Serum metabolomics reveals that gut microbiome perturbation

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Serum metabolomics reveals that gut microbiome perturbation mediates metabolic disruption induced by arsenic exposure in mice Jingchuan Xue, Yunjia Lai, Liang Chi, Pengcheng Tu, Jiapeng Leng, Chih-Wei Liu, Hongyu Ru, and Kun Lu J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.8b00697 • Publication Date (Web): 10 Jan 2019 Downloaded from http://pubs.acs.org on January 10, 2019

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Journal of Proteome Research

Serum metabolomics reveals that gut microbiome perturbation mediates metabolic disruption induced by arsenic exposure in mice Jingchuan Xue†, Yunjia Lai†, Liang Chi†, Pengcheng Tu†, Jiapeng Leng†, Chih-Wei Liu†, Hongyu Ru‡, and Kun Lu†,*

† Department

of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599; ‡ Department

of Population Health and Pathobiology, North Carolina State University, Raleigh,

NC 27607.

* Corresponding author Kun Lu, PhD Department of Environmental Sciences and Engineering University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 Tel.: 919 966 7337 Email: [email protected]

1 ACS Paragon Plus Environment

Journal of Proteome Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ABSTRACT: Arsenic contamination in drinking water has been a worldwide health concern for decades. Except for being a well-recognized carcinogen, arsenic exposure has also been linked with diabetes, neurological effects, and cardiovascular diseases. Recently, increasing evidence have indicated that gut microbiome is an important risk factor in modulating the development of diseases. In the present study, we aim to investigate the role of gut microbiome perturbation in arsenic-induced diseases by coupling a mass spectrometry-based metabolomics approach and an animal model with altered gut microbiome induced by bacterial infection. Serum metabolic profiling has revealed that gut microbiome perturbation and arsenic exposure induced the dramatic changes of numerous metabolite pathways, including fatty acid metabolism, phospholipids, sphingolipids, cholesterols, and tryptophan metabolism, which were not or less disrupted when gut microbiome stays normal. In summary, this study suggested that gut microbiome perturbation can exacerbate or cause the metabolic disorders induced by arsenic exposure.

KEYWORDS: arsenic, metabolomics, mouse model, bacterial infection, gut microbiome


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1. INTRODUCTION Human gut microbiota is composed of around 100 trillion microbes, including bacteria, viruses, archea, fungi, and protozoa.1 With the progress in characterizing the structure of gut microbiome, especially the complement of several large-scale projects including European Metagenomics of the Human Intestinal Tract (MetaHIT) and the NIH-funded Human Microbiome Project (HMP), increasing efforts are spent to study the interactions between the microbiota and the host.2-5 Studies have shown that gut microbiota is contributing to a variety of metabolic processes of the host such as food digestion and energy metabolism.6,7 Gut microbiota can digest food substrates that are otherwise indigestible by the host, and produce nutrients that are functionally important to the host physiology. For instance, gut bacteria can generate short chain fatty acids (SCFAs) from dietary fibers which cannot be digested by the host. SCFAs are playing crucial roles in regulating energy metabolism, modulating immune responses and tumorigenesis in the gut.4 On the other hand, the physiological change of host, such as the diseased state, can also lead to the changes in the microbiota. Dysbiosis of microbiota has been associated with a variety of human diseases, including cardiovascular diseases, obesity, diabetes, and inflammatory bowel disease, although it is challenging to distinguish the cause and effect.8-10 Furthermore, a variety of external factors, such as environment, bacterial infection, and antibiotics, can easily affect the composition and diversity of gut microbiota.11 This raises the hypothesis that gut microbiome may play a key role in mediating environmental toxicant-induced diseases. Arsenic contamination in drinking water has become a global health concern for decades, especially in South and East Asia, because of the widespread naturally occurrence of inorganic arsenic.12 In the U.S., it is estimated that more than 25 million people are drinking water with arsenic level higher than 10 µg/L, which is the acceptable arsenic level in drinking water



established by World Health Organization (WHO) and the U.S. Environmental Protection Agency (EPA).13 People can also be exposed to arsenic by ingesting the foods which are processed or irrigated with the contaminated water. A number of epidemiological studies and animal models have associated arsenic exposure with a variety of health outcomes, including cancers (bladder, liver, skin, and lung, etc.), skin lesion, cardiovascular diseases, diabetes, neurological and cognitive dysfunction.12,14-19 For example, blackfoot disease (BFD), an endemic peripheral vascular disease with high incidence rates in southwestern Taiwan, has been linked with the exposure to arsenic in artesian well water.20 However, the mechanisms underlying the arsenic-induced diseases are not clear although a variety of mechanisms have been proposed such as oxidative stress.21 Recently, accumulating evidence have showed that functional interaction between the gut microbiome and xenobiotics are contributing significantly to the development of xenobiotic-induced human diseases.11 Our earlier study has illustrated that arsenic exposure can significantly perturb the gut microbiome composition in C57BL/6 mice as well as the gut microbiome associated metabolites.11 Later we used a bacterial infection animal model and demonstrated that gut microbiome alteration from bacterial infection can significantly impact the biotransformation of inorganic arsenic in mice.22 However, it remains unclear how gut microbiome perturbation affects arsenic exposure-induced host health effects. Advances in omics technologies allow the simultaneous and non-targeted profiling of genes, proteins and metabolites in complex biological matrices, which are often used to identify the physiological changes induced by the treatment of environmental toxicants.23, 24 Compared with transcriptomics and proteomics, metabolomics can capture low molecular weight metabolites that are the closest to the phenotype.25 Thus, metabolomics has become one of popular and powerful techniques in studying the metabolic alteration associated with


