directly related to antibiotic resistance in the human gut, such as signal tracking and hydrophobic protein transport (Forsberg et al. 2012, Martínez 2008). In addition, previous research

f

reported the existence of the genes encoding resistance to tetracyclines in

oo

30,000-year-old Beringian permafrost sediments (D'Costa et al. 2011), which

pr

indicated that tetracycline resistance genes are ancient and their appearance predates

e-

the modern selective pressure of clinical antibiotic.

Pr

4.2 Bacterial microbiota in guts of healthy individuals and patients receiving antibiotic administration

al

Gut microbiota can process some indigestible complex molecules to simpler ones,

rn

providing essential nutrients for human life (Shreiner et al. 2015). Therefore, gut

Jo u

microbiota plays an important role in the metabolic functions that ultimately affect host physiology. Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria were detected in all individuals, which was concordant with the previous studies (Arumugam et al. 2011, Hooper and Gordon 2001, Khanna and Tosh 2014, Shreiner et al. 2015). The high proportion of Firmicutes and Bacteroidetes in the human gut might be attributed to their metabolic functions. Firmicutes are extensively involved in energy resorption and often account for the largest portion of the mouse and human gut microbiota (Ley et al. 2006a). Bacteroides could generate volatile fatty acids that

18

Journal Pre-proof

are reabsorbed through the large intestine and utilized as an energy source by the host (Wexler 2007). The shifts of gut microbiota were found to be involved in diseases and in some cases, the increases of resistant pathogens Clostridium difficile and Salmonella enterica abundances were precariously involved in multiple human diseases

f

(Dethlefsen et al. 2008, Lozupone et al. 2012). The gut microbiota of patients owned a

oo

lower diversity compared to healthy subjects, which was consistent with the results in

pr

present studies (Buelow et al. 2017, Shreiner et al. 2015). For example, the ICU

e-

patients receiving the prophylactic antibiotic regimen were observed to possess the

Pr

lower microbial diversity in gut microbiota (Buelow et al. 2017). The disruption of antibiotics on gut microbiota could be the reason for the decreased bacterial diversity.

al

In this study, the microbiota of patients owned increased abundance of Bacteroidetes

rn

and decreased abundance of Proteobacteria, which corroborated the results from one

Jo u

previous study that antibiotic administration decreased Escherichia coli level but increased the abundance of Bacteroidetes and enterococci in the gut of ICU patients (Buelow et al. 2017). The shifts in the communities of probiotics and pathogens were often associated with intestinal dysbiosis in patients (Jernberg et al. 2005, Shreiner et al. 2015). In present study, some opportunistic pathogens such as Helicobacter and Neisseria were detected in certain patients with high abundance, which was distinct from some healthy individuals that had abundant probiotics such as Lactobacillus and Bifidobacterium. Similarly, it was also reported that the microbiota of Crohn’s disease patients had increased abundances of Neisseriaceae and decreased abundances of 19

Journal Pre-proof

Bifidobacteriaceae (Shreiner et al. 2015).

4.3 Relationships among antibiotic residues, bacterial microbiota and antibiotic resistomes in human guts

We also analyzed the antibiotic residues in the collected fecal samples for further exploration of possible factors leading to the differences in gut antibiotic resistome

oo

f

and bacterial microbiome between Chinese healthy individuals and patients. In the present research, the most used antibiotics in the patients were beta-lactams and

pr

quinolones including cephalosporins, amoxicillin, and levofloxacin, which were also

e-

consistent with the antibiotic residues detected in fecal samples (Fig. 5A).

Pr

Although the concentrations of detected veterinary antibiotics in this study were relatively low in the level of ng/g, some previous studies have manifested that the low

al

concentrations of antibiotics from the environmental exposure such as food and

rn

drinking water could still exert the selective pressure on the spread of ARGs in the

Jo u

human guts (Barton 2007, C.J. 2002). This kind of selection pressure was also backed by the positive Spearman’s correlations between cfxA and tetQ with the corresponding antibiotic residues of beta-lactams and tetracyclines, respectively (Fig. 5B, C). Furthermore, we also correlate the total antibiotic concentrations with the total abundance of ARGs, and as expected, a statistically significant positive relationship was observed (Fig. 5D). Therefore, our results provided substantial evidence for the selection of gut ARGs induced by antibiotics. The increasing ARGs in human gut microbiota could be attributed to the use of corresponding antibiotics (Palleja et al.

