The carbamate aldicarb altered the gut microbiome, metabolome and

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The carbamate aldicarb altered the gut microbiome, metabolome and lipidome of C57BL/6J mice Bei Gao, Liang Chi, Pengcheng Tu, Nan Gao, and Kun Lu Chem. Res. Toxicol., Just Accepted Manuscript • DOI: 10.1021/acs.chemrestox.8b00179 • Publication Date (Web): 08 Nov 2018 Downloaded from http://pubs.acs.org on November 9, 2018

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Chemical Research in Toxicology

The carbamate aldicarb altered the gut microbiome, metabolome and lipidome of C57BL/6J mice

Bei Gao †, ‡, Liang Chi†, Pengcheng Tu†, Nan Gao§ and Kun Lu*†

†Department

of Environmental Sciences and Engineering, University of North Carolina at

Chapel Hill, Chapel Hill, North Carolina, USA, 27599 ‡NIH West Coast Metabolomics Center, University of California, Davis, CA, USA, 95616 §National

Engineering Research Center for Biotechnology, School of Biotechnology and

Pharmaceutical Engineering, Nanjing Tech University, Nanjing, China, 211816

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

ACS Paragon Plus Environment

Chemical Research in Toxicology 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

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Abstract The gut microbiome is highly involved in numerous aspects of host physiology, from energy harvest to stress response, and can confer many benefits to the host. The gut microbiome development could be affected by genetic and environmental factors, including pesticides. The carbamate insecticide aldicarb has been extensively used in agriculture, which raises serious public health concern. However, the impact of aldicarb on the gut microbiome, host metabolome and lipidome has not been well studied yet. Herein, we use multi-omics approaches, including16S rRNA sequencing, shotgun metagenomics sequencing, metabolomics and lipidomics, to elucidate aldicarb-induced toxicity in the gut microbiome and the host metabolic homeostasis. We demonstrated that aldicarb perturbed the gut microbiome development trajectory, enhanced gut bacterial pathogenicity, altered complex lipid profile, induced oxidative stress, protein degradation and DNA damage. The brain metabolism was also disturbed by the aldicarb exposure. These findings may provide a novel understanding of the toxicity of carbamate insecticides.

Key words: carbamate, gut microbiome, oxidative stress, lipidomics, protein degradation, DNA damage



Introduction Carbamates, a relatively old group of pesticides, have been frequently used because of their fast action on target pests and their relatively short half-life 1. Aldicarb [2-methyl-2(methylthio)propanal O-(N-methylcarbamoyl)oxime] is a potent oxime carbamate insecticide used on both food and non-food crops to control early-season insects and nematodes 2. Aldicarb is highly toxic and is labeled as a “restricted use pesticide” in the United States. It was responsible for several outbreaks of food poisonings 3, 4. Due to its highly toxic effects, a major aldicarb product, Temik, was phased out by the Environmental Protection Agency (EPA) in 2010. However, the EPA approved a new product, AgLogic 15G, which made its debut in Georgia in 2016 and is released in other states in 2017, increasing the risk of aldicarb exposure. The registered crops include cotton, dry beans, peanuts, soybeans, sugar beets and sweet potatoes. General population exposure to aldicarb may occur via inhalation of dust and ingestion of aldicarb-contaminated food and drinking water 1. Because of the extensive usage and highly toxic nature, aldicarb has become a serious public health concern.

Aldicarb exerts its neurotoxic effects through reversible inhibition of cholinesterase activity at nerve terminals 2. In addition to the nervous system, many adverse effects in liver, lungs, heart, kidney and immune system have been associated with aldicarb exposure 5. Aldicarb has been reported to induce oxidative stress in CHO-K1 cells by overproduction of reactive oxygen species (ROS) 6, 7. ROS are highly reactive molecules, which disturb normal cellular redox states and damage cellular components, such as nucleic acids, proteins, carbohydrates, and lipids 8. Several defense mechanisms have been developed to offset ROS and ROS-induced damages, including enzymatic and non-enzymatic scavengers 9. In addition to oxidative stress, aldicarb has


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been reported to induce DNA damage in human lymphocytes and Salmonella typhimurium TA1538 10, 11. Salmonella Typhimurium is a pathogen, which has a wide host range including humans, livestock, waterfowl, rodents and birds 12.