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environmental exposure.26, 27 Compared with nuclear magnetic resonance (NMR), high resolution mass spectrometry (HRMS) based metabolomics profiling has the following advantages: high sensitivity, a wide dynamic range, the ability to detect diverse molecules, quantitative capability, and the ease of interfacing with other separation techniques such as gas and liquid chromatography.28 All of these made HRMS-based metabolomics profiling more attractive in characterizing and developing biomarkers related to environmental exposure. In the present study, we employed a mouse model with perturbed gut microbiome phenotype from Helicobacter trogontum (H.t.) infection to study the impact of gut microbiome alteration on the arsenic-induced host health effects. Serum metabolome alterations from arsenic exposure were investigated by ultra-high performance liquid chromatography-quadrupole timeof-flight mass spectrometry (UPLC-Q-TOF) based metabolomics profiling. By providing knowledge associated with arsenic-induced serum metabolome disturbance, this is the first work to demonstrate that infection induced gut microbiome perturbation can significantly mediate arsenic effects on disrupting metabolic functions in the host.

2. EXPERIMENTAL SECTION Chemicals and Reagents Sodium arsenite, and LC-MS grade reagents used for the HPLC mobile phase and sample preparation were from Fisher Scientific (Pittsburgh, PA). Animal Infection and Exposure The animal infection and exposure procedure has been described previously.22 In brief, Helicobacter-free C57BL/6 (~8-week-old) mice, from Jackson Laboratories (Bar Harbor, ME), were provided with pelleted rodent diet (ProLab 3000, Purina Mills, St. Louis, MO) and filtered



water ad libitum. The animals were maintained in AAALAC-accredited facilities in microisolator caging under standard environmental conditions. All experiments were approved by the Massachusetts Institute of Technology Committee on Animal Care. The animals were classified into 4 groups: A, B, C, and D. Each grouping comprised of 10 mice unless stated otherwise. Mice in groups C and D were dosed at 9-10 weeks of age with 2 × 107 Helicobacter trogontum (H.t.) three times on alternate days by oral gavage. Inorganic arsenic (10 ppm) was administered to mice in groups B and D through drinking water for 4 weeks. Plasma samples were collected during necropsy at the end of the study. Metabolite Extraction The sample processing protocol for serum metabolomics has been described elsewhere with slight modification.11, 75 In brief, cold methanol (180 µL) was added to 20 µL of serum for the extraction of metabolites. The mixture was vortexed for 1 min, incubated at -20 ℃ for 30 min, and then centrifuged for 10 min at 15 000 rpm. The collected supernatant was speed vacuum dried and resuspended with 100 µL of 2% acetonitrile in water prior to instrumental analysis. Metabolomics Profiling by LC-MS A 1290 ultra-high performance liquid chromatography system from Agilent (Santa Clara, CA) coupled with Agilent 6530 accurate-mass Q-TOF and an electrospray ionization source was used for the profiling of metabolites in the serum samples. Chromatographic separation was performed on a Poroshell 120 EC-C18 column (150 mm × 2.1 mm, 1.9 µm) from Agilent. The sample injection volume was 10 µL, and the mobile phase comprised water containing 0.1% formic acid (mobile phase A) and methanol containing 0.1% formic acid (mobile phase B). The metabolites were profiled by gradient elution of mobile phase at a flow rate of 400 µL/min starting at 2% B, held for 2 min; increased to 80% B in 9 min and further increased to 98% B at


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12 min, then held for 4 min; decreased to 2% B at 16.1 min, and held for 3.9 min, for a total run of 20 min. The mass spectrometer was operated in positive ion mode with a mass range of 601000 m/z and scan rate of 2 spectra/sec. The source parameters were set as follows: drying gas flow rate, 12 L/min; drying gas temperature, 275 ℃; nebulizer, 40 psig; fragmentor, 120 V; VCap, 4 kV. Prior to analysis, serum samples were randomized to avoid possible uncertainties from gradual changes of instrument sensitivity in whole batch runs. Serum quality control (QC) samples were prepared by pooling and mixing the same volume of each sample. QC samples were injected for 10 times at the beginning of the batch run and then injected at an interval of six samples to check for the stability during the whole sequence. The instrument was calibrated daily with the standard tuning solution from Agilent. Two different reference masses, 121.0509 and 922.0098, were used for calibration during the entire run. Data Processing and Analysis Data acquired in Agilent MassHunter.d format were converted to mzdata.XML using Agilent MassHunter Qualitative Analysis Navigator software. The converted data were processed by XCMS (Scripps, La Jolla, CA) for peak picking, alignment, integration and extraction of the peak intensities. The parameters used for peak picking and alignment were as follows: tolerated m/z deviation, 15 ppm; signal/noise threshold, 6; allowable retention time deviation, 5 s; minimum fraction of samples necessary in at least one of the sample groups for it to be a valid group, 50%. To profile individual metabolite differences between different groups, a 2-tailed Welch’s t test was used (p ≤ 0.05). The molecular features, including m/z and retention time (RT), with significant changes (p ≤ 0.05) were used for the generation of MS/MS data to further identify the metabolites. Metabolite Identification