20

Journal Pre-proof

2018). However, the negative correlation between the total antibiotic concentrations and the Shannon indexes of ARGs indicated that antibiotics could decrease ARG diversity in human gut. This might be the reason for the lower diversity of ARGs observed in patients (Fig. 5E). Conceivably, antibiotics were considered as the most potent source of disturbance

f

for the human guts, which severely altered the gut bacterial communities (Lee et al.

oo

2010, Modi et al. 2014, Raymond et al. 2015, Toprak et al. 2011). Somewhat

pr

dysbacteriosis in the gut microbial community had been observed in the human

e-

undergoing antibiotics treatments (Dethlefsen and Relman 2011, Eckburg et al. 2005,

Pr

Mosca et al. 2016, Raymond et al. 2015, Sekirov et al. 2008). The reduced diversity of microbial compositions of patients has been proposed as an indicator of

al

disease-associated dysbiosis (Eckburg et al. 2005, Mosca et al. 2016, Vangay et al.

rn

2015). In the present research, we also found the negative correlation between the

Jo u

total antibiotic concentrations and the Shannon indexes of bacterial OTUs, which indicated the disruption of antibiotics on gut microbiota (Fig. 5F). Therefore, the antibiotic administrations could be the driver for the lower bacterial diversity in patients. Finally, to link the changed antibiotic resistomes with altered bacterial microbiomes in the guts of patients, we examined the relationship between bacterial diversity and ARG diversity. The results demonstrated that the bacterial diversities evaluated by Shannon indexes of bacterial OTUs were positively correlated with Shannon indexes of ARGs (Fig. 5G), suggesting that the gut microbiota shifts were 21

Journal Pre-proof

likely to reshape the gut resistomes in turn. For specifically, Spearman’s correlation between ARG diversity and the relative abundance of bacteria at either phylum or genus level was also evaluated (Table S9). As the members of four dominant phyla of gut microbiota, Firmicutes, Bacteroidetes and Actinobacteria did not exhibit significant correlation with the diversity of ARGs. However, Proteobacteria phylum

f

and several genera of Proteobacteria such as Escherichia-Shigella, Citrobacter,

oo

Klebsiella, Plesiomonas, Enterobacter, etc. had significantly positive correlation with

pr

diversity indexes of ARGs. It was reported that ARGs were less prone to exist in

e-

Bacteroidetes but more frequently observed in Proteobacteria (Hu et al. 2013). Hu et

Pr

al. also indicated that mobile ARGs were significantly enriched in Proteobacteria due to the exchanges of the mobile ARGs were more active in Proteobacteria than in

al

other phyla (Hu et al. 2016). Overall, the relationships among bacterial community,

rn

antibiotic resistome and antibiotic residues found in this study indicated that the

Jo u

antibiotic selective pressures could structure the gut microbiota carrying ARGs as well as the antibiotic resistome in gut bacteria.

4.4 Potential ARG hosts and discriminative ARG sets for patients receiving antibiotic administration

Some potential ARG hosts found in this study were consistent with previous findings in different environments (Table S10). For instance, Bacteroides was the potential host of tetQ, which was also found in another study that tetQ was commonly carried by Bacteroides and Escherichia (Eitel et al. 2013, Li et al. 2015, Sarkar et al.

22

Journal Pre-proof

2015, Zhang et al. 2009). Bifidobacterium was the potential host of ermX, which was indicated both in this study and in previous research (van Hoek et al. 2011). We found Escherichia-Shigella was host of blaSHV-01, blaSHV-02, ampC-02, tolC-01, tolC-02, acrA-01, acrA-02, and acrF, and the similar Escherichia as the host of many multidrug resistance genes such as acrA, acrB, acrF, emrK, mdtA, mdtB, ompF,

f

ompR, tolC, etc. and other ARGs such as aadA1, qnrA, qnrB, tetA, tetM, etc. was

oo

observed in previous studies (Li et al. 2015, van Hoek et al. 2011). These

pr

consistencies also indicated that the network analysis is a powerful method to provide

e-

a prediction of ARGs and their potential hosts. The exploration of ARGs and their

Pr

potential hosts could assist us to investigate the interactions and alteration of resistomes and microbiota in human gut better.

al

The patients in present research owned higher proportions of Bacteroidetes and

rn

tetQ than in healthy individuals, which was consistent with the previous result that

Jo u

patients were characterized by higher abundance of Bacteroidetes and tetQ than in healthy subjects (Buelow et al. 2017). Moreover, the cfxA gene had been connected to Bacteroidetes in guts, such as Bacteroides vulgatus (Parker and Smith 1993), and even the transferable element of transposon Tn4555 carrying cfxA gene was found to be harbored in Bacteroides distasonis (Ferreira et al. 2007). Some species of Bacteriodes, such as Bacteriodes fragilis, was opportunistic pathogens and these species carrying cfxA might pose threat to clinic treatment by beta-lactams (Niestępski et al. 2019). Bacterial phylogeny structures the resistomes, which have been verified by Forsberg’s study (Forsberg et al. 2014, Forsberg et al. 2012). 23

Journal Pre-proof

According to LEfSe analysis, the ARG types of MGEs, multidrug, and aminoglycoside were discriminative in healthy individuals and beta-lactam was discriminative ARG types in patients. The ermB, tetW, tetO, and tet32 were discriminative ARGs for healthy individuals, and cfxA was the discriminative ARG for the patients, which might be used as a potential biomarker for intestinal imbalance

f

for patients. Considering significant positive correlation between cfxA and

oo

beta-lactams concentrations, cfxA gene could be also used as a potential biomarker to

pr

indicate the prescription of beta-lactams.

