Zhou et al. BMC Pediatrics(2024) 24:562
https://doi.org/10.1186/s12887-024-05042-0

BMC Pediatrics

RESEARCH

Open Access

Exploring the effects of short-course
antibiotics on children’s gut microbiota
by using 16S rRNA gene sequencing: a casecontrol study
Yuhan Zhou1*, Xianglian Chen1, Tongtong Wang1 and Riyan Huang1
Abstract
Background With the widespread use of antibiotics, more attention has been paid to their side effects. We paid extra
attention to the impact of antibiotics on children’s bodies. Therefore, we analyzed the characteristic changes in thegut microbiota of children after antibiotic treatment to explore the pathogenesis of antibiotic-associated diseases inmore depth and to provide a basis for diagnosis and treatment.
Methods We recruited 28 children with bronchopneumonia in the western district of Zhuhai, China, and divided
them into three treatment groups based on antibiotic type. We took stool samples from children before and 3–5 days
after antibiotic treatment. 16S rRNA gene sequencing was used to analyze the effects of antibiotic therapy on the gut
microbiota of children. Continuous nonparametric data are represented as median values and analyzed using the
Wilcoxon rank-sum test.
Results While alpha diversity analysis found no significant changes in the mean abundance of the gut microbiotaof children after a short course of antibiotic treatment, beta diversity analysis demonstrated significant changes in
the composition and diversity of the gut microbiota of children even after a short course of antibiotic therapy. We
also found that meloxicillin sulbactam can inhibit the growth of Proteobacteria, Bacteroidetes, and Verrucomicrobia,
ceftriaxone inhibits Verrucomicrobia and Bacteroides, and azithromycin inhibits Fusobacteria, Actinobacteria,
Proteobacteria, and Verrucomicrobia. We further performed a comparative analysis at the genus level and foundsignificantly different clusters in each group. Finally, we found that azithromycin had the greatest effect on themetabolic function of intestinal microbiota, followed by ceftriaxone, and no significant change in the metabolic
process of intestinal microbiota after meloxicillin sulbactam treatment.
Conclusions Antibiotic treatment significantly affects the diversity of intestinal microbiota in children, even after
a short course of antibiotic treatment. Different classes of antibiotics affect diverse microbiota primarily, leading tovarying alterations in metabolic function. Meanwhile, we identified a series of intestinal microbiota that differed*Correspondence:
Yuhan Zhou
Zhouyuhan4406@outlook.com
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you
give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified thelicensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to thematerial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation
or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://
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significantly after antibiotic treatment. These groups of microbiota could be used as biomarkers to provide an
additional basis for diagnosing and treating antibiotic-associated diseases.
Keywords Gut microbiota, Effect, Children, Antibiotics, 16S rRNA

Introduction
Pathogenic bacteria are a significant cause of infectious
diseases in children, such as sepsis, bacterial meningitis, and infectious diarrhea. If not treated properly andtimely, it can cause serious consequences [1]. Antibiotics play a vital role in treating bacterial infectious diseases in children. It has also contributed significantly tothe reduction of complications and mortality. However,with the wide application of antibiotics, people havefound that antibiotics can also cause various harmful
effects on the human body, such as antibiotic-associateddiarrhea (AAD), an allergic rash, fungal infection, multidrug-resistant bacteria, and so on [2–4]. Researchers
also found that antibiotic exposure increases the risk ofnumerous diseases, such as obesity, diabetes, allergies,
asthma, and inflammatory bowel diseases [5].
In addition to killing disease-causing bacteria, antibiotics can affect bacteria that colonize the gut. It will
break the original microbial balance of the intestine andcause enteric dysbacteriosis, which is more evident inchildren [6]. Previous studies have shown that colonization of the gut microbiota begins during the fetal stage
and plays a crucial role in the development and maturation of the fetal gut. Similarly, gut microbiota’s role inchildren’s growth and development is not limited. Gut
microbiota can participate in or affect the body’s metabolic and immune processes by maintaining a dynamicbalance and producing chemicals [7]. Various studies
have recently examined the relationship between disease
and gut microbiota [8]. Researchers want to seek different approaches to diagnosis and treatment by uncovering
the role of gut microbiota in physiological processes anddisease progression.
It is known that antibiotics can cause dysbiosis of the
microbiota, inhibiting beneficial bacteria and causing
an overgrowth of opportunistic pathogens, resulting
in a wide range of clinical manifestations [9]. However,
the mechanism has yet to be particularly well known.
Most of the gut microbiota in previous studies was cultured by bacteria. Still, most of the culture conditions
were only suitable for the growth of some bacteria, so
the results were limited. The development of researchtechniques, particularly at the molecular level and biological information, has provided us with a different
perspective on understanding gut microbiota. In addition, previous studies have shown that probiotics are aneffective treatment for enteric dysbacteriosis and AAD[10, 11]. However, the mechanism of action needs tobe better understood, and it is also critical to note that

