Effects of chronic cadmium exposure at food limitation-relevant levels on energy metabolism in mice

Journal Pre-proof Effects of Chronic Cadmium Exposure at Food Limitation-Relevant Levels on Energy Metabolism in Mice Xiwei He, Zhaodong Qi, Hui Hou, Jie Gao, Xu-Xiang Zhang

PII:

S0304-3894(19)31745-5

DOI:

https://doi.org/10.1016/j.jhazmat.2019.121791

Reference:

HAZMAT 121791

To appear in:

Journal of Hazardous Materials

Received Date:

16 June 2019

Revised Date:

25 November 2019

Accepted Date:

29 November 2019

Please cite this article as: He X, Qi Z, Hou H, Gao J, Zhang X-Xiang, Effects of Chronic Cadmium Exposure at Food Limitation-Relevant Levels on Energy Metabolism in Mice, Journal of Hazardous Materials (2019), doi: https://doi.org/10.1016/j.jhazmat.2019.121791

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Effects of Chronic Cadmium Exposure at Food Limitation-Relevant Levels on Energy Metabolism in Mice Authors: Xiwei He, Zhaodong Qi, Hui Hou, Jie Gao, Xu-Xiang Zhang* Affiliations of authors:

Environment, Nanjing University, Nanjing 210023, China Corresponding author:

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State Key Laboratory of Pollution Control and Resource Reuse, School of the

*Xu-Xiang Zhang (Address: 163 Xianlin Road, Nanjing 210023, China; Phone: +86-

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25-89680363; Fax: +86-25-89680363; Email: [email protected])

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Graphical abstract

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Highlights 

Cd exposure at food limitation-relevant levels induced perturbation of energy metabolism in mice.



Cd induced structural/histological and functional alterations in liver and gut microbiome.



Correlations exist between certain liver transcription factors, gut microbes, and

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urinary metabolites.

Abstract

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Cadmium (Cd) exposure has been implicated in the perturbation of energy

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metabolism and the development of cardiometabolic disease, but disease predisposition from chronic low-dose Cd exposure remains unclear. This study employed a mouse

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model to investigate the toxic effects of chronic Cd exposure at food limitation-relevant

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levels on energy metabolism and the associated liver and gut microbiome functions. Results showed that the Cd exposure induced the perturbation of energy metabolism in

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mice, evidenced by the alteration of various metabolites associated with the phosphorogen (adenosine triphosphate-creatine phosphate) system, tricarboxylic acid

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cycle, and lipid metabolism, as well as the increase of the cardiometabolic risk factor, triglyceride. Moreover, both liver and gut microbiome underwent marked structural/histological and functional alterations, prone to the onset of cardiometabolic disease following the Cd exposure. Certain hepatic transcription factors and gut microbes,

specifically

PPARα,

SREBP1c, 2

HNF4A

and

the

Clostridiales_vadinBB60_group, were identified to be highly correlated with altered urinary metabolites, revealing potential toxicological interactions between the liver and gut microbiome, and energy metabolism. Our findings provide new insights into the progression of metabolic diseases induced by Cd exposure. We also propose a stricter Cd limitation in future food safety standards. Keywords: Cadmium; cardiometabolic risk; chronic low-dose exposure; energy

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metabolism; mice gut microbes. 1. Introduction

Cadmium (Cd) is one of the most prevalent toxic metal pollutants widely

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distributed in the environment. Exposure to Cd increases mortality in the human

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population from cardiovascular-related diseases including diabetes, peripheral artery disease, and nonalcoholic fatty liver disease (NAFLD) (Satarug et al. 2010). These

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diseases are characterized by cardiometabolic syndrome, primarily manifested in

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dysfunctions of energy metabolism (Grundy 2012; Kirk and Klein 2009). Both liver and gut microbiota play critical roles in controlling energy metabolism (Nicholson et

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al. 2012). Previous studies have indicated that orally ingested Cd can change gut microbiota (Go et al. 2015; Liu et al. 2014). Orally ingested Cd can be transported from

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intestine to liver where it accumulates and exerts adverse impacts (Liu et al. 2009). This raises questions about potential toxicological interactions between the liver and gut microbiome, and energy metabolism. Addressing these questions may provide novel insights into the health risks induced by Cd. Previous studies investigating toxicological effects of Cd on liver and gut 3

microbiome associated with energy metabolism have principally focused on Cd exposure at relatively high doses (parts per million ranges) (Go et al. 2014; Larregle et al. 2008; Liu et al. 2014; Zhang et al. 2015). For example, administration of 10 ppm Cd for 20 weeks was reported to significantly change lipids and fatty acids contents in the plasma of mice, and it altered mRNA expression of the functional genes responsible for the pathways associated with NAFLD (Go et al. 2015). Additionally, Zhang et al. (2015)

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revealed that Cd exposure at 10 ppm for 8 weeks also changed both the lipid and glucose

levels in mice serum and altered their gut microbiome composition that contributes to the induced perturbation of energy homeostasis. Recently, Ba et al. (2018) demonstrated

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that low-dose (100 nM) Cd exposure during fetal development could lead to life-long

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metabolic consequences with gut microbiota alterations and hepatic lipid metabolism disorder. Despite these pioneer studies, there is still much uncertainty about the post-

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developmental toxic effects of chronic Cd exposures at environmentally-relevant levels

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(e.g. parts per billion range).