e-

As we have known, the human gut is a very complex environment and there also

Pr

exists many other factors to impact the gut microbiota or resistome such as the medicines, lifestyles, age, geography, nations, incomes, etc. (Ley et al. 2006b, Lu et al.

al

2014, Pehrsson et al. 2016). It should be noticed that the relation between ARGs and

rn

the usage of antibiotics is complicated and previous studies provide various

Jo u

conclusions such as the enrichment of ARGs may not be only attributed to antibiotic abuse (Hu et al. 2013). The high abundance of the discriminative ARGs (tetO, tet32 and tetW) in healthy individuals was not likely to be the result of tetracycline antibiotics, because the concentrations of tetracycline antibiotics were low in the healthy individuals, and the tetracyclines were less used for prescription than before as the side effects to human. 5. Conclusions This study explored the profiles and characteristics of resistomes, microbiota and

24

Journal Pre-proof

antibiotic residues in guts of Chinese healthy individuals and patients receiving antibiotics. In general, we found the imbalance in gut microbiota of patients could be attributed to the effects of antibiotics, which might further reshape the gut resistomes. The discriminative cfxA gene was proposed as a potential biomarker to indicate the disturbance of antibiotic prescription on the human gut resistome, and ermB, tetW,

f

tetO, and tet32 were discriminative ARG subtypes for the healthy individuals. The

oo

veterinary antibiotics were also detected in both patients and healthy individuals,

pr

indicating that the contaminated food or drinking water from the environment might

e-

be relevant exposure sources. Considering Chinese population harbour the more

Pr

abundant and higher diverse ARGs than other countries, the investigation of the resistomes in diseases could help to inform the appropriate guidance of clinical

Jo u

Conflict of interest

rn

al

antibiotic prescription to the patients.

All authors have no conflict of interest to declare. Authors’ contributions

DQM, YL and YJD conceived the study. YJD analyzed the data and wrote the manuscript. ZYC, DQM, YL, RD revised the manuscript. DQM and ZYC provided guidance with statistical analysis and figure design. YJD, YGX, HL, XLW and LYL collected samples. YJD and SPW accomplished the experiment. LT, XLW, QW, LYL and JH provided valuable suggestions. YJD, XLW and LYL submitted the data to 25

Journal Pre-proof

NCBI. All authors read and approved the final draft. Acknowledgments The authors would like to thank all the participating volunteers. The authors are thankful for the collecting of samples by the helpers. This work was supported by the Key projects of the National Natural Science Foundation of China (41831287), the

oo

f

China National Science Fund for Distinguished Young Scholars (41525013), National Natural Science Foundation of China (31870351, 31670509, 41807483, 21607016,

pr

41703088, and 41977367), Key projects of Research and Development of Hebei

e-

Province (19273707D) and the T2017002 111Program, Ministry of Education, China,

Pr

and the Fundamental Research Funds for the Central Universities.

al

Appendix A. Supplementary data

References

Jo u

rn

Supplementary information to this article can be found online at

Allin, K.H., Tremaroli, V., Caesar, R., Jensen, B.A.H., Damgaard, M.T.F., Bahl, M.I., Licht, T.R., Hansen, T.H., Nielsen, T., Dantoft, T.M., Linneberg, A., Jørgensen, T., Vestergaard, H., Kristiansen, K., Franks, P.W., Hansen, T., Bäckhed, F. and Pedersen, O. (2018) Aberrant intestinal microbiota in individuals with prediabetes. Diabetologia 61(4), 810-820. Angulo, F.J., Collignon, P., Powers, J.H., Chiller, T.M., Aidara-Kane, A. and Aarestrup, F.M. (2009) World Health Organization Ranking of Antimicrobials According to Their Importance in Human Medicine: A Critical Step for Developing Risk Management Strategies for the Use of Antimicrobials in Food Production Animals. Clinical Infectious Diseases 49(1), 132-141. Arumugam, M., Raes, J., Pelletier, E., Le Paslier, D., Yamada, T., Mende, D.R., Fernandes, G.R., Tap, J., Bruls, T., Batto, J.-M., Bertalan, M., Borruel, N., Casellas, F., Fernandez, L., Gautier, L., Hansen, T., Hattori, M., Hayashi, T., Kleerebezem, M., Kurokawa, K., Leclerc, M., Levenez, F., Manichanh, C., Nielsen, H.B., Nielsen, T., Pons, N., Poulain, J., Qin, J., Sicheritz-Ponten, T., Tims, 26

