Metagenomic and Metabolomic Analysis of the Toxic Effects of

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Metagenomic and metabolomic analysis of the toxic effects of trichloroacetamide-induced gut microbiome and urine metabolome perturbations in mice Yan Zhang, Fuzheng Zhao, Yongfeng Deng, Yanping Zhao, and Hong-qiang Ren J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr5011263 • Publication Date (Web): 22 Jan 2015 Downloaded from http://pubs.acs.org on February 18, 2015

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Journal of Proteome Research

Title page Title: Metagenomic and metabolomic analysis of the toxic effects of trichloroacetamide-induced gut microbiome and urine metabolome perturbations in mice Authors: Yan Zhang, Fuzheng Zhao, Yongfeng Deng, Yanping Zhao, Hongqiang Ren* Affiliations: State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu 210023, China * Corresponding author: Hongqiang Ren E-mail: [email protected] Tel: +86-(0)25-89680160 Fax: +86-(0)25-89680160

1

ACS Paragon Plus Environment


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Abstract Disinfection by-products (DBPs) in drinking water has been linked to various diseases, including colon, colorectal, rectal, and bladder cancers. Trichloroacetamide (TCAcAm) is emerging nitrogenous DBPs, and our previous study found that TCAcAm could induce some changes associated with host-gut microbiota co-metabolism. In this study, we used an integrated approach combining metagenomics based on high-through sequencing and metabolomics based on nuclear magnetic resonance (NMR) to evaluate the toxic effects of TCAcAm exposure on the gut microbiome and urine metabolome. High-through sequencing revealed that the gut microbiome composition and function were significantly altered due to TCAcAm exposure for 90 days in Mus musculus mice. In addition, metabolomic analysis showed that a number of gut microbiota-related metabolites were dramatically perturbed in mice urine. These results may provide a novel sight to evaluate the health risk of environmental pollutants and reveal the potential mechanism of its toxic effects. Key words: trichloroacetamide, gut microbiome, metabolome, mice, health risk

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Introduction The diverse composition and function of gut microbiome can be affected by external factors such as environment, diet, and antibiotics 1. The gut microbiome may also play a role in risks from environmental pollutants exposure. It is documented that oral exposure to polychlorinated biphenyls (PCBs) significantly altered the composition of the gut microbiome and induce systemic toxicity 2. Heavy metal, such as cadmium, can inhibit the growth of gut microbiota and reduce the abundance of total intestinal bacteria of the mice 3. The intestinal microbiota may even be used as a biomarker for evaluation of responses to specific interventions induced by xenobiotics and play a far greater role in environmental health than ever imagined. Therefore, the health assessment for environmental pollutants should include greater emphasis on understanding the complex relationship between compounds toxicity and gut microbiota information, which yet is not completely understood. With the increasing shortage of water resources around the world, resource utilization of wastewater and advanced treatment of reclaimed water is becoming a new trend, and is gaining concern. Either reuse or advanced treatment, disinfection process is usually essential. However, this process will inevitably produce disinfection by-products (DBPs)

4

.

Accumulating evidence demonstrates that DBPs exposure is associated with a number of diseases such as colon, colorectal, rectal, and bladder cancers

5-7

. More recently, DBPs

exposure has been linked to the changes of intestinal microbial populations and microbial activity, which may play an important role in the development of human diseases

8, 9

.

Trichloroacetamide (TCAcAm), emerging nitrogenous DBPs, has been widely detected in drinking water. Previous studies indicated that TCAcAm could induce chronic cytotoxicity 3



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and genotoxicity 10, 11. In addition, our previous study demonstrated that TCAcAm exposure could induce alterations of serum metabolome, including altered choline, trimethylamino oxide (TMAO), tyrosine, and phenylalanine, and many of these changes associated with host-gut microbiota co-metabolism 12. This raises questions about potential toxicological interactions between host and gut microbiome, which may provide a novel sight to evaluate the health risks of DBPs. Changes in gut microbiome may trigger a variety of diseases, such as diabetes, obesity, metabolic disorders, inflammatory, and cardiovascular diseases 13, 14. The potential mechanism is that multiple bacterial genomes can modulate the co-metabolism by the gut microbiome and host, such as choline, bile acids, phenols, polyamines, lipids, vitamins, and short-chain fatty acids (SCFAs) metabolism 15. These metabolites contribute the host metabolic phenotype and are essential for host health. For example, Wang et al. confirmed a critical role of gut microbiota metabolism of choline in promoting cardiovascular disease by using germ-free mice 16. Swann et al. elucidated the detailed effects of gut microbial depletion on the bile acid sub-metabolome of multiple body compartments in rats 17. It was found that the presence of specific microbial bile acid co-metabolite patterns strongly influenced the signaling functions and homeostasis in mammalian. Therefore, it is of particular interest to evaluate the interactions of perturbed gut microbiome and associated metabolic changes due to TCAcAm exposure. In this aspect, metagenomics and metabolomics are powerful multi-omic approaches to reveal potential interactions between the altered gut microbiome and perturbed metabolic profiles 18-20. Given the essential role of the gut microbiome in a variety of aspects of human health 4


