Effect of chitooligosaccharides on human gut microbiota and antiglycation

Carbohydrate Polymers 242 (2020) 116413

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Carbohydrate Polymers
journal homepage: www.elsevier.com/locate/carbpol

Effect of chitooligosaccharides on human gut microbiota and antiglycation
a

b,

c

a

c

a

T

b

Wei Liu , Xiaoqiong Li *, Zhonglin Zhao , Xionge Pi , Yanyu Meng , Dibo Fei , Daqun Liu ,
Xin Wanga
a
State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Institute of Plant Protection and Microbiology, Zhejiang
Academy of Agricultural Sciences, Hangzhou 310021, PR China
b
Institute of Food Sciences, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, PR China

c
College of Sciences, Henan Agricultural University, Zhengzhou 450002, PR China

A R T I C LE I N FO

A B S T R A C T

Keywords:
AGEs
Antiglycation
Chitooligosaccharides
Gut microbiota
Prebiotics
SCFA

Chitooligosaccharides (COS) have garnered great attention in the field of human healthcare. The prebiotic activities and antiglycation of COS were investigated using a combination of in vitro and in vivo studies. COS
supplementation dramatically increased the levels of acetic acid, while reducing the concentrations of propionic
and butyric acids. It also decreased the total bacterial population; however, it did not affect diversity and
richness of the gut microbiota. In addition, COS modulated the gut microbiota composition by increasing
Bacteroidetes, decreasing Proteobacteria and Actinobacteria, and lowering the Firmicutes/Bacteroidetes ratio.
COS promoted the generation of beneficial Bacteroides and Faecalibacterium genera, while suppressing the pathogenic Klebsiella genus. The antiglycation activity of COS and acetic acid was dose-dependent. Furthermore,
COS prevented the decrease of serum Nε-(carboxymethyl) lysine (CML) level caused by CML ingestion in a mouse
model of diet-induced obesity. To improve host health, COS could be potential prebiotics in food products.

1. Introduction
Prebiotics are substrates that nourish beneficial bacteria in host
microorganisms (Gibson et al., 2017). Chitooligosaccharides (COS), the
oligomers of β-(1-4)-linked D-glucosamine, are compounds prepared
from chitosan, a N-deacetylated derivative of chitin (Thadathil &
Velappan, 2014). COS are water-soluble compounds characterized by

polymerization degrees (DP) less than 20 and an average molecular
weight (Mw) below 3.9 kDa (Liaqat & Eltem, 2018). Their biological
activity, including the antimicrobial, anti-tumor, antioxidant, anti-inflammatory, immunoregulatory, anti-obesity, anti-diabetics, anti-Alzheimer's disease, and anti-hypertension functions (Liaqat & Eltem,
2018; Muanprasat & Chatsudthipong, 2017; Vinsova & Vavrikova,
2011). Due to the properties of COS, they have been regarded as new
potential prebiotics, and applied in various industries such as food,
agriculture and medicine (Lee, Park, Jung, & Shin, 2002; Liaqat &
Eltem, 2018).
The biological functions of COS may be affected by interactions with
gut microbiota that are considered as an extra organ influencing host
health. However, the results from the studies available in the literature

regarding the relationship between COS and gut microbiota are not
consistent. In mice, one in vivo study demonstrates that COS (DP = 2-6,
Mw < 1 kDa, 200 mg kg-1 d-1) treatment promotes the population of
Bacteroidetes, but inhibits the Proteobacteria phylum. At the genus
level, COS treatment reduces the population of probiotic Lactobacillus,
Bifidobacterium, and harmful Desulfovibrio bacteria, while increasing the
abundance of Akkermansia (Zhang, Jiao, Wang, & Du, 2018). Meanwhile, in vitro fermentation assessments conducted in the same study
show that COS decreases the number of Escherichia/Shigella pathogens
(Zhang et al., 2018). In the diabetic db/db mice model, COS of the same
Mw relieve the gut dysbiosis by promoting Akkermansia and suppressing
Helicobacter (Zheng et al., 2018). However, in pigs, dietary supplementation of COS (Mw =1.5 kDa) increases the number of bifidobacteria and lactobacilli, without affecting the Escherichia coli counts
(Yang et al., 2012). In yet another study, COS (Mw =1 kDa) supplementation is shown to decrease the proportion of Escherichia coli in pig
colonic content, while increasing short chain fatty acids (SCFA) concentrations and the number of beneficial bacterial species such as Bifidobacterium spp., Faecalibacterium prausnitzii, Lactobacillus spp., Prevotella, Fusobacterium prausnitzii, and Roseburia, and SCFA

Abbreviations: AGEs, advanced glycation end products; CML, Nε-(carboxymethyl) lysine; COS, Chitooligosaccharides; DIO, diet-induced obesity; GC, gas chromatography; HF, high fat; LDA, Linear discriminant analysis; OTU, operational taxonomic unit; PCoA, principal-coordinate analysis; QIIME, quantitative insights into
microbial ecology; q-PCR, quantitative real-time PCR; RDA, Redundancy analysis; SCFAs, short chain fatty acids
⁎
Corresponding author at: No. 198 Shiqiao Road, Hangzhou 310021, PR China.

