Characteristics of Intestinal Microbiota and Mother's Reproductive Tract Flora in Children With Down Syndrome

Background: Patients with Down’s syndrome (DS) often have an increased rate of infections, hypertension, objectivity and gastrointestinal disorders, which are the most common abnormalities and have a signicant impact on their daily life. The gut microbiota plays an important role in maintaining gut homeostasis and improving immunity and has been linked to the development of obesity, hypertension, and colon cancer. However, there are few studies on the intestinal ora and the mother's reproductive tract ora of children with DS in childhood. Therefore, 16S sequencing technology was used to analyze and explore the intestinal ora of children with DS and CHD patients in DS and the microbial abundance and diversity composition in the mother's reproductive tract. Results: We found that the gut microbiota in children with DS was mainly composed of Escherichia, Bidobacterium, Clostridium and Bacteroides, which have signicant differences in the abundance and diversity of intestinal ora compared with healthy children, and the abundance of Enterococcus and Erysipelatoclostridium in the intestine of children with CHD was signicantly higher than that of children without CHD, and the relative abundance of Lactobacillus in the reproductive tract of mothers with DS was signicantly higher than that of mothers with healthy children, which may suggest potential ways of using microbiome composition for prognosis and diagnosis. Through functional analysis, it was found that Down’s syndrome patients signicantly downregulated immune system and their cell growth and nucleotide metabolism were lower than those of healthy children. Conclusion: We performed 16S rRNA gene sequencing on fecal samples from 60 children and vaginal swab samples from 63 mothers to identify a number of potentially important taxonomic, functional and microbiomes associated with congenital heart disease and Down’s syndrome. Structural changes and its correlation with the mother’s vaginal ora. Our analysis shows that the ecosystem associated with childhood congenital heart disease affects the selection of bacterial communities in the native microbiota, and we focus on specic bacteria and their relevance to disease. Catabolism, Cofactors Cell Carbohydrate Cellular Community -prokaryotes, Metabolism of Other Amino Acids and Amino Acid Metabolism Pathways. Group DSM signicantly downregulated Metabolism of Terpenoids and Polyketides and Endocrine System. The analysis of the L2 level metabolic pathway can provide a basis for exploring the pathogenesis and prevention of DS.


Background
Down syndrome, also known as trisomy 21 syndrome, is an autosomal dominant genetic disease in children caused by the absence of separation of egg cells or sperm during meiosis of chromosome 21 [1,2]. It is the most common chromosomal disease that causes developmental disorders and intellectual defects in children [3]. The incidence of DS in the world is approximately 1/500 ~ 1/1000 of live births [4].
In the United States, the incidence rate is approximately 1.26% [5]. DS children have many clinical phenotypes, such as mental retardation, growth retardation, and special facial features, accompanied by multiple system malformations, which affect their health [6]. The risk of developing deformities in each group is signi cantly higher than that in normal children. The most common complication of DS is congenital heart disease (CHD). It has been reported that approximately 40% − 60% of children with DS have CHD [7]. The main reason may be related to chromosome abnormalities in DS patients, and the differences in morbidity and mortality in DS are related to the type of congenital heart defect. Some studies have shown that microRNAs on chromosome 21 (mir-nov21) are involved in the regulation of cardiac development by regulating the expression of GATA4 and other genes, while the inactivation of the GATA4 gene in the early stage of cardiac development can lead to cardiac hypoplasia and endocardial cushion defects, which may be one of the reasons for the frequent combination of CHD in patients with DS[8,9]. In addition, aberrations on chromosome 21 have also been con rmed to affect the fusion of endocardial cushions, leading to CHD [10]. Studies have shown that AVSD, ASD and VSD are the most common CHD types in children with DS [11]. The main causes of death in children with DS and CHD are pulmonary hypertension, severe pneumonia and heart failure. In addition, some studies have found that the risk of DS has a stronger relationship with mothers [12]. For example, the risk of DS increases with the age of pregnant women. Women over 35 years old have the highest risk of giving birth to children with Down's syndrome. If the childbearing age of the older mother is 40 years old, the risk will increase to 1/110, while if the childbearing age is 45 years old, the risk will be higher [13]. The risk of DS will increase for fetuses with abnormal pregnancy histories and more pregnancies [14].
