Anesthetics change the oral microbial composition of children and increase the abundance of the genus Haemophilus

Introduction

Using anesthetics in treating children’s oral diseases has gradually developed into a relatively mature behavior management model (1). Anesthesia significantly improves the effect and quality of oral therapy and overcomes children’s dental fear and anxiety when facing oral treatment (2). In clinical practice, inhalation and intravenous anesthesia are often used for general anesthesia, while lidocaine is used for local anesthesia. However, whether these anesthesia methods affect the normal microbiome of children’s oral cavities is rarely reported.

Maintaining a healthy oral cavity in children is crucially dependent on the balance of their average microbial balance (3,4), and breaking this balance may cause various oral and systemic diseases. For example, specific microbiota components, such as Streptococcus mutans, Candida albicans, and Streptococcus sobrinus, are vital etiological factors in the initiation and progression of caries in the oral cavity (5-7). Oral microbiome characteristics in children with early childhood caries differed from those without (8). After dental caries treatment in children, there is a change in the diversity of their oral microbiota (9,10). Pathogenic oral bacteria can also participate in the pathogenesis of oral squamous cell carcinoma by promoting cell proliferation and angiogenesis and facilitating invasion and metastasis (11). Oral microbes involved in periodontitis can enter the bloodstream through inflamed periodontal tissues and infiltrate the systemic circulation, causing bacteremia and the formation of atherosclerotic plaques (12,13). Additionally, certain microbes have been reported to be an advantage to oral health. For instance, supplementation of Lacticaseibacillus rhamnosus, Lacticaseibacillus reuteri, and Lacticaseibacillus plantarum can prevent and improve clinical outcomes in caries and periodontal treatments (14,15). Adolescents who receive Lactobacillus treatment achieve improvements in terms of plaque (16). Consequently, maintaining the balance of oral microbes is essential for keeping children healthy.

Anesthesia can affect microbial homeostasis. In recent years, several studies have focused on the effect of anesthesia on the oral microbiome. For example, some local anesthetics can effectively inhibit bacterial growth in the oral cavity (17,18). A clinical study revealed that applying lidocaine spray on the buccal mucosa for 3 minutes causes a 60–95% biofilm reduction (19). Moreover, some studies have demonstrated that the abundance of caries-pathogenic bacteria is reduced after dental general anesthesia, and children’s oral health-related quality of life has improved (20,21). However, the effects of different anesthesia methods on children’s oral microbial community have not been reported.

In this study, 16S rDNA sequencing was used to investigate the effects of different anesthesia techniques on children’s oral microbiota. Our results demonstrated that anesthesia altered children’s oral microbial composition, leading to a homeostatic disruption of the latter, potentially impacting the children’s oral health. We present this article in accordance with the STROBE reporting checklist (available at https://tp.amegroups.com/article/view/10.21037/tp-24-336/rc).

Methods

Study design and subject selection

All participants and at least one of their parents or legal guardians gave their written informed consent to use their saliva samples for microbiome research. This consent form and study protocol were approved by the ethics committee of the Jiangxi Provincial Children’s Hospital (No. JXSETYY-YXKY-20220205). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

A cohort of 60 clinical individuals was analyzed. Among the patients with oral cysts who visited the Department of Stomatology of the Jiangxi Provincial Children’s Hospital, fifteen who did not receive anesthesia during the surgery were randomly selected and assigned to the control group [non-anesthesia (Noa) group]. Patients who received anesthesia during the surgery were collected and divided into the lidocaine (n=15), sevoflurane (n=15), and the intravenous injection-inhalation (intra-inhalation, n=15) groups according to their anesthesia method. None of the participants used antibiotics in the three months preceding sample collection and had no other type of oral disease. Subjects were instructed not to eat, smoke, chew gum, or drink 30 min before saliva collection. Before the clinical examination, each patient’s information (including sex, age, height, and weight) was collected via interviews and questionnaires.

