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J. Microbiol. Biotechnol. (2018), 28(4), 652–662https://doi.org/10.4014/jmb.1711.11065 Research Article jmbReview

Comparative Analysis of Gut Microbial Communities in Childrenunder 5 Years Old with DiarrheaHongyu Wen†*, Xin Yin†, Zhenya Yuan†, Xiuying Wang, and Siting Su

School of Life Science, Jiangsu Normal University, Xuzhou 221116, P.R. China

Introduction

Diarrhea in children is related to significant morbidity

and mortality rates worldwide and is an enormous global

health problem for children under 5 years old [1, 2]. In

China, diarrhea is still one of the most serious public

health problems, especially in low-income regions such as

southwest and northwest China. The China Information

System for Diseases Control and Prevention has marked

diarrhea as one of the top three among 39 notifiable

infectious diseases because of numerous diarrhea cases [3].

Diarrhea is manifested clinically by continual loose and

liquid stools with abdominal cramps. Pathogenic agents of

acute diarrhea are several viral, bacterial, and protozoan

species. Viral diarrhea is often caused by Rotavirus,

Adenovirus, Astrovirus, Calicivirus, and Norovirus [4-8].

Diarrheagenic Escherichia coli is a common pathogen of

bacterial diarrhea [9]. Non-typical Salmonella spp. cause

93,800,000 cases of gastroenteritis and 155,000 deaths each

year worldwide [10, 11]. Although there were reportedly

fewer cases of diarrhea caused by protozoa, recent research

has shown that gut pathogenic protozoa, including

Cryptosporidium spp., are on the second place for causing

gastroenteritis and death in infants and children [12].

Diarrhea is generally accepted as a complicated disease

because of various reasons [13]. The gut microbial

communities are composed of thousands of microbes

belonging to different genera [14]. Intestinal bacteria

perform an important role in the synthesis and metabolism

of certain nutrients, hormones, and vitamins, in the

clearance of drugs and toxic metabolites, and in the

resistance to the invasion of pathogenic agents [15]. Many

studies have proved that the main species presenting in the

gut are Firmicutes and Bacteroidetes [16]. Gut microbes

that are highly related to gut functions of the host can be

divided into two groups: predominant microbes and

subdominant microbes. Predominant microbes comprise

Bacteroides, Bifidobacterium, Clostridium, and other obligate

anaerobic bacteria [17]. They perform vital functions by

maintaining the intestinal homeostasis and resisting

pathogenic bacterial invasion [18]. Subdominant microbes

are aerobic bacteria or facultative anaerobic bacteria,

including Enterococcus, Escherichia coli, and Streptococcus,

which are considered as conditioned pathogens [19].

Received: December 5, 2017

Revised: January 31, 2018

Accepted: February 11, 2018

First published online

April 6, 2018

*Corresponding author

Phone: +86-138-1531-0346;

Fax: +0516-83403173;

E-mail: [email protected]

†These authors contributed

equally to this work.

pISSN 1017-7825, eISSN 1738-8872

Copyright© 2018 by

The Korean Society for Microbiology

and Biotechnology

Diarrhea is a global disease with a high morbidity and mortality rate in children. In this study,

25 fecal samples were collected from children under 5 years old. Seven samples had been

taken from healthy children without diarrhea and marked as the healthy control group; eight

samples had been sampled from children with diarrhea caused by dyspepsia and defined as

the non-infectious group; and ten samples had been taken from children with diarrhea

induced by intestinal infections and identified as the infectious group. We detected the

microbial communities of samples by using high-throughput sequencing of 16S rRNA genes.

The proportion of aerobic and facultative anaerobic microbes in samples of the infectious

group was much higher than in the non-infectious group. In addition, the relative abundance

of Enterococcus in the healthy control group was significantly higher than in the non-infectious

group and infectious group. This can be used as a potential diagnostic biomarker for diarrhea.

Keywords: Children diarrhea, gut microbial communities, high-throughput sequencing, 16S

rRNA genes

Gut Microbial Communities in Children with Diarrhea 653

April 2018⎪Vol. 28⎪No. 4

Subdominant microbes that occupy a small proportion of

gut microbiota communities consume the intestinal oxygen

that enables the growth of anaerobic bacteria.