Due to sewage irritation and increased use of groundwater, chronic low-dose Cd

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(CLD-Cd) exposure from ingestion of contaminated food and water has become more and more prevalent. It is estimated that over 16% of the total farmland in China has

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been contaminated by Cd, which is endangering the health of more than 200 million people (Ran et al. 2011). Although the Food and Agriculture Organization and the World Health Organization have recommended a provisional tolerable daily intake (PTDI) of Cd at 58 μg/day (for a person who weighs 70 kg), a recent study showed that over 30% of the general population in China have a dietary daily Cd intake above the 4

PTDI, with a 95th percentile being 193.2% of PTDI (Liu et al. 2018). This raises a safety concern about the current standard for maximum limits (MLs) of Cd in food. According to CODEX STAN 193-1995 (Fan et al. 2005), an adult could ingest 135-225 μg Cd in one day, given that the ingested food and water (250-400 g grain, 300-500 g vegetables, 200-400 g fruits, 150-250 g meat, 1-2 L water) contain Cd at MLs. However, potential toxic effects of chronic Cd exposure at relevant MLs on energy metabolism

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and the associated liver and gut microbial functions remains largely unknown.

In this study, we employed a 26-week-exposure mouse model with a daily Cd intake of around 30 μg/kg body weight to investigate the toxic effects of CLD-Cd

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exposure on energy metabolism. We examined the changes of cardiometabolic risk

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factors and urinary metabolites associated with energy metabolism. In addition, both the histopathological/structural and functional alterations in the liver and gut

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microbiome were investigated to determine their contribution to the perturbation of

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energy homeostasis. The results could provide new insights into the metabolic health

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hazards induced by exposure to Cd at its MLs required by current food safety standards.

2. Materials and Methods

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2.1. Animals and Treatment Male C57BL/6 mice (6 weeks old) obtained from the Experimental Animal Center

of the Academy of Military Medical Science of China and housed in stainless steel cages were used for toxicity test. After acclimated for two weeks under the ambient conditions of 25±2 oC temperature, 50±5% relative humidity, and a 12/12 h light/dark 5

cycle, sixteen mice were randomly assigned to one control and one treatment group (eight mice per group, four mice per cage), which were fed with pure water and 150 μg/L CdCl2 spiked in pure water, respectively, for 26 weeks. The dose was chosen based on a human Cd intake of 3.2 μg/kg body weight (225 μg daily intake for a person who weighs 70 kg) multiplied by the mouse-human dose ratio in toxicology (9, calculated based on the body surfaces of human and mouse, see Method S1 in the Supplementary

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Materials for details). Food and water were provided ad libitum during the whole exposure time. The male C57BL/6 mouse model was chosen because it is a well-

established animal model extensively used for studies of energy metabolism and related

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diseases, with detailed prior characterization of Cd sensitivity. We used male mice

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instead of females because greater absorption of Cd has been reported in females, which suggests that any observed toxicity in the male at this dose would replicate or be

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worsened in a female cohort. Given that the development of male mice usually

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completes within six weeks after birth, we chose an age of eight weeks at the start of the Cd exposure experiment (after two weeks of acclimation) to minimize the effects

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of development on energy metabolism and gut microbiome. After 26 weeks of exposure, the mice were sacrificed with diethyl ether (Xu et al.

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2008). Blood, liver and feces samples were collected and immediately put into ice-cold tubes or into liquid nitrogen. Serum was separated from the blood samples by centrifugation at 3,000 rpm for 15 min and stored at −80 oC for further use. The animal experiment was approved by the Institutional Animal Care and Use Committee of Nanjing University. 6

2.2. Determination of serum biochemistry indices The serum triglyceride (TG), glucose (GLU), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) of the mice (eight mice per group) were determined in duplicate using kits purchased form the Nanjing Jiancheng Institute of Biotechnology (Nanjing, China) according to the manufacturer’s instructions.

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2.3. H&E and immunohistochemical (IHC) staining of the livers

Liver tissues from four mice in each group (randomly chosen) were used for the

H&E and IHC staining. Liver tissues were fixed in 10% formalin solution for 4 h at

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room temperature and then overnight at 4 oC with fresh fixative. The fixed tissues

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were embedded in paraffin and sectioned to 5 μm-thick slices, which were then stained with H&E solutions. Images of the stained sections were obtained using a light

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microscope. For the immunohistochemistry experiment, paraffin-embedded 5 μm-

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thick sections were blocked with 0.5% H2O2 in methanol to reduce endogenous peroxidase activity, hydrated, and stained. Sections were incubated overnight at 4 oC

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with an anti-CD68 primary antibody diluted 1:200 (Abcam, Cambridge, MA, USA), and then incubated with HRP-conjugated anti-mouse secondary antibody for 2 h at

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room temperature and washed with PBS three times. Images of the sections were obtained with a light microscope, and the Image J software (version 1.8) was used to analyze the images of H&E and IHC staining.