Subjects in the lidocaine group were treated with 1% lidocaine for local anesthesia. Inhalation of 8% sevoflurane was used for anesthesia induction, and the continuous inhalation of 3–5% sevoflurane was used for anesthesia maintenance in the sevoflurane group. In the intra-inhalation group, subjects inhaled 8% sevoflurane for anesthesia induction by combining with inhalational 2–3% sevoflurane and compound alfentanil with an intravenous injection (0.5–1 µg/kg/min) for anesthesia maintenance.

Sample collection and DNA extraction

Subjects rinsed their oral cavities with drinking water to clean out any debris. Saliva samples from subjects were held in the mouth for over one minute and then spat into the centrifuge tube. A 2–5 mL saliva sample in each subject was collected and immediately kept in liquid nitrogen and stored −80 ℃ until use. Per the manufacturer’s protocols, microbial DNA from the saliva samples was extracted using the Bacterial DNA Extraction Mini Kit (Mabio, China).

16S rDNA sequencing

The 16S V4 region of bacteria was amplified by polymerase chain reaction (PCR, 94 ℃ for 5 min, followed by 30 cycles at 94 ℃ for 30 s, 52 ℃ for 30 s, and 72 ℃ for 30 s, and a final extension at 72 ℃ for 10 min) using primers (forward: 5'-GTGCCAGCMGCCGCGGTAA-3', reverse: 5'-GGACTACHVGGGTWTCTAAT-3') and TaKaRa Premix Taq^® Version 2.0 (TaKaRa Biotechnology Co., Dalian, China). Amplicons were extracted from 1% agarose gels and purified using the EZNA^® Gel Extraction Kit (Omega, USA). The DNA library was constructed as outlined in NEBNext^® Ultra™ II DNA Library Prep Kit for Illumina^® (New England Biolabs, USA). Then, the Illumina Nova 6000 platform (Guangdong Magigene Biotechnology Co., Ltd. Guangzhou, China) was paired-end sequenced (2×250) for amplicon.

Operational taxonomic units (OTU) clustering and taxonomy assignment

The collected paired-end raw reads were filtered for quality to generate paired-end clean reads using the fastp (an ultra-fast all-in-one FASTQ preprocessor, v0.14.1), in which primer sequences were removed from assembled reads using Cutadapt software (v1.14). The USEARCH-fastq_mergepairs (v10.0.240) was performed to obtain the raw tags based on the overlapping parts of clean read pairs, which was further filtered to obtain quality tags using the fastp. UPARSE was used to generate OTUs. The taxonomy assignment was performed using the SILVA database as a reference with the assistance of the USEARCH-sintax program (v10.0.240). OTUs and tags annotated as chloroplasts and mitochondria and cannot be annotated to the kingdom level were removed. Then, the OTU table with comprehensive OTU information was generated.

Community composition analysis of oral microbiota

R software (v3.5.1) was used for coherent and endemic species statistics, community composition analysis, and species abundance cluster analyses. Based on the phylogenetic relationship and relative OTU abundance in samples, KRONA software was used to visualize the annotation results of species in each sample. We also utilized the GraPhlAn software to acquire the circle plot of OTU annotation in every sample. Phylogenetics trees were generated and visualized using FastTree software and the ggtree package.

Alpha and beta diversity analyses

Various diversity indices (Pielou, Chao1, and Simpson) were calculated using USEARCH -alpha_div (v10.0.240) to analyze the alpha diversity. Student’s t-test and the Kruskal–Wallis signed-rank test were used to compare the two groups and multiple groups, respectively. In the beta diversity analysis, the vegan package in R software, the Bray-Curtis algorithm, and the Eucliden algorithm was used for the principal coordinate analysis (PCoA). In addition, the unweighted pair-group method with arithmetic mean (UPGMA) was performed for sample clustering.