inappropriate use of probiotics may lead to drug-induced
intestinal microbiota disorders.
For these reasons, we sequenced the 16S rRNA V3/ V4
region of stool samples from children treated with different antibiotics. We want to know the changes in the gut
microbiome after antibiotic therapy to provide additional
evidence to study the mechanism and treatment of intestinal microbiota disorders.

Methods
Human subjects

For the study, we collected 56 stool samples from 28 children, 17 boys, and 11 girls, aged between 5 months and13 years, in Zhuhai, China. According to different antibiotics, we divided all samples into three research groups,
namely research group 1 (RG1, n = 13), research group 2(RG2, n = 8), and research group 3 (RG3 n = 7). The medicine used in RG1 was meloxicillin sulbactam(Suzhou
Erye Pharmaceutical Co. LTD). The medicine used in
RG2 was ceftriaxone(Shenzhen Lijian Pharmaceutical
Co. LTD), and the RG3 was medicine(Hainan Puli Pharmaceutical Co. LTD). We collected specimens from thethree research groups before drug treatment (RGA) andafter 3–5 days of treatment (RGB). Key laboratory test
data of each research group have been collated in the
additional table, including WBC, CRP, PCT, and pathogens (Additional table). All children in our study weretreated according to antibiotic use criteria. In our study,
we relied on the criteria for antibiotic use: Communityacquired pneumonia diagnosis and treatment standard
for children (2019 edition), published by the National
Health Commission of the People’s Republic of China
and the State Administration of Traditional Chinese
Medicine.
Inclusion criteria:(a) Inclusion age range: children aged 1 month to 14
years; (b) Children with bronchopneumonia; (c) All participants voluntarily joined the study and informed consent from their legal guardians.
Exclusion criteria:(a) Participants had a history of antibiotic use withinfour weeks before the study; (b) Participants had a history of gastrointestinal diseases within four weeks before
the study, such as abdominal pain, vomiting, diarrhea,
constipation, etc.; (c) Participants with a history of probiotics, prebiotics, or any other medications three months
before the study that could affect their gut microbiota.

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Sample collection and genomic DNA extraction

This study was approved by the Ethics Committee ofthe Fifth Affiliated Hospital of Zunyi Medical University(Zhuhai). All participants and legal guardians volunteered to participate in this study, and all legal guardianssigned informed consent forms. We obtained 56 stool
samples from 28 children in three groups. Stool samples
were collected and frozen at -80℃ within 15 min. Afterall the samples are collected, they are transported to
the laboratory of the research institution. The microbialcommunity DNA was extracted using MagPure Stool
DNA KF kit B (Magen, China) following the manufacturer’s instructions. DNA was quantified with a Qubit Fluorometer using a Qubit dsDNA BR Assay kit (Invitrogen,USA), and the quality was checked by running an aliquot
on 1% agarose gel.
Library construction