2.4. Hepatic oxidative stress analysis Hepatic oxidative stress was determined by measuring malondialdehyde (MDA). 7

Four mice were randomly chosen from each group, and part of the left lobe of the liver (300-500 mg) was homogenized and centrifuged. The supernatants were collected and MDA was detected in duplicate for each mouse by using the Enzyme-Linked Immunosorbent Assay kits (Jiancheng, Bioeng. Inst., China). 2.5. Quantitative real-time RT-PCR analyses To determine hepatic mRNA expressions of the genes associated with oxidative

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stress and inflammation, as well as transcription factors (TFs) related to hepatic

metabolism and phenotype, total RNA was extracted from mouse liver (eight mice per

group) using the TaKaRa MiniBEST Universal RNA Extraction kit (TaKaRa, Dalian,

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China), and the first strand cDNA was synthesized using the PrimeScripTM RT Reagent

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Kit (TaKaRa). Diluted cDNA was amplified using the SYBR Premix EX Taq Super Mix (TaKaRa) in an ABI 7500 Real-Time PCR system (Applied Biosystems, Inc., Foster

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City, CA, US) with glyceraldehyde-3-phosphate dehydrogenase as the housekeeping

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gene. Each reaction was run in duplicate in a final volume 20 μl containing 10 μl of SYBR Premix EX Taq Super Mix (TaKaRa), plus 0.5 μl of each primer (250 nM), 0.5

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μl of Rox dye, 4.5 μl of ddH2O, and 4 μl of diluted cDNA. The following polymerase chain reaction (PCR) protocol consisted of a denaturation for 30 s at 95 oC, followed

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by 40 cycles of 15 s at 95 oC and 30 s at 60 oC. The efficiency of each PCR was estimated from a serially diluted cDNA curve. Based on the cycle thresholds (Cts), the relative concentration of the target and reference cDNA and their ratio was determined. The primer sequences of the genes and TFs of interest are listed in Table S1. 2.6. Determination of Cd concentrations 8

After 26 weeks of exposure, the Cd concentrations in both liver and feces samples were measured. Briefly, wet liver tissues and dried fecal samples collected from six mice (randomly chosen) in each group were accurately weighted and digested in concentrated HNO3 for the complete removal of bio-organic material. The concentrations (μg/g) of Cd in the samples were determined in duplicate by a NexION 300 inductively coupled plasma mass spectrometry (PerkinElmer, Shelton, CT, US).

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2.7. 16S rRNA gene sequencing of gut microbiota and data analysis

Total DNA from fecal sample of the mice (eight mice per group) was extracted

with a FastDNA Soil Kit (MP Biomedicals, Solon, OH, US) according to the

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manufacturer’s instructions. The concentration and quality of acquired DNA were

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measured by Nano-Drop 2000 microspectrophotometry (Thermo Scientific, Wilmington, DE, US) and the DNA samples were stored at −80 oC for further analysis.

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Bacterial 16S rRNA genes were amplified using universal primers 16s-F (5’-

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AGAGTTTGATYMTGGCTCAG-3’) and 16s-R (5’-TGCTGCCTCCCGTAGGAGT3’) targeting the V1-V2 region. Individual samples were barcoded and then pooled to

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construct the sequencing library before sequenced using an Illumina Miseq (Illumina, San Diego, CA). The raw sequencing data were processed with the open-source

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expandable mothur software (Schloss, 2019) to remove low-quality and chimeric reads. After denoising and quality checking, each dataset was rarefied to achieve same sequencing depth. The Quantitative Insights into Microbial Ecology (QIIME, version 1.9.1) release was used to cluster high quality reads into operational taxonomic units (OTUs) at a 97% identity level. The alpha-diversity indices (Chao, ACE, Simpson, and 9

Shannon) were calculated in QIIME. Representative sequence of each OTU was assigned

to

taxonomy

in

Ribosomal

Database

Project

(RDP)

Classifier

(http://rdp.cme.msu.edu/) using a confidence threshold of 80% (Caporasoet al. 2010). The sequencing data have been deposited in the NCBI Sequencing Reads Archive database under the accession number SUB6567518. Bacterial metagenome content was predicted from 16S rRNA gene-based microbial compositions, and functional

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inferences were made from the Kyoto Encyclopedia of Gene and Genomes (KEGG) catalog, using the PICRUST algorithm (Kanehisa et al. 2012). 2.8. Metabolomics profiling

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Urine metabolomics profiles of the mice (eight mice per group) were determined

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by 1H NMR (nuclear magnetic resonance) according to Zhang et al. (2013). Briefly, equal volumes of serum and phosphate sodium buffer (70 mM Na2HPO4; 20% (v/v)

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D2O; 3 mM sodium trimethylsilyl [2,2,3,3-2H4] propionate (TSP); pH 7.4) were mixed

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and centrifuged at 10,000 rpm for 10 min, and the supernatants were transferred into 5 mm NMR tubes for analysis. The 1H NMR spectra were acquired using a Bruker AV600

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spectrometer (Bruker Biospin GmbH, Karlsruhe, Germany) equipped with a cryoprobe (5 mm TCl cryo 1H, 15N, 13C Z-GRD) at 298 K. Water resonances were suppressed by