Trillions of microorganisms colonize the mammalian gastrointestinal tract and are known as the gut microbiome. The past few years have seen a surge in research of the gut microbiome, which has firmly established the critical role of the gut microbiome to host health 13-16. From energy production to stress response, the gut microbiome is involved in numerous aspects of host metabolism and physiology and can confer many benefits to the host 17, 18. The gut microbiome also plays a role in modulating lipid metabolism potentially through bacterial-derived signaling molecules such as short chain fatty acids and bile acids, and their receptors including G proteincoupled receptors and nuclear receptor 19. In addition, the gut microbiome is a rich source of effector molecules involved in redox regulation. The redox tone is critical to microbial and host cell functions and pathogenic-metabolic-redox activity 20. Numerous gastrointestinal diseases are associated with oxidative stress, such as irritable bowel syndrome, Crohn’s disease and ulcerative colitis 21-23. Oxidative stress also plays a key role in neurodegenerative diseases 24. Interestingly, a recent study showed the supplementation of probiotic E. coli CFR 16 to 1,2dimethylhydrazine(DMH)-treated Charles Forster rats reduced systemic oxidative stress and restored brain neurotransmitter status 25, suggesting the role of probiotics against oxidative stress and its potential to modulate gut–brain axis. However, further investigations are needed to explore the impact redox status on the gut-brain axis. The microbiome-gut-brain axis has received extensive attention recently and the routes of communication between the brain, gut and microbiome has been slowly unraveled, including vagus nerve, gut hormone, immune system,



tryptophan metabolism and bacterial-derived metabolites 26.

Environmental toxicants, such as heavy metals and air pollutants, have been shown to affect gut microbiome development 27-29. Previously, we demonstrated that the organophosphate insecticides diazinon and malathion altered the gut microbiome community structure and its functional metagenome 30, 31. However, the toxicity of carbamate insecticide on the gut microbiome and host metabolic homeostasis is not well studied. In the present study, we applied 16S rRNA sequencing, shotgun metagenomics sequencing, mass spectrometry-based metabolomics and lipidomics to elucidate the functional impacts of a carbamate insecticide, aldicarb, on gut microbiome and host metabolic homeostasis.

Materials and Methods Animals and exposure Seven-week-old specific pathogen-free C57BL/6 male mice (Jackson Laboratory) were housed in the University of Georgia animal facility for a week before the experiments, where they consumed tap water ad libitum. All the mice were previously cohoused. The mice were weaned and maintained in the same weaning cage at 21 days of age. Before and throughout the experimental period, mice were provided with a standard pellet rodent diet and housed under environmental conditions of 22°C, 40–70% humidity, and a 12:12 h light:dark cycle. At the beginning of the experiment, mice were randomly assigned to either a control or an aldicarbtreated group (five mice per group). The aldicarb exposure started when the mice were eight weeks old. Aldicarb was purchased from Sigma-Aldrich with Purity ≥ 98.0% (catalog number 33386). The structure was confirmed by proton NMR spectrum. Aldicarb was administered in


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the drinking water at a concentration of 2 ppm for 13 weeks. Control mice received water alone. The animal protocol was approved by the University of Georgia Institutional Animal Care and Use Committee. The mice were treated humanely and with regard to alleviation of their suffering. PicoLab Rodent Diet 20 (LabDiet, St. Louis, MO), Bed-o’Cobs Combo (The Andersons Lab Bedding, Maumee, Ohio) and Dura Cage (Alternative Design Manufacturing & Supply, Siloam Springs, AR) were used in this study.

16S rRNA gene sequencing The 16S rRNA gene sequencing was performed as described previously 28. Briefly, total DNA of individual mouse was extracted from fecal pellets collected before exposure and 13 weeks postexposure using a PowerSoil® DNA Isolation Kit (MO BIO Laboratories, Carlsbad, CA). The extracted DNA was quantified by NanoDrop (Thermo Fisher Scientific, Waltham, MA) and stored at -80°C. Primers 515F and 806R were used to amplify the V4 region of the 16S rRNA gene. PCR products were normalized and barcoded. The resulting DNA samples were pooled, quantified by a Qubit 2.0 Fluorometer and sequenced using an Illumina MiSeq v2 500 cycle kit at the Georgia Genomics Facility. Paired-reads were assembled in Geneious (Biomatters, Auckland, New Zealand), followed by trimming the ends with an error probability of 0.01 as initial quality filtering. The Quantitative Insights into Microbial Ecology (QIIME) software package (version 1.9.1) was used for operational taxonomic unit (OTU) assignment and diversity analysis.