E-mail address: (X. Li).
/>Received 7 November 2019; Received in revised form 30 April 2020; Accepted 30 April 2020
Available online 11 May 2020
0144-8617/ © 2020 Elsevier Ltd. All rights reserved.


Carbohydrate Polymers 242 (2020) 116413

W. Liu, et al.

had not received any medications, including antibiotics, for at least
three months prior to sample collection. The written informed consent
obtained from each volunteer was approved by the Ethics Committee of
the Zhejiang Academy of Agricultural Sciences. The collected fresh fecal
samples were kept in an anaerobic jar and processed within 3 h.

concentrations (Kong, Zhou, Lian, Liu, & Tan, 2014).
To the best of our knowledge, only limited reports have investigated
the role of COS on human gut microbiota. The results of these studies
are also inconsistent, due to the diversity of COS sources, Mw, and
enterotype backgrounds, as well as to the dissimilar experimental settings. Vernazza, Gibson, and Rastall (2005) show that COS (Mw < 5000
Da) fermentation stimulates the growth of bacteroides, without affecting bifidobacterial. It also increases the concentration of butyric
acid. More recently, Mateos-Aparicio, Mengíbar, and Heras (2016) reported that the biofunctionality of COS compounds is closely related to
their main physico-chemical characteristics (Mw and acetylation degree). COS compounds (Mw = 2 kDa, 1% w/v) with many acetylated
residues increase Lactobacillus/Enterococcus population and the production of SCFAs (mainly acetic acid), while significantly deacetylated
COS compounds (Mw = 3 kDa) might decrease some human microbiota
populations. This suggests that the COS compounds corresponding to
acetylated chitosans are not potential prebiotics, while those produced
from deacetylated chitosans could induce a colonic microbiota imbalance.
Despite the importance of COS, the molecular mechanism of their

biological activity remains unclear. Pathogenic pathways involved in
the development of obesity, diabetes, chronic inflammation,
Alzheimer's disease, and cancer are promoted by the advanced glycation end products (AGEs) generated via non-enzymatic reactions of
reducing sugars and amino groups (Xue et al., 2014). Therefore, AGEs
inhibition constitutes a potential therapeutic approach for the prevention of obesity and other chronic diseases. Although the antiglycation
activity of certain polysaccharides and their degradation products has
been reported (Zhu et al., 2019), the efficiency of COS compounds in
preventing obesity and age-related dysfunctions via the antiglycation
mechanism has not yet been investigated. Such investigation is essential, particularly considering that different COS compounds are expected to show varying antiglycation activity, due to differences in
structural characteristics, such as Mw and degree of esterification.
To confirm the prebiotic potency of COS in humans and to clarify
the mechanism of the compounds’ beneficial activity, we investigated
the effect of COS on human gut microbiota by conducting16S rRNA
gene high-throughput sequencing. we also used a BSA/glucose system
and a mouse model of high-fat (HF) diet-induced obesity (DIO) to
evaluate the antiglycation effect of COS on AGEs formation and accumulation.

2.3. In vitro batch culture fermentation
The batch cultures were fermented in 10 mL vials containing 5 mL
of basal medium VI, as per the method described previously (Wu et al.,
2017). To evaluate the effect of COS compounds on human feces, a
filter-sterilized (0.2-μm PTFE membrane) stock COS solution (100 mg
mL-1) was added to the fecal samples at a final concentration of 30 mg
mL-1 prior to fermentation. A control medium containing no COS was
also prepared. The medium was autoclaved at 115 °C for 15 min, and
the initial pH was adjusted to 6.5. To prepare the inoculum, fresh fecal
samples (0.8 g) were suspended in 8.0 mL of 0.1 M anaerobic phosphate-buffered saline (pH = 7.0) using an automatic fecal homogenizer
(Halo Biotechnology Co. LTD., Jiangsu, China) to make 10% (w/v)
slurries. Batch fermentation was performed by inoculating 1% of the
fecal slurry into each vial at 37 °C for 24 h. Aliquots (1 mL) of the

culture broth were taken from the vials at 4, 8, 12, and 24 h for further
analysis. The cultures were centrifuged, and the precipitates were collected and stored at -20 °C before use.
2.4. Fecal SCFAs quantification
The concentrations of SCFAs, such as acetic, propionic and butyric
acids in the culture filtrates were measured on a gas chromatograph
(GC, Shi-madzu, GC-2010 Plus, Japan) equipped with a DB-FFAP
column (Agilent Technologies, USA) and an H2 flame ionization detector. Trans-2-butenoic acid was used as an internal standard (Bai
et al., 2017).
2.5. Fecal DNA extraction and quantitative real-time PCR
Microbial genomic DNA was extracted from the culture broth at 24
h, using a QIAamp DNA Stool Mini Kit, according to the manufacturer's
instructions (Qiagen, Germany). The concentration of extracted DNA,
stored at −80 °C, was determined using a NanoDrop 2000 UV spectrophotometer (Thermo Scientific, Wilmington, USA), and the integrity
and size of this DNA were confirmed by agar gel electrophoresis (1.0%).
Quantitative real-time PCR (qPCR) assessments were used to quantify
the copy numbers of the bacterial 16 s rRNA gene, using an ABI PRISM
7500 Real-Time PCR Detection System (Applied Biosystems) with the
341 F (5’-CCTACGGGNGGCWGCAG-3’) and 805R (5’-GACTACHVGGGTATCTAATCC-3’) primer pair. DNA standards of bacteria were prepared by serial dilutions of the pGEM-T Easy Vector (Promega) containing the 16S rRNA gene of Escherichia coli. The PCR reaction and
amplification were performed according to the method described in a
previous study (Yin et al., 2013).