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coupled with the potential health risk of TCAcAm, there is a need to elucidate the effects of TCAcAm exposure on the gut microbiome and its functions. In this study, we used a multi-omic approach combining high-throughput sequencing and metabolomics to investigate the effects of TCAcAm exposure on the gut microbiome and urine metabolic profiles. Materials and methods Animal treatment and sample collection Ten-week-old male mice (Mus musculus, ICR) were purchased from experimental animal center of Academy of Military Medical Science of China. The mice were housed in stainless-steel cages and acclimated for two weeks at 25 ± 3 °C, 50 ± 5 % relative humidity, and a 12/12 h light/dark cycle. A total of 28 healthy mice were randomly divided into four groups (seven mice in each group), including the control group was fed with 0.1 % vehicle (DMSO) and the TCAcAm-teated groups fed with 50 (T1), 500 (T2), and 5000 (T3) µg/L TCAcAm solution, respectively. The concentrations selected for TCAcAm were based on our preliminary work 12. During exposure, food and water were provided ad libitum. TCAcAm was purchased from Sigma-Aldrich. All experimental processes were in accordance with NIH Guide for the Care and Use of Laboratory animals. Mice were euthanized with diethyl ether and necropsied after exposed to TCAcAm for 90 days. Urine samples were collected over a 24-h period using metabolic cages on day 90. One milliliter of urine samples were added to 100 µL 0.02% NaN3 on ice, then centrifuged at 3000 rpm for 10 min and supernatants were stored at -80 °C. Fecal samples were collected during necropsy. Parts of intestine were dissected and fixed in 10% formalin solution. Histological analysis 5



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After 24-28 h, the intestine samples were routinely processed, dehydrated in a grade alcohol series, embedded in paraffin wax, sectioned at 4 µm, stained with hematoxylin-eosin, and evaluated by a board-certified veterinary pathologist blinded to the sample identity (seven tissue samples for each group and five slices for each tissue). Illumina high-throughput sequencing Fecal samples were collected during necropsy and DNA from the fecal samples was isolated using FastDNA SPIN Kit for Soil (MP Biomedicals, CA, USA). The extracted DNA was quantified by Nanodrop (NanoDrop® ND-1000, USA) and stored at -80 °C for further analysis. The resultant DNA samples from each group were barcoded, pooled to construct the sequencing library, then sequenced using Illumina Hiseq 2500 platform (Illumina, USA). The sequencing strategy was paired end sequencing, 101-bp reads and 8-bp index sequence. Nearly equal amount of sequencing reads was generated for each sample. For quality control, the raw data were filtered using the method described by Wang et al. 21. Finally, a total of 12,783,228 quality-filtered reads (clean reads) were extracted for each group and applied for subsequent metagenomic analysis. Taxonomic classification and functional analysis The clean reads were deposited in the MG-RAST (Meta Genome Rapid Annotation using Subsystem Technology, v3.3) server (http://metagenomics.anl.gov/) under accession numbers of 4558697.3 (Control), 4558692.3 (T1), 4558694.3 (T2) and 4558693.3 (T3). Alpha diversity, which can summarize the diversity of organisms in a sample, was estimated from the distribution of the species-level annotations in the combined MG-RASR dataset. For taxonomic classification analysis, the metagenomic data were submitted to the Metagenomic 6