Variable regions V4 of bacterial 16S rRNA gene wasamplified with degenerate PCR primers, 515F (5’-GTGCCAGCMGCCGCGGTAA-3’) and 806R (5’- GGACTACHVGGGTWTCTAAT-3’). Both forward and reverse
primers are labeled with Illumina adapter, pad, and linker
sequences. PCR enrichment was performed in a 50 µL
reaction containing a 30ng template, fusion PCR primer,
and PCR master mix. PCR cycling conditions were as follows: 95 °C for 3 min, 30 cycles of 95 °C for 45 s, 56 °C
for 45 s, 72 °C for 45 s, and a final extension for 10 min
at 72 °C for 10 min. The PCR product was purified using
Agencourt AMPure XP beads and eluted in an elution buffer. The Agilent Technologies 2100 Bioanalyzer
qualifies the library. The validated libraries were used forsequencing on DNB MGISeq 2000 platform (BGI, Shenzhen, China) following the standard pipelines of DNB
and generating 2 × 300 bp paired-end reads.
Sequencing and bioinformatics analysis

Raw reads were filtered to remove adaptors and lowquality and ambiguous bases, and then paired-end
reads were added to tags by the Fast Length Adjustment of Short reads program (FLASH, v1.2.11) to get
the tags [12]. The tags were clustered into Operational
Taxonomic Units (OUTs) with a cutoff value of 97%
using UPARSE software (v7 0.0.1090) [13] and chimera
sequences were compared with the Gold database using
Table 1 Characteristics of the study participants
Variables
Sample size
Gender(%,n)
MaleFemaleAge, year

RG1
13

RG2
8

RG3
7

46.15%(6)53.85%(7)3.00 ± 1.78

87.50%(7)12.50%(1)2.75 ± 2.25

57.14%(4)42.86%(3)
7.00 ± 3.92

Data of age are expressed as mean ± standard deviation

UCHIME (v4.2.40) [14] to detect. Then, OTU representative sequences were taxonomically classified using
Ribosomal Database Project (RDP) Classifier v.2.2 with aminimum confidence threshold of 0.6 and trained on theGreengenes database v201305 by QIIME v1.8.0 [15]. The
USEARCH global was used to compare all Tags back to
OTU to get the OTU abundance statistics table of eachsample [16]. The OTU Rank curve was plotted using
the R package version 3.1.1. Alpha and beta diversity
were estimated by MOTHUR (v1.31.2) [17] and QIIME(v1.8.0) [15] at the OTU level, respectively. The sample
cluster was conducted by QIIME (v1.8.0) [15] based on
UPGMA. MetaCyc functions were predicted using the
PICRUSt software [18]. Principal Coordinate Analysis(PCoA) was performed by QIIME (v1.8.0) [15]. Barplot ofdifferent classification levels was plotted with R package
v3.4.1 and R package “gplots”, respectively. LEfSe cluster or LDA analysis was conducted by LEfSe. Significant
Species or functions were determined by R (v3.4.1) basedon Wilcox-test or Kruskal-Test.
Statistical analysis

We use IBM SPSS Statistics 27.0 software for data documentation and statistical analysis. Parametric data of age
are expressed as the mean and standard deviation. Continuous nonparametric data are represented as medianvalues and analyzed using the Wilcoxon rank-sum test.
P < 0.05 is considered statistically significant.

Results
Study participants feature

We recruited 28 children with bronchopneumonia (male:
female, 17: 11; average age 3.93 ± 3.06 years). According to different antibiotics, they were divided into threeresearch groups. RG1 was treated with meloxicillin sulbactam (male: female, 6: 7; average age 3.00 ± 1.78 years),study group 2 was treated with ceftriaxone (male: female,
7: 1; average age 2.75 ± 2.25 years), and study group 3 wastreated with azithromycin (male: female, 4: 3; average age
7.00 ± 3.92 years) (Table 1).
Species sequencing coverage

The rarefaction curves (Fig. 1a) reflect the depth and coverage of sequencing. In this study, the ends of the mostrarefaction curves tend to be flat, demonstrating that thecurrent amount of data can reflect the vast majority of thespecies information in the sample and that the sequencing depth and representation are acceptable. More data
will yield only a few new OTUs. The OTU Rank curve(Fig. 1b) had a wide abscissa but a steep slope, indicatingthat the species richness in the samples was high, but the
species composition was not uniform.