2. Materials and methods
2.1. Chitooligosaccharides
COS with deacetylation degrees greater than 95% and average Mw
below 1 kDa were kindly provided by Dalian Glycobio Co., Ltd (Dailian,
China). The monosacchaaride composition in COS was confirmed by
pre-column (1-phenyl-3-methyl-5-pyrazolone) PMP derivatization
high-performance liquid chromatograph (HPLC) method, using an
Agilent 1260 HPLC system (Waldbronn, Germany) with a Thermo-C18
column (4.6 mm × 250 mm, 5μm). The DP of COS was determined by

Acchrom S6000 HPLC system (Acchrom, China) equipment with an
Acchrom XAmide column (4.6 mm × 250 mm × 5 μm). The relative
abundance of COS oligomers was calculated from their peak area of
each oligosaccharide component using COS oligomer standards as external standard (Qingdao Marine Biomedical Research Institute Inc.,
Testing Center).

2.6. 16S rRNA gene pyrosequencing and bioinformatic analysis
The V3–V4 region of the bacterial 16S rRNA gene was amplified
using the primers 338 F (5’-ACTCCTACGGGAGGCAGCA-3’) and 806R
(5’-GGACTACHVGGG TWTCTAAT-3’). The amplicons were extracted
and further purified using the AxyPrep DNA Gel Extraction Kit (Axygen
Biosciences, Union City, CA, USA) and quantified with a QuantiFluor™ST (Promega, USA). Next generation sequencing (2 × 300 paired-end)
was performed on an Illumina MiSeq platform, according to the standard protocols of Majorbio Bio-pharm Technology Co., Ltd (Shanghai,
China). The raw sequence data were deposited in the NCBI Sequence
Read Archive (SRA) database under accession number SRR8361789.
After sequencing, raw fastq files were demultiplexed and quality-filtered by QIIME software package (version 1.17). Operational

2.2. Collection of fecal samples from human volunteers
A total of eleven healthy human volunteers living in Hangzhou,
China and aged between 34 and 60 years participated in the study. All
donors of fecal samples were in good health and physical condition,
followed a normal Chinese diet, presented no digestive diseases, and
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taxonomic units (OTUs) were clustered at 97% similarity using UPARSE

(version 7.1 and chimeric sequences were
identified and removed by UCHIME. Taxonomic annotation of OTUs
was performed with RDP Classifier against the SILVA database v. 128,
with a confidence threshold of 0.7.
Rare OTUs (< 0.001%) were removed in order to reduce sampling
heterogeneity for further alpha and beta diversity calculations. Alpha
(observed OTUs (sobs), Chao, Shannon and Simpson) and beta diversity
(weighted and unweighted UniFrac-based principal coordinate (PCoA)
analyses were performed using the R software and vegan package
(version 3.3.1). The linear discriminant analysis (LDA) effect size
(LEfSe) method was used to identify the effect of each differentially
abundant taxon and distinguish the one with the greatest biological
activity among the two groups (Segata et al., 2011). Additionally, redundancy analyses (RDA) and Spearman correlations were used to associate abundant differential taxa with SCFAs. A Wilcoxon rank-sum
test and Welch’s t-test were used to compare the data, and the significance value was set to 0.05.

according to the Km factor ratio of 3 and 37 for mice (20 g) and humans
(60 kg), respectively (Reagan‐Shaw, Nihal, & Ahmad, 2008).
Body weight and food intake were monitored every week. After 2
weeks treatment, blood was collected from the tail vein and centrifuged
at 2000 g for 15 min. Serum was then separated and stored at −20 °C.
All animal study protocols were approved by Zhejiang Academy of
Agricultural Sciences (approval number: ZAAS2020041).

2.7. Antiglycation activity assay

Except for the bioinformatic information, all data recorded in this
study were analyzed using the SPSS 12.0 software (IBM Corp., Armonk,
N.Y., USA). The data obtained for CTR and COS were compared using
the Student’s t-test (qualitative data, equal variance), or Welch’s t-test
(qualitative data, unknown variance). The differences between fluorescence intensity, AGEs inhibition and CML concentration were assessed by using an analysis of variance (ANOVA) with a (post hoc)

Turkey test. Differences with p values less than 0.05 were considered
statistically significant.