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a Carr-Purcell-Merbom-Gill (CPMG) spin-echo pulse sequence with 32 free induction decays collected into 64K data points. Exponential line broadenings of 0.3 HZ were applied before Fourier transformation, and spectra were phase and baseline corrected using MestRec 4.9 software. Each spectrum was referenced to 3-(trimethylsilyl)2,2,3,3-tetradeuteropropionic acid sodium salt (TSP) at δ = 0.00 ppm before segmented 10

into 0.005 ppm chemical shift bins corresponding to the range from 0.00 to 10.00 ppm. Water resonance (4.50–5.00 ppm) was removed prior to normalization. All remaining regions were scaled to the total integrated area of the spectra to facilitate comparison among samples. The metabolites were identified according to the Human Metabolome Database (HMDB, http://www.hmdb.ca/) (Table S2). 2.9. Statistical analysis

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Experimental data obtained in this study were expressed as the mean  SEM. Statistical differences of body weights, water intakes, hepatic mRNA levels, liver and fecal Cd concentrations, gut microbiome taxa, predicted functional pathways, and

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urinary metabolites between the control and Cd-treated group were evaluated using a

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two-tailed Welch’s t-test by the SPSS 15 software (SPSS Inc., USA). The gut microbiome profiles of the two groups were compared using principal coordinate

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analysis (PCoA) of the OTUs with abundances greater than 0.5%, and the statistical

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difference was evaluated using the One-way PERMANOVA test by PAST (version 3.16) (Hammer et al. 2001). For all the above analyses, p < 0.05 was considered statistically

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significant.

Partial least-squares discriminant analysis (PLS-DA) was used to explore

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differences in the hepatic transcription factors’ (TFs’) transcriptional and urinary metabolic profiles between the control and Cd-treated group by MetaboAnalyst 3.0 (http://www.metaboanalyst.ca/). Specific TFs and metabolites with a variable influence on projection (VIP) score > 1 and p < 0.05 were deemed to be significantly altered (Jordan et al. 2012). The TF z-scores were calculated with the following formula: z11

score = (treatment metabolite abundance – control mean) / standard deviation of control (Jordan et al. 2012). A heat map was generated using a hierarchical clustering algorithm to visualize the transcriptional differences of the TFs between the two groups. The significantly altered metabolites (SAMs) were used for further metabolic pathway enrichment analysis using MetaboAnalyst 3.0. Correlations between SAMs and hepatic TFs or gut bacterial genera were generated using Pearson’s correlation coefficient by

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the PAST, and p < 0.05 was considered statistically significant. 3. Results

3.1. Cardiometabolic risk factors and energy metabolism in response to the CLD-

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Cd exposure

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During the 26-week exposure, the mean daily water intake of the controls and Cdtreated mice ranged from 4.0 to 5.9 and 4.1 to 5.7 mL, respectively (Figure S1A), with

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no significant difference between the two groups (p=0.766, Figure S1B). For the Cd-

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treated mice, the water intake resulted in a mean daily Cd intake of 23.3 to 36.0 μg/kg body weight. The cardiometabolic risk of CLD-Cd exposure was evaluated by

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examining the cardiometabolic risk factors, including body weight, serum TG, GLU, LDL-C, and HDL-C. As shown in Figure S1C, body weights of the controls and Cd-

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treated mice showed temporally increasing trends during the whole exposure time. The controls and Cd-treated mice showed no significant difference in the gained weight after the 26-week exposure (p=0.194, Figure S1D). Similarly, the Cd exposure posed no statistically significant effects on serum GLU, LDL-C, and HDL-C, but seemed to evidently increase serum TG levels (Table S3). 12

We then investigated the effects of CLD-Cd exposure on energy metabolism by metabolomic analysis of urine samples. Supervised multivariate PLS-DA of the NMR data showed that the Cd-treated group could be clearly separated from the control group (R2X=0.63, R2Y=0.75, Q2=0.72, Figure S2), indicating that the Cd exposure markedly altered the metabolic profile of the mice. Table 1 highlights urinary metabolites that were significantly changed (VIP > 1 and p < 0.05) in response to the Cd exposure. The

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rapid energy suppliers creatine and phosphocreatine showed to have significantly lower

levels in the Cd-treated mice than the controls, indicating impairment of the phosphorogen system, e.g. adenosine triphosphate-creatine phosphate (ATP-CP)

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system. In addition, the Cd exposure significantly decreased the levels of the key

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intermediates within the tricarboxylic acid (TCA) cycle, citrate and 2-oxoglutarate, suggesting a potential disturbance of oxidative energy metabolism. Disrupted lipid

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metabolism was also observed in the Cd-treated mice, indicated by a significant

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increase in trimethylamine oxide (TMAO) and a decrease in choline and phosphorylcholine in their urine samples. Notably, TMAO, choline, and

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phosphorylcholine were host-gut microbiota co-metabolites, and their alterations indicate possible perturbations in the gut microbiome. Taken together, the CLD-Cd

