Structural and functional changes in the oral microbiome of the patients with craniofacial microsomia

doi:10.21203/rs.3.rs-4673616/v1

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Structural and functional changes in the oral microbiome of the patients with craniofacial microsomia

https://doi.org/10.21203/rs.3.rs-4673616/v1

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Background

Craniofacial microsomia (CFM), the second most common congenital craniofacial deformity, has manifold clinical manifestations and treatments that may interact with oral bacteria dysbiosis (OBD). However, studies exploring the relationship between CFM and OBD are scanty.

Methods

Saliva samples of 20 patients with CFM and 24 controls were collected, and oral microflora and gene function annotation were compared using 16S ribosomal RNA and metagenomics. The correlation between clinical phenotypes of CFM and microbiota community structure was also evaluated.

Results

The oral microflora of CFM patients exhibited higher richness and evenness. The dominant genera in CFM were mainly pathogenic and included Actinomyces, Fusobacterium, and Prevotella. The severity of CFM was significantly positively correlated with the abundance of Neisseria and Porphyromonas. The upregulated pathways were primarily enriched in biotin and amino acid metabolism, including Tryptophan metabolism, which positively correlated with the abundance of Neisseria.

Conclusion

This study suggests for the first time that patients with CFM exhibit distinct oral bacterial dysbiosis, characterized by an elevated prevalence of opportunistic pathogens and upregulated pathways associated with oral and systemic health. These findings provide evidence for corresponding preventions and therapeutic interventions of CFM.

craniofacial microsomia

microflora

oral cavity

16S rRNA

metagenomics

During embryogenesis, the craniomaxillofacial region is developed from frontonasal prominence, maxillary prominence, mandibular prominence, median nasal prominence, and lateral nasal prominence. Disturbances in the development of the first and second pharyngeal arches during the first 6 weeks of the embryonic stage may lead to a series of deformations and craniofacial microsomia (CFM). CFM, the second most common congenital craniofacial malformation after cleft lip and palate, manifests as mandible, maxilla, ear, facial nerve, and muscle underdevelopment. CFM is characterized by dental anomalies resulting from first pharyngeal arch dysplasia ^1,2,3.

Children born with craniofacial deformity are susceptible to poor oral health ⁴, often caused by oral structural deficiencies (OSD). The deformed oral structure may result in poor oral hygiene and disorderly oral microenvironment homeostasis, further inhibiting alveolar bone homeostasis, masseter dysfunction, delayed development of permanent teeth, and even the disruption of orthodontic and orthognathic treatment planning ^{5, 6, 7, 8}. In CFM patients, OSD includes abnormal maxilla and mandible, dental agenesis, delayed dental development, tooth size anomalies, abnormal tooth morphology, and enamel defects ⁹. Meanwhile, obstructive sleep apnea syndrome (OSAS), one of the manifestations of CFM, and typical treatments of CFM, including mandibular distraction osteogenesis, orthodontics, or orthognathic surgery, have been associated with oral bacteria dysbiosis (OBD). However, the oral health status of patients with CFM remains to be fully elucidated.

Oral microenvironment homeostasis comprises oral anatomy, microbiota, mucosal immune system, epithelial barrier, and other factors. All the elements interact with each other in a complex, diverse, and dynamic way. The oral microbiota, an essential part of oral homeostasis, is the second largest microbiota in humans and encompasses bacteria, fungi, viruses, and some low-abundance microbiota. Among these, bacteria constitute the main component of the oral microbiota because of their rich types and quantities. Various endogenous and exogenous factors, such as anatomical structure, oral hygiene habits, and OSAS, interact with the oral bacteria. Then, the interaction and balance of oral bacteria further affect oral health and even systemic diseases. Therefore, change in the abundance, structure, and function of oral bacteria is a vital evaluation indicator of oral health status ^{10, 11,12, 13}.

Multiple clinical manifestations and treatments have been shown to influence and interact with oral health in patients with CFM. Therefore, exploring the potential structural and functional changes in the oral microbiota of CFM patients is imperative to establish a basis for the inclusion of oral hygiene therapies in future treatment regimens for CFM. However, there is currently no comparative analysis of the oral flora between CFM patients and normal controls. Thus, the present study sought to compare the characteristics and changes in oral bacteria of CFM patients using 16S ribosomal RNA (rRNA) and evaluate potential corresponding functional molecular change using metagenomics. Combined multi-omics approaches were utilized to preliminarily characterize the oral microbiota of CFM and assess whether changes in the microbiota of CFM patients mediate functional changes.

Subject selection

CFM patients were recruited from Maxillo-facial Surgery Center, XXXXXX, from November 2022 to April 2023. Healthy controls diagnosed with hypertrophic scarring were recruited from the Department of Scar & Wound Treatment of Plastic Surgery Hospital from October 2022 to April 2023.