General approaches for enteric pathogen detection include

bacterial culture-based diagnostics, microscopy, and

antigen-based tests. However, these approaches are unable

to reflect the diversity of the bacterial communities or the

relative abundance of microbes [20, 21]. High-throughput

sequencing is an efficient and accurate method, which is

enabled by the development of next-generation sequencing

platforms [22, 23]. The 16S rRNA gene-based sequencing

can reveal the diversity of gut microbiota communities.

Metagenome sequencing can precisely represent the gut

microbiota communities and their functional genes based

on gene annotation and bacterial pathways. This method

rapidly proceeds our understanding of bacterial community

functions in highly diverse microenvironments [24].

Currently, an increasing number of studies have been

performed with detection of the intestinal bacteria by 16S

rRNA gene sequencing. In our study, we collected 25 stool

samples from children with diarrhea and healthy children,

in order to investigate the gut pathogenic bacteria by using

high-throughput sequencing. Bacteria were classified by

analyzing 16S rRNA genes. We found that diarrhea could

be divided into two types according to different bacterial

species, which may be a new detection index for diarrhea.

Gut microbiota communities deserve further study for

detection of more potential non-randomized characteristics of

intestinal pathogen communities in children with diarrhea.

Materials and Methods

Ethical Approval

All procedures performed in studies involving human participants

were in accordance with the ethical standards of the institutional

and/or national research committee and with the 1964 Helsinki

declaration and its later amendments or comparable ethical

standards. Informed consent was obtained from all parents of the

individual participants who were included in the study.

Definitions of Diarrhea Cases and Healthy Controls

In accordance with the WHO’s definition of diarrhea (World

Health Organization. http://www.who.int/mediacentre/factsheets/

fs330/en/. Accessed April 2013), acute gastroenteritis is a condition

of having three or more episodes of abnormal stools within 24 h

(e.g., loose, watery, bloody, and mucousy stool) that lasts less than

2 weeks. A healthy control is a person who did not have any

diarrheal symptoms within the 7 days before inclusion in the study.

Study Population and Sample Collection

A randomized clinical study was performed for investigating

gut microbiota communities, with the inclusion criterion of

patients being younger than 5 years of age. The stool samples

were collected from 18 children who were diagnosed with

diarrhea and from 7 healthy children in March 2016 in Xuzhou

Children’s Hospital and Xuzhou Maternity and Child Health Care

Hospital in Xuzhou, China. Each stool sample was collected with

the help of sterile sampling cups and sterile cotton swabs. The

stool routine test was made by clinical labs. Test indicators were

the stools’ color and form, white blood cells, red blood cells, occult

blood tests, worm eggs, fat globules, and starch granules. All

children had regular bowel habits and had no history of

gastrointestinal diseases such as gastritis, peptic ulcers, gastric

cancer, colorectal cancer, and inflammatory bowel disease, and

had no diarrheal symptoms within the previous month. The

exclusion criteria were any evidence of gastrointestinal symptoms

and antibiotics use within one month before sampling.

Seven samples from healthy children were marked as the

healthy control group; eight samples from children with diarrhea

caused by dyspepsia were defined as the non-infectious group;

and ten samples from children with diarrhea induced by intestinal

infections were identified as the infectious group.

Before sampling in the non-infectious group, for at least 1 month,

three children under 1-year-old had the formula with carbohydrate

dietary supplements feeding; four children under 1-year-old had

human milk mixed with the formula with carbohydrate dietary

supplements feeding; and one child, older than 3-years-old, had a

common feeding.

In the infectious group, one child under 1-month-old had only

the formula feeding; three children under 1-year-old had the

formula with carbohydrate dietary supplements feeding; and four

children under 3-years-old and two kids older than 3-years-old

had a common feeding.

In the healthy control group, three children under 1-month-old

had human milk-only feeding; three children under 1-year-old

had carbohydrate dietary supplements and mixed milk feeding;

and one child older than 3-years-old had a common feeding.

