Subscriber access provided by READING UNIV
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
Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.
is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.
Page 1 of 53
Journal of Agricultural and Food Chemistry
1 2 3 4
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
5
[Wanting Qu]a, [Chenxi Nie]a, [Jinsong Zhao]a, [Xiyang Ou]a, [Yingxiao
6
Zhang]a, [Shanchun Yang]a, [Xue Bai]a, [Yong Wang]bc, [Jiawei Wang]c,
7
[Juxiu Li]a*
8
a[College of Food Science and Engineering, Northwest A&F University, 22
9
Xinong Road, Yangling, Shaanxi Province, 712100, P. R. China.]
10
b[Shaanxi Research Institute of Agricultural Products Processing Technology,
11
Xi’an, Shaanxi Province, 710016, P. R. China.]
12
c[Shaanxi University of science and Technology, Xi’an, Shaanxi Province,
13
710016, P. R. China.]
14
*Correspondence: [Juxiu Li]. Phone: +86-29-87092486.
15
Fax: +86-29-87092486. E-mail:
[email protected] ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
16
ABSTRACT
17
Thermal-processed diets are widely consumed, although advanced glycation
18
end products (AGEs) are unavoidably formed. AGEs, a cluster of
19
protein-cross-linking products, become less digestible for they impair intestinal
20
peptidase proteolysis. We characterized the impacts of dietary AGEs on gut
21
microbiota through microbiome-to-metabolome association study. C57BL/6
22
mice were fed heated-treated diet (H-AGEs), or standard AIN-93G diet
23
(L-AGEs) for 8 months. Fecal microbiota composition was examined by 16S
24
rDNA sequencing, and fecal metabolome profile was evaluated by gas
25
chromatography tandem time-of-flight mass spectrometry (GC-TOF-MS).
26
Reduced α-diversity and altered microbiota composition with elevated
27
Helicobacter were found in H-AGEs group, and protein fermentation products
28
(i.e., p-cresol, putrescine) were increased among 57 perturbed metabolites.
29
Major dysfunctional metabolic pathways were associated with carbohydrate and
30
amino acid metabolism in two groups. Moreover, high correlations were found
31
between fluctuant gut microbiota and metabolites. These findings might reveal
32
underlying mechanisms of the detrimental impacts of dietary AGEs on host
33
health.
34
Keywords: Advanced glycation end products (AGEs), Gut microbiota,
ACS Paragon Plus Environment
Page 2 of 53
Page 3 of 53
Journal of Agricultural and Food Chemistry
35
Metabolome, Short-chain fatty acids (SCFAs), Protein fermentation
36
1. INTRODUCTION
37
Modern diets have embraced excessive amounts of processed foods, primarily
38
including heat-treated foods, such as grilled and fried foods, which are often
39
featured in Western diets. Although diets differ among nations and regions,
40
eating habits have changed remarkably due to changes in income, urbanization
41
and globalization. One of the most profound results of those changes is that
42
many Western-style fast food outlets have become widely distributed
43
worldwide, and their rate of distribution is increasing1. Western diets are largely
44
heat-processed, which results in the inevitable formation of extensive advanced
45
glycation end products (AGEs). In Maillard reaction, reducing sugars react with
46
free amino groups in proteins, thus AGEs formation is accompanied by protein
47
cross-linking. The most well-known representative AGEs are
48
NƐ-(carboxymethyl)-lysine (CML), carboxyethyllysine, pyrraline and
49
pentosidine. There is emerging evidence that chronic exposure to excessive
50
AGEs is correlated with the pathogenesis of diabetes mellitus, cardiovascular
51
diseases, hypertension and nephropathy2. AGEs exert pro-inflammatory
52
bioactivity under the condition in which they are first absorbed. As a result of
53
cross-linking and protein aggregation, only 10-30% of dietary AGEs are
54
absorbed and enter circulation, according to kinetic studies3. Additionally, the
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
55
CML recovery rate never attain 100% in the excreta based on early oral CML
56
administration studies4, 5. Recently, researchers have taken an interest in the role
57
of AGEs that are not absorbed. These unabsorbed AGEs are deemed to change
58
the gut microbiota composition, with an adverse effect on gastrointestinal (GI)
59
tract health6. However, these issues have not been investigated completely to
60
date, and there is a limited knowledge regarding the influence of diet-derived
61
AGEs on the gut microbiota and metabolome.