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Fig. 1 Rarefaction curve and OTU Rank curve (a) The rarefaction curves of sample species. The abscissa is the amount of sample sequencing data, andthe ordinate is the actual number of OTUs measured. Blue is for the pre-antibiotic treatment group, and orange is for the post-antibiotic group. (b) OTU
Rank curves. The abscissa is ordered according to the number of OTUs, with the ordinate being the relative abundance of OTUs. The different color curves
represent different samples, with M for the meloxicillin sulbactam-treated group, X for the ceftriaxone-treated group, and Z for the azithromycin-treatedgroup

Analysis of gut microbiota diversity

We performed diversity analysis separately for each of thethree research groups. In alpha diversity, chao1 algorithm
results represent species richness within each group andare shown as boxplots (Fig. 2a, b, c). There were no significant differences in mean species richness among thethree study groups before and after antibiotic treatment(P = 0.05, P = 0.33, P = 0.80), which might be related tothe shorter duration of antibiotic treatment. We would
obtain a different result if the course of antibiotic therapy were longer. The beta diversity was analyzed by theunweighted-unifrac algorithm and shown by box plots(Fig. 2d, e, f ). There were significant differences in microbiota composition before and after antibiotic treatmentin the three study groups (P <​ 0.01, P < 0.01, P = 0.04).
The diversity of gut microbiota increased significantly
after meloxicillin sulbactam and ceftriaxone treatment.
But result decreased substantially after treatment withazithromycin.
​Changes in gut microbiota after antibiotic therapy

The stacked bar chart of species composition shows thatat the phylum level, Actinobacteria, Firmicutes, Proteobacteria, Fusobacteria, Verrucomicrobia, and Bacteroidetes were the main compositions of gut microbiota inchildren (Fig. 3a, b, c). The composition of the gut microbiota differed in the three study groups after antibiotic
treatment (Table 2). The relative abundance of Fusobacteria (0.95%, 3.19%) and Actinobacteria (2.01%, 11.71%)
increased significantly, while the relative abundance ofProteobacteria (13.61%, 9.24%), Bacteroidetes (44.12%,
37.71%) and Verrucomicrobia (4.33%, 1.79%) decreased

significantly after treatment with meloxicillin sulbactam.
There was no significant difference in Firmicutes (34.96%,
35.62%). After ceftriaxone treatment, the relative abundance of Proteobacteria (9.43%, 16.67%), Actinobacteria(6.20%, 12.39%), Firmicutes (28.73%, 38.51%), and cyanobacteria (<​0.01%, 2.21%) increased significantly. The relative abundance of Verrucomicrobia (8.72%, 2.17%) and
Bacteroidetes (45.77%, 26.37%) significantly decreased,
and there was no significant difference in Fusobacteria(1.00%, 0.94%). After azithromycin treatment, only the
relative abundance of Bacteroidetes (40.19%, 58.67%)
increased significantly, and the relative abundance ofFusobacteria (1.28%, 0.17%), Actinobacteria (4.45%,
2.99%), Proteobacteria (15.49%, 2.23%), and Verrucomicrobia (0.55%, 0.28%) decreased significantly. There wasno significant difference in Firmicutes (37.92%, 35.54%).
No data indicates

Linear discriminant analysis Effect Size (LEfSe) is used to
identify species with significant differences in abundance
between different groups. The microbiota abundance of
LDA Score > 2 in each group was considered significantly
higher than that in the other group, and the larger thescore, the more pronounced the difference (P < 0.05). We
use the evolutionary clade diagram (Fig. 4a, c, e) and thehistogram of the distribution of LDA values (Fig. 4b, d,f ) to demonstrate. In this study, 27 microbiota relative
abundance increased significantly after meloxicillin sulbactam treatment. The most obvious one is Lactococcus(LDA value 4.17, P < 0.01), 13 microbiota relative abundance decreased significantly, and the most obvious one
is Prevotellaceae (LDA value 4.83, P < 0.05) (Fig. 4a, b).