Inclusion criteria were as follows: (1) patients diagnosed with CFM and classified according to the Pruzansky-Kaban Classification and healthy controls diagnosed with hypertrophic scarring; (2) aged 6–12 years old (the mixed dentition stage); (3) parents or guardians signed the informed consent form.

Exclusion criteria were as follows: (1) no other systemic diseases reported except for CFM or hypertrophic scarring; (2) those who had not taken probiotics, fluoride, or antibiotics for at least 3 months; (3) those who had not received periodontal and/or OSAS treatment in the last 1 month; (4) particular dietary preference such as vegetarianism; (5) pet feeding; (6) those without discomfort or symptoms on the day of sampling; (7) females who were not in the physiological period.

All Subjects understood the nature of the experiment and provided informed consent before participating in the study. This study was approved by the ethical review board of the Plastic Surgery Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College (approval no. 2021 − 199) on Month 12, 2021. All methods were performed in accordance with the relevant guidelines and regulations.

Sample Collection

Non-stimulated saliva samples were collected from CFM patients and healthy controls. All participants were instructed not to brush their teeth for 24 hours and eat or drink for 1 hour before sampling. After rinsing their mouths with 20 ml of physiological saline for 1 minute, eight sterile cotton swabs were put into the participant’s mouth for 2 minutes to absorb saliva and then into the 5 ml sterilized Cryogenic Storage Tube. The tubes were frozen with liquid nitrogen and stored in the refrigerator at -80℃.

The demographic data of each participant were recorded, including age, gender, and body mass index (BMI). All patients underwent sleep monitoring with respiratory polysomnography (RP), and the obstructive apnea-hypopnea index (OAHI) and blood oxygen saturation (SpO₂) levels were recorded.

16S rRNA Diversity Sequencing

DNA extraction and amplification

Genomic DNA was extracted from saliva samples using the MagPure Soil DNA KF Kit and DNA concentrations and integrity were measured using the NanoDrop 2000 (Thermo Fisher Scientific, USA). Polymerase chain reaction (PCR) amplification of bacterial 16S rRNA genes was performed using barcoded primers and Takara Ex Taq (Takara) using extracted DNA stored in a -20°C refrigerator. V3-V4 regions of 16S rRNA genes were amplified using primers 343F (5’-TACGGRAGGCAGCAG-3’) and 798R (5’-AGGGTATCTAATCCT-3’) ¹⁴.

Library Construction and Sequencing

PCR products were purified with AMPure XP beads (Agencourt) and re-amplified for quality assessment using agarose gel electrophoresis. AMPure XP beads were used to purify the final amplicon, and the Qubit dsDNA Assay Kit (Thermo Fisher Scientific, USA) was used to measure the DNA concentration of samples for sequencing using an Illumina NovaSeq 6000 platform with 250 base pairs (bp) paired-end reads (Illumina Inc.; OE Biotech Company, Shanghai, China).

Bioinformatic Analysis

Sequencing and data processing were performed by OE Biotech Co., Ltd. (Shanghai, China). Raw sequencing data were in the FASTQ format. Preprocessing was then carried out using Cutadapt software to detect and remove adapters from the paired-end reads. DADA2 ³ with QIIME2 ⁴ (2020.11) default parameters were used to filter low-quality reads, denoise, merge, detect, and cut off the chimera reads after trimming paired-end reads. The QIIME2 package was used to select the representative read of each amplicon sequence variant and output the abundance table. With default parameters, a q2-feature classifier was used to classify all representative reads against the Silva database (Version 138).

A MagPure Soil DNA KF Kit was used to isolate total DNA from the sample. An agarose gel electrophoresis and NanoDrop2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) were used to measure DNA concentration and integrity. DNA was fragmented using an S220 Focused-ultrasonicator (Covaris, USA) and cleansed using Agencourt AMPure XP beads (Beckman Coulter Co., USA). Subsequently, libraries were constructed using the TruSeq Nano DNA LT Sample Preparation Kit (Illumina, San Diego, CA, USA) following the manufacturer’s instructions. The Metagenome was sequenced and analyzed by OE Biotech Co., Ltd. (Shanghai, China).