Journal Pre-proof S., Torrents, D., Ugarte, E., Zoetendal, E.G., Wang, J., Guarner, F., Pedersen, O., de Vos, W.M., Brunak, S., Doré, J., Meta, H.I.T.C., Weissenbach, J., Ehrlich, S.D. and Bork, P. (2011) Enterotypes of the human gut microbiome. Nature 473, 174. Barton, M.D. (2007) Antibiotic use in animal feed and its impact on human health. Nutrition Research Reviews 13(2), 279-299. Bastian, M., Heymann, S. and Jacomy, M. (2009) Gephi: An Open Source Software for Exploring and Manipulating Networks, Proceedings of the Third International ICWSM Conference Becattini, S., Taur, Y. and Pamer, E.G. (2016) Antibiotic-Induced Changes in the Intestinal Microbiota and Disease. Trends Mol Med 22(6), 458-478. Buelow, E., Bello González, T.d.j., Fuentes, S., de Steenhuijsen Piters, W.A.A., Lahti, L., Bayjanov, J.R., Majoor, E.A.M., Braat, J.C., van Mourik, M.S.M., Oostdijk, E.A.N., Willems, R.J.L., Bonten, M.J.M., van Passel, M.W.J., Smidt, H. and van Schaik, W. (2017) Comparative gut microbiota and

f

resistome profiling of intensive care patients receiving selective digestive tract decontamination

oo

and healthy subjects. Microbiome 5(1), 88.

C.J., T. (2002) Antimicrobial resistance and the food chain. Journal of Applied Microbiology 92(s1),

pr

85S-89S.

Casén, C., Vebø, H.C., Sekelja, M., Hegge, F.T., Karlsson, M.K., Ciemniejewska, E., Dzankovic, S., Frøyland, C., Nestestog, R., Engstrand, L., Munkholm, P., Nielsen, O.H., Rogler, G., Simrén, M.,

e-

Öhman, L., Vatn, M.H. and Rudi, K. (2015) Deviations in human gut microbiota: a novel & Therapeutics 42(1), 71-83.

Pr

diagnostic test for determining dysbiosis in patients with IBS or IBD. Alimentary Pharmacology Costea, P.I., Zeller, G., Sunagawa, S., Pelletier, E., Alberti, A., Levenez, F., Tramontano, M., Driessen, M., Hercog, R., Jung, F.-E., Kultima, J.R., Hayward, M.R., Coelho, L.P., Allen-Vercoe, E.,

al

Bertrand, L., Blaut, M., Brown, J.R.M., Carton, T., Cools-Portier, S., Daigneault, M., Derrien, M., Druesne, A., de Vos, W.M., Finlay, B.B., Flint, H.J., Guarner, F., Hattori, M., Heilig, H., Luna,

rn

R.A., van Hylckama Vlieg, J., Junick, J., Klymiuk, I., Langella, P., Le Chatelier, E., Mai, V., Manichanh, C., Martin, J.C., Mery, C., Morita, H., O'Toole, P.W., Orvain, C., Patil, K.R., Penders,

Jo u

J., Persson, S., Pons, N., Popova, M., Salonen, A., Saulnier, D., Scott, K.P., Singh, B., Slezak, K., Veiga, P., Versalovic, J., Zhao, L., Zoetendal, E.G., Ehrlich, S.D., Dore, J. and Bork, P. (2017) Towards standards for human fecal sample processing in metagenomic studies. Nature Biotechnology 35, 1069.

D'Costa, V.M., King, C.E., Kalan, L., Morar, M., Sung, W.W., Schwarz, C., Froese, D., Zazula, G., Calmels, F., Debruyne, R., Golding, G.B., Poinar, H.N. and Wright, G.D. (2011) Antibiotic resistance is ancient. Nature 477(7365), 457-461. Dethlefsen, L., Huse, S., Sogin, M.L. and Relman, D.A. (2008) The Pervasive Effects of an Antibiotic on the Human Gut Microbiota, as Revealed by Deep 16S rRNA Sequencing. PLOS Biology 6(11), e280. Dethlefsen, L. and Relman, D.A. (2011) Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proceedings of the National Academy of Sciences 108(Supplement 1), 4554-4561. Duvallet, C., Gibbons, S.M., Gurry, T., Irizarry, R.A. and Alm, E.J. (2017) Meta-analysis of gut microbiome studies identifies disease-specific and shared responses. Nature communications 8(1), 1784. Eckburg, P.B., Bik, E.M., Bernstein, C.N., Purdom, E., Dethlefsen, L., Sargent, M., Gill, S.R., Nelson, 27

Journal Pre-proof K.E. and Relman, D.A. (2005) Diversity of the Human Intestinal Microbial Flora. Science 308(5728), 1635-1638. Eitel, Z., Soki, J., Urban, E., Nagy, E. and Infection, E.S.G.o.A. (2013) The prevalence of antibiotic resistance genes in Bacteroides fragilis group strains isolated in different European countries. Anaerobe 21, 43-49. Fang, H., Han, L., Zhang, H., Long, Z., Cai, L. and Yu, Y. (2018) Dissemination of antibiotic resistance genes and human pathogenic bacteria from a pig feedlot to the surrounding stream and agricultural soils. Journal of Hazardous Materials 357, 53-62. Feng, J., Li, B., Jiang, X., Yang, Y., Wells, G.F., Zhang, T. and Li, X. (2018) Antibiotic resistome in a large-scale healthy human gut microbiota deciphered by metagenomic and network analyses. Environmental Microbiology 20(1), 355-368. Ferreira, L.Q., Avelar, K.E.S., Vieira, J.M.B.D., de Paula, G.R., Colombo, A.P.V., Domingues, R.M.C.P.