MS/MS was generated on the same instrument to further confirm the identity of the metabolites. The column and gradients were the same as those used for metabolite profiling as well as the source parameters. The typical mass accuracy was less than 20 ppm. Metabolites were extracted from 20 µL of pooled serum samples for MS/MS experiments. A target list, which included previously determined exact masses and the relevant retention time, was generated for fragmentation. The parameters used for MS/MS data generation were as follows: collision energy, 20 eV; isolation width, ~1.3 amu; delta RT, 1.4 min. MS/MS data were processed using the freely available software MS-DIAL and MSFINDER according to the user manual.29-31 Briefly, the raw MS data were converted from the Agilent MassHunter.d format into the Analysis Base File format *.abf using the Reifycs Abf converter (http://www.reifycs.com/AbfConverter/index.html). After conversion, the MS-DIAL software was used for peak detection, identification and alignment between samples. The MS/MS spectra based metabolite identification was performed in MS-FINDER by searching the acquired MS/MS spectra against the public available databases including Human Metabolome Database (HMDB) (http://www.hmdb.ca) as well as the in silico fragmentation software. The tolerance for MS1 and MS/MS search were set to 0.03 and 0.05 Da, respectively. The ion types checked were [M+H]+, [M+Na]+, [M+NH4]+, and [M+K]+. The obtained results were further checked manually to confirm the identity of the metabolites. METLIN (http://metlin.scripps.edu) database was also searched in manual confirmation procedure. Statistical Analysis Multivariate statistical methods were employed for the data analysis. Principal component analysis (PCA) was performed with MetaboAnalyst 4.0 to examine intrinsic clusters and obvious outliers within the observations.32 Hierarchical clustering heat maps were also generated using


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MetaboAnalyst 4.0 to visualize the metabolite difference within the data set.32 metaXCMS was used to compare the difference of arsenic-induced significantly changed metabolites between gut microbiome stays normal (A vs. B) and gut microbiome was perturbed (C vs. D).33

3. RESULTS 3.1 Quality evaluation and overview of the metabolomics analysis Quality control samples were injected after every six samples to check for the stability during the whole sequence. The LC-MS system showed good reproducibility in the analysis, with less than ~30 sec retention time shifts from run to run and the abundance variation is less than 10% across samples. This helps the alignment of features across samples. After the MS data was acquired, a 2-tailed Welch’s t test was used to identify the features with significant differences (p ≤ 0.05) induced by arsenic exposure (~4 week) when gut microbiota was in normal state (A vs. B) and when gut microbiota was disrupted (C vs. D), respectively. From Table 1, we can see that approximately 8% of the total features were changed significantly after 4-week exposure to arsenic, with the majority (~90%) being down-regulated. However, when homeostasis of gut microbiota was disrupted by H.t. infection, 4-week arsenic exposure exerted a significant different effect on the serum metabolic profiles: a higher percentage (~13%) of the total features were changed; the majority changes (~60%) were upregulated instead of down-regulated. Multivariate analyses were later conducted to investigate whether features in group A and B, and C and D can be differentiated using metabolic profiles; to compare the goodness of separation between A vs. B and C vs. D. As illustrated from Figure 1a and 1c, PCA analysis



showed better separation when differentiating the metabolites between group C and D. The hierarchical clustering heat maps shown in Figure 1b and 1d also revealed better separation of metabolite features between group C and D. These information supports that arsenic exposure is driving more significant changes to the host metabolic profiles after the homeostasis of gut microbiome was disrupted. The hierarchical clustering heat map across four types of samples (A vs. B vs. C vs. D) was shown in Figure S1. A metaXCMS analysis was further conducted to identify the significantly changed metabolites induced by arsenic exposure only or mainly occurred when gut microbiome was perturbed. As shown in Figure 1e, 434 features were changed significantly only when gut microbiome was disrupted, while this number was 161 when gut microbiome stays normal. The common features between the two were only 24. These evidences suggested that arsenic exposure has more profound health effects on the host after gut microbiota was perturbed. For the identification of metabolites in the serum samples, software (MS-DIAL and MSFINDER) identification was followed with manual confirmation. The exact masses of metabolites were firstly searched against the databases such as HMDB and METLIN, then followed by the comparison of MS/MS product ion spectra. Although there are near 1000 significantly changed features after the treatment of both arsenic and H.t., we mainly focus on those metabolites which changed significantly after the treatment of both arsenic and H.t. whereas no significant changes when only arsenic was treated, as summarized in Table 2. The detailed information of identification of major metabolites was summarized in Table S1 and the MS/MS spectrums used for comparison were provided for representative metabolites (Figure S3S7) in all the chemical classes, including fatty acids, phospholipids, sphingolipids, cholesterols


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and amino acids. A hierarchical clustering heat map across four types of samples (A vs. B vs. C vs. D) constructed using identified metabolites was shown in Figure S2.