Meanwhile,150 bp paired-end reads were generated from the libraries on an Illumina Novaseq 6000 platform. Sequences in the FastQ file were trimmed and filtered using Fastp (v 0.20.1) ¹⁵, low-quality primary groups were filtered out, and reads containing N bases were removed. The host pollution was controlled by aligning the post-filtered pair-end reads against the human genome using bowtie2 (v 2.2.9) and discarding the aligned reads. MEGAHIT (v 1.1.2) was used for genome assembly after obtaining valid reads ^16,17. Gaps inside the scaffold were used to break the scaffold into new contigs (Scaftigs), and new Scaftigs with a length > 500 bp were retained. Using Prodigal (v 2.6.3), open reading frames (ORFs) were predicted on assembled scaffolds and translated into amino acid sequences ¹⁸. CDHIT (v 4.5.7) was used to construct non-redundant gene sets from all predicted genes. The clustering parameters had a 95% identity and 90% coverage. The longest gene represented each gene set. Then, clean reads of each sample were aligned against the non-redundant gene set (95% identity) using bowtie2 (v 2.2.9), and the abundant information of the gene in the corresponding gene set was analyzed. The species’ taxonomy was determined using the Non Redundant Protein (NR) database, and gene abundance was used to calculate the abundance of the species. Abundance statistics were performed at Domain, Kingdom, Phylum, Class, Order, Family, Genus, and Species levels to construct abundance profiles on the corresponding taxonomy level. The amino acid sequence representing the gene set was compared and annotated using DIAMOND (v 0.9.7) with NR, Kyoto Encyclopaedia of Genes and Genomes (KEGG), eggNOG, SWISS-PROT, and GO databases at an e-value of 1e-5 ^19,20,21. Gene sets were compared with the CAZy database using hmmscan (v 3.1) to obtain the carbohydrate-active enzyme corresponding to the gene. The carbohydrate activity was calculated by summing the gene abundances corresponding to the carbohydrate-active enzyme abundance ²².

Statistical Analysis

Normally distributed data were compared using the independent samples t-test; otherwise, the Wilcoxon rank-sum test was adopted. In 16S rRNA sequencing, QIIME2 software was used to analyze alpha and beta diversity. The Shannon index was used to estimate microbial diversity in samples ^{23, 24}. Principal coordinate analysis (PCoA) was performed to estimate beta diversity using an unweighted Unifrac distance matrix. A T-test was used to analyze the differences in diversity and taxonomic composition between the two groups. Spearman correlation analysis was applied to measure the correlation between the clinical characteristics of patients and microbial structure. In metagenome sequencing, the taxonomic or functional abundance spectrum was analyzed using R software (version 3.2.0), and an equidistant matrix of PCoA was calculated and analyzed. The Student’s t-test was used to analyze significant differences between groups. Taxonomic and functional abundance spectra were compared using linear discriminant analysis effect size (LEfSe). All data were analyzed using R software. P < 0.05 was considered statistically significant.

Baseline Demographic and Clinical Characteristics

Forty-four participants were recruited, of whom 20 cases belonged to the CFM group (CFM) and 24 to the healthy control group (Ctrl). The baseline demographic and clinical characteristics of different groups are shown in Table 1. No significant difference was found between CFM and Ctrl groups in terms of age, gender, and BMI (P = 0.446, 0.876, and 0.229, respectively). According to the Pruzansky-Kaban classification, 1, 6, 9, and 4 CFM patients were classified as Type I, Type IIa, Type IIb, and Type III, respectively. Sixteen patients were diagnosed with OSAS, accounting for 80% of all patients. The average OAHI was 2.8 and the average SpO2 was 74.2% (65.4–83.0%).

16S rRNA gene sequencing of 44 samples yielded 3,518,300 raw reads. The average number of sequences per sample was 79,961. After preprocessing the sequencing data, 2,969,017 high-quality sequences were obtained, averaging 67,477 sequences per sample. Among the 44 samples, 2,789 amplicon sequence variants were detected. The sequences were categorized based on their taxonomic group using the Human Oral Microbiome Database (HOMD). There were 15 Phyla, 26 classes, 73 orders, 126 families, 247 genera, and 479 species in the saliva sample of 44 participants.

Meanwhile, metagenomics sequencing was further performed on samples from 6 patients and 6 healthy controls. The effective data size distribution of each sample was between 11.96 and 15.93 G, and the N50 statistical distribution of Contigs was between 454 and 938 bp. After removing redundancy, 1,499,952 ORFs were identified in the gene catalog (non-redundant gene set). Compared with KEGG, NR, and CAZy databases, the annotation rates of non-redundant genes were 94.15, 51.21, and 1.69%, respectively.

Table 1

Baseline demographic and clinical characteristics of two groups.
Characteristic	Patients	Controls	P-value
No. of Patients	20	24
Gender, male, n%	12 (60%)	15 (62.5%)	0.876
Age	8.1 (7.1–9.1)	7.7 (6.8–8.7)	0.446
BMI (kg/m²)	16.4 ± 2.2	15.9 ± 1.3	0.299
Pruzansky-Kaban
I	1
IIa	6
IIb	9
III	4
OAHI	2.8 ± 2.3
SpO₂ (%)	74.2 (65.4–83.0)

Normally distributed data were expressed as mean ± SD (standard deviations) and non-normally distributed data were expressed as median interquartile range. P<0.05 was considered statistically significant.