2.8.1. CML quantification
CML is of used as a marker of AGEs formulation. Serum CML content
was measured with mouse CML ELISA kit (Andy gene Co., Ltd,
Shanghai, China) following manufacturer’s instructions. The serum was
diluted with kit-provided diluent to fall within the measurable concentration range of the kit, and measured in duplicate. The CML ELISA
kit is a colorimetric immunoassay comparing samples to a standard
curve.
2.9. Statistical analysis

The effect of COS and acetic acid in inhibiting the formation of AGEs
was evaluated using the BSA/glucose system, based on a previously
reported method (Meng, Xiao, & Zhang, 2019), with some modifications. Briefly, 10 mL of 20 mg mL-1 BSA, 5 mL of 0.5 M glucose, 0.02%
sodium azide, and 0.2 M phosphate buffer (pH = 7.4) were mixed and
reacted with 5 mL samples of COS compounds (concentrations of 2, 4,
and 8 mg mL-1) or acetic acid (8.3, 25, and 75 mM) dissolved in
phosphate buffer (0.2 M, pH = 7.4). A Mixture containing aminoguanidine (AG, 4 mg mL-1) instead of COS was used as the positive control,
whereas the negative control contained neither AG nor COS. Samples
containing only COS (concentrations of 2, 4, and 8 mg mL-1) were also
run, so as to measure any fluorescence emissions caused by endogenous
substances in these samples. All mixtures were incubated in the dark at
37 °C for 2 weeks. Subsequently, 0.5 mL of the glycated solution were
diluted with PBS (0.2 M, pH = 7.4) to a final volume of 10 mL. The
AGEs content in the diluted solutions was determined using fluorospectrophotometry, at excitation and emission wavelengths of 370 and
440 nm, respectively (SpectraMax® M5, Molecular Devices, Sunnyvale,
United States). The percentage of antiglycation activity was calculated
according to the following equation:
% antiglycation = [(ANC−Asample)/ANC] × 100, where ANC and

Asample represent the absorbance values of the negative control and
COS, acetic acid or AG groups, respectively.

3. Results
3.1. Structure characterization of COS
The analysis of monosaccharide composition showed that the content of glucosamine in COS was 100%, and glucosamine was the only
monosaccharide presented in COS (Fig. S1). The COS were found to be
composed of 2–8 DP oligomers (Mw ≈ 856 Da), with 33.6% disaccharide, 16.9% trisaccharide, 15.8% tetrasaccharide, 12.4% pentasaccharide, 8.3% hexasaccharide, 7.1% heptasaccharide, and 5.9%
octasaccharide, respectively (Fig. 1).
3.2. Effect of COS on SCFAs production
SCFAs were produced during the fermentation of COS in human
fecal samples (Fig. 2). Noticeably, the concentration of acetic acid was
significantly higher in the COS group than that in the CTR group during
the fermentation. However, the amounts of propionic and butyric acid
produced in the CTR group were much greater than those observed for
the COS group.

2.8. Animal experiments
DIO and control C57BL/6 J mice (male, 18-week-old, DIO:
38.4 ± 4.1 g, control: 27.0 ± 1.8 g) were purchased from
GemPharmatech Co., Ltd (Jiangsu, China). To generate DIO models, HF
diet (60% kcal/fat, D12492, Research Diets) were introduced at 6week-old and fed for 12 consecutive weeks before purchase. All mice
were housed in an air-conditioned room at 20 − 22 °C with alternating
12 h cycles of light and dark, and with free access to pellet food and
water. After 10 days of acclimatization, mice in NC group (n = 6) were
continued to feed a normal diet (10% kcal/fat, MD12031), and orally
gavaged with 300 μL sterile physiological saline (SPS). The rest of DIO
mice were continued to feed a HF diet, and randomly assigned to 4
groups (n = 7): 1) HC, mice orally gavaged with equivalent volume of
SPS; 2) COS, mice orally gavaged with 500 mg kg-1 body weight COS

dissolved in SPS; 3) CML, mice orally gavaged with 10 mg kg-1 body
weight Nε-carboxymethyllysine (CML) (≥97%, Perfemiker Co., Ltd.
Shanghai, China) dissolved in SPS; and 4) C + M, mice orally gavaged
with equivalent concentration of COS and CML dissolved in SPS. The
dose of 500 mg/kg/day COS and 10 mg/kg/day CML was equivalent to
consumption of 2.4 g COS and 48.8 mg CML/day by a 60 kg human

3.3. Effect of COS on the microbial abundances
The total copy numbers of the bacterial 16S rRNA gene in fecal
samples were estimated by quantitative real-time PCR (qPCR). As
shown in Fig. 3, after 24 h of incubation, the copy numbers of the gene
significantly decreased in the COS group compared to the CTR group
(8.01 vs 6.92 Log10 copies mL-1, p < 0.001). Such an inhibitory effect
of COS on fecal bacteria was expected.
3.4. Effect of COS on the composition of the bacterial community
A total of 1,124,106 high-quality sequences with a minimum of
30,602 sequences per sample (mean = 52,096; read length = 265 to
509) were obtained. Based on the 97% sequence similarity criterion, the
sequences were assigned to 339 OTUs, representing 11 phyla and 185
genera. After removing the rare OTUs (< 0.001% of total sequences),
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Fig. 1. Relative contents of COS mixture by peak area ratio method using HPLC analysis. COS with the polymerization degree 2-8, their weight percentages were
3.12%, 11.30%, 19.82%, 17.90%, 34.35%, 3.06%, and 10.45%, respectively.