3.2 Altered Phospholipid Metabolism in Mice Treated with Arsenic and H.t. As shown in Figure 2 and Table 2, approximately 20 phospholipids, including phosphatidic acid (PA), phosphocholine (PC), phosphatidylcholine (PTC), lysophosphatidylcholine (LysoPC), phosphoserine (PS), diacylglycerol (DG), and phosphatidylglycerol (PG), were only up-regulated (+1.31-4.51 fold) in the arsenic and H.t. treatment group. This indicates that disrupted gut microbiome significantly enhance arsenic exposure-induced alteration of phospholipid metabolism in the host. Phospholipid metabolism alteration has been associated with a variety of human diseases, including atherosclerosis, hyperhomocysteinemia, and lipotoxic cardiac diseases.34-38 Furthermore, the increased amounts of

PG, a precursor of cardiolipin synthesis, and DG, a precursor to triacylglycerol, indicated that arsenic exposure also disrupted the energy metabolism in the host when gut microbiota was perturbed.39, 40

3.3 Altered sphingolipid metabolism in Mice Infected with Arsenic and H.t. 3-Ketosphingosine, which is synthesized from the condensation of serine and fatty acylCoAs with the catalyzation of serine palmitoyl-transferase, increased approximately 2.5-fold in arsenic and H.t. treated mice, as shown in Figure 3.41 Synthesis of 3-ketosphinganine is the first step in de novo sphingolipid metabolism, then 3-ketosphinganine can be reduced to sphinganines.41 This is supported by the up-regulation of C16 sphinganine (+3.27 fold) in mice



treated with both arsenic and H.t. A series of reactions in the de novo sphingolipid biosynthesis lead to the formation of ceramide.41 In the present study, we also detected increased (+2.12 fold) amounts of Cer(d18:0/25:0), which is a long-chain dihydroceramide. No significant changes in the serum levels of sphingolipids were observed in mice treated with arsenic alone. These evidences indicated that arsenic exposure had stronger effects in disrupting sphingolipid metabolism in C57BL/6 mice when the gut microbiota was perturbed with H.t. treatment. The impaired sphingolipid metabolism pathway in arsenic and H.t. treated mice is illustrated in Figure 3.

3.4 Altered Metabolism of Fatty Acids and Acylcarnitine in Mice Treated with Arsenic and H.t. The serum metabolomics profiling also shows that a number of unsaturated fatty acids were only up-regulated (+1.6-3.0 fold) upon arsenic and H.t. treatment (Figure 4 and Table 2), including alpha-linolenic acid, 3-acetoxy-eicosanoic acid, and 13(Z)-docosenoic acid. This may be the consequences of decreased fatty acid oxidation as the results show that serum levels of a group of acylcarnitine, including glutarylcarnitine, acetylcarnitine, 3-hydroxyisovalerylcarnitine, and 3-carboxypropyl trimethylammonium, decreased more significantly, if not only, in arsenic and H.t. treated mice (Figure 4 and Table 2). Taken together, these results clearly demonstrate that perturbation of gut microbiota by H.t. infection exacerbated the host health effects from arsenic exposure.

3.5 Altered Cholesterol Metabolism and Bile Acids Biosynthesis in Mice Infected with Arsenic and H.t.


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Serum levels of two important intermediates in the biosynthesis and metabolism of cholesterol, 4α-carboxy-5α-cholesta-8-en-3β-ol (+18 fold) and 7α-hydroxy-4-cholesten-3-one (+3.88 fold), only increased significantly after the treatment of both arsenic and H.t., as shown in Figure 5. 4α-Carboxy-5α-cholesta-8-en-3β-ol is an intermediate in cholesterol biosynthesis, upregulation of which can lead to the upregulation of cholesterol in mice. 7α-Hydroxy-4cholesten-3-one is an intermediate in the biochemical synthesis of bile acids from cholesterol and it has been increasingly used as a biomarker for bile acid synthesis.42,43 Upregulation of 7αhydroxy-4-cholesten-3-one can lead to the alteration of the bile acids concentrations and the change in bile acids compositions.42,43 The potential biosynthesis pathway of cholesterol and bile acids is illustrated in Figure 5.

3.6 Altered Tryptophan Metabolism in Mice Infected with Arsenic and H.t. Serum level of 3-indolepropionic acid (IPA), a metabolite of tryptophan which can only be metabolized by Clostridium sporogenes, decreased approximately 1.56-fold in arsenic treated mice, as shown in Figure 6. Down-regulation of this indole-containing biomolecule was also found in arsenic and H.t. treated mice. Furthermore, 2-methyl-5-hydroxytryptamine, a serotonin related derivative from tryptophan metabolism, was only up-regulated (+1.68 fold) in arsenic and H.t. treated mice. This suggests that after gut microbiota was perturbed, more metabolic pathways of tryptophan were disrupted from arsenic exposure. The altered tryptophan metabolism pathways were demonstrated in Figure 6.