Metagenomics sequencing Total DNA was extracted from fecal pellets of individual mouse (Week 13). It was normalized to



10 ng/µL and fragmented to 500 bp using the Covaris E220 Evolution Device. The sequencing library was constructed using the Kapa Hyper Prep Kit (Kapa Biosystems, Wilmington, MA) according to the manufacturer’s instructions. The resulting DNA was pooled, quantified, and sequenced using an Illumina NextSeq High Output Flow Cell at the Georgia Genomics Facility. The raw FASTQ files were imported into the MG-RAST metagenomics analysis server (version 3.5) with MG-RAST ID 280772. The sequences were assigned to the M5NR Subsystems database for functional analysis with a maximum e-value cutoff 10-5, a 60% minimum identity cutoff, and a minimum alignment length cutoff of 15.

GC-MS metabolomics profiling GC-MS was used for the analysis of primary metabolites for both control group and aldicarbtreated group. Fecal, liver and brain (10mg each) samples were extracted by 1mL extraction solvent, 3:3:2 acetonitrile, isopropanol and water. The extraction solvent was degassed and prechilled at −20°C. Fecal, liver and brain samples were homogenized using Genogrinder at 1,500 rpm for 30s. Samples were shaken at 4°C for 5 min and centrifuged for 2 min at 18,440 g. 450μL supernatant was transferred to a new tube and dried down using Centrivap cold trap concentrator. 10uL methoxyamine hydrochloride was added to each dried sample followed by shaking at 30°C for 1.5 hours for methoximation. 91μL N-methyl-N-(trimethylsilyl)-trifluoroacetamide (MSTFA) was added to each sample followed by shaking at 37°C for 30 mins for trimethylsilylation. C8– C30 fatty acid methyl esters (FAMEs) were used as internal standards for retention time correction. Agilent GC-quadrupole mass spectrometer 5975 (Agilent, Santa Clara, CA) was used for primary metabolite profiling. Agilent J&W DB-5ms Ultra Inert column was used (Agilent, Santa Clara, CA). The column was hold at 60°C for 0.5 min, ramped to 325°C at 10°C/min and


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hold at 325°C for 10 min.

HILIC metabolomics profiling Hydrophilic interaction liquid chromatography (HILIC) was used for both control group and aldicarb-treated group for the analysis of biogenic amines, dipeptides, methylated and acetylated metabolites, etc. Fecal, liver and brain (10mg each) samples were extracted using 225μL methanol and 750μL methyl tert-butyl ether (MTBE). The extraction solvent was degassed and pre-chilled at −20°C. Fecal, liver and brain samples were homogenized using Genogrinder at 1,500 rpm for 30s. Samples were vortexed for 10s and shaken for 6 min at 4°C. 188μL water was then added to the samples, followed by centrifugation at 18,440 g for 2 min. The bottom polar phase (125μL) was collected and evaporated. Dried samples were resuspended using 60μL 4:1 acetonitrile and water (v/v) containing internal standards. 5μL of resuspended samples were injected onto a Acquity UPLC BEH Amide column (150mm x 2.1mm; 1.7μm) (Waters, Milford, MA). Acquity VanGuard BEH Amide pre-column (5mm × 2.1mm; 1.7μm) was used as a guard column (Waters, Milford, MA). The column was maintained at 45°C and coupled to Vanquish UHPLC (Thermo Scientific, Waltham, MA). 100% LC-MS grade water with 10 mM ammonium formate and 0.125% formic acid (Sigma–Aldrich) was used as mobile phase A. 95:5 acetonitrile and water (v/v) with 10 mM ammonium formate and 0.125% formic acid (Sigma-Aldrich) was used as mobile phase B. The gradient was as follows: 0 - 2 min 100% B, 7.7 min 70% B, 9.5 min 40% B, 10.25 min 30% B, 12.75 min 100% B, isocratic until 16.75 min. The flow rate was 0.4 mL/min. Samples were maintained at 4°C. Spectra were collected using Q-Exactive Plus (Thermo Scientific, Waltham, MA) with data dependent mode for MS/MS spectra acquisition. Data was collected in ESI (+) mode with a mass range of m/z 50–1700.