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exposure posed diverse impacts on energy metabolism, including alterations in the ATP-CP system, TCA cycle, and lipid metabolism. 3.2. Hepatic tissue damage and transcription profile alteration induced by the CLD-Cd exposure Results showed that hepatic Cd concentration in the Cd-treated mice was 13

significantly higher than that of the controls (p=0.009, Table S4). No significant difference in relative liver weight (vs. body weight) was observed between the two groups of mice (p=0.248, Figure S3). However, H&E staining of the liver sections revealed lesions of hepatocellular necrosis and inflammatory infiltration in the Cdtreated mice (Figure 1A). Meanwhile, IHC analysis of the macrophage activation marker CD68 showed an increased number of activated Kupffer cells in the Cd-treated

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mice (Figure 1B). In accordance with the tissue observation, quantitative real-time RTPCR analysis also revealed significantly higher hepatic mRNA levels of the two

inflammatory cytokines, TNF-a and IL-6, in the Cd-treated mice compared to the

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controls (Figure 1C). In addition, oxidative damage took place in the liver of the Cd-

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treated mice, as indicated by the increased hepatic MDA level (Figure 1D) and

system (Figure 1C).

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decreased mRNA levels of the key genes HO-1 and GPx involved in the anti-oxidation

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The hepatic expression of 35 TFs associated with energy metabolism (Benet et al. 2014) were thereby investigated to determine the effects of the CLD-Cd exposure on

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the expression of the TFs and their contribution to the disrupted energy homeostasis. A heat map produced by unsupervised hierarchical clustering of the Z-scores of the 35

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TFs expression profiles revealed that the controls and Cd-treated mice were clearly clustered into two separate groups (Figure 2). Moreover, supervised multivariate data analysis using PLS-DA showed clear discrimination between the two groups (Figure S4). These results indicated the profound effect of CLD-Cd exposure on the hepatic transcriptional regulation of energy metabolism. Table 2 lists the significantly changed 14

hepatic TFs (VIP > 1 and p < 0.05) under the stress of Cd exposure, indicating that the CLD-Cd exposure evidently decreased the mRNA levels of HNF1A and HNF4A (responsible for organ development and energy metabolism), and SRC1 and SRC3 (uniquely important for mediating steroid hormone and systems metabolism, as well as PPARα, a master regulator of lipid metabolism). In contrast, the CLD-Cd exposure

genes required for glucose and fatty acid production.

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seemed to promote the mRNA expression of SREBP1c that regulates the functional

To identify the TFs that are most closely linked to the metabolic alterations, a

correlation matrix was generated based on Pearson’s correlation coefficient between the

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changed TFs and altered urinary metabolites (Figure 3, Table S5). It was found that

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most of the TFs were highly correlated with one or more altered metabolites. For example, HNF1A was negatively correlated with TMAO while SRC3 was positively

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correlated with phosphocreatine, 2-oxoglutarate, and glutamine. Notably, among the

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changed TFs, PPARα, HNF4A, and SREBP1c showed the highest correlations primarily with the metabolites of the ATP-CP system and TCA cycle.

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3.3. Changes of gut microbiome induced by the CLD-Cd exposure This study investigated the effects of the CLD-Cd exposure on both structure and

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function of the gut microbial community. The CLD-Cd exposure was found to induce obvious accumulation of Cd in the mice feces (Table S4). The Chao and Shannon indices of the gut microbiota were significantly higher in CLD-Cd group, while the Simpson index was significantly lower in the CLD-Cd group when compared to the control (Figure S5). One-way PERMANOVA analysis based on 16S rRNA gene 15

sequencing showed that the Cd exposure significantly changed the gut microbiome patterns (p=0.002), which was further confirmed by the PCoA plot (Figure 4A) and hierarchical clustering analysis (Figure 4B). Table S6 shows the differentiated taxa assigned at different taxonomical levels. At the family level (Figure S6), relative abundance of a total of six bacterial families were significantly changed (p<0.05) after the Cd exposure, including the enriched Helicobacteraceae, Bacteroidaceae, and and

the

decreased

Porphyromonadaceae,

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Rikenellaceae,

Clostridiales_vadinBB60_group, and unclassified Gastranaerophilales (Figure 4C). Correlation analysis showed that the changes in the Porphyromonadaceae and

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Clostridiales_vadinBB60_group were significantly correlated with the host-gut

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microbiota co-metabolites TMAO, choline and phosphorylcholine (Figure 3, Table S7). Functional analysis using the PICRUST algorithm revealed that the CLD-Cd

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exposure significantly altered 68 KEGG pathways among the 262 ones tested (p < 0.05,

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Figure S7). Table 3 highlights the altered pathways within the metabolism category. Interestingly, we found that the altered metabolic pathways related to carbohydrate

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metabolism, lipid metabolism, and energy metabolism were all significantly depleted in the Cd-treated mice, whereas the pathways related to glycan biosynthesis and

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metabolism were significantly strengthened by the exposure. The CLD-Cd exposure also altered the pathways related to the metabolisms of other substances, including amino acid, vitamins, and xenobiotics (Table 3).