4. DISCUSSION



A variety of environmental epidemiological studies have associated arsenic exposure with a number of human diseases, although the underlying mechanisms are still vague.14-19 The proposed mechanisms of action for arsenic carcinogenicity include genotoxicity, cell proliferation, altered DNA repair and DNA methylated oxidative stress, cocarcinogenesis, and tumor promotion.21, 44 Besides the neoplastic outcomes, arsenic exposure also accounts for the increased risk of various non-cancer related health outcomes, including vascular diseases, diabetes, neurotoxicity, and nepatotoxicity.21 Proposed mechanisms for these non-cancer related diseases include increased oxidative stress, decreased expression of PPAR-γ, and inhibition of PDK-1.21 Recently, accumulating evidences have shown that gut microbiome is playing an essential role in mediating chemical toxicity and causing or exacerbating human disease.11, 22 This raises the possibility that perturbation of gut microbiome phenotypes and functions may contribute to the development of arsenic exposure-related diseases. Therefore, this study was designed to investigate the role of gut microbiome in the pathogenesis of arsenic-induced disease. Studies have illustrated that Helicobacter trogontum (H.t.) infection can successfully induce the change of gut microbiome at family level.22, 45, 46 Specifically, H. t. infection significantly up-regulated two Bacteroidetes families besides H. t. and down-regulated five bacterial families belonging to Firmicutes.22 Both forward read and reverse read identified similar numbers and consistent types of significantly changed gut bacteria families.22 And the observations were consistent across several independent studies.22, 45, 46 Bacterial infections are naturally occurring, however, the impact of infection on the toxicity of environmental chemical exposure is not well studied. Therefore, in the present study, a H.t. infection animal model together with host serum metabolomics analysis was employed to investigate the impact of


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bacterially induced gut microbiome perturbation on the host health effects from arsenic exposure. This is the first study regarding the relationships between bacterial infections, arsenic exposure, and host metabolic alterations. We grouped 40 mice into four different categories, with each group being control (A), arsenic exposure (B), H.t. infection (C), and H.t. infection plus arsenic exposure (D), respectively. By comparing the differences between the significantly changed metabolite profiles in H.t. and arsenic treatment group (C vs. D) with the significantly changed metabolite profiles in arsenic treatment group (A vs. B), we can recognize those metabolites which only or mainly changed when both H.t. and arsenic were treated. Metabolite identification was completed by coupling the information of molecules including exact masses, MS/MS fragments, and retention times with the databases such as HMDB and METLIN. A large number of metabolites changed significantly in C57BL/6 mice following 4 week arsenic exposure, with the majority being down-regulated in exposed animals. However, after gut microbiota was perturbed in C57BL/6 mice with H.t. infection, both the number and regulation pattern of the metabolites with significant differences changed dramatically. Pathway analysis revealed that after gut microbiome change, phospholipid metabolism in the host was the major perturbed pathway by arsenic exposure, followed by sphingolipid and fatty acid metabolism, cholesterol biosynthesis and metabolism, and tryptophan metabolism. These data clearly demonstrated that gut microbiota perturbation expedited, exacerbated or caused the toxicity of arsenic exposure. The impact of gut microbiome disruption on the arsenic exposure related health effects can be reflected by altered phospholipid metabolism. We detected a large number of increased phospholipids including PCs and LysoPCs in arsenic and H.t. treatment group while none in arsenic treatment group. Plasma lipids are potential biomarkers of lipid composition in the liver



and other tissues.47-49 Previous studies also found the marked increased levels of phospholipids such as LysoPCs after arsenic exposure.25, 50, 51 Our results clearly revealed that gut microbiome change is playing a key role in mediating the toxicity of arsenic exposure. It is of interesting to note that the majority of up-regulated phospholipids are choline related lipids, which might contribute to the increase of the level of choline in the mice. For instance, phosphatidylcholine (PTC), one of the major classes of up-regulated phospholipids identified in this study, can be converted into phosphatidic acid and choline by bacterial phospholipase D (PLD).52 Similarly, lysophospholipase D enzymes in the intestine can catalyze lysophosphatidylcholine (LysoPC) to generate lysophosphatidic acid (LPA) and choline.53 The unabsorbed choline can reach the large intestine and further metabolized into trimethylamine by bacteria residing in the gastrointestinal tract. Then trimethylamine was transported to the host blood and yield a product of trimethylamine oxide (TMAO), which has been identified as one of the independent predictors for the risk of atherosclerosis.54, 55 Many studies have linked arsenic exposure with the incidence of atherosclerosis, but the underlying mechanisms are still far from unambiguous.56-59 Here, our data suggest that gut microbiome perturbation and arsenic exposure induced the metabolic disorder of phospholipids which in turn may contribute to the incidence of atherosclerosis. Besides the lipid metabolism, a few up-regulated phospholipids are also involved in the energy metabolic pathway. Phosphatidylglycerol is an important intermediate in the cardiolipin biosynthesis, which is known to provide essential structural and functional support to several enzymes involved in mitochondrial energy metabolism.60 Alteration of cardiolipin has been linked with mitochondrial dysfunction in multiple tissues leading to a variety of diseases such as ischemia and hypothyroidism.60 Phosphatidic acid and diacylglycerol are critical components in