At present, the prevention of DS mainly occurs through prenatal screening during pregnancy to effectively reduce and prevent the birth of children with Down's syndrome [15]. Testing mainly includes serological screening, fetal malformation ultrasound screening, and cell-free DNA testing technology. When screening high-risk pregnant women, it is necessary to perform chorionic villus biopsy in early pregnancy or amniocentesis in middle pregnancy to avoid the birth of DS babies after the diagnosis of chromosome karyotype analysis. Serological screening is a more common screening method because it is less invasive for pregnant women, detection is convenient and quick, and the cost of the test is low. Lattice is easy to accept and is generally carried out in the clinic at home and abroad, but there are problems with this test, such as a low detection rate and high screening error rate. Radioimmunoassay (RIA), chemiluminescence immunoassay (CLIA), enzyme-linked immunosorbent assay (ELISA), colloidal gold immunochromatography (GICA), time-resolved uorescence immunoassay (TRFIA) and other detection methods have a longer detection time, more complex operation, increased sample demand, and higher cost [16][17][18], which limits the application and development to a certain extent. As a result, due to many economic, educational and cultural level reasons, many pregnant women do not participate in the pregnancy screening on time, so the birth rate of DS is still high [2,19,20]. After birth, children with DS will face the risk of repeated lung infection, heart failure, severe malnutrition and even death. At present, it is relevant to correct the heart malformation and reduce the death risk of children by surgical treatment in the early stage [21], but the vulnerability, immune function, intelligence level and cognitive development level of children with DS are far lower than those of normal children, and the overall increase in life expectancy in DS comes with a parallel increase in risk for age-related diseases, such as Alzheimer's disease, increased rate of infections, hypertension and obesity [22] and gastrointestinal disorders, which are the most common anomalies in people with DS and have a signi cant impact on their daily life [23]. Ottaviano et al. found that patients with DS are characterized by increased susceptibility to autoimmunity and respiratory tract infections that are suggestive of humoral immunity impairment [24].
The gut microbiota plays an important role in maintaining homeostasis and improving immunity [25,26]. The intestinal microbiota develops during early childhood, and this normal colonization is critical for immune system development in infants and young children, as gut dysbiosis can lead to long-term disease [25,27]. In particular, dysbiosis has been linked to the development of obesity[28], hypertension, and colon cancer [29]. In addition, children and adults with DS may experience unexplained neurodevelopmental regression [30]; however, in the current study, it was found that the intestinal ora is also closely related to autism and depression in children.
Based on this information, this study used 16S rRNA sequencing as a technical means to explore the microbial diversity and species distribution of intestinal ora in children with DS and CHD for the rst time to analyze the difference in reproductive tract ora between mothers of DS and normal healthy children, determine the key difference in intestinal ora between DS and healthy children, and provide a theoretical basis for disease-related research and treatment methods.

Overall structural changes in microbiota composition
Good's coverage for the six groups was greater than 98.0%, indicating a considerable sequencing depth for the analysis of the microbiota (Fig. 1a-c). For alpha diversity analysis, Chao and ACE estimators and the Shannon and Simpson indexes were used to assess community richness and diversity, respectively. As shown in Fig. 1a, the richness of the bacterial population (Chao1 and ACE indices) in the DSC group was signi cantly higher than that in the NC group (p < 0.01), while there was no signi cant difference between the species richness of intestinal ora in CHD and NCHD group (Fig. 1b), and there was no signi cant difference between the species richness of the reproductive tract of DSM and that of NM (Fig.  1c). For the Shannon and Simpson analyses, we can see that the Shannon index of the DSC group is signi cantly lower than that of normal group (p < 0.05), which indicated that the diversity of intestinal ora of children with DS was less than that of healthy children, while the diversity index of children with congenital heart disease was not signi cantly different from that of children without congenital heart disease, and the reproductive tract microbial species diversity of DS mothers was not signi cantly different from that of healthy children.