f

and Ferreira, M.C.S. (2007) Association Between the cfxA Gene and Transposon Tn4555 in

oo

Bacteroides distasonis Strains and Other Bacteroides Species. Current Microbiology 54(5), 348-353.

pr

Forsberg, K.J., Patel, S., Gibson, M.K., Lauber, C.L., Knight, R., Fierer, N. and Dantas, G. (2014) Bacterial phylogeny structures soil resistomes across habitats. Nature 509, 612. Forsberg, K.J., Reyes, A., Wang, B., Selleck, E.M., Sommer, M.O.A. and Dantas, G. (2012) The Shared

e-

Antibiotic Resistome of Soil Bacteria and Human Pathogens. Science 337(6098), 1107. Goossens, H., Ferech, M., Vander Stichele, R. and Elseviers, M. (2005) Outpatient antibiotic use in

Pr

Europe and association with resistance: a cross-national database study. The Lancet 365(9459), 579-587.

Hammer, O., Harper, D. and Ryan, P. (2001) PAST : Paleontological Statistics Software Package for

al

Education and Data Analysis Palaeontologia Electronica. Palaeontologia Electronica 1, 9. Heaton, K.W., Radvan, J., Cripps, H., Mountford, R.A., Braddon, F.E. and Hughes, A.O. (1992) Gut 33(6), 818.

rn

Defecation frequency and timing, and stool form in the general population: a prospective study.

Jo u

Hooper, L.V. and Gordon, J.I. (2001) Commensal Host-Bacterial Relationships in the Gut. Science 292(5519), 1115-1118.

Hu, X., Zhou, Q. and Luo, Y. (2010) Occurrence and source analysis of typical veterinary antibiotics in manure, soil, vegetables and groundwater from organic vegetable bases, northern China. Environmental Pollution 158(9), 2992-2998. Hu, Y., Yang, X., Li, J., Lv, N., Liu, F., Wu, J., Lin, I.Y.C., Wu, N., Weimer, B.C., Gao, G.F., Liu, Y. and Zhu, B. (2016) The Bacterial Mobile Resistome Transfer Network Connecting the Animal and Human Microbiomes. Applied and environmental microbiology 82(22), 6672-6681. Hu, Y., Yang, X., Qin, J., Lu, N., Cheng, G., Wu, N., Pan, Y., Li, J., Zhu, L., Wang, X., Meng, Z., Zhao, F., Liu, D., Ma, J., Qin, N., Xiang, C., Xiao, Y., Li, L., Yang, H., Wang, J., Yang, R., Gao, G.F., Wang, J. and Zhu, B. (2013) Metagenome-wide analysis of antibiotic resistance genes in a large cohort of human gut microbiota. Nature communications 4. I, A. and AE, W. (2009) Establishment of the gut microbiota in Western infants. Acta Paediatrica 98(2), 229-238. Jackson, M.A., Verdi, S., Maxan, M.-E., Shin, C.M., Zierer, J., Bowyer, R.C.E., Martin, T., Williams, F.M.K., Menni, C., Bell, J.T., Spector, T.D. and Steves, C.J. (2018) Gut microbiota associations with common diseases and prescription medications in a population-based cohort. Nature 28

Journal Pre-proof communications 9(1), 2655. Jernberg, C., Löfmark, S., Edlund, C. and Jansson, J.K. (2007) Long-term ecological impacts of antibiotic administration on the human intestinal microbiota. The ISME journal 1, 56. Jernberg, C., Löfmark, S., Edlund, C. and Jansson, J.K. (2010) Long-term impacts of antibiotic exposure on the human intestinal microbiota. Microbiology 156(11), 3216-3223. Jernberg, C., Sullivan, Å., Edlund, C. and Jansson, J.K. (2005) Monitoring of Antibiotic-Induced Alterations in the Human Intestinal Microflora and Detection of Probiotic Strains by Use of Terminal Restriction Fragment Length Polymorphism. Applied and environmental microbiology 71(1), 501-506. Karkman, A., Pärnänen, K. and Larsson, D.G.J. (2019) Fecal pollution can explain antibiotic resistance gene abundances in anthropogenically impacted environments. Nature communications 10(1), 80. Khanna, S. and Tosh, P.K. (2014) A Clinician's Primer on the Role of the Microbiome in Human Health

f

and Disease. Mayo Clinic Proceedings 89(1), 107-114.

oo

Lacy, E.B. and Patel, K.N. (2017) Rome Criteria and a Diagnostic Approach to Irritable Bowel Syndrome. Journal of Clinical Medicine 6(11).

pr

Lee, H.H., Molla, M.N., Cantor, C.R. and Collins, J.J. (2010) Bacterial charity work leads to population-wide resistance. Nature 467, 82.

