MicrobiomeâMetabolomics Analysis of the Impacts of Long-Term

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Omics Technologies Applied to Agriculture and Food

Microbiome-metabolomics Analysis of the Impacts of Long-term Dietary Advanced Glycation End Products Consumption on the C57BL/6 Mouse Fecal Microbiota and Metabolite Perturbation Wanting Qu, Chenxi Nie, Jinsong Zhao, Xiyang Ou, Yingxiao Zhang, Shanchun Yang, Xue Bai, Yong Wang, Jiawei Wang, and Juxiu Li J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b01466 • Publication Date (Web): 23 Jul 2018 Downloaded from http://pubs.acs.org on July 27, 2018

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Journal of Agricultural and Food Chemistry

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Microbiome-metabolomics Analysis of the Impacts of Long-term Dietary Advanced Glycation End Products Consumption on the C57BL/6 Mouse Fecal Microbiota and Metabolite Perturbation

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[Wanting Qu]a, [Chenxi Nie]a, [Jinsong Zhao]a, [Xiyang Ou]a, [Yingxiao

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Zhang]a, [Shanchun Yang]a, [Xue Bai]a, [Yong Wang]bc, [Jiawei Wang]c,

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[Juxiu Li]a*

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a[College of Food Science and Engineering, Northwest A&F University, 22

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Xinong Road, Yangling, Shaanxi Province, 712100, P. R. China.]

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b[Shaanxi Research Institute of Agricultural Products Processing Technology,

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Xi’an, Shaanxi Province, 710016, P. R. China.]

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c[Shaanxi University of science and Technology, Xi’an, Shaanxi Province,

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710016, P. R. China.]

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*Correspondence: [Juxiu Li]. Phone: +86-29-87092486.

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Fax: +86-29-87092486. E-mail: [email protected]

ACS Paragon Plus Environment


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ABSTRACT

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Thermal-processed diets are widely consumed, although advanced glycation

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end products (AGEs) are unavoidably formed. AGEs, a cluster of

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protein-cross-linking products, become less digestible for they impair intestinal

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peptidase proteolysis. We characterized the impacts of dietary AGEs on gut

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microbiota through microbiome-to-metabolome association study. C57BL/6

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mice were fed heated-treated diet (H-AGEs), or standard AIN-93G diet

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(L-AGEs) for 8 months. Fecal microbiota composition was examined by 16S

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rDNA sequencing, and fecal metabolome profile was evaluated by gas

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chromatography tandem time-of-flight mass spectrometry (GC-TOF-MS).

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Reduced α-diversity and altered microbiota composition with elevated

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Helicobacter were found in H-AGEs group, and protein fermentation products

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(i.e., p-cresol, putrescine) were increased among 57 perturbed metabolites.

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Major dysfunctional metabolic pathways were associated with carbohydrate and

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amino acid metabolism in two groups. Moreover, high correlations were found

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between fluctuant gut microbiota and metabolites. These findings might reveal

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underlying mechanisms of the detrimental impacts of dietary AGEs on host

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health.

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Keywords: Advanced glycation end products (AGEs), Gut microbiota,


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Metabolome, Short-chain fatty acids (SCFAs), Protein fermentation

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1. INTRODUCTION

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Modern diets have embraced excessive amounts of processed foods, primarily

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including heat-treated foods, such as grilled and fried foods, which are often

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featured in Western diets. Although diets differ among nations and regions,

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eating habits have changed remarkably due to changes in income, urbanization

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and globalization. One of the most profound results of those changes is that

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many Western-style fast food outlets have become widely distributed

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worldwide, and their rate of distribution is increasing1. Western diets are largely

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heat-processed, which results in the inevitable formation of extensive advanced

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glycation end products (AGEs). In Maillard reaction, reducing sugars react with

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free amino groups in proteins, thus AGEs formation is accompanied by protein

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cross-linking. The most well-known representative AGEs are

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NƐ-(carboxymethyl)-lysine (CML), carboxyethyllysine, pyrraline and

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pentosidine. There is emerging evidence that chronic exposure to excessive

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AGEs is correlated with the pathogenesis of diabetes mellitus, cardiovascular

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diseases, hypertension and nephropathy2. AGEs exert pro-inflammatory