This clinical study was approved by the Clinical Research

Ethics Committee of the Xuzhou Maternity and Child Health Care

Hospital (Approval No. 2017FL016).

DNA Extraction, PCR Amplification, and High-Throughput

Sequencing

DNA extraction. Bacterial DNAs from the samples were

extracted by using an E.Z.N.A. Stool DNA Kit (Omega Bio-Tek,

USA) according to the manufacturer’s protocol and stored at -

80°C for PCR. PCR amplification. The bacterial forward primer

338F (5’-ACTCCTACGGGAGGCAGCAG-3’) and reverse primer

806R (5’-GGACTACHVGGGTWTCTAAT-3’) were used to amplify

the 468 bp DNA fragment of the 16S rRNA genes. TransStart

Fastpfu DNA Polymerase and ABI GeneAmp 9700 PCR Amplifier

were used. The amplification conditions were as follows: an initial

denaturation step at 95°C for 3 min, followed by 27 cycles of

denaturation at 95°C for 30 sec, annealing at 55°C for 30 sec,

654 Wen et al.


elongation at 72°C for 45 sec, with a final extension step at 72°C

for 5 min, and halted at 10°C. The PCR products were purified

with the help of the AxyPrep DNA Gel Extraction Kit. The DNA

was detected and quantified by using the QuantiFluor-ST Real-time

PCR System (Promega, USA) before sequencing. High-throughput

sequencing. 16S rRNA gene sequencing was performed on an

Illumina Miseq 300 Platform by Majorbio Bio-Pharm Technology

Co., Ltd. (China).

Data Statistics

All clinical parameters of the children and treatment records

were presented in the form of tables and statistically analyzed by

using the software SPSS 21.0. All statistical analyses were based

on the SPSS 21.0, including the t-test in the pairwise comparison,

the Pearson correlation analysis, and the Chi-square independence

test. Each statistical method was remarked in annotations to the

table or in notes to the figure. The Illumina reads were filtrated

and merged into the single reads with the help of the QIIME

program, and Chimera was removed by using USEARCH.

Sequences were clustered into operational taxonomic units

(OTUs) at the 97% sequence similarity level by using the UCLUST

algorithm and adjusted by reference alignment to the SILVA 16S

rRNA database 123 Release. The 16S rRNA gene function

prediction was performed based on PICRUSt. Bioinformatics

analysis was performed by using the R script. The data processing

and histogram plotting were based on the R script (ver. 3.3.2) and

GraphPad. The heatmap was drawn by using the “vegan”

package in the R script clustering method: by using the complete

distance algorithm Bray-Curtis. Linear discriminate analysis

(LDA) effect size (LEfSe) was based on the software LEfSe (https://

bitbucket.org/biobakery/biobakery/wiki/lefse).

Results

Basic Information of the Children

During the study period, a total of 25 stool samples were

collected from 7 healthy children (numbered G5, G6, G12,

Z1, Z4, Z7, and Z10) and 18 children with diarrhea

(numbered G3, G11, G13, G14, G15, G16, S5, S6, S8, S10, G1,

G4, G7, G9, G10, S1, S2, and S7). The patient’s basic

information and fecal examination reports are listed in

Table 1. The children were from 17 days to 5 years of age

(mean age = 13 months old). The sex ratio (male/female) of

all children was 2.215 (17/8). Ten children with diarrhea

according to fecal examination reports had leukocytosis,

which is one of the diagnostic markers of bacterial infection

[25]. Therefore, we marked these 10 children as the

infectious group (G3, G11, G13, G14, G15, G16, S5, S6, S8,

and S10), in order to find if their intestinal bacteria were

different from bacteria of the non-infectious group (G1, G4,

G7, G9, G10, S1, S2, and S7). Moreover, in eight children

from the non-infectious group, fecal manifestations of

lactose intolerance, starch granules, and fat droplets were

detected. These three substances were undigested food

residues.