Lewis, S.J. and Heaton, K.W. (1997) Stool Form Scale as a Useful Guide to Intestinal Transit Time.

e-

Scandinavian Journal of Gastroenterology 32(9), 920-924.

Ley, R.E., Peterson, D.A. and Gordon, J.I. (2006a) Ecological and Evolutionary Forces Shaping

Pr

Microbial Diversity in the Human Intestine. Cell 124(4), 837-848. Ley, R.E., Turnbaugh, P.J., Klein, S. and Gordon, J.I. (2006b) Human gut microbes associated with obesity. Nature 444, 1022.

al

Li, B., Yang, Y., Ma, L., Ju, F., Guo, F., Tiedje, J.M. and Zhang, T. (2015) Metagenomic and network analysis reveal wide distribution and co-occurrence of environmental antibiotic resistance genes.

rn

ISME J 9(11), 2490-2502.

Lozupone, C.A., Stombaugh, J.I., Gordon, J.I., Jansson, J.K. and Knight, R. (2012) Diversity, stability

Jo u

and resilience of the human gut microbiota. Nature 489, 220. Lu, N., Hu, Y., Zhu, L., Yang, X., Yin, Y., Lei, F., Zhu, Y., Du, Q., Wang, X., Meng, Z. and Zhu, B. (2014) DNA microarray analysis reveals that antibiotic resistance-gene diversity in human gut microbiota is age related. Sci Rep 4, 4302. Luo, Y., Mao, D., Rysz, M., Zhou, Q., Zhang, H., Xu, L. and J. J. Alvarez, P. (2010) Trends in Antibiotic Resistance Genes Occurrence in the Haihe River, China. Environmental Science & Technology 44(19), 7220-7225. Luo, Y., Xu, L., Rysz, M., Wang, Y., Zhang, H. and Alvarez, P.J.J. (2011) Occurrence and Transport of Tetracycline, Sulfonamide, Quinolone, and Macrolide Antibiotics in the Haihe River Basin, China. Environmental Science & Technology 45(5), 1827-1833. Martínez, J.L. (2008) Antibiotics and Antibiotic Resistance Genes in Natural Environments. Science 321(5887), 365-367. Modi, S.R., Collins, J.J. and Relman, D.A. (2014) Antibiotics and the gut microbiota. The Journal of clinical investigation 124(10), 4212-4218. Mosca, A., Leclerc, M. and Hugot, J.P. (2016) Gut Microbiota Diversity and Human Diseases: Should We Reintroduce Key Predators in Our Ecosystem? Frontiers in Microbiology 7(455). Niestępski, S., Harnisz, M., Korzeniewska, E., Aguilera-Arreola, M.G., Contreras-Rodríguez, A., 29

Journal Pre-proof Filipkowska, Z. and Osińska, A. (2019) The emergence of antimicrobial resistance in environmental strains of the Bacteroides fragilis group. Environment International 124, 408-419. Ouyang, W.-Y., Huang, F.-Y., Zhao, Y., Li, H. and Su, J.-Q. (2015) Increased levels of antibiotic resistance in urban stream of Jiulongjiang River, China. Applied Microbiology and Biotechnology 99(13), 5697-5707. Palleja, A., Mikkelsen, K.H., Forslund, S.K., Kashani, A., Allin, K.H., Nielsen, T., Hansen, T.H., Liang, S., Feng, Q., Zhang, C., Pyl, P.T., Coelho, L.P., Yang, H., Wang, J., Typas, A., Nielsen, M.F., Nielsen, H.B., Bork, P., Wang, J., Vilsbøll, T., Hansen, T., Knop, F.K., Arumugam, M. and Pedersen, O. (2018) Recovery of gut microbiota of healthy adults following antibiotic exposure. Nature Microbiology 3(11), 1255-1265. Parker, A.C. and Smith, C.J. (1993) Genetic and biochemical analysis of a novel Ambler class A beta-lactamase responsible for cefoxitin resistance in Bacteroides species. Antimicrobial agents

f

and chemotherapy 37(5), 1028-1036.

oo

Parks, D.H., Tyson, G.W., Hugenholtz, P. and Beiko, R.G. (2014) STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30(21), 3123-3124.

pr

Pehrsson, E.C., Tsukayama, P., Patel, S., Mejía-Bautista, M., Sosa-Soto, G., Navarrete, K.M., Calderon, M., Cabrera, L., Hoyos-Arango, W., Bertoli, M.T., Berg, D.E., Gilman, R.H. and Dantas, G. (2016) Interconnected microbiomes and resistomes in low-income human habitats. Nature 533, 212.

e-

Pruden, A., Pei, R., Storteboom, H. and Carlson, K.H. (2006) Antibiotic resistance genes as emerging contaminants: studies in northern Colorado. Environmental Science & Technology 40(23),

Pr

7445-7450.