269 and 266 OTUs were retained in the CTR and COS groups, respectively. Veen analysis shows that 220 out of 315 OUTs (∼70%) are
shared by both groups, compared to 49 and 46 unique OTUs in the CTR
and COS groups, respectively (Fig. S3).
Bacteroidetes, Firmicutes, Proteobacteria, Fusobacteria, and
Actinobacteria were the prominent phyla identified in the tested samples (Fig. 4A). After 24 h of fermentation in the presence of COS,
phylum Bacteroidetes remarkably increased in abundance from 15.5 to
53.0% (p < 0.001), while the abundances of phyla Proteobacteria and
Actinobacteria significantly decreased from 41.2% to 16.8% and from
2.0% to 0.7%, respectively (p < 0.05) (Fig. 4B). Furthermore, COS
treatment reduced the ratio of Firmicutes to Bacteroidetes (F/B) from
2.27 in the CTR group to 0.56 in the COS group (Fig. S4). The bacterial
genera detected at ≥ 1% average relative abundance were showed in
Fig. 4C. Among these genera, Bacteroides, Faecalibacterium, and Alistipes
were found to be more abundant in the COS group (41.4%, 2.8%, and
1.1%, respectively) than in the CTR group (12.8%, 0.2%, and 0.1%,
respectively, p < 0.05). The abundance of Klebsiella, on the other hand,
was reduced from 5.6% to 0.7% upon COS treatment (p < 0.05). Interestingly, COS increased the abundance of Prevotella from 0.1% to
7.1%, while decreasing the amount of Escherichia-Shigella from 28.7%
to 13.1% after COS treatment; however, the variations showed no significant effect (Fig. 4D).

Fig. 3. Effect of COS on the population of the bacterial community. The
abundance of bacteria was estimated by quantitative PCR, based on the copy
numbers of the bacterial 16S rRNA gene in the fermentation culture at 24 h.
Data are presented as mean ± SD (n = 11, ***p < 0.001).

Fig. 2. Acetic (A), propionic (B), and butyric acids (C) concentration produced during in vitro fecal fermentation. Data are represented as the means ± SEM (n = 6 at
4, 8 and 12 h, n = 11 at 24 h). Values with significant correlations at the same fermentation time are marked by *p < 0.05; **p < 0.01; ***p < 0.001.
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Fig. 4. Effect of COS on microbial composition, alpha, and beta-diversity. Relative abundance of bacterial phyla (A) and genera (C). Lanes CTR1–CTR11 and
COS1–COS11 correspond to the samples in the CTR and COS groups, respectively. Heat maps of the mean relative abundances of the prominent phyla (B) and genera
(D). Wilcoxon rank-sum test was used to compare bacterial abundances at phylum and genus levels between the CTR and COS groups. Significant differences are
marked by *p < 0.05; **p < 0.01; ***p < 0.001. The cladogram of Linear discriminant analysis (LDA) effect size (LEfSe) analysis of microbial abundance from
phylum to genus level (E). LDA score assessments of the size of differentiation between the CTR and COS groups, with a score threshold of 4.0 (F). Bacterial richness
(observed OTUs (Sobs) and Chao index) and diversity comparison (Shannon and Simpson index) between the two groups (G). Principal-coordinate analysis (PCoA)
based on weighted UniFrac distances (H) and unweighted UniFrac distances (I) of samples from CTR and COS.
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3.7. The antiglycation activity of COS and acetic acid in vitro

The linear discriminant analysis (LDA) effect size (LEfSe) method
was used to identify the classified bacterial taxa with significant
abundance differences between the CTR and COS groups. As shown in
Fig. 4E, 23 bacterial clades present statistically significant differences
with an LDA score of 4.0 (Fig. 4F). In accordance with Wilcoxon ranksum test, COS stimulated the growth of Bacteroides and Faecalibacterium, while suppressing Klebsiella.

The antiglycation activity was conducted in vitro for COS and acetic
acid. As shown in Fig. 6A, COS exhibited strong autofluorescence, with
intensities increasing as a function of COS concentration. When added
to the incubation system, COS inhibited the formation of AGEs, with

greater inhibition observed at higher concentrations (Fig. 6B). The solution with the highest COS concentration (8 mg mL-1) showed the
strongest suppression of AGEs formation (85.57% ± 15.19%) during
the 14 days of incubation, followed by the solutions with intermediate
(4 mg mL-1, 51.22% ± 14.87%) and low (2 mg mL-1, 20.10% ± 8.02%)
COS concentrations. The AG solution (4 mg mL-1) yielded the least
AGEs suppression (13.48% ± 9.52%, p < 0.05), and the inhibition
percentages observed for this solution are similar to those recorded for
2 mg mL-1 COS. For acetic acid, the highest dose (75 mM) had the
lowest fluorescence intensity and greatest AGEs inhibition rate of
79.06% at day 7 (Fig. 6C&D). However, lower doses (25 and 8.3 mM) of
acetic acid did not show antiglycation activity.