High-Throughput Sequencing Dataset and Overall Bacterial

Distribution

The sequencing of 16S rRNA genes amplified by PCR

resulted in 317,657 reads that passed quality checks. The

sequence taxonomy was used for taxonomic assignment of

V3-V4 regions in the 16S rRNA genes. The reads were

clustered into 148 OTUs. A representative region with the

highest abundance in each OTU was selected for taxonomy

assignment. Six main phyla were determined by the

sequences from all samples; namely, Firmicutes,

Proteobacteria, Actinobacteria, Bacteroidetes, Fusobacteria,

and Saccharibacteria. We compared the bacterial communities

from the healthy children and from children with diarrhea,

in order to find out the difference of their bacterial

diversities. Fig. 1 shows the compositions and proportions

of intestinal bacteria in the healthy and diseased children

on the phylum level. In the healthy control group,

Firmicutes was the predominant phylum and accounted

for approximately 65% of all species, which was significantly

Table 1. Basic information and fecal inspection report information

of the 25 children.

Basic information Number (%)

Ages

<1 month 4 (16%)

1 month ~1 year 13 (52%)

1 year ~3 years 4 (16%)

3 years ~5 years 4 (16%)

Sex

Male 17 (68%)

Female 8 (32%)

Feeding patterns

Human milk only 3 (12%)

Formula only 8 (32%)

Mixed 14 (56%)

Route of delivery

C-section 14 (56%)

Vaginal 11 (44%)

Characteristics of fecal inspection report information

Leukocytosis 10 (36.4%)

Undigested food residue 8 (27.2%)

Normal 7 (36.4%)



higher than in the non-infectious group (p = <0.001) and in

the infectious group (p = 0.011) (Table 2). It was worth

noting that there were almost 15% Bacteroidetes in the non-

infectious and infectious groups that had been proved to

contain many known pathogenic bacteria, such as E. coli.

Pathogenic bacteria were mostly aerobic and facultative

anaerobic, so we compared the bacterial dissimilarity of

oxygen demands in 18 samples with diarrhea [26-28].

According to their Aerotolerant or Obligate Anaerobic

Metabolism (http://www.mediterranee-infection.com/article.

php?laref=374&titre=list-of-prokaryotes-according-to-their-

aerotolerant-or-obligate-anaerobic-metabolism), we calculated

the proportion of anaerobic bacteria in all strains. Gut

microbiota of the children with diarrhea was divided into

groups according to their requirements of oxygen. One

group was anaerobic bacteria and the other one was

aerobic and facultative anaerobic bacteria. We selected 14

bacterial genera that had the highest proportion among all

the detected strains. Seven species were anaerobic bacteria

Fig. 1. Bacterial proportion of the three sample groups on the

phylum level.

The control at x-axis represented the healthy control group and the

diarrhea at x-axis represented the infectious group and the non-infectious

group. The figures at the y-axis showed the relative abundance of the

phyla in samples. The phyla were displayed by different colors.

Table 2. Relative abundance of microbes at phylum level.

TaxonomyRelative abundance of microbes, mean (%) ± SD (%)

Healthy control group p-valuea Non-infectious group p-valueb Infectious group p-valuec

Firmicutes 64.28 ± 30.57 <0.001### 10.67 ± 8.97 0.098 19.73 ± 17.04 0.011#

Proteobacteria 23.72 ± 25.94 0.972 25.81 ± 27.49 0.923 52.21 ± 29.38 0.896

Actinobacteria 11.63 ± 12.96 0.001## 56.74 ± 35.70 <0.001### 5.68 ± 10.46 0.142

Bacteroidetes 0.05 ± 0.05 0.052 6.77 ± 16.43 0.172 22.02 ± 27.76 0.008##

Saccharibacteria 0.25 ± 0.66 0.023# 0 0.003## 0.01 ± 0.02 0.014#

Fusobacteria 0 0.049# <0.01 ± <0.01 0.063 0.28 ± 0.87 0.082

Others - - - - - -

Welch T test for differences in pairwise comparison between groups, “a” represented P-value between healthy control group and non-infectious group, “b” represented

p-value between non-infectious group and infectious group, “c” represented P-value between healthy control group and infectious group. p=<0.05 was considered

significant, #p=<0.05, ##p=<0.01, ###p=<0.001.