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de novo biosynthesis of triacylglycerol, which act as the main energy store for animals.61 Triacylglycerol metabolism disorder has been associated with the incidence of obesity and related metabolic disorders such as hepatic steatosis and Type 2 diabetes.62 A number of sphingolipids, including 3-ketosphinganine, C16 sphinganine, and cer(d18:0/25:0), were also up-regulated in the mice treated with both arsenic and H.t. When only treated with arsenic, serum sphingolipid levels maintain the same. Up-regulation of 3ketosphinganine and C16 sphinganine can lead to the accumulation of ceramide through de novo ceramide synthesis, which has been confirmed by the increased amount of cer(d18:0/25:0) in the serum. Increased levels of ceramide has been related with cell apoptosis, oxidative stress, and proteolysis, which could serve as a mechanism in arsenic-induced diseases.63, 64 Previous studies have also showed that arsenic exposure can induce the accumulation of sphingolipids such as ceramide in both in vitro and in vivo models.25, 65 Here, for the first time, we demonstrated that gut microbiota disruption is vital in mediating arsenic effects on sphingolipid metabolism in mice. Altered fatty acid metabolism can also be used to explain the effects of gut microbiome perturbation on the arsenic exposure induced toxicity, as shown by a number of up-regulated polyunsaturated fatty acids in arsenic and H.t. treated mice. Evidence has showed that fatty acids can be metabolized into a range of phospholipid and sphingolipid species in the liver, which is in good accordance with the increased levels of these lipids observed in the present study.47-49 Increased amounts of fatty acids in the blood may be ascribed to the decreased levels of acylcarnitine which are of great importance for fatty acid β-oxidation, the major pathway for the metabolism of fatty acids. Formation of acylcarnitine is catalyzed by carnitinepalmitoyltransferase I (CPT I), then acylcarnitine is converted into long chain acyl-CoA



molecules by carnitinepalmitoyltransferase II (CPT II) for β-oxidation in the mitochondria.25 Down-regulation of acylcarnitine can lead to decreased fatty acid β-oxidation, which in turn result in the accumulation of fatty acids in the serum. Although a variety of factors are involved in fatty acid synthesis, the increased serum levels of fatty acids in arsenic and H.t. treatment group could be impacted by the changes in gut microflora phenotypes and functions. This indicates that gut microbiome perturbation exacerbated the toxicity of arsenic exposure. We also discovered that 4α-carboxy-5α-cholesta-8-en-3β-ol increased 18-fold in animals treated with both arsenic and H.t., which didn’t change in arsenic treatment group. 4α-Carboxy5α-cholesta-8-en-3β-ol functions as an intermediate in cholesterol biosynthesis. Cholesterol is a biological molecule that has unreplaceable roles in cell membrane structure as well as being a precursor for the synthesis of the steroid hormones, bile acids and vitamin D3. Up-regulation of 4α-carboxy-5α-cholesta-8-en-3β-ol can lead to the over-accumulation or abnormal deposition of cholesterol, which is often associated with a variety of health outcomes such as rheumatoid arthritis.66 Increased amounts of 7α-hydroxy-4-cholesten-3-one (+3.88 fold) in mice treated with both arsenic and H.t. were also observed in the present study, which is a key intermediate in the biochemical synthesis of bile acids from cholesterol.67 It can be catalyzed by Cyp8B1 to 7α, 12αdihydroxy-4-cholesten-3-one and then to cholic acid, which is the major primary bile acid in mammals.67 It can also be catalyzed by Akr1D1 to 5β-cholestane-3α,7α-diol and then to chenodeoxycholic acid, another major primary bile acid in mammals.67 Bile acids are physiologically important biomolecules which can facilitate intestinal absorption and transport of lipids, nutrients, and vitamins.68 Bile acids can also act as signaling molecules and inflammatory agents that regulate lipid, glucose, and energy metabolism.68 A few studies have used the analysis of 7α-hydroxy-4-cholesten-3-one in serum as a novel, simple and sensitive method for


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the detection of bile acid malabsorption in patients.42, 43 Up-regulation of this metabolite can lead to the manipulation of bile acid homeostasis, which is often associated with many adverse health outcomes such as Type 2 diabetes.69 Taken together, these evidence indicate that after the perturbation of gut microbiota, arsenic exposure impaired the balance and homeostasis of cholesterol and bile acids in the host. Disruption of tryptophan metabolism was also observed in this study. 3-Indolepropionic acid (IPA), a metabolite from tryptophan through a gut bacteria-involved process, decreased ~1.56 fold in mice treated with arsenic. This indole-containing metabolite was also downregulated (-1.51 fold) in the arsenic and H.t. treatment group. IPA is a highly potent neuroprotective antioxidant and can act on intestinal cells via pregnane X receptors (PXR) to maintain mucosal homeostasis and barrier function.70 It also can exert a neuroprotective effect against cerebral ischemia and Alzheimer’s disease when it is transported to brain.70 Furthermore, 2-methyl-5-hydroxytryptamine (2-methyl-5-HT), a tryptamine derivative of serotonin, was only up-regulated (+1.68 fold) in mice treated with both arsenic and H.t. Serotonin has been recognized as a key contributor to the modulation of mood and anxiety and strongly associated to the etiology of major depressive disorder.71