To measure the extent of the similarity between microbial communities, PCoA plots of unweighted and weighted UniFrac distances were generated. From the PCoA analysis, we can see that the composition of intestinal ora in children with DS is signi cantly different from that in the healthy control group (Fig. 1d). Among these algorithms, the PC1 distribution with the largest contribution rate revealed changes of 16.32%, and the PC2 distribution revealed changes of 6.5%, which showed that there was a signi cant difference between the two groups. However, the distribution of ora with CHD and NCHD was similar.
There was a partially signi cant difference between the reproductive tract ora of DSM and NM (Fig. 1e). Among these algorithms, the PC1 distribution with the largest contribution rate revealed changes of 57.1%, and the PC2 distribution revealed changes of 18.1%; that is, the total microbial composition was not similar between the two groups. We also used cluster analysis to explore the similarity between samples. From Fig. 1f, we can see that DSC group samples tended to gather together and had similar microbial composition and diversity distribution, and it can be seen that the composition and diversity of intestinal ora in NC were similar as a whole, with Bacteroides as the majority. At the same time, it shows the accuracy of the results of our sample collection. We found that the composition of the diversity of the microbial ora in the group of mothers of children with DS tended to clump together, had a similar microbial composition and had a relatively high abundance of Lactobacillus (Fig. 1g), indicating that there is a signi cant difference in the overall composition compared with the mothers of healthy children. Taxonomy-based comparisons at the phylum and genus levels From the phylum-level analysis (Fig. 2a), we could clearly see that the DSC group was mainly composed of 16.67% Bacteroides, 37.35% Firmicutes, 26.13% Proteobacteria, 50% Verrucomicrobia and 15.64% Actinobacteria. The normal group was mainly composed of 45.30% Bacteroides, 22.85% Firmicutes, 15.64% Proteobacteria, 28% Verrucomicrobia and 3.02% Actinobacteria. Therefore, we found that Bacteroides is the most different intestinal ora between children with DS and healthy children. The DSM group was mainly composed of 9.36% Bacteroides, 28.99% Firmicutes, 6.55% Proteobacteria and 29.96% Actinobacteria. The NM group was mainly composed of 13.39% Bacteroides, 25.42% Firmicutes, 6.51% Proteobacteria and 24.14% Actinobacteria. The most different intestinal ora were Firmicutes and Bacteroides.
We further analyzed the composition of intestinal ora at the genus level (Fig. 2b), and we could clearly see that the DSC group was mainly composed of 18.42% Escherichia, 14.93% Bi dobacterium and 14.66% Bacteroides. The normal group was mainly composed of 44.30% Bacteroides, 9.94% Escherichia and 2.90% Bi dobacterium. We found that the relative abundance of Escherichia in the intestines of children with DS was signi cantly higher than that of healthy children (p < 0.01) (Fig. 2c), followed by that of Bi dobacterium (Fig. 2d), and the relative abundance of Bacteroides in the intestines of children with DS was signi cantly lower than that of healthy children (p < 0.01) (Fig. 2e). Therefore, we speculated that the greater abundance of Escherichia and the lower abundance of Bacteroides in the intestines of children with DS might be related to the susceptibility of children with DS to intestinal diseases.
Through the analysis of the intestinal ora of the DSC and NC groups at the level of Metastats (Fig. 2f), we found that the main differences between the two groups were Bacteroides, Bi dobacterium, Escherichia, Clostridioides, Erysipelatoclostridium, Faecilibacterium, Haemophilus, Klebsiella, Parabacteroides and Ruminococcus. The results show that the distribution and differential ora of DS patients in the intestinal ora are different from those of healthy children and provide a theoretical basis for revealing the related disease mechanisms of DS. Through analysis of the intestinal ora of groups CHD and NCHD through Metastats (Fig. 2g), we found that the main differences between the two groups were Enterococcus and Erysipelatoclostridium, indicating that DS patients with CHD and DS patients without CHD have signi cant distribution of intestinal ora, which indicated that the relative abundance of these two intestinal bacteria was higher in children with CHD. This related discovery provides a certain theoretical basis for exploring the pathogenesis of CHD; then we analyzed the ora of DSM and NM at the genus level of Metastats (Fig. 2h), and we found the main differences in ora between the two groups were Lactobacillus, Atopobium and Corynebacterium. The ndings provide a basis for exploring the correlation between children with DS and their mothers.