Raymond, F., Ouameur, A.A., Déraspe, M., Iqbal, N., Gingras, H., Dridi, B., Leprohon, P., Plante, P.-L., Giroux, R., Bérubé, È., Frenette, J., Boudreau, D.K., Simard, J.-L., Chabot, I., Domingo, M.-C.,

al

Trottier, S., Boissinot, M., Huletsky, A., Roy, P.H., Ouellette, M., Bergeron, M.G. and Corbeil, J. ISME journal 10, 707.

rn

(2015) The initial state of the human gut microbiome determines its reshaping by antibiotics. The Riegler, G. and Esposito, I. (2001) Bristol scale stool form. A still valid help in medical practice and

Jo u

clinical research. Techniques in Coloproctology 5(3), 163-164. Rizzo, L., Manaia, C., Merlin, C., Schwartz, T., Dagot, C., Ploy, M.C., Michael, I. and Fatta-Kassinos, D. (2013) Urban wastewater treatment plants as hotspots for antibiotic resistant bacteria and genes spread into the environment: A review. Science of The Total Environment 447, 345-360. Ryan, K.J. and Ray, C.G. (2004) Sherris medical microbiology. McGraw Hill. Sánchez, A.R., Rogers III, R.S. and Sheridan, P.J. (2004) Tetracycline and other tetracycline-derivative staining of the teeth and oral cavity. International Journal of Dermatology 43(10), 709-715. Salyers, A.A., Gupta, A. and Wang, Y. (2004) Human intestinal bacteria as reservoirs for antibiotic resistance genes. Trends Microbiol 12(9), 412-416. Sarkar, A., Pazhani, G.P., Dharanidharan, R., Ghosh, A. and Ramamurthy, T. (2015) Detection of integron-associated gene cassettes and other antimicrobial resistance genes in enterotoxigenic Bacteroides fragilis. Anaerobe 33, 18-24. Scott, K.P., Melville, C.M., Barbosa, T.M. and Flint, H.J. (2000) Occurrence of the New Tetracycline Resistance Gene tet(W) in Bacteria from the Human Gut. Antimicrobial agents and chemotherapy 44(3), 775. Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W.S. and Huttenhower, C. (2011) Metagenomic biomarker discovery and explanation. Genome Biology 12(6), R60-R60. 30

Journal Pre-proof Sekirov, I., Tam, N.M., Jogova, M., Robertson, M.L., Li, Y., Lupp, C. and Finlay, B.B. (2008) Antibiotic-Induced Perturbations of the Intestinal Microbiota Alter Host Susceptibility to Enteric Infection. Infection and immunity 76(10), 4726-4736. Shreiner, A.B., Kao, J.Y. and Young, V.B. (2015) The gut microbiome in health and in disease. Current opinion in gastroenterology 31(1), 69-75. Simon, G.L. and Gorbach, S.L. (1984) Intestinal flora in health and disease. Gastroenterology 86(1), 174-193. Sommer, M.O., Church, G.M. and Dantas, G. (2010) The human microbiome harbors a diverse reservoir of antibiotic resistance genes. Virulence 1(4), 299-303. Toprak, E., Veres, A., Michel, J.-B., Chait, R., Hartl, D.L. and Kishony, R. (2011) Evolutionary paths to antibiotic resistance under dynamically sustained drug selection. Nature Genetics 44, 101. van Hoek, A.H., Mevius, D., Guerra, B., Mullany, P., Roberts, A.P. and Aarts, H.J. (2011) Acquired

f

antibiotic resistance genes: an overview. Front Microbiol 2, 203.

oo

Vangay, P., Ward, T., Gerber, Jeffrey S. and Knights, D. (2015) Antibiotics, Pediatric Dysbiosis, and Disease. Cell Host & Microbe 17(5), 553-564.

pr

Wang, F.-H., Qiao, M., Su, J.-Q., Chen, Z., Zhou, X. and Zhu, Y.-G. (2014) High Throughput Profiling of Antibiotic Resistance Genes in Urban Park Soils with Reclaimed Water Irrigation. Environmental Science & Technology 48(16), 9079-9085.

e-

Wang, Q., Duan, Y.J., Wang, S.P., Wang, L.T., Hou, Z.L., Cui, Y.X., Hou, J., Das, R., Mao, D.Q. and Luo, Y. (2019) Occurrence and distribution of clinical and veterinary antibiotics in the faeces of a

Pr

Chinese population. J Hazard Mater 383, 121129.

Wexler, H.M. (2007) Bacteroides: the Good, the Bad, and the Nitty-Gritty. Clinical Microbiology Reviews 20(4), 593.

al

Xie, J., Jin, L., Luo, X., Zhao, Z. and Li, X. (2018) Seasonal Disparities in Airborne Bacteria and Associated Antibiotic Resistance Genes in PM2.5 between Urban and Rural Sites. Environmental

rn

Science & Technology Letters 5(2), 74-79.

Zhang, X.-X., Zhang, T. and Fang, H.H.P. (2009) Antibiotic resistance genes in water environment.