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Phylogenetic

Analysis

(MetaPhlAn)

online

platform

(v1.7.0)

in

GALAXY

(http://huttenhower.sph.harvard.edu/galaxy) to accurately profile the microbial community 22. For functional annotation, the metagenomic data were annotated against Clusters of Orthologous Groups (COGs) in MG-RAST with the cutoff of e-value < 10-5, the cutoff of nucleotide sequence identity > 60%, and the cutoff of alignment length > 15 bp. Metabolomic profiling A total of 200 µL sodium phosphate buffer (0.2 M Na2HPO4/0.2 M NaH2PO4, pH 7.4) was mixed with 400 µL of urine samples and centrifuged at 1000 g for 5 min. Aliquots of the supernatant (500 µL) from each sample were mixed with 50 µL TSP/D2O (1 mM final concentration) and transferred into 5 mm NMR tubes. 1

H NMR spectra for all samples were acquired using a Bruker AV600 MHz spectrometer

(Bruker Co., Germany) operating at 600.13 MHz and 298 K. For each urine sample, the spectrum was acquired with a standard pulse sequence (NOESY), using 64 FIDs, 64k data points. After Fourier transformed, the phase and baseline of spectra were manually corrected using MestRec software. All the spectra were referenced to TSP (δ = 0.00 ppm). Each spectrum was segmented into 0.04 ppm chemical shift bins corresponding to the range from 0.20 to 10.00 ppm. The region (4.50-5.00 ppm) was excluded to remove the variation in water suppression efficiency. For urine samples, the urea signals (5.00-6.00 ppm) were also removed. Then, all remaining regions were scaled to the total integrated area of the spectra to facilitate comparison among the samples. The metabolite resonances were identified according to the Human Metabolome Database (www.hmdb.ca). SAM software was used to identify significant changes between control and 7



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TCAcAm-treated groups with FDR < 0.01. Partial least-squares discriminant analysis (PLS-DA) was used to explore the main effects in the NMR data sets by using SIMCA-P software (Umertric, Umeå, Sweden). The data processing and models validating in this study were previously described in detail by Jiang et al. 23. Significantly changed metabolites were identified based on the criteria that the FDR < 0.01 and the Variable Influence on Projection (VIP) score > 1 (which contributed relative large to the PLS-DA model). Statistical analysis The heat maps were generated using R language to visualize the gut microbiome differences between treatment groups. The correlation matrix between metabolites and gut bacterial species was generated using Pearson’s correlation coefficient and visualized by using R language. The Benjamin Hochberg method was used for FDR control (FDR < 0.05).

Results Water consumption, food intake and body weight Throughout the experiments, no death was observed and no significant changes were found in daily food intake between the TCAcAm-treated groups and control group. The average water consumption was 6.62, 6.74, 6.72, and 6.78 mL/day for individual mouse in three treated groups (T1, T2 and T3) and control, respectively and no significant differences were observed. However, the final body weight was significantly decreased in T3 group (42.81 ± 2.29 g) compared with control group (46.70 ± 2.79 g). In addition, the animal doses were converted to the human equivalent dose (HED) based on body weight. The HED was 8.86, 90.35, and 967.99 mg/kg for T1, T2 and T3 groups, respectively (Table S1). The possible exposure dose for an adult was 2 µg/kg/day, which was calculated by Equation S1 8


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(Supporting Information). Although the HEDs of TCAcAm used in this study were higher than the possible human exposure dose, the animal doses were acceptable to study the toxic effects of TCAcAm. Intestinal histological changes induced by TCAcAm Representative histological sections of intestine from control and TCAcAm-treated mice are shown in Figure 1, and the mean percentages with adverse histological changes are listed in Table S2. With the increase of TCAcAm concentration, varying degrees of lesions were induced in intestinal tissue. For example, intestinal mucosal congestion (76%) was observed in T1 group, hemorrhagic infiltration (78%) was observed in T2 group, and ulceration and necrosis (86%) was observed in T3 group. TCAcAm-induced gut microbiome changes Gut microbiota community in mice was determined by Illumina high-throughput sequencing. The α-diversity, calculated by MG-RST dataset, was 239.9, 200.0, 198.5 and 175.1 for control, T1, T2 and T3 groups, respectively. It means that the diversity of gut microbiome was reduced due to the TCAcAm exposure. Figure 2A shows the identified gut bacteria assigned at the phylum level from high-throughput sequencing reads, with each color representing an individual bacterial phylum. Results showed that Firmicutes (44.60-66.13 %) and Bacteroides (22.99-41.67 %) were predominant phyla in the gut bacteria of mice, followed by Proteobacteria (5.70-9.39 %), Tenericutes (1.01-1.28 %), Chlamydiae (0.64-0.81 %) and Fusobacteria (0.62-1.25 %). Our observations and assignments of gut bacteria at phylum level were consistent with previous studies for mammalian intestinal environment