5. CONCLUSIONS Taken together, more significant metabolic disruption was induced by arsenic exposure after the perturbation of gut microbiome, indicating that the gut microbiome is an important factor that mediates arsenic toxicity. This has been evidenced by a variety of altered lipids (fatty acids, phospholipids, sphingolipids, and cholesterols) and amino acids (tryptophan). Homeostasis of these lipids is controlled by diverse and intersecting metabolic pathways, especially those



pathways involved in energy and lipid metabolism. In the present study, an elevated lipid flux was observed in the arsenic and H.t. treatment group, which is proposed to be related with a variety of diseases including cancer development.72 The altered tryptophan metabolites mainly contribute to the disruption of neurological signaling pathway. Furthermore, studies have reported that upregulated sphingolipids and decreased acylcartinine are also associated with the disruption of nervous system.73, 74 In summary, this study employed a bacterial infection animal model and a global metabolomics approach to examine the role of gut microbiome perturbation in mediating the toxicity of arsenic exposure. We found that several classes of metabolites were only dysregulated by arsenic exposure when the gut microbiome was perturbed, including phospholipids, sphingolipids, fatty acids, and cholesterols. For some metabolic pathways, such as acylcartinine and tryptophan metabolism, arsenic exposure induced more significant metabolic changes when gut microbiota was disrupted. Our study clearly demonstrated that intestinal bacteria are playing important roles in mediating arsenic toxicity and provides novel insights into the mechanism of arsenic-induced diseases. Associated Content The Supporting Information is available free of charge. A list of supporting information components is as follows. Table S1-Title: The detailed information of identification of major different metabolites in serum samples; Figure S1-Title: The hierarchical clustering heat map across four groups (A vs. B vs. C vs. D) constructed using molecular features with significant changes (p ≤ 0.05); Figure S2-Title: The hierarchical clustering heat map across four groups (A vs. B vs. C vs. D) constructed using molecular features of identified metabolites; Figure S3-Title: MS/MS spectrum of m/z=204.1229 at 1.27 min identify this metabolite as acetylcartinine; Figure S4-Title: MS/MS spectrum of m/z=652.4225 at 12.81 min identify this metabolite as azelaoylPAF; Figure S5-Title: MS/MS spectrum of m/z=191.1095 at 17.30 min identify this metabolite 20 ACS Paragon Plus Environment

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as 2-methyl-5-hydroxytryptamine; Figure S6-Title: MS/MS spectrum of m/z=401.3446 at 15.26 min identify this metabolite as 7a-hydroxy-cholestene-3-one; Figure S7-Title: MS/MS spectrum of m/z=274.2735 at 9.06 min identify this metabolite as C16 sphinganine.

Acknowledgments This work was supported in part by the NIH grant (R01ES024950) and the University of North Carolina Center for Environmental Health and Susceptibility with the NIH grant (P30-ES010126).

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Table 1. Summary of Metabolite Profiling and Comparison between Groups (A vs. B; C vs. D), Acquired by LC-MS under Positive Mode total features

group

na

treatment

A

10

6442

B

10

control As exposure for 4 weeks

C

10

H.t. infection for 5 weeks

6566

D

10

H.t. infection for 5 weeks+ As exposure for 4 weeks

6566

anumber

0.05;

6442

total significantly changed featuresb

total decreased featuresc

total increased featuresc

484

432

52

862

314

548

of mice in eachgroup; bsignificantly changed features denote those features with p value below or equal to or increased features are relative to the corresponding controls.

cdecreased


Page 27 of 35 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47


Table 2. Significantly Changed Metabolites Only or Mainly in Mice Treated with Both Arsenic and H.t. metabolites

pathway

Acetylcarnitine

adduct ions [M+H]+

p-value (A vs. B) 0.0500

Fold change (C vs. D) -1.20

p-value (C vs. D) 0.0017

m/z

Fatty acid metabolism

fold change (A vs. B) -1.16

MW

HMDB

204.1229

RT (min) 1.27

203.1158

0000201

METLIN ID 956

3-Hydroxyisovalerylcarnitine

[M+H]+


-1.27

0.0390

-1.25

0.0069

262.1635

2.89

261.1576

0061189

6505

3-Carboxypropyl trimethylammonium Glutarylcarnitine

[M+H]+


-1.45

0.0057

-1.63

0.0018

146.1158

1.37

145.1103

[M+H]+


-2.08

0.0682

-1.93

0.0215

276.1441

2.46

275.1369

0013130

alpha-Linolenic acid

[M+H]+


-1.22

0.5223

1.61

0.0354

279.2318

11.54

278.2246

0001388

3-Acetoxy-eicosanoic acid

[M+Na]+


13(Z)-Docosenoic Acid

[M+H]+


1.26

0.5833

1.84

0.0467

393.2934

14.72

370.3083

2.70

0.0980

3.02

0.0393

339.3454

15.20

338.3185

3555

PA(O-18:0/15:0)

[M+H]+

Phospholipid metabolism

1.68

0.3578

4.51

0.0078

649.4957

16.44

648.5094

82169

LysoPC(22:0)

[M+H]+


1.23

0.5768

2.47

0.0268

580.3659

12.41

579.4264

PC(O-16:2(9E,10E)/0:0)[U]

[M+H]+


-1.14

0.2988

1.31

0.0015

478.3284

11.98

477.3219

LysoPC(16:0)

[M+H]+


-1.10

0.3164

1.31

0.0259

496.3321

11.96

495.3325

1-Palmitoyl-2-(5-oxovaleroyl)-snglycero-3-phosphorylcholine PA(P-18:0/14:1(9Z))

[M+H]+


1.16

0.7442

2.51

0.0241

594.3802

12.63

593.3693

82378

[M+H]+


3.63

0.0042

631.5016

16.45

630.4624

82271

PC(0:0/18:1(9e))