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Fig. 2 Alpha and beta diversity. (a, b, c) Alpha diversity box plot. The five lines from bottom to top are minimum, first quartile, median, third quartile,and maximum. The abscissa denotes the group, and the ordinate is the Chao index. (d, e, f) beta diversity box plot. The five lines from bottom to top areminimum, first quartile, median, third quartile, and maximum. The abscissa denotes the group; the ordinate is the Unweighted Unifrac index. Different
colors indicate different study groups. RG1A is the pre-treatment group of meloxicillin sulbactam, and RG1B is the post-treatment group of meloxicillin
sulbactam. RG2A is the pre-treatment group of ceftriaxone, and RG2B is the post-treatment group of ceftriaxone. RG3A is the pre-treatment group ofazithromycin, and RG3B is the post-treatment group of azithromycin

Fig. 3 Bar graph of species composition The abscissa represents the groups, RG1A is the pre-treatment group of meloxicillin sulbactam, and RG1B is thepost-treatment group of meloxicillin sulbactam. RG2A is the pre-treatment group of ceftriaxone, and RG2B is the post-treatment group of ceftriaxone.
RG3A is the pre-treatment group of azithromycin, and RG3B is the post-treatment group of azithromycin. The ordinate is the proportion of species composition (phylum level). Different colors correspond to different species. Species with abundances less than 0.5% of the sample not annotated at thistaxonomic level were combined into Others

After ceftriaxone treatment, the relative abundance of 11
bacterial groups increased significantly, the most obvious
one is Actinomycetales (LDA value 4.69, P < 0.05), and 13
bacterial groups decreased significantly, the most obvious
one is Bacteroidaceae (LDA value 5.08, P < 0.05) (Fig. 4c,d). After azithromycin treatment, the relative abundance of 6 bacteria groups increased significantly, themost obvious is Bacteroidia (LDA value 4.69, P < 0.05).
17 bacteria groups decreased significantly, and the mostobvious is Proteobacteria (LDA value 4.83, P < 0.05)(Fig. 4e, f ). These groups of microbiota can be used as
biomarkers. They could combine with the microbiota’s
biological function to further investigate the mechanismsof antibiotic effects on the children, thus providing additional methods and evidence for diagnosis and treatment.

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Table 2 Proportionality of bacterial community composition

Firmicutes
Bacteroidetes
Actinobacteria
Proteobacteria
Fusobacteria
Verrucomicrobia
OthersCyanobacteria

RG1(%)
RG1A
34.96
44.12
2.01
13.61
0.95
4.33
0.01
-

RG1B
35.62
37.71
11.71
9.24
3.19
1.79
0.70
-

RG2(%)
RG2A
28.73
45.77
6.20
9.43
1.00
8.72
0.01
<0.01

RG2B
38.51
26.37
12.39
16.67
0.94
2.17
0.66
2.21

RG3(%)
RG3A
37.92
40.19
4.45
15.49
1.28
0.55
-

RG3B
35.54
58.67
2.99
2.23
0.17
0.28
-

RG1A is the pre-treatment group of meloxicillin sulbactam, RG1B is the post-treatment group of meloxicillin sulbactam, RG2A is the pre-treatment group of ceftriaxone,
RG2B is the post-treatment group of ceftriaxone, RG3A is the pre-treatment group of azithromycin, and RG3B is the post-treatment group of azithromycin.No data
indicates -Functional difference analysis of metabolic levels