Fig. 2. Proportions of anaerobic bacterial expression among

the three groups.

The x-axis represented the healthy control group, the non-infectious

group and the infectious group. The y-axis represented the proportions

of anaerobic bacteria of samples in the groups. The black circle

represented the samples in the healthy control group (n = 7), the black

square represented the samples in the non-infectious group (n = 8)

and the black triangle represented the samples in the infectious group

(n = 10). ***Represented the significant difference (p < 0.001) in the

comparison of two groups.

656 Wen et al.


and seven species were aerobic and facultative anaerobic

bacteria (Table 3). Fig. 2 clearly displays the anaerobic

bacteria content in all cases. As shown in the figure, the

non-infectious group and the healthy control group had

the higher proportion of anaerobic bacteria than did the

infectious group, indicating that anaerobic bacteria may

play an important role in stabilizing intestinal function.

Therefore, anaerobic bacteria could be used as a potential

diagnostic biomarker for infectious diarrhea.

In order to search the main determining factor for the

difference, we performed a deep analysis of gut microbiota

at the genus level. The heatmap (Fig. 3A) shows the relative

abundance of different taxonomic groups. A deep color

represented the genus with high abundance whereas a

light color represented the genus with low abundance.

Escherichia, Bifidobacterium, and Enterococcus were genera

with superior relative abundance in all samples. The

relative abundance of Enterococcus in the healthy control

group was significantly higher than in the non-infectious

group (p < 0.001) and in the infectious group (p < 0.001)

(Fig. 3B, Table 3). This reminded us that Enterococcus plays

a significant role in the maintenance of gut microbiota

communities.

Comparison of Bacterial Community Structures among

Samples

We used the LDA effect size (LEfSe), a statistical tool

designed for finding biomarkers, to predict potential

discriminating taxonomic groups among the three groups

of children [29]. The results showed that there were 35

taxonomic groups with an LDA score greater than 3 that

were distinguishing in the three groups (Fig. 4). In the

non-infectious group, Bifidobacteriales, Geobacillus, and

Tyzzerella_3 were enriched from phylum to genus or species

levels. Staphylococcaceae, Hungatella, Erysipelotrichales,

Selenomonadales, and Pasteurellales showed LDA scores

higher than 3 in the infectious group. In addition, microbes

of Lactobacillales, Clostridiaceae_2, and Mycoplasmatales

were significantly more predominant in the healthy control

group. We used LEfSe to identify discriminate taxonomic

groups among the three children groups (Fig. 5). From the

perspective of the phylum level, we could find that the

healthy control group had a large distribution in the

cladogram, and many of them belonged to Firmicutes.

Moreover, the percentage of Bacteroidetes was higher in

the infectious group, and Actinobacteria was the most

abundant phylum in the non-infectious group.

Table 3. Relative abundance of microbes at genus level.

Taxonomy Relative abundance of microbes, mean (%) ± SD (%)

AnaerobicHealthy control

groupp-valuea

Non-infectious

groupp-valueb

Infectious

groupp-valuec

Bifidobacterium 38.12 ± 9.68 <0.001### 58.26 ± 36.15 <0.001### 3.32 ± 7.13 0.222

Prevotella_9 7.07 ± 8.29 0.001## 0.04 ± 0.07 0.067 1.60 ± 5.04 0.080

Bacteroides 1.45 ± 3.45 0.116 7.01 ± 19.96 0.191 5.78 ± 6.95 0.289

Veillonella 2.31 ± 5.21 0.053 0.41 ± 0.69 <0.001### 5.57 ± 7.78 0.088

Faecalibacterium 0.19 ± 0.50 0.038# 0.02 ± 0.05 0.066 1.19 ± 3.77 0.136

Peptoclostridium 2.08 ± 4.90 0.024# 0 0.022# 0.21 ± 0.51 0.022#

Lactobacillus 0.73 ± 0.91 0.276 0.96 ± 1.47 0.608 0.53 ± 1.57 0.851

Facultative anaerobic and aerobic

Enterococcus 38.66 ± 17.43 <0.001### 0.33 ± 0.42 0.083 0.69 ± 1.30 <0.001###

Escherichia-Shigella 3.95 ± 6.57 0.007## 19.93 ± 31.78 0.778 55.45 ± 31.39 0.036#