62
Dietary composition undoubtedly plays a key role in the intestinal ecosystem,
63
long-term dietary habits undoubtedly have a crucial effects on human gut
64
microbiota. Many studies have demonstrated relationships between an
65
imbalanced microbiota structure and inflammatory disorders7-9, and microbiota
66
metabolites is one of the most predominant connection9. During food digestion,
67
intestinal microbiota coproduces a large sorts of small molecules which can
68
enter the bloodstream through absorption, enterohepatic circulation or a leaky
69
gut10. Those small molecules play critical roles in shuttling information between
70
the microbial symbionts and their host’s cells11. Nicholson et al.12defined the
71
axis of host and microbe metabolic as ‘a multidirectional interactive chemical
72
communication highway between specific host cellular pathways and a series of
73
microbial species and activities’. Some metabolites play positive impacts on the
74
host health, such as antioxidant and anti-inflammatory activities. One example
ACS Paragon Plus Environment
Page 4 of 53
Page 5 of 53
Journal of Agricultural and Food Chemistry
75
is short chain fatty acids (SCFAs), which are mainly produced through
76
carbohydrate fermentation. SCFAs regulate immunity and energy metabolism,
77
improve insulin secretion and exert antidiabetic effect by binding to the GPR41
78
and GPR439. In contrast, other metabolites are deleterious to the host and cause
79
toxicity, cytotoxicity and genotoxicity, such as the fermentation of
80
proteinaceous material in the distal colon13.
81
Hellwig et al.14 first investigated the stability of CML after 24 h of incubation
82
with human faeces microbiota in vitro, 40.7 ± 1.5% of the CML was founded to
83
be degraded at least. Our early study confirmed that intake of dietary AGEs for
84
18 weeks in rats reduced the α-diversity and richness of the cecal microbiota
85
and adversely altered the gut microbiota composition15. Those results suggested
86
that mutual modulation might exist between the intestinal microflora and dietary
87
AGEs. Additionally, AGEs have been reported to increase proteolytic
88
metabolism, leading to secrete a range of putrefactive metabolites16. The
89
negative effects of dietary AGEs on GI tract disease, such as inflammatory
90
bowel disease (IBD) and colorectal cancer, are likely caused not only by the
91
adverse actions of glycated amino acids themselves but also by the their
92
associated microbial metabolites17. However, whether other diverse metabolites
93
are also influenced by dietary AGEs is not clear. Therefore, more research is
94
urgently required to identity the microbial metabolites of dietary AGEs and to
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
95
determine whether AGEs can influence host metabolic pathways. Untargeted
96
metabolomics can capture variations of metabolites in biological samples to
97
determine changes in metabolic phenotypes in nutrition intervention18. This
98
process could also be applied to explore the specific metabolites in the feces
99
after dietary AGEs intervention. In this study, we fed C57BL/6 mice a
100
heat-treated AIN-93G diet for 8 months and identified the specific microbiota
101
and their metabolites in the feces by developing a multivariate strategy
102
employing 16S rDNA gene sequencing and fecal metabolite profiling of the gut
103
microbiota structure using gas chromatography tandem time-of-flight mass
104
spectrometry (GC-TOF-MS).