Species difference analysis

Distinguishment of the oral microbiota specific to the different anesthesia techniques was identified with the linear discriminant analysis (LDA) effect size (LEfSe) method. The non-parametric factorial Kruskal-Wallis sum-rank test was used to identify microbes with significant differences, and the Wilcoxon signed-rank test was used to determine differences between the two groups. The effect of the abundance of each component on the differential impact was estimated by the LDA score (LDA score >3). Differential analyses in species between the two groups were performed using the Wilcox signed-rank test, and the false discovery rate (FDR) was used for adjustment. The Kruskal-Wallis signed-rank test was used to perform multiple comparisons. A P value of <0.05 was considered statistically significant.

Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis

OTU table was normalized by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) to eliminate the effect of 16S marker genes on the species genome. Subsequently, each OTU was paired into the functional categories in the KEGG.

Results

Anesthesia technologies changed the relative abundance of children’s oral microbiota

We recruited 60 participants who were divided into the following four groups (n=15): the Noa, lidocaine, sevoflurane, and intra-inhalation groups. The essential characteristics of patients in the four groups are outlined in Table S1. Saliva samples from subjects were collected for 16S rDNA sequencing. The analysis of microenvironmental factors revealed that the correlation between individual characteristics (sex, age, height, and weight) and oral microbiota exhibited no significant differences (Tables S2,S3), indicating that these particular factors had no impact on our data.

A total of 1,316 OTUs were identified in overall samples, and 75,275 reads per sample were obtained on average. As shown in Figure 1A, genera in the four groups overlapped, and 137 genera were shared among them. Based on the OTUs table, the relative abundance of oral microbiota at the genus level was conducted. It showed that the genera Neisseria, Prevotella-7, Streptococcus, and Veillonella had a higher abundance in the four groups (Figure 1B). Neisseria was the most abundant genus among the three anesthesia groups, whereas the Noa group had the highest proportion of Prevotella-7 (Figure 1B). Despite Prevotella-7 being the most predominant genus in the Noa group (20.75%), its abundance was reduced by anesthesia in the sevoflurane (9.937%), intra-inhalation (11.89%), and lidocaine (13.56%) groups. Compared with the Noa group, the abundance of Streptococcus and Veillonella in the three anesthesia groups was increased and reduced, respectively (Figure 1B). Notably, the abundance of the genus Haemophilus was increased in the sevoflurane (1.660%), intra-inhalation (1.812%), and lidocaine (1.343%) groups compared with the Noa group (0.8228%). The heatmap of community structure also confirmed that the genus Haemophilus was enriched in the three anesthesia groups (Figure 1C). Taken together, the application of anesthesia changed oral microbiota abundance.

Figure 1 Oral microbial composition in the Noa, intra-inhalation, sevoflurane, and lidocaine groups. (A) Venn diagram showing the number of common and unique genera among the intra-inhalation, lidocaine, Noa, and sevoflurane groups. (B) Histogram of oral microbial composition at the genus level. The abscissa shows the groups, and the ordinate shows the relative abundance of microbes. (C) Heatmap indicating the oral microbial abundance at the genera level in the four groups. Noa, non-anesthesia; intra-inhalation, intravenous injection-inhalation.

Alpha and beta diversity analyses revealed the differences between the Noa and anesthesia groups

We performed alpha and beta analyses to determine the differences in taxonomic composition and oral microbial diversity among the four groups. First, we calculated the indices of alpha diversity among the four groups at the OTU level. We observed that the Simpson index in the Noa group was significantly higher than that in the sevoflurane and intra-inhalation groups (Figure 2A) but showed no significant differences between the Noa and lidocaine groups. Subsequently, Bray-Curtis algorithm-based PCoA was first performed to compare the beta diversity between groups at the OTU level. We found that the oral microbial community of Noa samples was close to that of lidocaine samples; however, it was separate from the intra-inhalation and sevoflurane samples (Figure 2B). The Bray-Curtis algorithm-based multi response permutation procedure (MRPP) analysis was used to calculate differences in community composition between groups, and the results of the MRPP analysis showed significant differences between the Noa group and the other three anesthesia groups (Table 1). In addition, the smaller the expected delta, the smaller the difference among groups, and the comparison of lidocaine vs. Noa had the smallest expected delta compared with comparisons in other groups, indicating that the difference in community composition between the lidocaine and the Noa groups was mild. These findings revealed that the diversity of oral microbiota in the Noa group was significantly different from those in the sevoflurane and intra-inhalation groups, and the difference between the Noa and lidocaine treatments was the smallest.