3.5. Effect of COS on alpha and beta bacterial communities
Before calculating the alpha diversity indices, the samples were
rarefied to 28,204 sequences in order to account for the unequal
numbers of sequences between the groups. The results presented in
Fig. 4G showed that the CTR and COS groups exhibited relatively similar community diversity (Shannon and Simpson) and richness indices
(sobs and chao), however the richness values of the COS group were
constantly higher than those of the CTR group. Collectively, the data
indicate that COS did not affect bacterial alpha diversity. As for beta
diversity, weighted UniFrac-based principal-coordinate analysis (PCoA)
demonstrated a distinct clustering of bacterial composition in the two
groups (with the expectation of COS1) (Fig. 4H). Contrarily, no clear
visual separation between the COS and CTR groups could be observed
in the plot of unweighted UniFrac distances (Fig. 4I). However, the
ANOSIM of both, weighted and unweighted UniFrac, showed that the
treatments are significantly different (p = 0.001). These results indicate
that COS treatment markedly affected the beta diversity of the bacterial
community.

3.8. Effect of COS on serum CML concentration
Comparison of serum CML levels in different groups was conducted
to investigate the effects of COS on AGEs accumulation (Fig. 7). After
administration of free CML for 2 consecutive weeks, serum CML level in
CML mice was lower than that in NC group (27.5 vs 20.5 μg/L, p =
0.0312). Two mice in CML group died during the experiment, and the
rest of CML mice showed reduced weight gain (Fig. S6). However, due
to the short-term intake of COS and CML, no differences were observed
in the comparison between other groups.

3.6. The relationship between bacterial community and SCFAs profiles
4. Discussion
The correlation between the prominent gut microbes and SCFAs was
detected using redundancy (RDA) and Spearman’s correlation analyses.
The results presented in Fig. 5 indicate that, as expected, enriched
Bacteroidetes in the COS group are positively correlated with the concentration of acetic acid (Fig. 5A). This relationship was confirmed by
the statistically significant positive correlation between the Bacteroides
and acetic acid (rs = 0.525, p < 0.05) (Fig. 5B). Faecalibacterium (rs =
0.601, p < 0.01) and Blautia (rs = 0.442, p < 0.05) of the Firmicutes
also showed positive correlations with acetic acid. Moreover, the Fusobacterium and Klebsiella genera enriched in the CTR group are positively related to propionic and butyrate acids, respectively.

COS have garnered a lot of attention in the field of human healthcare, particularly for the treatment and prevention of obesity and diabetes. However, the detailed mechanisms of the anti-obesity and antidiabetics activities of COS remain unclear (Karadeniz & Kim, 2014). In
vitro gut fermentation remains an irreplaceable tool for screening as
well as studying the mechanisms of action of prebiotics. Being host-free,
it makes an ideal system in which to study microbial perturbations
resulting from prebiotic interventions, as microbial changes can be
measured without host interference (Elzinga, van der Oost, de Vos, &
Smidt, 2019; Payne, Zihler, Chassard, & Lacroix, 2012). In this study,

Fig. 5. The correlation between microbial structure and SCFA indices. Redundancy analysis (RDA) of the prominent phyla responding to SCFA (A); A heatmap of

Spearman’s correlation between the prominent genera and SCFA (B). The intensity of the colors represents the degree of association (red, positive correlation; blue,
negative correlation). Significant correlations are marked by *p < 0.05; **p < 0.01; ***p < 0.001.
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Fig. 6. Inhibition of advanced glycation end products (AGEs) formation by chitooligosaccharides (COS) and acetic acid. Fluorescence intensity of total fluorescent
AGEs (A) and (C); The antiglycation activities of Aminoguanidine (AG) and COS (B) and acetic acid (D). Fluorescence intensity was determined at 370 (excitation)
and 440 nm (emission). The lowercase letters over each bar for comparison between treatment and fraction for a given incubation time. Bars with different letters
differ significantly (p < 0.05).

(Koliada et al., 2017; Ley, Turnbaugh, Klein, & Gordon, 2006), it is
expected that COS supplementation may prevent obesity by reducing
the F/B ratio and reshaping the structure of gut microbiota. Coincidentally, COS treatment relieves gut dysbiosis in diabetic mice by
suppressing Firmicutes and Helicobacter, while promoting Bacteroidetes
and Akkermansia (Zheng et al., 2018).
At the genus level, COS supplementation increased Bacteroides,
Faecalibacterium, Alistipes, and Prevotella. The phylum Bacteroidetes
possess a broad saccharolytic potential (Martens, Koropatkin, Smith, &
Gordon, 2009), and are represented mainly by the genera Bacteroides
and Prevotella genera in the human gut. These genera are normally
symbiotic and saccharolytic, and they produce acetate, propionate, and
succinate as the major metabolic end products (Downes, Sutcliffe,
Booth, & Wade, 2007; Robert, Chassard, Lawson, & Bernalier-Donadille,
2007). The increase in the abundance of Bacteroides and Prevotella is
probably due to the effect of COS in facilitating their proliferation.
Faecalibacterium prausnitzii, a major representative of the