Lipidomics profiling Lipidomics profiling was used for both control group and aldicarb-treated group for the analysis of complex lipids. Fecal, liver and brain samples were extracted using the same methods as described previously in HILIC metabolomics profiling, except after the centrifuge step, upper organic layer (350μL) was collected and evaporated. Dried samples were resuspended using 110μL 9:1 methanol and toluene containing 150ng/mL CUDA as an internal standard. Lipids were separated using an Acquity UPLC CSH C18 column (100mm x 2.1mm, 1.7μm) (Waters, Milford, MA). Acquity UPLC CSH C18 VanGuard precolumn (5mm x 2.1mm; 1.7μm) was used as guard column (Waters, Milford, MA). The column was maintained at 45°C and coupled to Vanquish UHPLC (Thermo Scientific, Waltham, MA). The mobile phase A consisted of 60:40 acetonitrile and water. Mobile phase B consisted of 90:10 isopropanol and acetonitrile. For positive mode, 10 mM ammonium formate and 0.1% formic acid (Sigma-Aldrich) were used as mobile phase modifier and 10 mM ammonium acetate (Sigma-Aldrich) was used for negative mode. The gradient was as follows: 0 min 15% B, 2.0-2.5 min 48% B, 2.5-11 min 82% B, 1111.5 min 99% B, 11.5-12 min 99% B, 12-15 min 15% B. Samples were maintained at 4°C. Spectra were collected using Q-Exactive HF (Thermo Scientific, Waltham, MA) with data dependent mode for MS/MS spectra acquisition. Data was collected in ESI (+) and ESI (-) modes with a mass range of m/z 50–1700.

Statistical analysis of data A nonparametric test via Metastats software was used to assess the difference in the gut microbiome composition as described previously 32. The gut microbiome profiles between control and aldicarb-treated samples were compared using principle coordinate analysis (PCoA).


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Beta diversity difference was calculated based on the UniFrac distance metric 33. DESeq was used in the MG-RAST pipeline for data normalization 34. Metabolomics and lipidomics raw data were processed using MS-DIAL for peak detection, MS/MS deconvolution and compound identification 35. Mann-Whitney U test was used to calculate the p-value in the metabolomics and lipidomics datasets. ChemRICH was used for chemical similarity analysis 36. Metamapp was used for mapping and visualization of metabolomics data 37.

Results Aldicarb disturbed the gut microbiome development and enhanced the pathogenicity of the gut bacteria The chemical structure of carbamate aldicarb was shown in Figure 1A. To test the impact of carbamate aldicarb on the gut microbiome, 16S rRNA sequencing and shotgun metagenomics sequencing were performed on both control group and aldicarb-treated group. 16S rRNA sequencing analysis showed that the gut microbiome development was disrupted by aldicarb exposure, as shown in the PCoA plot (Figure 1B) using the beta diversity as the metrics. The gut microbial community structures at the genus level were similar for all mice on day 0. However, the community structures were notably different between the two groups at week 13 (Figure 1B). Consistent with the altered gut microbiome development trajectory, 16S rRNA sequencing data showed after thirteen weeks of exposure, a total of seventeen genera were significantly different between the control group and aldicarb-treated group after thirteen weeks (Figure 1C, Supporting Information Table S1, S2). The abundance change for each bacterial genus was showed in Figure S1. Along with the change of the gut microbiome community, shotgun metagenomics data showed that aldicarb exposure increased the pathogenicity of the gut bacteria. Genes involved in



regulation of virulence (Figure 2A), adhesion (Figure 2B) and bacteriocins (Figure 2C) were enriched in aldicarb-treated mice.

Figure 1. Chemical structure of carbamate aldicarb (A). Aldicarb disturbed the gut microbiome development trajectory of C57BL/6J mice, as demonstrated by 2D PCoA plot using the beta diversity metrics. PC1: percent variation explained 15.36% (X axis); PC2: percent variation explained by 11.83% (Y axis) (B). The fold changes of significantly altered bacterial genera compared to the control mice after 13-week aldicarb exposure (C). Fold changes were calculated using the group means for each genus; ∞ showed that genus was completely depleted in the aldicarb group.


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Figure 2. Pathogenicity related genes were enriched in the gut microbiome of mice exposed to aldicarb for 13 weeks, including genes involved in regulation of virulence (A) adhesion (B) and bacteriocins (C). (All comparisons listed are statistically significant, p

The carbamate aldicarb altered the gut microbiome, metabolome and

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