4. Discussion 16

This study revealed the disruption of energy metabolism in mice induced by the CLD-Cd exposure, which was evidenced by the significant changes of a set of urinary metabolites associated with the ATP-CP system, TCA cycle, and lipid metabolism. Previous studies have indicated a link between these form’s perturbation of energy homeostasis and the development of cardiometabolic disease. For example, chronic deficiency of phosphocreatine is a hallmark of cardiovascular disease and has been

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observed in cardiomyopathy patients (Scally et al. 2017), while lowered levels of citrate and 2-oxoglutarate have been associated with insulin resistance, a primary

manifestation of cardiometabolic syndrome (Souto et al. 2011). Additionally, although

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body weight, and serum GLU, HDL-C, and LDL-C were unchanged after the Cd

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exposure, serum TG, an important cardiometabolic risk factor, was significantly increased in the Cd-treated mice. These biochemistry changes in both urine and serum

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indicate that the CLD-Cd exposure may increase the risk of cardiometabolic disease.

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The liver usually performs essential metabolic functions in the body. As a main bioaccumulation organ for Cd, the liver is susceptible to Cd toxicity (Rikans and

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Yamano 2000). Following Cd exposure at ppm levels, hepatic tissue damage associated with perturbation of energy metabolism in rodent animals has been well documented

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(Cao et al. 2017; Chen et al. 2003; Miltonprabu et al. 2016; Yazihan et al. 2011). In the present study, we found that chronic Cd exposure at ppb levels also induced hepatic tissue damage, including hepatic oxidative damage and inflammation (Figure 1). Oxidative stress can directly cause activation of NF-κB, resulting in the transcription of pro-inflammatory mediators such as TNF-α and IL-6 (Yin 2001). We found that 17

hepatic mRNA levels of TNF-α and IL-6 were up-regulated along with MDA production, indicating that the Cd-induced oxidative stress may elicit inflammatory responses in the liver. Liver-enriched TFs act as master regulators in the expression of hepatocytespecific genes encoding proteins and enzymes involved in energy metabolism of various forms (Costa et al. 2003). Similar to Go et al. (2015), this study also showed

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that hepatic TFs related to energy metabolism and liver phenotypes underwent

prominent alterations after the Cd exposure (Figure 2). The CLD-Cd exposure posed a prominent inhibitory effect on the hepatic TFs regulating the metabolism of energy

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substances and xenobiotics, including HNF4A, PPARα, SRC3, RXR, P300, and SHP

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(Figure 2 and Table 2). It is well known that the down-regulation of these TFs is associated with poor metabolic conditions and the pathogenesis of metabolic diseases

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such as NAFLD (Baciu et al. 2017; Feingold et al. 2004; Tsai et al. 2017; Wan et al.

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2000; Watanabe et al. 2017). However, some TFs may respond inconsistently to different doses and durations of Cd exposure. For example, 8-week exposure to 10 mg/L

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Cd could enhance hepatic mRNA levels of ChREBP, PPARγ, and PPARα in mice (Zhang et al. 2015), whereas this study revealed no significant changes in ChREBP and

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PPARγ and a decrease in PPARα mRNA, although both studies shared the same results in SREBP1c expression. In addition, the changes of PPARα, SREBP1c, and HNF4A were significantly correlated with most of the altered urinary metabolites in the mice tested (Figure 3), indicating that these TFs may be mainly responsible for the Cdinduced perturbation of energy homeostasis. This agrees with a previous study showing 18

that that several rate-limiting enzymes within energy metabolism pathways are regulated via PPARα and SREBP1c (Liu et al. 2012). Meanwhile, a recent study has indicated that HNF4A acts as a central TF in the network connected to metabolic diseases, which regulates additional TFs involved in energy metabolism (Baciu et al. 2017). The sensitivity to the Cd exposure and its relevance to energy metabolism of PPARα, SREBP1c, and HNF4A suggest that these TFs may serve as promising

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biomarkers for assessing metabolic complications induced by CLD-Cd exposure.

The present study and previous ones (Breton et al. 2013; Zhang et al. 2015) consistently showed that most orally-ingested Cd was retained in the gut and excreted

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by feces. Although previous studies have shed light on the alteration of gut microbiome

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by Cd (Liu et al. 2014; Zhang et al. 2015), this study affirmed that exposure at a much lower dose for a longer time can also evidently affect gut microbial structure and

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function (Figure 4, Figure S7 and Table 3). It is known that the host and its gut

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microbiota have an intrinsic mutually beneficial relationship resulting in the production of metabolites by microbes that contributes to the host metabolic phenotype (Hosokawa

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et al. 2006). Accumulating evidence suggests that metabolic changes associated with gut microbiome perturbations are important risk factors for inducing metabolic diseases

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(Abushanab and Quigley 2010; Boursier et al. 2016; Wang et al. 2011). In this study, several significantly changed taxa were identified following Cd exposure, including increased Porphyromonadaceae and Helicobacteraceae and decreased Bacteroidaceae and Rikenellaceae (Figure 4C), which have been linked with negative effects on lipid metabolism and the induction of hepatic steatosis and diabetes (Bajaj et al. 2015; Del 19

et al. 2017; Marques et al. 2015; Sheng et al. 2017). Previous studies have identified a large array of host-microbiota cometabolites, with some being essential to the modulation of host’s energy metabolism, including short-chain fatty acids, bile acids, and choline metabolites (Nicholson et al. 2012). Perturbations of gut microbiome are correlated with alterations of host-microbiota cometabolites in the host’s biofluid, such as urine and plasma (Nicholson et al. 2012). Specifically, we found that the