Faecalibacterium genus in the Firmicutes phylum, represents more than
5% of the total bacterial population in healthy human gut microbiota
(Miquel et al., 2013). It produces butyrate and other SCFAs in the colonic epithelium, possesses anti-inflammatory properties, and protects
against colorectal cancer and inflammatory bowel diseases (Lopez-Siles,
Duncan, Garcia-Gil, & Martinez-Medina, 2017). Chinese subjects diagnosed with type 2 diabetes (Zhang et al., 2013) or other gut diseases,
exhibit depleted levels of F. prausnitzii. Therefore, this bacterium is
considered as a new generation of probiotic (Martín et al., 2017).
Moreover, the interesting phenomenon observed from our study that an
increase in the abundance of Faecalibacterium was accompanied with
Bacteroides enrichment, probably due to the cross-feeding interactions
between the two members. Bacteroides produces acetate, whereas Faecalibacterium consumes acetate to produce butyrate; thus, the two
genera are metabolically complementary (Wrzosek et al., 2013). The
effect of COS consumption in increasing the population of F. prausnitzii
has also been observed in a pig model (Kong et al., 2014). Based on
these results, it could be postulated that COS are potential prebiotics.
SCFAs, the most abundant products of the bacterial fermentation of
undigested dietary fibers, mainly consist of acetate, propionate, and
butyrate (typically occurring in the ratio of 3:1:1). These compounds
drive the crosstalk between the host and gut microbiota, and the

Fig. 7. CML concentration in serum (μg/L) in mice in NC, HC, COS, CML and C
+ M groups. Data are presented as mean ± SD (n = 5-7, *p < 0.05).

the impact of COS on human gut microbial ecology and metabolic endproducts was investigated using the in vitro batch fermentation model.
The antimicrobial activities of COS against various microorganisms
are well known (Kittur, Vishu Kumar, Varadaraj, & Tharanathan, 2005;
Liaqat & Eltem, 2018). In our study, COS treatment not only reduced
the whole bacterial flora population, but also reduced abundances of
Proteobacteria and Klebsiella, as well as to a relatively decreased Escherichia-Shigella proportion. Similar results are reported by Zhang et al.
(2018), from which the inhibitory effect of bacterial populations could

be enhanced through increasing concentrations of COS (0.1-3 g L-1), but
declined with extended treatment time. Klebsiella (Paczosa & Mecsas,
2016) and Escherichia-Shigella (Li et al., 2006), key pathogens of the
Proteobacteria phylum, are major contributors to infections worldwide.
The antibacterial activity of COS against E. coli and K. pneumoniae is
confirmed in yet another study (Fernandes et al., 2010).
Although COS significantly inhibited the growth of the bacterial
community as a whole, it did not affect the alpha diversity. Beta diversity analysis, on the other hand, showed a relatively strong influence
of COS treatment on gut microbiota composition. The concurrent increase in Bacteroidetes and decrease in Proteobacteria were in line with
previous findings concerning the administration of 3 g L-1 COS in mice
fecal fermentation (Zhang et al., 2018). Knowing that obese individuals
and mice exhibit increased Firmicutes/Bacteroidetes (F/B) ratios
7


Carbohydrate Polymers 242 (2020) 116413

W. Liu, et al.

a mouse model of DIO. CML, commonly known as a biomarker of oxidative stress, is a major antigenic AGE structure. Accumulation of CML
in adipose tissue of obese subjects is able to activate inflammatory
signaling pathways contributing to obesity-related insulin resistance,
and meanwhile lends to a decreased circulating CML blood levels
(Gaens et al., 2015). In line with this finding, we observed a lower
circulating CML serum levels in DIO mice after a short-term consumption of free CML, which indicate that CML ingestion promotes
AGEs accumulation in adipose tissue. Nevertheless, no significant
change in serum CML level was found when ingestion of COS and CML
simultaneously. Therefore, COS may prevent the accumulation of AGEs
in adipose tissue to a certain extent. However, no statistical decrease of
CML in blood is found in rat fed with long-term CML-rich diets (Li et al.,

2015). Whether blood CML may serve as an inversely correlated marker
of CML accumulation in adipose tissue need to be further verified.