24

. In addition, the relative abundance of Bacteroidetes was elevated with the 9



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increase of TCAcAm concentration. For the relative abundance of Firmicutes, no similar result was observed (Figure 2B). But the ratio of Firmicutes and Bacteroidetes (F/B ratio) was decreased with the increase of TCAcAm (Figure 2B). The microbial composition of

Firmicutes and Bacteroidetes was further analyzed at the family level. As a result, compared with control group, high similarity for the families was observed in TCAcAm-treated groups (Figure 2C). Specific changes associated with TCAcAm treatment included an increase in abundance of Bacteroidaceae, Porphyromonadaceae, Sphingobacteriaceae, Aerococcaceae and Erysipelotrichaceae families, and a decrease in Bacillaceae, Heliobacteriaceae and

Syntrophomonadaceae families. In addition, the families of Cyclobacteriaceae and Thermodesulfobiaceae were not found in TCAcAm-treated groups. TCAcAm-induced functional changes of the gut microbiome To assess the functional changes of gut microbiome, the mouse fecal metagenome was analyzed using MG-RST program. The metagenomic data sets were annotated against COGs database. As a result, the functional genes were divided into four categories, including metabolism, cellular processes and signaling, information storage and processing, and poorly characterized. Among these genes, metabolism-related genes accounted for the greatest abundance and were selected for further analysis. The results are shown in Figure 3. TCAcAm exposure increased the abundance of genes associated with amino acid transport and metabolism, energy production and conversion, and secondary metabolites biosynthesis, transport and catabolism, but decreased the abundance of genes related to lipid transport and metabolism. TCAcAm-induced changes in urine metabolic profiles 10


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Metabolomic alterations in urine were determined by 1H NMR. To characterize the variations of metabolic profiles, PLS-DA models were conducted on the 1H NMR spectra of all individuals. The discrimination between TCAcAm-treated and control mice was calculated on the basis of the first three components, and the results showed that the TCAcAm-treated groups and control group were readily separated (Figure 4). The validation of the models was evaluated by the default leave-one-out procedure with the parameters of R2X, R2Y and Q2. As a result, all of these models are applicable and have predictability values (R2X = 0.63, R2Y = 0.79 and Q2 = 0.66). The pattern recognition indicated that the metabolic profiles of urine altered with the increase of TCAcAm concentration. In addition, compared with control group, a number of metabolites were significantly changed (p < 0.05) in at least two TCAcAm-treated groups (Table 1, the rest metabolites were listed in Table S3). The structures of these metabolites were diverse, including choline metabolites, phenolic derivatives, polyamines, indole derivatives, short-chain fatty acids (SCFAs) and other organic acids. Many of these metabolites are associated with diseases and derived from host-gut microbiota co-metabolism

15

. Therefore, further study is needed to reveal the relationship between

metabolic perturbations and alterations in the composition of the gut microbiota. Correlation between the gut microbiome and host metabolites Metabolomics analysis has demonstrated that TCAcAm exposure altered metabolic profiles in mice urine, and many host-gut microbiota co-metabolites were also identified. Alterations in host-gut microbiota co-metabolites were previously reported as a consequence of perturbation in gut microbiota 25. To explore the functional correlation between the gut microbiome changes and metabolite perturbations, a correlation analysis was conducted based 11



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on the Pearson’s correlation coefficient. Clear correlations could be identified between the perturbed gut microbiome and altered metabolite profiles (r > 0.5 or < -0.5, p < 0.05). It was found that several typical metabolites were highly correlated with specific gut bacteria, which demonstrated the functional correlation between the gut microbiome and associated metabolites (Figure 5). For example, choline metabolites were highly correlated with

Bacteroidetes, Firmicutes, and Proteobacteria families. Phenyl derivatives and polyamines were significantly correlated with Bacteroidetes, Firmicutes and Proteobacteria families. While indole derivatives and SCFAs were only significantly correlated with the

Oxalobacteraceae family of Proteobacteria. The corresponding correlation coefficients are listed in Table S4.