Figure 2 Alpha and beta diversity analyses for oral microbiota in children. (A) Box plots showing the Simpson index in the comparison of Noa vs. lidocaine, Noa vs. sevoflurane, and Noa vs. intravenous injection-inhalation (intra-inhalation). P values of less than 0.05 indicate significant differences between groups. (B) The PCoA analysis calculated by the Bray-Curtis algorithm is performed in the comparison of Noa vs. lidocaine (left), Noa vs. sevoflurane (middle), and Noa vs. intra-inhalation (right). Noa, non-anesthesia; PCoA, principal coordinate analysis.

Differential analyses of oral microbiota

To further identify vital taxonomic differences among the Noa, lidocaine, sevoflurane, and intra-inhalation groups, we conducted LEfSe analysis to explore differential microbes between groups (LDA >3, P<0.05). In the comparison of Noa vs. lidocaine and Noa vs. intra-inhalation, there were 16 and 37 significantly different microbes, respectively (Figure 3A and Figure S1A). The cladogram revealed evolutionary relationships of species between groups, in which Gammaproteobacteria was present in the lidocaine group and Campylobacteria and Spirochaetia occupied the oral cavity in the intra-inhalation group (Figure 3B and Figure S1B). Notably, there were 52 differential microbes between the Noa and the sevoflurane groups (Figure 3C), and Bacteroidia, Gammaproteobacteria, and Deinococci appeared in the sevoflurane group from phylum to species (Figure 3D). We noticed that Haemophilus was enriched in the lidocaine and sevoflurane groups (Figure 3C), consistent with the result of Figure 1C. To sum up, the three anesthesia groups had distinct microbe signatures.

Figure 3 LEfSe analysis for children’s oral microbiota between groups. (A) The LEfSe analysis indicates the distribution of LDA scores of differential microbes between the Noa and lidocaine groups. The red and green entries represent differential microbes in the Noa and sevoflurane groups, respectively. (B) Cladogram of differential microbes between the Noa and lidocaine groups from phylum (inside circle) to species (outside circle). (C) LDA scores of differential microbes in the LEfSe analysis between the Noa and sevoflurane groups are revealed. The red and green entries represent differential microbes in the Noa and sevoflurane groups, respectively. (D) The cladogram depicts the differential microbes between the Noa and sevoflurane groups from phylum (inside circle) to species (outside circle). LDA, linear discriminant analysis; Noa, non-anesthesia; LEfSe, linear discriminant analysis effect size.

The differential distribution of oral microbiota between the four groups was also analyzed using the Kruskal-Wallis test (Figure 4A), and the results revealed that species of Prevotella nanceiensis and Campylobacter showae exhibited significant differences (Table S4). When comparing the differential distribution of oral microbiota in the Noa group against the other three groups at the genus level using the Wilcoxon rank-sum test, only the comparison between the Noa and sevoflurane groups had statistically significant differences in taxa (Table 2). The sevoflurane group significantly enriched the genera Streptococcus, Haemophilus, and Aggregatibacter (Figure 4B). To determine whether prolonged sevoflurane inhalation specifically altered the oral microbiota, we also compared the intra-inhalation group with the sevoflurane group; however, there were no significant differences in bacteria (Table S5). These findings suggested the induction of sevoflurane inhalation to Haemophilus.

Figure 4 Differential oral microbes between groups. The differential oral microbes at the species level among the four groups (A) are analyzed, as well as at the genus level between the Noa and sevoflurane groups (B). The Y-axis represents the microbes, and the X-axis indicates the average abundance of microbes in groups. Noa, non-anesthesia; intra-inhalation, intravenous injection-inhalation.