Previous studies have found that gut microbiota participates in the body’s life activities and metabolic processes
by producing chemicals. This study confirmed that antibiotics affect the composition ratio and abundance of gut
microbiota. After antibiotic treatment, we also analyzed
differences in gut microbiota function at the metaboliclevel to explore how the shift in microbiota affected the
body’s metabolic process. As shown in the figure, in the
RG1 group, glycan degradation decreased after meloxicillin sulbactam treatment, but the difference was insignificant (Fig. 5a, P = 0.07). In the RG2 group, antibiotic
resistance increased significantly after ceftriaxone treatment (Fig. 5b, P = 0.01), while the polymeric compound
degradation decreased significantly (Fig. 5b, P < 0.05). In
group RG3, nucleoside and nucleotide biosynthesis function, glycolysis function, and secondary metabolite biosynthesis were increased significantly (Fig. 5c, P < 0.01,
P = 0.01, P < 0.05), aldehyde and polymeric compound
degradation, aldehyde degradation, alcohol degradation,
and aromatic compound degradation were decreased significantly (Fig. 5c, P < 0.01, P < 0.05, P < 0.05).

Discussion
The side effects of antibiotic treatment on the humanbody should not be ignored, especially in children. Early
exposure to antibiotics can significantly increase the riskof certain diseases, which may be related to a shift in
the colonizing microbiota of the child’s gut [19]. Even a
short course of antibiotics can take a long time to restore
balance among the microbiota and may have long-term
effects on colonizing the gut microbiota. This study further seeks to understand antibiotics’ impact on children
from a gut microbiota perspective.
From the alpha diversity results, we can see that shortcourse antibiotic therapy may not significantly affect themean abundance of gut microbiota, which is the sameconclusion reached in other similar studies [20]. It may
be related to the course of antibiotics, and the outcome
may be different if treatment is prolonged. However,beta diversity analysis showed that antibiotic therapy,
even short approaches, significantly affected the composition and homogeneity of gut microbiota. The study
showed that gut microbiota diversity increased dramatically after meloxicillin sulbactam and ceftriaxone.
However, it decreased significantly after treatment withazithromycin.
From the sample analysis, at the phylum level, the gut
microbiota of the children consisted mainly of Actinobacteria, Firmicutes, Proteobacteria, Fusobacteria, Verrucomicrobia, and Bacteroides, in agreement with otherstudies [21]. At the same time, we found that different
antibiotics had different effects on different groups ofbacteria. Meloxicillin sulbactam inhibited the growthof Proteobacteria, Bacteroidetes, and Verrucomicrobia,
while Fusobacteria and Actinobacteria showed significant increases but had limited effect on Firmicutes. Ceftriaxone had an inhibitory effect on the Verrucomicrobia
and Bacteroides, and a substantial increase in Proteobacteria, Actinobacteria, Firmicutes, and Cyanobacteria,with little impact on Fusobacteria. Azithromycin treatment inhibited Fusobacteria, Actinobacteria, Proteobacteria, and Verrucomicrobia, with a significant increasein Bacteroides, but had little effect on Firmicutes. By
killing or inhibiting the growth of some bacteria, antibiotics cause a considerable increase in the abundance ofothers, thus disrupting the original homeostasis of thegut, which may include harmful bacteria or opportunistic pathogens. Cyanobacteria, for example, showeda significant increase in abundance after treatment withceftriaxone, which may have been acquired by eating seafood. Treatment with ceftriaxone creates an imbalancein the gut microbiota, which may be responsible for theapparent increase in the abundance of Cyanobacteria andother microbiota. Studies have found that Cyanobacteria
can produce neurotoxins that may cause neurodegeneration in humans [22].
LEfSe was used to analyze further and compare themicrobiota at the genus level of each study group. The
microbiota affected by the treatment with meloxicillin

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Fig. 4 Cluster diagram of LEfSe and LDA diagram (a, c, e). LEfSe cluster graph. The nodes with different colors represent microbial communities that play
an essential role in the groups. A colored circle represents a biomarker, and the legend in the upper right corner is the name of the biomarker. The diameter of the circle is proportional to the relative abundance. From the inside out, the circles are the species at the level of phylum, class, order, family, andgenus. (b, d, f) LDA diagram. It is the distribution map of LDA values of different species, the color represents the corresponding groups, and the lengthof the bar chart represents the contribution of different species (LDA Score). The figure shows species with significant differences in abundance between
different groups under the condition that the LDA Score is greater than the set value (default setting is 2)