Discussion In this study, we used Illumina high-throughput sequencing and metabolomics profiling to research the impact of TCAcAm exposure on the gut microbiome and urine metabolic profiles. The results showed that TCAcAm exposure induced significant changes in the gut microbiome composition and alterations in the urine metabolic profiles. Furthermore, a number of perturbed gut microbes were strongly associated with changes of specific gut microflora-related metabolites. These results suggested that TCAcAm exposure not only changed the community of the intestinal flora, but also substantially induced host metabolic disorders after TCAcAm exposure. These finds may provide a new perspective regarding perturbations of gut microbiome to reveal the mechanism of DBPs or other environmental pollutants induced health risk. Accumulated evidences strongly suggest that metabolic alterations associated with gut 12


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microbiome perturbations have become important health risks to result in tissue dysfunction or disease, such as obesity, insulin resistance, and cardiovascular disease 26-28. In addition, the composition of gut microbiota greatly influences host immune system and energy metabolism. Intestinal dysbiosis can trigger inflammatory in colon 29 or even in the remote tissues such as liver 30. In this study, various intestinal lesions were observed in the TCAcAm-treated mice. Our previous study demonstrated that TCAcAm could also induce inflammatory and hepatotoxicity in mouse liver 12. Furthermore, the shifts in the Firmicutes/Bacteroidetes ratio have significant correlation with host energy harvest and obesity 31. The gut microbiota of the obese mice contains more Firmicutes and fewer Bacteroidetes than that of lean mice. Conversely, the low Firmicutes/Bacteroidetes ratio was found in the case of weight loss 32. In this study, the Firmicutes/Bacteroidetes ratio of the gut microbiota was decreased in the high TCAcAm exposure dose groups of mice. At the meanwhile, weight loss was induced in the mice treated with 5000 µg/L TCAcAm. Choline is primarily metabolized in liver, but can also be conversed into methylamine, trimethylamine and trimethylamine-N-oxide by intestinal microbes

33

. These microbial

conversions may decrease the availability of choline and trigger non-alcoholic fatty-liver and cardiovascular diseases 16, 34. In this study, choline metabolites were significantly altered in urine samples after TCAcAm exposure in mice (Table 1). The presence of the altered choline metabolites is highly correlated with perturbed gut bacterial families. For example, the choline’s conversion into methylamine is modulated by gut microbial and host enzymatic activities. While, dimethylamine and trimethylamine can be directly produced by intestinal microbes. Thus, decreased excretion of methylamine, dimethylamine and trimethylamine in 13



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urine may indicate that imbalanced gut bacteria were induced by TCAcAm exposure. In this study, the bacteria of Firmicutes;f_Clostridiales Family XI had positive correlation with dimethylamine.

Both

Bacteroidetes;f_Rikenellaceae

and

Proteobacteria;f_Pseudomonadaceae had positive correlation with trimethylamine (Figure 5). Previous studies have documented that Faecalibacterium is related to the conversion process discussed above and this gut bacteria family is classified into the same order of Clostridiales 35

. It has been demonstrated that alterations of phenolic derivatives in urine are associated

with various diseases such as weight loss and inflammatory 36. For example, altered levels of urine cresols, produced from tyrosine in mammals, have been associated with inflammatory bowel disease 37. In agreement with this, decreased urine tyrosine and intestinal lesions were observed in TCAcAm-treated mice (Table 1 and Figure 1). In addition, phenolic derivatives are also sensitive to gut microbiome changes. For example, the production of cresols is highly correlated with gut bacteria genus of Clostridium, Bifidobacterium, and Bacteroides

15

.

Lactobacillus and Bacteroides species decreased in abundance in the case of inflammatory bowel disease

38

. In this study, phenylacetate and hydcoxyphenylacetate had positive

correlation with Rikenellaceae and Pseudomonadaceae, and negative correlation with

Syntrophomonadaceae and Thermoanaerobacteraceae. Hippurate and phenylacetylglycine had negative correlation with Oxalobacteraceae (Figure 5). Hippurate is the most widely detected urinary metabolite of host-microbial origin in humans and rodents, and has become an important biomarker for disease or gut microbial activity 39. Decreased urinary excretion of these metabolites may suggest that an impaired intestinal function was induced by a perturbed 14