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bioactivity under the condition in which they are first absorbed. As a result of

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cross-linking and protein aggregation, only 10-30% of dietary AGEs are

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absorbed and enter circulation, according to kinetic studies3. Additionally, the



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CML recovery rate never attain 100% in the excreta based on early oral CML

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administration studies4, 5. Recently, researchers have taken an interest in the role

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of AGEs that are not absorbed. These unabsorbed AGEs are deemed to change

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the gut microbiota composition, with an adverse effect on gastrointestinal (GI)

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tract health6. However, these issues have not been investigated completely to

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date, and there is a limited knowledge regarding the influence of diet-derived

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AGEs on the gut microbiota and metabolome.

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Dietary composition undoubtedly plays a key role in the intestinal ecosystem,

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long-term dietary habits undoubtedly have a crucial effects on human gut

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microbiota. Many studies have demonstrated relationships between an

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imbalanced microbiota structure and inflammatory disorders7-9, and microbiota

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metabolites is one of the most predominant connection9. During food digestion,

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intestinal microbiota coproduces a large sorts of small molecules which can

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enter the bloodstream through absorption, enterohepatic circulation or a leaky

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gut10. Those small molecules play critical roles in shuttling information between

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the microbial symbionts and their host’s cells11. Nicholson et al.12defined the

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axis of host and microbe metabolic as ‘a multidirectional interactive chemical

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communication highway between specific host cellular pathways and a series of

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microbial species and activities’. Some metabolites play positive impacts on the

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host health, such as antioxidant and anti-inflammatory activities. One example


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is short chain fatty acids (SCFAs), which are mainly produced through

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carbohydrate fermentation. SCFAs regulate immunity and energy metabolism,

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improve insulin secretion and exert antidiabetic effect by binding to the GPR41

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and GPR439. In contrast, other metabolites are deleterious to the host and cause

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toxicity, cytotoxicity and genotoxicity, such as the fermentation of

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proteinaceous material in the distal colon13.

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Hellwig et al.14 first investigated the stability of CML after 24 h of incubation

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with human faeces microbiota in vitro, 40.7 ± 1.5% of the CML was founded to

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be degraded at least. Our early study confirmed that intake of dietary AGEs for

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18 weeks in rats reduced the α-diversity and richness of the cecal microbiota

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and adversely altered the gut microbiota composition15. Those results suggested

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that mutual modulation might exist between the intestinal microflora and dietary

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AGEs. Additionally, AGEs have been reported to increase proteolytic

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metabolism, leading to secrete a range of putrefactive metabolites16. The

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negative effects of dietary AGEs on GI tract disease, such as inflammatory

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bowel disease (IBD) and colorectal cancer, are likely caused not only by the

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adverse actions of glycated amino acids themselves but also by the their

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associated microbial metabolites17. However, whether other diverse metabolites

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are also influenced by dietary AGEs is not clear. Therefore, more research is

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urgently required to identity the microbial metabolites of dietary AGEs and to



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determine whether AGEs can influence host metabolic pathways. Untargeted

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metabolomics can capture variations of metabolites in biological samples to

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determine changes in metabolic phenotypes in nutrition intervention18. This

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process could also be applied to explore the specific metabolites in the feces

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after dietary AGEs intervention. In this study, we fed C57BL/6 mice a

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heat-treated AIN-93G diet for 8 months and identified the specific microbiota

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and their metabolites in the feces by developing a multivariate strategy

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employing 16S rDNA gene sequencing and fecal metabolite profiling of the gut

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microbiota structure using gas chromatography tandem time-of-flight mass

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spectrometry (GC-TOF-MS).