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Clostridiales_vadinBB60_group had negative correlations with urinary choline and phosphorylcholine, and a positive correlation with TMAO (Figure 3). The pathways of

choline metabolism involve a complex host/symbiont molecular cross-talk (Dumas et

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al. 2006; Spencer et al. 2011), which is emerging as a metabolic hallmark for liver and

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cardiovascular diseases (Nicholson et al. 2012). In the host liver, choline is converted into phosphatidylcholine, which is necessary for the assembly and secretion of very-

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low-density lipoprotein (VLDL) (Jiang et al. 2005). Choline can also be processed by

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gut microbiota and eventually metabolized to TMAO, a toxic metabolite that has adverse effects on glucose homeostasis and can induce hepatotoxicity (Lin and Ho

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1992). The lowered levels of choline and phosphatidylcholine and the increased level of TMAO in the Cd-treated mice suggests a reduced bioavailability of choline and

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increased choline metabolism in the gut. This may result in the inability to synthesize VLDL and the subsequent accumulation of TG. A recent study has shown that the conversion process of choline is related to Faecalibacterium (Schnabl and Brenner 2014), which is classified into the same order of Clostridiales with the Clostridiales_vadinBB60_group, indicating that this taxon is potentially involved in 20

this process. To gain a deep insight into the functional differences between the gut microbiomes of the controls and Cd-treated mice, we used PICRUSt to predict the metagenomics potential by imputing the available annotated genes within the KEGG catalogue. To our knowledge, this is the first study of investigating the functional impact of Cd on gut microbiome. The PICRUSt results showed an imbalanced representation of gene

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pathways between the Cd-treated and control samples (Figure S7), which agrees with the structural observations. Notably, although statistically significant, the magnitude of

the changes of the predicted pathways was relatively small, which might result from

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the application of PICRUSt in this study, as the particular algorithm often provides

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small but significant changes of predicted gene pathways of gut microbiome even though remarkable alterations of the corresponding community structure (Guo et al.

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2018; Yu et al. 2018; Zeng et al. 2019; Zou et al. 2019; Mao et al. 2019). Unexpectedly,

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this study showed that the CLD-Cd exposure posed a marked inhibitory effect on the mRNA expression of the genes responsible for the metabolism of carbohydrates, lipids,

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and energy in gut microflora, and similar results have also been reported in cardiovascular disease patients with type-2 diabetes mellitus (Sanchez-Alcoholado et

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al. 2017). It is worth noting that the expression of the genes responsible for glycan biosynthesis and metabolism was obviously inhibited in the Cd-treated mice (Table 3). Glycan is essential for the biosynthesis of lipopolysaccharide (LPS), an endotoxin originating from the gut microbiome that can induce inflammation in peripheral organs (Rietschel et al. 1998). Over-expression of this gene in the gut likely results in an 21

increased serum level of LPS and worsened inflammation in liver (Zhang et al. 2015). These results demonstrated that the CLD-Cd exposure structurally and functionally altered the gut microbial community, which likely influenced the host energy metabolism indirectly.

5. Conclusions

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This study revealed the perturbation of energy metabolism in mice and an

increased risk for the development of cardiometabolic disease after chronic exposure to Cd at food limitation-relevant levels, evidenced by the alteration of the metabolites

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associated with the ATP-CP system, TCA cycle, and lipid metabolism, as well as the

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increased cardiometabolic risk factor TG. Moreover, both the liver and gut microbiome underwent marked structural/histological and functional alterations prone to the onset

microbes,

specifically

PPARα,

SREBP1c,

HNF4A

and

the

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gut

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of cardiometabolic disease following Cd exposure. Furthermore, some hepatic TFs and

Clostridiales_vadinBB60_group, were highly correlated with altered urinary

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metabolites, indicating potential toxicological interactions between the liver and gut microbiome, and energy metabolism. Our findings provide new insights into the

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progression of metabolic diseases induced by CLD-Cd exposure, which calls for a stricter Cd limitation in future food safety standards. Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal 22

relationships that could have appeared to influence the work reported in this paper.

Authors' Individual Contributions

Xiwei He: Methodology, Data curation, Roles/Writing - original draft. Zhaodong Qi: Formal analysis, Validation. Hui Hou: Methodology. Jie Gao: Software. Xu-Xiang

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Zhang: Conceptualization, Funding acquisition, Project administration, Writing review & editing.

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Acknowledgments

This study was financially supported by the National Key Research &

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Development Program of China (2018YFF0214105), the Key R&D Program of Jiangsu

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Province, China (BE2018632) and the Fundamental Research Funds for the Central

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Universities, China (14380116).