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gut microbiome as a result of TCAcAm exposure. Indole derivatives, including serotonin, 3-indolepropionate and indoleacetate, were significantly perturbed in TCAcAm-treated mice (Table 1). These metabolites are also sensitive to gut microbiome changes. For example, serotonin is derived from tryptophan by enterochromaffin cells in the gastrointestinal tract and its production is indirectly mediated by gut bacteria 40. This molecule has been identified as biomarker for irritable bowel syndrome and Crohn’s disease

41

. The alterations of serotonin may give rise to gastrointestinal

dysfunction. Through germ-free mice colonization analysis, it has been demonstrated that 3-indolepropionate is only produced by gut bacterium Clostridium sporogenes 42. Although no significant

correlation

was observed

between

Clostridiaceae

family

and

altered

indole-containing metabolites, one Proteobacteria family (Proteobacteria;f_Oxalobacteraceae) increased in abundance after TCAcAm exposure, which had high negative correlation with 3-indolepropionate and indoleacetate, and positive correlation with serotonin. The underlying mechanisms remain unclear, however, TCAcAm-induced gut microbiome perturbations may play a role in this process, which need further study. Cadaverine and spermine are both polyamines (PAs) metabolites. They were significantly altered in the urine samples of TCAcAm-treated mice. It has been demonstrated that PAs can induce genotoxicity on the host, because PAs may affect the synthesis and stabilization of DNA, RNA, and protein, and simulate cell proliferation or differentiation 43. In addition, PAs also have strong anti-inflammatory and antitumoral effects. PAs have been used as potential biomarkers for tumor. For example, colonic epithelial cells original from colonic cancer are characterized by a very high capacity for PAs synthesis 44. Furthermore, the presence of the 15



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altered polyamines metabolites is highly correlated with perturbed gut bacterial families. Recent studies demonstrated that PAs could be produced by specific gut bacteria, such as

Escherichia, Bacteroides, Lactobacillus, Veillonella, Bifidobacterium, and Clostridium 45. Some of these bacteria genera were found to have high correlation with altered cadaverine and spermine (Figure 5). Cadaverine is the decarboxylation product of lysine degraded by

Escherichia coli in intestinal

46

. In particular, it has been reported that increased amount of

5-aminopentanoic acid, a metabolite of cadaverine, has high correlation with intestinal inflammation in mice 47. Among PAs, spermine has the strongest physiological function and toxicity, and its metabolism is strictly modulated by both gut microbiota and host

48

. In the

present study, we also found the levels of cadaverine and spermine were significantly decreased in urine (Table 1), possibly reflecting cell genotoxicity or inflammation may be induced by the perturbed gut microbiome as a result of TCAcAm-exposure. In agreement with this result, hepatic transcriptomic analysis also found pathways related to cell process were significantly altered due to TCAcAm exposure 12. In this study, we demonstrated that TCAcAm exposure altered the gut microbiome and associated urine metabolomic profiles. Clearly, the in vivo experiments on the toxic effects of TCAcAm exposure included in this study are only preliminary, and further research should be targeted towards elucidating the underlying mechanisms of these perturbations. Future studies should strive towards the following directions: (1) Further comprehensive top-down systems biology analyses, such as metatranscriptomic and metaproteomic studies, should be conducted to reveal the response of microbial function as a result of environmental chemicals stimulus. (2) The cross-talk between the microbes and the host is complicated. Altered composition of 16


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gut microbiome can result in perturbations of host metabolic profiles. Conversely, metabolic disorders can also induce altered gut microbial ecology. Therefore, further studies are warranted to confirm whether the relationship between altered gut microbiome and perturbed metabolic profiles is just an epiphenomenon or is some kind of causal link. (3) A deeper research is required to clarify the impact of TCAcAm on gut microtiota by using an in vitro model of intestinal microbiota.