The differences in the dominant members of the microbiota From 3.2, we determined the different species in the DSC and DSM groups from the relative abundance level of OTUs. To verify and further determine the more signi cant microorganisms in different groups, we also conducted LEfSe analysis. LEfSe was used to identify the speci c phylotypes related to the DSC and DSM groups. As shown in Fig. 3a-c, the main differential microbial species between the control group and the DSC groups were Bacteroides, Clostridioides, Pseudomonadales, Bi dobacterium, Streptococcus and Escherichia. The main differential microbial species between the CHD group and the NCHD groups were Enterococcus, Pseudomonadales, Bacteroides, Haemophilus, Romboutsia and Paeniclostridium. The main differential microbial species between the DSM group and the NM groups were Lactobacillus, Pseudomonas, Corynebacterium, Peptostreptococcus, Sneathia and Gaiella.

Functional gene prediction
The intestinal ora plays a certain role in the intestine and is closely related to the function of the body. Since we found that the intestinal microbes changed through 16S rRNA sequencing technology, we further analyzed and predicted the functions between different groups.
Through the L1 level KEGG analysis of the gene pathway (Fig. 4a), we found that the expression of the functional pathway of the group NC was mainly concentrated in Metabolism, Cellular Processes and Organic Systems, and the DSC and CHD groups were signi cantly lower than the normal group in terms of the Genetic Information Processing pathway, indicating that DS patients were weak in the transmission and expression of genetic information. The expression of group DSM functional pathways was mainly concentrated in Genetic Information Processing and Environmental Information Processing, and the functional pathways of group NM were signi cantly lower in Metabolism, Cellular Processes and Organic Systems. Among other groups, the difference in ora between mothers of children with DS and healthy mothers is also revealed here, which provides a reference basis for exploring the relevant mechanism of mothers' in uence on children with DS.
We further analyzed the L2 KEGG of the gene pathway (Fig. 4b) and found that group DSC and CHD signi cantly downregulated Immune System, Cell Growth and Death, Replication and Repair, Nucleotide Metabolism and Folding, Sorting and Degradation compared with the other groups. The expression of signaling molecules and interacting genes was signi cantly upregulated, indicating that the immune function of DS patients was weak, and their cell growth and nucleotide metabolism were lower than those of healthy children, which was consistent with the content reported in the literature.

Discussion
Human intestinal microbes are closely related to health, as intestinal microbes help regulate host metabolism and immune system development [31]. Numerous studies have shown that intestinal microbes in uence and are signi cantly associated with many diseases of the human body, such as intestinal in ammation, obesity, diabetes, and tumors [32,33]. An important stage of intestinal microecology colonization occurs during infancy. The colonization pattern of intestinal microbiota in infants directly affects the diversity of the microbiota, physiological development and the development of the immune system, thus exhibiting a long-term impact on the risk of long-term disease. The human intestinal microbiota is established from the time of the baby's birth, and the intestinal microbiota becomes increasingly complex as the baby grows [34,35]. As the baby grows and develops, the intestinal microbiota continues to evolve into an adult state, and the abundance of Bacteroides gradually increases[36-38]. Kamng'ona et al. found that the dominant intestinal microorganisms are Bacteroides, which is consistent with our results in this research [39]. We found that the relative abundance of Bacteroides in the intestinal tract of healthy children was the highest, accounting for 50% of the entire intestinal microbiota, thereby making it the dominant component of the microbiota. We also found that the intestinal ora of children with DS was signi cantly different from that of healthy children. The relative abundance of Escherichia in the intestine of children with DS was signi cantly higher than that of healthy children, while the relative abundance of Bacteroides was signi cantly lower than that of healthy children. In the sampling results of healthy children, the relative abundance of Bacteroides accounted for approximately half (44%) of the whole intestinal ora, while Escherichia accounted for 9.94%, which was signi cantly lower than the 18.42% of children with DS. The relative abundance of Bi dobacterium and Clostridioides in children with DS was signi cantly higher than that in healthy children, which was consistent with the results of Biagi et al. [22]. They found that the gut microbiota of persons with DS was well known to provide the host with short-chain fatty acids (SCFAs), butyrate, propionate and acetate, from fermentation of indigestible polysaccharides in the gut, and their results showed that the DS microbiota was largely dominated by the butyrate producers Clostridium cluster, propionate producing Bacteroidetes, and the acetate producer Bi dobacterium. However, they also found that persons with DS were enriched in Sutterella and reduced in Veillonelaceae, which was inconsistent with our results. The reason may be because the population they studied was adult patients with DS (19-35 years), and the human intestinal ora changed more with age. The distribution of bacteria in adults is quite different from that in childhood.