Pseudomonas 0.02 ± 0.02 0.226 0.03 ± 0.04 0.063 8.20 ± 25.85 0.082

Klebsiella 3.26 ± 3.88 0.103 8.17 ± 12.93 0.407 9.10 ± 28.49 0.149

Streptococcus 1.22 ± 0.99 0.014# 4.25 ± 2.85 0.244 3.81 ± 4.18 0.007##

Enterobacteriaceae_unclassified 0.09 ± 0.17 0.043# 0.01 ± 0.01 0.064 1.32 ± 4.14 0.096

Staphylococcus 0.84 ± 2.22 0.288 0.59 ± 0.99 0.104 3.21 ± 10.11 0.187

Sum of facultative anaerobic and aerobic 48.04 ± 15.29 0.311 33.30 ± 34.00 <0.001### 81.78 ± 12.84 <0.001###

Welch T test for differences in pairwise comparison between groups, “a” represented p-value between healthy control group and non-infectious group, “b” represented

p-value between non-infectious group and infectious group, “c” represented p-value between healthy control group and infectious group. p=<0.05 was considered

significant, #p=<0.05, ##p=<0.01, ###p=<0.001.



Discussion

In this study, we aimed to detect the specific gut

microbiota communities in children under 5-years of age

with diarrhea and if there would be some consistent and

constant characteristics of their gut microbiota and whether

Fig. 3. Abundance of bacterial genera among the three groups.

(A) Heatmap showing the top 50 bacteria among the three groups at the genus level. (B) The content of Enterococcus was significantly different

among the three groups of children. In healthy controls, it occupied a large percentage.

658 Wen et al.


age would be relevant to the gut microbiota communities.

We demonstrated that diarrhea was relevant to gut microbes

in children under 5 years old. Many types of diarrhea are

caused by an ecological imbalance in the gut, and

dysbacteriosis is one of its symptoms [30]. The imbalance

of gut microbiota will give rise to many gastrointestinal

diseases [31]. We discovered that intestinal infection could

affect the intestinal bacteria that demand oxygen. Oxygen

enabled different bacteria to obtain energy and metabolism

in other ways [32]. The proportion of anaerobic bacteria

decreases when the gut is stimulated by foreign pathogens.

The decrease of obligate anaerobic bacteria, which are the

predominant bacteria in the gut, may lead to the different

degrees of diarrhea. The aerobic bacteria and the facultative

anaerobic bacteria, including some well-known pathogenic

bacteria such as Escherichia and Streptococcus, were dominant

bacteria in the infectious group [33, 34]. The healthy control

group contained a large proportion of Firmicutes (64.28%).

Firmicutes are beneficial to intestinal epithelial cells and

are the dominant bacteria in the normal bacterial community

[35, 36]. In our previous research of diarrhea in children,

we found that intestinal dysbacteriosis was the leading

cause of diarrhea, and Lactobacillaceae could relieve the

severity of diarrhea whereas Enterobacteriaceae had an

opposite effect [37]. It was also proved that the predominant

intestinal microbes play a critical role in maintaining the

gut homeostasis, which matched the conclusion of this

study. According to previous studies, the gut microbiota

community composition of children is quite different from

that of adults [38]. The newborn infant’s digestive system is

sterile. Their intestinal bacterial compositions enriched

slowly from an initial low diversity at birth and reach the

adult state at around 3 years of age [39]. Therefore,

intestinal microbes of infants are in constant variation and

more unstable. In this study, the relative abundance of

Enterococcus was significantly higher in the healthy control

group than in the non-infectious and infectious groups.