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2. MATERIALS AND METHODS

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2.1. Animals and diets

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Four-week-old male C57BL/6 mice were acquired from Beijing Vital River

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Laboratory Animal Technology Co., Ltd. (Beijing, China) and housed with 2 or

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3 animals per cage. The controlled environmental conditions: temperature 22 ±

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1°C, humidity at 55 ± 15%, 12/12 h light-dark cycle. After an adaptation period

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of 2 weeks, 8 mice were randomly chosen for each group. L-AGEs group fed a

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standard AIN-93G diet (low-AGEs diet); H-AGEs group fed a heat-treated

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AIN-93G diet (high-AGEs diet) for 8 months. The mice were free to access


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rodent chow and water. The L-AGEs and H-AGEs diets were manufactured by

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Trophic Animal Feed High-Tech Co., Ltd., Nantong, China. The specific diet

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production process was reported previously by our group15 with the slight

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modification that the H-AGEs diet was exposed to 175°C for 45 min. In order to

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avoid nutrients difference, identical AIN-93G vitamin pre-mixture and mineral

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pre-mixture were added and mixed well after the heating step. Diet

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fluorescent19,CML, CEL20, GO and MGO21 contents were measured. As shown

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in Table 2, both diets were isocaloric and identical in the levels of fiber,

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minerals and vitamins, but the fluorescent, CML, CEL, GO and MGO contents

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were significantly higher in the H-AGEs diet. The food intakes and body

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weights were measured twice per month. In the last week at the end of the study

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protocol, each mouse was housed individually in a metabolic cage for the

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collection of fecal sample, and it was divided into two parts for the subsequent

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gut microbiota and metabolome analyses. For the microbiota analysis, feces

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were collected immediately in a sterile chilled tube, quickly frozen with liquid

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nitrogen immediately. For the metabolome analysis, the sample was mixed with

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one drop of 1% (w/v) NaN3 and then quickly frozen. All of the animal

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experimental procedures followed the Guide for the Care and Use of Laboratory

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Animals: NIH Publication no. 80-23 (revised in 1996) by Northwest A&F

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University Animal Care and Use Committee.



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2.2. Fecal microbiota--16S rDNA sequencing

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Microbial DNA was extracted from each stool sample (200 ± 20 mg) using the

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E.Z.N.A.® Stool DNA Kit (Omega Bio-tek, Inc., GA, USA). To ensure

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complete cell lysis, almost 200 mg of 1 mm sterile glass beads was added with

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the lysis buffer, the samples were completely homogenized with a vortex for 10

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minutes. To increase the extracted DNA concentration, the elution step in which

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the DNA from the HiBind® DNA column was dissolved with the Elution

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Buffer was repeated twice. The integrity of the extracted DNA sample was

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characterized and determined by Nano-200 nucleic acid and protein

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spectrophotometry. The V3-V4 region of the 16S rDNA gene was amplified

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using forward (5’-ACTC CTAC GGGA GGCA GCA-3’) and reverse primers

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(5’-GGAC TACH VGGG TWTC TAAT-3’) with dual-index barcodes. After

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quantification, equimolar concentrations of 16 PCR products were sequenced on

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the Illumina HiSeq platform at Biomarker Technologies Co, Ltd. (Beijing,

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China).

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2.3. Bioinformatics and statistical analyses

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Paired-end raw reads were merged from the original DNA fragments with

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FLASH (version 1.2.7). After quality filtering with Trimmomatic (version 0.33),

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a total of 1,098,978 clean reads were extracted. Then, effective tags were


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produced by removing the chimeric sequences with UCHIME (version 4.2).

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Finally, after stringent quality checking and data cleaning, high-quality effective

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tags with 97.70% Q20 bases and 95.48% Q30 bases (base quality greater than

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20 or 30) were applied for the subsequent bioinformatics analysis. Using QIIME

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(version 1.8.0), all effective reads from each sample were clustered into

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operational taxonomic units (OTUs) based on 97% sequence similarity

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according to UCLUST, and the representative sequence of each OTU was

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aligned against the Silva database for taxonomy analysis. For α-diversity

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analysis, rarefaction and Shannon index curves were generated, and the ACE

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and Chao1 estimators and Simpson and Shannon indices were calculated using

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Mothur (version v.1.30). The β-diversity of the microbial communities was

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explored by principal coordinate analysis (PCoA) plots based on unweighted

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and weighted UniFrac distances. Unweighted pair group method with arithmetic

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mean (UPGMA) was also performed using QIIME. Taxonomy-based analyses

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were performed to identify the significantly different phylotypes between the

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H-AGEs- and L-AGEs-treated groups. In the Mann-Whitney test, only taxa with

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an average abundance >1%, p-value 3% relative abundance).

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Fig. 3. Significant differences (p

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