The distribution of the intestinal microbiota in infants at 6 months and 12 months of age is unstable [40].
In the later stages of childhood, chronic in ammatory diseases are caused by a balance of intestinal microbiota damage [41]. By constructing a mouse model, it was shown that after a reasonable change in the intestinal microbiota of mice, the progeny of the offspring had a reduced incidence of allergic diseases and in ammatory diseases [42]. During this period, the construction of the intestinal microbiota in infants and young children is easily affected by various factors, such as dietary intake, probiotics and dietary supplements. In a study of the intestinal microbiota composition in 12-month-old infants, infants were given different diets [43]. Compared with the infants in the other two groups, the infants in the ironforti ed cereal group had prominent differences in the bacterial composition of the gut microbiota, and the abundance of the Lactobacilli populations was signi cantly reduced. Therefore, we speculate that we can improve the diversity of the intestinal ora of children with DS to make it consistent with the composition of the intestinal ora of healthy children to reduce or slightly alleviate the internal in ammation or other disease reactions of children with DS to a certain extent and increase intestinal immunity. Zhang et al. carried out metagenomic shotgun sequencing and a metagenome-wide association study (MGWAS) of fecal, dental and salivary samples from a cohort of individuals with rheumatoid arthritis (RA) and healthy controls [44]. They found that dysbiosis was detected in the gut and oral microbiomes of RA patients, but it was partially resolved after RA treatment, and alterations in the gut, dental or saliva microbiome distinguished individuals with RA from healthy controls, were correlated with clinical measures and could be used to stratify individuals on the basis of their response to therapy. Their results establish speci c alterations in the gut and oral microbiomes in individuals with RA and suggest potential ways of using microbiome composition for prognosis and diagnosis. Khocht et al. used checkerboard DNA-DNA hybridization to explore the subgingival microbiota in DS and non-DS adults [ ! ]. They found that most microbial species were present in DS subjects at levels similar to those in non-DS subjects, except for higher proportions of Selenomonas noxia, Propionibacterium acnes, Streptococcus gordonii, Streptococcus mitis and Streptococcus oralis in DS subjects than in non-DS study subjects, higher proportions of Treponema socranskii in DS subjects than in non-DS mentally retarded subjects, and higher proportions of Streptococcus constellatus in DS subjects than in mentally normal subjects. DS adults with periodontitis had higher subgingival levels of T. socranskii than DS subjects without periodontitis. Higher subgingival proportions of S. constellatus, Fusobacterium nucleatum ssp. nucleatum, S. noxia and Prevotella nigrescens showed signi cant positive correlations, and higher proportions of Actinomyces naeslundii and Actinomyces odontolyticus showed negative correlations, with increasing mean subject attachment loss in DS adults.