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Fig. 5 Analysis of the functional differences. Path difference of the Wilcox test results. Shown on the left is a bar plot showing the relative abundance ofthe channels for each group. In the middle is the log 2 value of the mean close abundance ratio for the same path in both groups and the right panel
shows the p-values and FDR values obtained from the Wilcox test. If the p-value is less than 0.05, the pathway is significantly different between the twogroupssulbactam had the most significant number of species, with 40 groups of microbiota found to be significantly different. Ceftriaxone was followed by 26 speciesof microbiota that differed significantly. The effects ofazithromycin were relatively minor, with 23 groups of
microbiota found to be quite different. In terms of bacterial metabolic function, we discovered that azithromycin
has the most significant effect on bacterial microbiota’s

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metabolic process and considerably inhibits the degradation function of amines, aldehydes, aromatic compounds,
and other chemical substances. Ceftriaxone promotes
antibiotic resistance in the body, while meloxicillin sulbactam has little effect on metabolism. Whether thesebacterial imbalances and changes in metabolic function
cause clinical symptoms or even have a more significant impact on the body could be a direction for future
research.
According to the findings, while most children did not
develop significant symptoms after a short course of antibiotics, they significantly altered the composition andfunction of the microbiota. Clinically, a precise diagnosis
is lacking, even when symptoms are present. In our study,
the groups of gut microbiota that changed substantiallycould be used as biomarkers to provide additional evidence for diagnosing antibiotic-associated diseases. In
terms of treatment, we can supplement with different
probiotics based on changes in specific microbial communities, which play a very significant role in restoringand establishing colonizing bacteria in the child’s gut.
There are still some limitations to our study. The time
range of the study was narrow, and only the short-term
effects of antibiotic treatment on the gut microbiota ofchildren were analyzed, while the long-term effects werelacking. In addition, the sample size of this study is small,and more samples are needed to prove our findings further. In the future, we may also find specific changes in
the microbiota after each antibiotic treatment throughmore different types of antibiotic studies, thus providing
an additional basis for diagnosis and treatment.

Conclusions
Antibiotic treatment significantly affects the diversity ofgut microbiota in children, even with short courses ofantibiotics. This study confirmed that different classesof antibiotics mainly affected diverse microbiota, resulting in various metabolic function changes. We identified
a range of gut microbiota that significantly differed after
antibiotic treatment, and they could be used as biomarkers to diagnose and treat antibiotic-associated disease.
Abbreviations
AAD	Antibiotic-Associated Diarrhea
OUTs	Operational Taxonomic Units
LEfSe	Linear Discriminant Analysis Effect Size

Supplementary Information

The online version contains supplementary material available at https://doi.
org/10.1186/s12887-024-05042-0.
Supplementary Material 1
Acknowledgements
The authors would like to thank all the participants for their participation inthis clinical trial.

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Author contributions
YHZ conceived the study and designed the experiments. TTW and RYH
recruited subjects and collected specimens. XLC performed experiments andanalyzed the data. TTW wrote the manuscript. YHZ revised the manuscript. All
authors read and approved the final manuscript.
Funding
This study was supported by the Science and Technology Foundation of
Guizhou Provincial Health Commission (gzwkj2022-137).
Data availability
The raw sequence data reported in this paper have been deposited in the
Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021)
in National Genomics Data Center (Nucleic Acids Res 2022), China National
Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of
Sciences (GSA: CRA010070) that are publicly accessible at https://ngdc.cncb.
ac.cn/gsa[23, 24].

Declarations
Ethics approval and consent to participate
This study was approved by the Ethics Committee of the Fifth AffiliatedHospital of Zunyi Medical University (Zhuhai) and was conducted by theprinciples of the Helsinki Declaration. All participants and their guardians
provided informed consent and signed informed consent forms.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Author details
1
Department of Pediatrics, The Fifth Affiliated (Zhuhai) Hospital of Zunyi
Medical University, Zhuhai 519100, China
Received: 4 May 2024 / Accepted: 30 August 2024

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