105
2. MATERIALS AND METHODS
106
2.1. Animals and diets
107
Four-week-old male C57BL/6 mice were acquired from Beijing Vital River
108
Laboratory Animal Technology Co., Ltd. (Beijing, China) and housed with 2 or
109
3 animals per cage. The controlled environmental conditions: temperature 22 ±
110
1°C, humidity at 55 ± 15%, 12/12 h light-dark cycle. After an adaptation period
111
of 2 weeks, 8 mice were randomly chosen for each group. L-AGEs group fed a
112
standard AIN-93G diet (low-AGEs diet); H-AGEs group fed a heat-treated
113
AIN-93G diet (high-AGEs diet) for 8 months. The mice were free to access
ACS Paragon Plus Environment
Page 6 of 53
Page 7 of 53
Journal of Agricultural and Food Chemistry
114
rodent chow and water. The L-AGEs and H-AGEs diets were manufactured by
115
Trophic Animal Feed High-Tech Co., Ltd., Nantong, China. The specific diet
116
production process was reported previously by our group15 with the slight
117
modification that the H-AGEs diet was exposed to 175°C for 45 min. In order to
118
avoid nutrients difference, identical AIN-93G vitamin pre-mixture and mineral
119
pre-mixture were added and mixed well after the heating step. Diet
120
fluorescent19,CML, CEL20, GO and MGO21 contents were measured. As shown
121
in Table 2, both diets were isocaloric and identical in the levels of fiber,
122
minerals and vitamins, but the fluorescent, CML, CEL, GO and MGO contents
123
were significantly higher in the H-AGEs diet. The food intakes and body
124
weights were measured twice per month. In the last week at the end of the study
125
protocol, each mouse was housed individually in a metabolic cage for the
126
collection of fecal sample, and it was divided into two parts for the subsequent
127
gut microbiota and metabolome analyses. For the microbiota analysis, feces
128
were collected immediately in a sterile chilled tube, quickly frozen with liquid
129
nitrogen immediately. For the metabolome analysis, the sample was mixed with
130
one drop of 1% (w/v) NaN3 and then quickly frozen. All of the animal
131
experimental procedures followed the Guide for the Care and Use of Laboratory
132
Animals: NIH Publication no. 80-23 (revised in 1996) by Northwest A&F
133
University Animal Care and Use Committee.
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
134
2.2. Fecal microbiota--16S rDNA sequencing
135
Microbial DNA was extracted from each stool sample (200 ± 20 mg) using the
136
E.Z.N.A.® Stool DNA Kit (Omega Bio-tek, Inc., GA, USA). To ensure
137
complete cell lysis, almost 200 mg of 1 mm sterile glass beads was added with
138
the lysis buffer, the samples were completely homogenized with a vortex for 10
139
minutes. To increase the extracted DNA concentration, the elution step in which
140
the DNA from the HiBind® DNA column was dissolved with the Elution
141
Buffer was repeated twice. The integrity of the extracted DNA sample was
142
characterized and determined by Nano-200 nucleic acid and protein
143
spectrophotometry. The V3-V4 region of the 16S rDNA gene was amplified
144
using forward (5’-ACTC CTAC GGGA GGCA GCA-3’) and reverse primers
145
(5’-GGAC TACH VGGG TWTC TAAT-3’) with dual-index barcodes. After
146
quantification, equimolar concentrations of 16 PCR products were sequenced on
147
the Illumina HiSeq platform at Biomarker Technologies Co, Ltd. (Beijing,
148
China).
149
2.3. Bioinformatics and statistical analyses
150
Paired-end raw reads were merged from the original DNA fragments with
151
FLASH (version 1.2.7). After quality filtering with Trimmomatic (version 0.33),
152
a total of 1,098,978 clean reads were extracted. Then, effective tags were
ACS Paragon Plus Environment
Page 8 of 53
Page 9 of 53
Journal of Agricultural and Food Chemistry
153
produced by removing the chimeric sequences with UCHIME (version 4.2).
154
Finally, after stringent quality checking and data cleaning, high-quality effective
155
tags with 97.70% Q20 bases and 95.48% Q30 bases (base quality greater than
156
20 or 30) were applied for the subsequent bioinformatics analysis. Using QIIME
157
(version 1.8.0), all effective reads from each sample were clustered into
158
operational taxonomic units (OTUs) based on 97% sequence similarity
159
according to UCLUST, and the representative sequence of each OTU was
160
aligned against the Silva database for taxonomy analysis. For α-diversity
161
analysis, rarefaction and Shannon index curves were generated, and the ACE
162
and Chao1 estimators and Simpson and Shannon indices were calculated using
163
Mothur (version v.1.30). The β-diversity of the microbial communities was
164
explored by principal coordinate analysis (PCoA) plots based on unweighted
165
and weighted UniFrac distances. Unweighted pair group method with arithmetic
166
mean (UPGMA) was also performed using QIIME. Taxonomy-based analyses
167
were performed to identify the significantly different phylotypes between the
168
H-AGEs- and L-AGEs-treated groups. In the Mann-Whitney test, only taxa with
169
an average abundance >1%, p-value 3% relative abundance).
716
Fig. 3. Significant differences (p