Oral microbiota responded to anesthesia by regulating multiple metabolic pathways

Based on the OTU table, we further predicted the abundances of functional categories of OTU in the KEGG database using PICRUSt. The result of pathways in comparing the four groups is shown in Figure S2. Three metabolic pathways exhibited significant differences, including D-glutamine and D-glutamate metabolism, one carbon pool by folate, and the biosynthesis of valine, leucine, and isoleucine. We also compared the KEGG pathways in the Noa group with those in the other three anesthesia groups. Thirteen pathways differed significantly between the Noa and lidocaine groups, and these pathways were associated with amino acid metabolism, lipid metabolism, biofilm formation-Vibrio cholerae, insulin signaling pathway, and olfactory transduction (Figure 5A). In the comparison of Noa vs. intra-inhalation and Noa vs. sevoflurane, there were 20 pathways with significant differences (Figure 5B,5C). In addition to amino acid metabolism and energy metabolism, these differential pathways were also associated with the metabolism of cofactors and vitamins (such as folate biosynthesis, thiamine metabolism, one carbon pool by folate, nicotinate and nicotinamide metabolism, pantothenate and CoA biosynthesis, vitamin B6 metabolism, and riboflavin metabolism) and streptomycin biosynthesis (Figure 5B,5C). These results indicated that anesthesia may affect the stability of oral microbiota through amino acid metabolism, energy metabolism, biofilm formation, cofactor and vitamin metabolism, and antibiotic synthesis.

Figure 5 KEGG analysis between groups. KEGG analysis reveals significant differences in metabolic pathways in the lidocaine vs. Noa (A), intra-inhalation vs. Noa (B), and Noa vs. sevoflurane (C). The Y-axis and X-axis represent the KEGG enrichment pathways and the average enrichment levels of samples in different KEGG pathways, respectively. Noa, non-anesthesia; intra-inhalation, intravenous injection-inhalation; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Discussion

The community balance of oral microbiota is required for children’s health. Oral microbiota dysregulation can result in multiple dental diseases and systematic diseases (13). We utilized the 16S rDNA sequencing to investigate the effect of different anesthesia methods on children’s oral microbiota and found that anesthesia changed the distribution of children’s oral microbiota. This study provides theoretical guidance for selecting anesthesia methods based on oral microbial homeostasis, which contributes to maintaining children’s oral health.

The composition of oral microbiota in children differed between the Noa and the anesthesia groups. According to a previous study, Streptococcus is the most abundant genus in most adolescents, and it has an average relative abundance of 22.3%, followed by Prevotella, Haemophilus, Neisseria, and Veillonella (22). Similarly, the oral microbial composition in healthy individuals and patients with periodontitis indicates a predominance of Streptococcus, Prevotella, Haemophilus, and Veillonella in saliva samples (23). In our study, Neisseria, Prevotella-7, Streptococcus, and Veillonella had a higher abundance in the Noa, sevoflurane, intra-inhalation, and lidocaine groups. Compared with the Noa group, the abundance of the Prevotella-7 and Veillonella genera was reduced in the three anesthesia groups, whereas the abundance of Streptococcus and Haemophilus was increased. Prevotella-7 in oral microbiota exhibits heterogeneous changes in different diseases. Previous study demonstrated that the relative abundance of Prevotella-7 is reduced in the oral saliva of patients with chronic kidney disease (24), suggesting that Prevotella-7 may be crucial for oral heathy. Streptococcus plays an essential role in the assembly of the oral microbiota. Different species of Streptococcus can cause various diseases, such as endocarditis (25) and periodontitis (26). Our results showed that the relative abundance of the genus Streptococcus was elevated in the sevoflurane and intra-inhalation groups compared with the Noa and lidocaine groups, suggesting the sevoflurane induction in the genus Streptococcus. A significant health threat to children, Haemophilus can cause multiple diseases, such as meningitis (27) and acute bacterial paranasal sinusitis (28). Notably, applying sevoflurane anesthesia significantly increased the abundance of Haemophilus relative to the Noa group. The oral microbiome in the sevoflurane group indeed differed from that in the Noa group. Sevoflurane inhalation has been proven to change the gut microbiota composition (29), and its results show that the abundance of Firmicutes, Proteobacteria, Clostridia, Clostridiales, and Lachnospiraceae is significantly increased in the experimental group. The composition of the oral microbial community is very different from that of the gut microbial community. Further investigation is required to explore the effects of sevoflurane on oral microbiota.