23

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Figure legends Figure 1. Liver damages induced by the Cd exposure. (A) H&E staining of liver tissue sections. The black arrow indicates the area of hepatic inflammation. (B) Immunohistochemical staining of macrophage cell surface marker CD68 in the liver tissue sections. The red arrow indicates CD-68 positive cell. The graph beside the image shows the ratios of CD68-positive cells to total liver cells. The ratios were determined

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by counting the CD68-positive and total cells in three random fields per sample. (C) Hepatic mRNA levels of inflammation markers (TNF-a and IL-6) and oxidative stress

markers (HO-1 and GPx) determined by quantitative real time RT-PCR. (D) Hepatic

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MDA levels. * p<0.05, ** p<0.01, *** p<0.001 compared with the controls.

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Figure 2. Heat map for 35 hepatic TFs calculated by Z scores. The heat map was generated using a hierarchical clustering algorithm to visualize the transcriptional

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difference of the TFs between the control and Cd-treated groups.

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Figure 3. Correlation plot showing the functional correlation between altered hepatic

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TFs, urinary metabolites, and perturbed gut bacteria families.

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Figure 4. Effects of Cd exposure on gut microbiome. (A) The gut microbiome patterns

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of the controls and Cd-treated mice differentiated by principal coordinate analysis. (B) Hierarchical clustering analysis of gut microbiome patterns by UPGMA. (C) Relative abundance of significantly changed gut bacteria taxa classified at phylum and family

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levels.

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Table 1. Alterations of urinary metabolites induced by the Cd exposure determined by H NMR Fold change 0.859 0.743 0.743 0.762 0.735 0.901 0.837 1.206

VIP 2.372 2.253 2.020 2.002 1.825 1.718 1.828 1.524

p value 0.00065 0.00161 0.00677 0.00746 0.01749 0.02722 0.01396 0.04836

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Metabolite Phosphocreatine Creatine Glutamine 2-Oxoglutarate Citrate Phosphorylcholine Choline TMAO

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1

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Table 2. Alterations of hepatic transcriptional factors induced by the Cd exposure determined by quantitative real time RT-PCR. Fold change 0.602 0.730 0.568 0.728 0.793 0.736 0.766 0.110

SREBP1c

1.784

NCOR1 PPARa

0.811 0.639

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p value 6.56E-08 9.16E-05 0.00011 0.00016 0.00085 0.00023 0.00050 0.00065

1.269

0.00080

1.267 1.261

0.00082 0.00091

-p 0.810 0.617 0.613 0.510

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P300 GATA6 SOX13 SHP

VIP 1.588 1.390 1.382 1.362 1.357 1.344 1.299 1.283

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Abbreviation HNF4A RXR PXR SRC1 SRC3 HNF1A CBP DBP

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Transcriptional factor Hepatocyte nuclear factor 4 alpha Retinoid X receptor Pregnane X receptor Steroid receptor coactivator-1 Steroid receptor coactivator-3 Hepatocyte nuclear factor 1 alpha CREB binding protein D site of albumin promoter binding protein Sterol regulatory element-binding protein 1c Nuclear receptor co-repressor 1 Peroxisome proliferator-activated receptor alpha E1A binding protein p300 GATA-binding factor 6 SRY-BOX 13 Small heterodimer partner

1.257 1.171 1.158 1.062

0.00096 0.00291 0.00339 0.00909

Table 3. Alterations of microbiota functional metabolic pathways induced by the Cd exposure. The bacterial metagenome content was predicted from 16S rRNA gene-based microbial compositions, and functional inferences were made from the KEGG catalog using the PICRUST algorithm. KO2

KO3

Histidine metabolism Phenylalanine metabolism Tyrosine metabolism Biosynthesis of Butirosin and neomycin biosynthesis other secondary Phenylpropanoid biosynthesis metabolites Butanoate metabolism Fructose and mannose metabolism Galactose metabolism Carbohydrate Glycolysis / Gluconeogenesis metabolism Pentose and glucuronate interconversions Pentose phosphate pathway Starch and sucrose metabolism Carbon fixation in photosynthetic Energy metabolism organisms Glycan biosynthesis and Various types of N-glycan biosynthesis metabolism Linoleic acid metabolism Primary bile acid biosynthesis Lipid metabolism Secondary bile acid biosynthesis Steroid hormone biosynthesis Metabolism of cofactors and Nicotinate and nicotinamide metabolism vitamins Metabolism of Cyanoamino acid metabolism other amino acids Selenocompound metabolism Metabolism of Geraniol degradation terpenoids and Zeatin biosynthesis polyketides Atrazine degradation Xenobiotics biodegradation and Bisphenol degradation metabolism Fluorobenzoate degradation acid

p value 0.04141 0.03160 0.01344 0.00983

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Amino metabolism

Fold change 0.965 0.961 0.966 0.940

38

0.905

0.01585

1.0274 0.916 0.948 0.983 0.939 0.972 0.946

0.01297 0.00021 0.00192 0.03165 0.00509 0.02179 0.00804

0.979

0.00864

2.157

0.00662

0.872 0.919 0.919 0.682

8.62E-06 0.01061 0.01084 0.00073

0.964

0.03784

0.936 1.025 0.900

0.02200 0.03645 0.01321

0.960

0.01434

1.624 0.897 0.426

0.00585 6.76E-06 0.03653

hydrocarbon

1.312

0.01398

0.944

0.02460

1.353

0.00187

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Nitrotoluene degradation Polycyclic aromatic degradation Styrene degradation

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