Conclusions In the present study, Illumina high-throughput sequencing and metabolomics were combined to analyze the impact of TCAcAm exposure on the gut microbiome and urine metabolic profiles in mice. The high-throughput sequencing revealed that TCAcAm exposure significantly changed the composition of gut microflora, whereas the metabolomics analysis demonstrated that a number of metabolites related to diverse metabolic pathways were dramatically perturbed after exposure to TCAcAm. In addition, correlation analysis identified that some gut bacteria families were highly correlated with the altered urine metabolites. Taken together, these data indicated that TCAcAm exposure not only altered the gut microbiome at the abundance level but that it also disrupted the metabolic function of the gut microbiota with corresponding metabolic disorders. These results supported the hypothesis that perturbations of gut microbiome may be a new mechanism for the toxic effects induced by TCAcAm. Furthermore, these highly correlated metabolites may be potential biomarkers for identification of health risks of TCAcAm. Integration of high-throughput sequencing of gut microbiome and metabolomics should provide new insight into the toxicity evaluation of environmental pollutants. 17



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Acknowledgements This work was financially supported by the National Science Foundation of China (No. 51348009 and No. 21407076), Jiangsu Natural Science Foundation (BK2011016) and the Fundamental Research Funds for the Central Universities (20620140487).

Supporting Information Available: This material is available free of charge via http://pubs.acs.org. Table S1 summarizes the human equivalent dose for the treatment groups; Table S2 summarizes the mean percentage with adverse histological changes for control and TCAcAm-treated groups; Table S3 summarizes the alterations of urine metabolites induced by TCAcAm treatment; Table S4 summarizes the correlation coefficients of correlation analysis for perturbed gut bacteria families and altered urine metabolites. Equation S1 summarizes the calculation of possible human exposure dosage of TCAcAm.

18


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Figure Legends Figure 1 Representative histological sections of intestine from control and TCAcAm-treated group mice. Black arrows show the histological changes.

Figure 2 (A) The gut microbiome composition profiles at the phylum level in the control and TCAcAm-treated mice. (B) The relative abundance and ratio of Firmicutes to Bacteroidetes in the control and TCAcAm-treated mice. (C) The microbial community of Firmicutes and

Bacteroidetes at the family level in the control and TCAcAm-treated mice. Figure 3 Annotated metabolism-related genes of gut microbiota in the control and TCAcAm-treated mice.

Figure 4 Partial least-squares discriminant analysis (PLS-DA) of urine 1H NMR spectra. Figure 5 Correlation plot showing the correlation between perturbed gut bacteria families of and altered urine metabolites (*, p < 0.05).

26


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Table 1 Alterations of urine metabolites induced by TCAcAm treatment (FDR < 0.01 and VIP >1). Metabolite

Chemical shift

Choline metabolites

Fold change 50

500

5000

µg/L

µg/L

µg/L

Methylamine

2.59(s)

0.52*

0.48*

0.32*

Dimethylamine

2.50(s)

1.12

1.02*

1.09*

Dimethylglycine

2.91(s)

0.96

1.00*

1.95*

Trimethylamine

2.89(s)

1.18*

0.82

0.71*

Phenylacetate

3.53(s)

1.08

0.84*

0.78*

Hippurate

7.66(t)

1.05

0.76*

0.76*

Hydroxyphenylacetate

3.48(s)

1.12

0.65*

0.50*

Phenylacetylglycine

3.67(s)

1.05

0.91*

0.91*

Tyrosine

7.16(m)

0.88*

0.84*

0.81*

Tryptophan

7.18(d)

1.10*

0.90

0.91*

Indoleacetate

3.65(s)

1.05

0.91*

0.91*

3-Indolepropionate

7.20(s)

0.99

0.83*

0.83*

Serotonin

3.10(t)

1.12

1.37*

1.36*

Cadaverine

2.68(t)

3.35*

2.98*

1.40

Spermidine

2.60(m)

0.52*

0.48*

0.32*

SCFAs

Acetate

1.92(s)

1.03

1.75*

1.80*

Others

Alanine

1.47(d)

0.95

1.09*

1.10*

Phenolic derivatives

Indole derivatives

Polyamines

27



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Citrate

2.54(d)

1.57*

1.92*

2.16*

Pyruvate

2.41(s)

1.36*

1.16

1.50*

Succinate

2.42(s)

1.00

1.29*

1.34*

*

p < 0.05 (One-Way ANOVA)

28


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TOC Art

a

29



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Figure 1 232x188mm (300 x 300 DPI)


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Figure 2A 219x226mm (300 x 300 DPI)



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Figure 2B 185x158mm (300 x 300 DPI)


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Figure 2C 152x232mm (300 x 300 DPI)



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Figure 3 249x173mm (300 x 300 DPI)


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Figure 4 215x189mm (300 x 300 DPI)



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Figure 5 474x381mm (96 x 96 DPI)


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Metagenomic and Metabolomic Analysis of the Toxic Effects of

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