Some species of Enterococcus were related to the leading

causes of diseases, especially to the nosocomial infections,

and to the growing antibiotic resistance [40]. Enterococcus is

common for children’s gut and some species, including

Enterococcus faecium, are used as probiotics for improving

the microbial balance of the intestine and for treating

gastroenteritis in humans and animals, although the

probiotic mechanism of these probiotics has remained

equivocal [41-43]. In this study, the relative abundance of

Enterococcus in the healthy control group was significantly

higher than in the non-infectious and infectious groups. It

is suggested that a certain proportion of Enterococcus could

become a biomarker of intestinal health and contribute to

maintaining the intestinal homeostasis.

It has been proved that antibiotics, intestinal infection, and

diets are the crucial factors for gut microbiota communities

in children. Additionally, the gut microbiota composition

differs in various age groups. However, it seems that some

non-randomized features of microbial communities were

detected extensively in children with diarrhea. These

features are likely to be consistent in all children under

5 years of age with diarrhea. For instance, we detected that

the relative abundance of Enterococcus in the healthy control

group was significantly higher than in the non-infectious

group (p = 0.004) and in the infectious group (p = 0.002);

and the proportion of aerobic and facultative anaerobic

microbes to anaerobic microbes in the infectious group was

much higher than in the non-infectious group (p = 0.001)

and in the healthy control group (p = <0.001). The age of

the children was nonsignificantly relevant to the relative

abundance of Enterococcus (Pearson, r = 0.019, p = 0.928)

and to the proportion of aerobic and facultative anaerobic

to anaerobic microbes (Pearson, r = 0.123, p = 0.557).

Furthermore, the age was independent to the relative

abundance of Enterococcus (Chi-square independence test,

p = 0.213) and to the proportion of aerobic and facultative

anaerobic microbes to anaerobic microbes (Chi-square

independence test, p = 0.271). The age was not the

determining factor for gut microbiota communities in

children with diarrhea, which was determined by using the

Fig. 3. Continued.



Chi-square test. In fact, in this study, we proved that the

causes of diarrhea determine and affect the gut microbiota

communities more than the age of the children.

In this study, we have used Illumina Miseq high-

throughput sequencing, which is based on detecting the

variable region of 16S rRNA genes. The results presented

in this article were obtained through the careful manual

annotation of all the OTUs. The high-throughput technology

could accurately reflect the microbial communities, and the

depth of high-throughput sequencing was significantly

Fig. 4. Indicator microbial groups within the three types of sediments with a linear discriminate analysis (LDA) value higher than 3.

The color lump represented the microbes with significant difference at different taxonomic levels. Red, green and blue represented the healthy

control group, the infectious group and the non-infectious group, respectively. The x-axis represented the LDA score of the microbes in samples.

The y-axis represented the microbes which were detected to be significantly different in the groups from the phylum to the genus level.

660 Wen et al.


higher than the molecular biological method. However,

this study has one limitation – the sample size. The

extremely limited sample size of every age group was not

as the desired one, so more studies are needed for the final

conclusion. The variation of gut microbiota communities

in different age groups of children with diarrhea is also

required.

Acknowledgments

The authors gratefully acknowledge the financial support

that was given by Priority Academic Program Development

(PAPD) of Jiangsu Higher Education Institutions. The

authors thank Xuzhou Children’s Hospital and Xuzhou

Maternity and Child Health Care Hospital for their help in

collecting the stool samples used in this study. The authors

are also grateful to Majorbio Biotech Co., Ltd. (China) for

their help in sample sequencing.

Conflict of Interest

The authors have no financial conflicts of interest to

declare.

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Fig. 5. Linear discriminate analysis (LDA) effect size (LEfSe).

Cladogram indicating the phylogenetic distribution of microbial lineages associated with the three types of sample characteristics; lineages with

LDA values of 3 or higher determined by LEfSe are displayed. Differences are represented in the color of the most abundant class (red indicating

Healthy control group, green for the Infectious group, blue for the Non-infectious group, and yellow being nonsignificant). Each circle’s diameter

is proportional to the taxon’s abundance. The strategy of multiclass analysis is nonstrict (at least one class differential). Circles represent

phylogenetic levels from domain to genus inside out. Labels show the phylum and class levels.



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