Jo u

Applied Microbiology and Biotechnology 82(3), 397-414. Zhao, L. (2013) The gut microbiota and obesity: from correlation to causality. Nature Reviews Microbiology 11, 639.

31

Jo u

rn al P

re

-p

ro

of

Journal Pre-proof

Fig. 1. Heatmap of relative abundances of detected 118 genes in healthy individuals and patients with hierarchical clustering (Relative abundances: ARG relative copy number per 16S rRNA gene relative copy number, log 10 conversion). The dark blue (ND) indicated the results were below the threshold of detection limit.

32

Jo

ur

na

lP

re

-p

ro

of

Journal Pre-proof

Fig. 2. Comparison of resistome between the two groups of healthy individuals (H) and patients (P) (*p < 0.05, **p < 0.01). (A) Total relative abundances of ARGs between group H and P showed the significant differences. (B) Comparison of Shannon indexes for ARGs between group H and P. (C) The relative abundances and the numbers of detected ARGs for top five most abundant ARG types. (D) The principal component analysis (PCA) of 122 ARGs. 33

ur

na

lP

re

-p

ro

of

Journal Pre-proof

Jo

Fig. 3. Analyses for discriminatory ARGs in two groups of healthy individuals (H) and patients (P). (A) The discriminatory genes of 10 ARG types and MGEs in LEfSe analysis (LDA > 3.5). (B) The LDA score distribution of discriminative genes in LEfSe analysis (LDA > 3.5). (C) STAMP analysis of discriminatory ARGs (Red and green circles represent ARGs with higher proportions in the patients and healthy individuals, **q value < 0.01). (D) Significant difference of relative abundance of cfxA in the two groups (**p value < 0.01).

34

-p

ro

of

Journal Pre-proof

re

Fig. 4. Comparison of gut microbiota between two groups of healthy individuals (H)

lP

and patients (P) (*p < 0.05, **p < 0.01). (A) The significant difference of absolute

na

quantification of 16S rRNA gene in two groups (copies per gram of feces). (B) The significant difference of Shannon index of OTUs in two groups. (C) The relative

ur

abundances for the top ten dominant bacterial phyla in two groups. (D) The relative

Jo

abundances of four dominant phyla (Firmicutes, Bacteroidetes, Proteobacteria and Actinobacteria) in two groups. (E) The relative abundances for the top ten dominant bacterial genera in two groups.

35

Jo

ur

na

lP

re

-p

ro

of

Journal Pre-proof

Fig. 5. Concentration of antibiotics and analyses of Spearman’s correlation coefficient 36

Journal Pre-proof

(ρ) among antibiotics, gut microbiota and resistomes between healthy individuals (green) and patients (red) (A correlation was considered to be statistically evident if ρ > 0.5 or ρ < -0.5, and the p value < 0.05). (A) Heatmap of concentration for antibiotics in patients and healthy individuals (log2 conversion, ppb: ng per gram of feces). (B) Beta-lactams were significantly correlated to the relative abundance of

of

cfxA gene excluding three outliers (ρ = 0.544, p < 0.01, n = 34). (C) Tetracyclines were significantly correlated to the relative abundance of tetQ gene excluding three

ro

outliers (ρ = 0.536, p < 0.01, n = 34). (D) Total relative abundances of ARGs were

-p

significantly correlated to the total antibiotic concentrations excluding two outliers (ρ

re

= 0.633, p < 0.01, n = 35). (E) Shannon indexes of ARGs were negatively correlated

lP

with total antibiotic concentrations separately (ρ = -0.542, p < 0.01, n = 34, three

na

outliers were excluded in the analysis). (F) Shannon indexes of bacterial OTUs were negatively correlated with total antibiotic concentrations excluding three outliers (ρ =

ur

-0.788, p < 0.01, n = 34). (G) The diversity of ARGs was positively correlated with

Jo

the diversity of OTUs of bacteria in almost individuals (ρ = 0.505, p < 0.05, n = 29, eight outliers were excluded in the analysis).

37

ur

na

lP

re

-p

ro

of

Journal Pre-proof

Jo

Fig. 6. The network analysis between the gut microbiota and ARGs. The orange nodes represented bacterial phyla and the other nodes colored according to the ARG types. The size of each node was proportional to its number of connections. A correlation was considered to be statistically robust if the ρ > 0.6 and p value < 0.01 between nodes.

38

Journal Pre-proof Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Jo

ur

na

lP

re

-p

ro

of

☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

39

Journal Pre-proof

Jo

ur

na

lP

re

-p

ro

of

Graphical abstract

40

Journal Pre-proof

Highlights Gut resistome, microbiota and antibiotic residues were quantified in patients.



Patients harbor lower diverse but higher abundant ARGs than healthy individuals.



Antibiotic therapy shaped the gut microbiota and resistome.



cfxA was proposed as potential biomarker to indicate intestinal imbalance.



Some veterinary antibiotics were detected in human gut.

Jo

ur

na

lP

re

-p

ro

of



41