mechanism by which gut microbiota affects the host’s physiological and
pathological processes is partly mediated by SCFAs (Koh, De Vadder,
Kovatcheva-Datchary, & Bäckhed, 2016; Rooks & Garrett, 2016). According to Mateos-Aparicio et al. (2016), low Mw COS compounds
promote the production of total SCFAs and increase the concentration
of acetate. In vitro fermentation assessments show that the continuous
accumulation of acetate and butyrate in samples of mice feces with COS
is significantly greater than that in the control sample (-Zhang et al.,
2018). Consistently, our results indicate that acetate was the main organic acid product of fermentation, and that its concentration significantly increased when the fecal samples were supplemented with
COS. Contrarily, COS appreciably inhibited the production of propionate and butyrate. Acetate is a co-substrate used by cross-feeding species to produce butyrate. It exhibits anti-inflammatory effects, and
contributes to the biosynthesis of cholesterol and fatty acids (Rivière,
Selak, Lantin, Leroy, & De Vuyst, 2016). Acetate is mainly consumed by
two butyrate-producing bacteria, namely, Faecalibacterium prausnitzii
and Roseburia intestinalis/Eubacterium rectale (Duncan et al., 2004).
Knowing that Faecalibacterium was enriched upon COS treatment, we
assume that the effect of high COS concentration in reducing the
amounts of propionate and butyrate is transient, and probably due to an
initial decline in the absolute number of butyrate-producing bacteria.
However, the gut microbiota is resilient and will likely recover with
extended incubation time (Zhang et al., 2018).
The onset of obesity and age-related disorders, such as cancer and
Alzheimer’s disease, is associated with the accumulation of AGEs
(Yamagishi, Nakamura, Suematsu, Kaseda, & Matsui, 2015). It remains
unclear whether COS protect against these diseases through antiglycation mechanism. The analyses performed herein confirmed that highly
deacetylated COS compounds exhibited a strong inhibitory effect on the
formation of AGEs in the BSA/glucose system, which was consistent
with previously reported results (Zhang, Yu, Zhang, Zhao, & Dong,
2014). Nevertheless, Wang et al. (2018) show that, in a complex real

food system, COS and lysine undergo non-enzymatic glycation reactions, resulting in formation of CML. Whereas in the presence of
transglutaminase, the formation of AGEs is inhibited by transglutaminase and COS-induced glycosylation. Such inhibition may be attributed
to a competing reaction wherein the carbonyl groups in the reducing
sugar bind to the amino groups of COS, thereby limiting the combination of glucose and BSA. Furthermore, our results indicate that the
antiglycation activity of COS depends on the concentrations of these
compounds. This is consistent with the observations of Zhang et al.
(2014), who show that high antiglycation activity is correlated with
high glucosamine acid content. Therefore, the wide bioactivity range of
COS can be partly explained by the strong antiglycation effect of COS.
So far, inconsistent findings have been reported regarding the impact of AGEs on the gut microbiota composition, which may be due to
the different glycated substrates used (Snelson & Coughlan, 2019).
Nevertheless, intestinal microflora and its metabolites (e.g. SCFAs) may
in turn have an impact on the formation of AGEs. Acetic acid, which
was dramatically boosted (75.71 mM at 24 h) by COS fermentation was
evaluated for its impact on the AGEs formation. Interestingly, we found
that high-dose (75 mM), but not low-dose acetic acid significantly
prevented AGEs formation. Besides, the inhibitory effect of acetic acid
on AGEs formation tended to wane with time. It has been reported that
new AGEs formation during cooking can be inhibited following exposure to acidic solutions of lemon juice or vinegar (acetic acid), due to
a low or acidic pH arrests AGEs development (Uribarri et al., 2010).
Similarly, an acidified environment in the gut may also limit new AGEs
formation. Qu et al. (2017) show that dietary AGEs exhibit an adverse
effect on gut microbiota by reducing their diversity and richness. Both
high-dose COS and acetate acid showed strong antiglycation activities.
Therefore, synergistic effect of them on antiglycation may benefit the
gut microbial ecology as a whole and contribute to the prevention of
obesity and age-related dysfunctions.
We further extended these in vitro observations to in vivo ones using

5. Conclusions

COS supplementation in a batch fermentation model did not significantly influence the richness and diversity of the bacterial community; however, it dramatically altered the structure and functions of
microbiota. Furthermore, COS treatment reduced the population of the
bacterial community as a whole and increased the production of acetic
acid. It also enhanced the abundance of phylum Bacteroidetes and
genus Bacteroides which were positively correlated with acetic acid.
Finally, COS compounds modulated the bacterial composition by increasing the abundance of beneficial genus Faecalibacterium and decreasing the levels of harmful genus Klebsiella. COS and acetic acid
inhibited AGEs formation in BSA/glucose systems via the antiglycation
effect. Furthermore, COS may prevent AGEs accumulation in adipose
tissue caused by CML ingestion in a mouse model of DIO. Based on
these results, we supposed that the prebiotic and antiglycation properties of COS might have a synergistic effect on benefiting the gut microbial ecology and contribute to the prevention of obesity and agerelated dysfunctions.
Author contributions
XQL, WL, and ZLZ conceived the study and designed the experiments. WL, XEP, DQL, and WCW conducted the experiments. XQL, WL,
and XW analyzed the data and wrote the paper with input from all the
other authors. All authors read and approved the final version of this
manuscript.
CRediT authorship contribution statement
Wei Liu: Conceptualization, Methodology, Data curation, Writing original draft, Resources. Xiaoqiong Li: Conceptualization, Writing review & editing, Visualization. Zhonglin Zhao: Resources,
Methodology. Xionge Pi: Methodology, Resources. Yanyu Meng:
Methodology, Resources. Dibo Fei: Methodology, Resources. Daqun
Liu: Methodology, Resources. Xin Wang: Supervision, Funding acquisition.
Declaration of Competing Interest
The authors declare that they have no conflicts of interest.
Acknowledgements
This research was financially supported by the National Natural
Science Foundation of China (2019R17A32B01) and Primary Research
and Development Plan of Zhejiang Province (2018R17B88D03).
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