In general, the microbiota of children with DS was mainly composed of Escherichia, Bi dobacterium, Clostridium and Bacteroides, which have signi cant differences in the abundance and diversity of intestinal ora compared with healthy children. Enterococcus and Erysipelatocolostridium were the main differentiating microorganisms in children with and without CHD, and the relative abundance of Lactobacillus in the reproductive tract of mothers with DS was signi cantly higher than that of mothers with healthy children, which may suggest potential ways of using microbiome composition for prognosis and diagnosis.

Study design
The experiment was divided into six groups: 30

Bioinformatics and statistical analysis
Sequences with ≥ 97% similarity were assigned to the same operational taxonomic units (OTUs). QIIME (version 1.7.0) was used for the analysis of alpha diversity, including observed species, Chao1, Shannon, Simpson, and ACE. Beta diversity analysis was used to evaluate differences in samples in species complexity. Beta diversity on both weighted and unweighted UniFrac was calculated by QIIME software (Version 1.7.0). Cluster analysis was preceded by principal component analysis, which was used to reduce the dimension of the original variables using the FactoMineR package and ggplot2 package in R software (Version 2.15.3). Principal Components Analysis (PCoA) was performed to obtain principal coordinates and visualize complex, multidimensional data. A weighted UniFrac distance matrix among samples obtained previously was transformed to a new set of orthogonal axes, by which the maximum variation factor was demonstrated by the rst principal coordinate, the second maximum variation factor was demonstrated by the second principal coordinate, and so on. Graphics drawing was calculated by using the ggplot2 package in R software (Version 2.15.3).
Data shown are the mean ± SD. Data between 2 groups were analyzed by the unpaired t-test (Prism 6.0; GraphPad Software) if the data were in a Gaussian distribution and had equal variance, by unpaired t-test with Welch's correction (Prism 6.0; GraphPad Software) if the data were in Gaussian distribution but with unequal variance, or by nonparametric test (Mann-Whitney U test, Prism 6.0; GraphPad Software) if the data were not normally distributed. Data among more than two groups were analyzed by one-way ANOVA followed by Dunnett's multiple comparisons (Prism 6.0; GraphPad Software) if the data were Gaussian distributed and had equal variance or analyzed by Kruskal-Wallis followed by Dunn's multiple comparisons (Prism 6.0; GraphPad Software) if the data were not normally distributed. The Gaussian distribution of data was analyzed by using the D'Agostino-Pearson omnibus normality test (Prism 6.0; GraphPad Software) and Kolmogorov-Smirnov test (Prism 6.0; GraphPad Software). Differences with p < 0.05 were considered signi cant.

Conclusions
In this study, we con rmed the microbiomes structural changes in the child fecal and mother vaginal ora related to congenital and Down's syndrome. The results showed that signi cant differences in the abundance and diversity of intestinal ora compared with healthy children. Especially, the abundance of Enterococcus and Erysipelatoclostridium in the intestine of children with CHD was signi cantly higher than that of children without CHD, and the relative abundance of Lactobacillus in the reproductive tract of mothers with DS was signi cantly higher than that of mothers with healthy children, which may suggest potential ways of using microbiome composition for prognosis and diagnosis. Using functional analysis, it was discovered that Down's syndrome patients' immune systems were signi cantly downregulated and their cell growth and nucleotide metabolism were lower than healthy children. Our analysis shows that the ecosystem associated with childhood congenital heart disease affects the selection of bacterial communities in the native microbiota, and we focus on speci c bacteria and their relevance to disease. Declarations method with arithmetic mean (UPGMA) clustering tree to study the similarity between different samples based on Bray-Curtis distance. The data were analyzed by one-way ANOVA (*p <0.05; **p < 0.01). DSC: Down syndrome, NC: normal, CHD: Child with congenital heart disease in DS; NCHD: child without congenital heart disease in DS; DSM: the mother of DS; NM: the mother of healthy child. n=25-40/group.  Major differential microbial species a-c) Taxonomic cladogram obtained from LEfSe in the DSC and NC, CHD and NCHD, DSM and NM groups. Biomarker taxa are highlighted with colored circles and shaded areas. Each circle's diameter re ects the abundance of those taxa in the community. Figure 4