[M+H]+


-1.06

0.6745

1.38

0.0270

522.3514

12.24

521.3481

40346

LysoPC(18:0)

[M+H]+


-1.10

0.5653

1.41

0.0295

524.3652

12.23

523.3638

0010384

61694

DG(15:0/14:1(9Z)/0:0)

[M+H]+


-1.20

0.5449

1.36

0.0500

525.3618

12.24

524.4441

0007067

80028

PC(19:1(9Z)/0:0)

[M+H]+


-1.39

0.2992

1.97

0.0060

536.3350

9.27

535.3638

LysoPC(24:0)

[M+H]+


1.03

0.8828

1.65

0.0038

608.3758

10.99

607.4213

0010405

39262

PC(16:0/22:5(4Z,7Z,10Z,13Z,16Z))

[M+H]+


-1.17

0.5260

1.56

0.0316

808.5805

12.40

807.5778

0007989

39385

Azelaoyl-PAF

[M+H]+


1.26

0.6277

2.50

0.0240

652.4225

12.81

651.4475

PC(14:1(9Z)/18:4(6Z,9Z,12Z,15Z))

[M+H]+


2.05

0.0445

724.4891

12.60

723.4839

0007910

PG(a-13:0/a-13:0)

[M+H]+


1.22

0.5659

3.00

0.0098

639.4036

12.49

638.4159

0116636

PG(i-12:0/i-12:0)

[M+H]+


1.43

0.5047

3.29

0.0199

611.3761

12.12

610.3846

0116660

1-O-Palmitoyl-2-O-acetyl-snglycero-3-phosphorylcholine PG(i-14:0/i-14:0)

[M+H]+


-1.63

0.0721

1.89

0.0384

538.3474

9.40

537.3430

[M+H]+


1.09

0.8516

3.38

0.0178

667.4322

12.08

666.4472

0116695

PG(i-13:0/i-14:0)

[M+H]+


1.29

0.6230

2.85

0.0175

653.4265

12.69

652.4315

0116680


34501

0010398

61707 40371

0010382

61692

76572

62938 59360

43419

Journal of Proteome Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

3-Ketosphingosine

[M+H]+

Sphingolipid metabolism

C16 Sphinganine

[M+H]+


1.71

0.1224

3.27

Cer(d18:0/25:0)

[M+H]+


-1.87

0.1211

7a-Hydroxy-cholestene-3-one

[M+H]+

Cholesterol metabolism

-1.07

4alpha-Carboxy-5alpha-cholesta-8en-3beta-ol 3-Indolepropionic acid

[M+H]+

Cholesterol biosynthesis

[M+H]+

Tryptophan metabolism

2-Methyl-5-hydroxytryptamine

[M+H]+

Tryptophan metabolism

Page 28 of 35

298.2762

12.05

297.2668

43208

0.0238

274.2735

9.06

273.2668

41556

2.12

0.0409

666.6370

17.25

665.6686

0011770

0.8840

3.88

0.0078

401.3446

15.26

400.3341

0001993

3.46

0.2396

18.0

0.0469

431.3832

13.76

430.3447

0012166

57667

-1.56

0.0357

-1.51

0.0358

190.0874

7.57

189.0790

0002302

6602

1.68

0.0159

191.1095

17.30

190.1106


62476

69619

（%）

（%）

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


（%）

Page 29 of 35

（%）

Figure 1. Separation of mice in different groups by principal component analysis (a. A vs. B; c. C vs. D) and the hierarchical clustering heat maps (b. A vs. B; d. C vs. D) constructed using molecular features with significant changes (p ≤ 0.05). Comparison of significantly changed metabolites between A vs. B and C vs. D (e).



Figure 2. Serum levels of selected statistically significantly changed phospholipids in arsenic and H.t. treatment group. Groups: (A) Control. (B) Arsenic exposure. (C) H.t. infection. (D) H.t. infection plus arsenic exposure.


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Figure 3. Serum levels of statistically significantly changed sphingolipids in arsenic and H.t. treatment group (I) and the potential metabolic pathway involved by these metabolites (II). Groups: (A) Control. (B) Arsenic exposure. (C) H.t. infection. (D) H.t. infection plus arsenic exposure.



Figure 4. Serum levels of selected statistically significantly changed fatty acids and acylcarnitine in arsenic and H.t. treatment group and/or in arsenic treatment group. Groups: (A) Control. (B) Arsenic exposure. (C) H.t. infection. (D) H.t. infection plus arsenic exposure.


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Figure 5. Serum levels of statistically significantly changed cholesterols in arsenic and H.t. treatment group (I) and the potential metabolic pathway involved by these metabolites (II). Groups: (A) Control. (B) Arsenic exposure. (C) H.t. infection. (D) H.t. infection plus arsenic exposure.



Figure 6. Serum levels of statistically significantly changed tryptophan metabolites in arsenic and H.t. treatment group and/or in arsenic treatment group (I) and the potential metabolic pathway involved by these metabolites (II). Groups: (A) Control. (B) Arsenic exposure. (C) H.t. infection. (D) H.t. infection plus arsenic exposure.


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For TOC Only


Serum metabolomics reveals that gut microbiome perturbation

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