The oral microbial community did not differ significantly between the Noa and lidocaine groups in our findings. Lidocaine has been widely used as a local anesthetic by needle infiltration, a technique that can assure efficient drug delivery by overcoming the oral mucosal barrier (30,31). The oral microbiota mainly attaches to the surface of the oral mucosa, which may be one of the reasons why lidocaine has mild effects on the oral microbiota. We also found that the samples in the sevoflurane and intra-inhalation groups differed significantly from those in the Noa group regarding oral microbiota composition. Anesthetics can impact hemodynamic stability, oxygen supply, and electrolyte balance (32). Mechanically, the partial pressure of oxygen in arterial blood (PaO₂) was 2–4 times that of the physiological state, and oxygen saturation (SaO₂) was >98% following general anesthesia, both of which led to a hyperoxic environment that was not conducive for bacterial growth.

In summary, this study shows that the combination of intravenous, inhaled, and sevoflurane anesthesia significantly changed the oral microbiota, and sevoflurane anesthesia had more excellent effects on oral microbiota than the other two. However, this study has some limitations. First, in this experiment, we collected saliva samples for investigation. Saliva samples are considered easily accessible and can be used to study a variety of oral diseases as well as some systemic diseases. However, representative tissue specimens should be collected to observe the effects of anesthesia on different tissues in the oral cavity. For example, gum plaque can be used to assess the microbial community on the tooth surface. Submucosal plaques in peri-implant and subgingival plaques contribute to exploring the pathogenesis of peri-implant mucositis and periodontal disease, respectively. The root canal plaque provides information about root canal infection or periapical infection. Oral mucosal specimens facilitate the study of mucosal diseases. Second, because of the small number of children receiving intravenous anesthesia, this experiment did not include their data. Third, the sample size in this study is relatively small, which may affect the statistical power and generalizability of the findings. Fourthly, the differential distribution of oral microbiota caused by varying dietary or hygiene habits may affect the homogeneity of data across different subjects. Fifth, our study population consists entirely of Chinese individuals, which may limit the generalizability of the results to other ethnic groups in different countries. In addition, the differential analysis between oral and intestinal microbiota under different anesthesia techniques should be investigated. The specific interference factors of different anesthesia techniques can also be included as individual factors in the list of analysis criteria.

Conclusions

We revealed the effects of different anesthesia techniques on children’s oral microbiota using 16S rDNA sequencing. Lidocaine had a mild impact on children’s oral microbiota, while sevoflurane inhalation and compound anesthesia significantly disrupted the oral microbial balance. These results suggest that anesthesia may affect children’s oral health.

Acknowledgments

We thank the Central Laboratory of Jiangxi Provincial Children’s Hospital for supporting this project.

Funding: This work was supported by the Jiangxi Province Natural Science Foundation Project (No. 20232BAB206125).

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tp.amegroups.com/article/view/10.21037/tp-24-336/rc

Data Sharing Statement: Available at https://tp.amegroups.com/article/view/10.21037/tp-24-336/dss

Peer Review File: Available at https://tp.amegroups.com/article/view/10.21037/tp-24-336/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-24-336/coif). All authors report that this work was supported by the Jiangxi Province Natural Science Foundation Project (No. 20232BAB206125). The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study protocol received approval from the ethics committee of the Jiangxi Provincial Children’s Hospital (No. JXSETYY-YXKY-20220205). All participants and at least one of their parents or legal guardians signed an informed consent form to use their saliva samples for microbiome research. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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