BMI: Body Mass Index; OHAI: obstructive apnea/hypopnea index; SpO₂: oxygen saturation

Diversity, Composition, and Comparison of the Salivary Microbiome

According to the rarefaction curve, the plateauing stage had been reached (Supplementary Fig. 1), indicating that all samples were sequenced to a reasonable depth and the results reflected microbial information of saliva samples.

The Shannon index differed significantly between the CFM group and the Ctrl group (P = 0.024) (Fig. 1A). PCoA was performed to assess the variation of salivary microbial community structure using the unweighted UniFrac distance, with axis 1 (PC1) explaining 9.56% of the variability and axis 2 (PC2) explaining 9.23%. Salivary microbial communities differed significantly between the CFM group and the Ctrl group (P = 0.001), indicating significant differences in the phylogenetic structure (Fig. 1B).

The relative abundance of the top 10 phyla is summarized in Fig. 1C. Proteobacteria (36.46%), Firmicutes (31.34%), and Bacteroidota (20.07%) were the three most abundant phyla in the Ctrl group. Proteobacteria (31.82%), Bacteroidota (23.34%), Firmicutes (20.82%), Actinobacteriota (11.35%), and Fusobacteriota (10.34%) were the five most abundant phyla in the CFM group. Detailed statistics are presented in Supplementary Table 1. Actinobacteriota (Ctrl = 0.04, CFM = 0.11, P < 0.001), Campilobacterota (Ctrl = 0.006, CFM = 0.02, P = 0.013); Firmicutes (Ctrl = 0.31, CFM = 0.21, P = 0.012), and Desulfobacterota (Ctrl = 0.0007, CFM = 0.0002, P = 0.002) differed significantly between the two groups (Fig. 2A).

The top 15 most abundant taxa are shown in Fig. 1D. Streptococcus (24.20%),

Haemophilus (18.23%), and Neisseria (11.63%) were significantly enriched in the Ctrl group. Neisseria (18.07%) and Streptococcus (14.34%) were highly abundant in the CFM group. Detailed statistics are shown in Supplementary Table 2. The top 10 most abundant genera are shown in Fig. 2B. Corynebacterium (Ctrl = 0.01, CFM = 0.05, P < 0.001), Capnocytophaga (Ctrl = 0.03, CFM = 0.07, P = 0.002), Rothia (Ctrl = 0.01, CFM = 0.03, P = 0.021), Campylobacter (Ctrl = 0.01, CFM = 0.02, P = 0.013), Actinomyces (Ctrl = 0.02, CFM = 0.04, P = 0.022), and Prevotella (Ctrl = 0.05, CFM = 0.09, P = 0.048) were the most predominant genera in the CFM group. Haemophilus (Ctrl = 0.18, CFM = 0.07, P < 0.001), Gemella (Ctrl = 0.03, CFM = 0.01, P < 0.001), Streptococcus (Ctrl = 0.24, CFM = 0.14, P = 0.008), and Porphyromonas (Ctrl = 0.06, CFM = 0.03, P = 0.04) were the most abundant genera in the Ctrl group.

Based on the relative abundance of community structure, the correlations between patient clinical phenotype and microbial community structure were determined using Spearman analysis. Neisseria and Porphyromonas were positively correlated with the Pruzansky-Kaban classification, with a Spearman’s rank correlation coefficient (rs) of 0.66 (P = 0.0016) and 0.52 (P = 0.019), respectively. Prevotella and Kingella were negatively correlated with the Pruzansky-Kaban classification, with an rs of -0.57 (P = 0.009) and − 0.45 (P = 0.046), respectively. Campylobacter, Leptotrichia, and Kingella were positively correlated with SpO2, with an rs of 0.54 (P = 0.014), 0.53 (P = 0.03), and 0.48 (P = 0.016), respectively. Staphylococcus significantly negatively correlated with SpO2, with an rs of 0.46 (P = 0.04). Meanwhile, no correlation was found between microbial community structure and BMI and OAHI (Fig. 2C).

To identify potential biomarkers, the LEfSe method was used to analyze differences in the composition of the microbial communities between the two groups. The cladogram of salivary microbial structure and dominant bacteria was generated based on a linear discriminant analysis (LDA) threshold of 3, which indicated the most significant differences between CFM and Ctrl groups. Neisseriaceae was the most predominant at the family level and Prevotella, Capnocytophaga, Corynebacterium, Leptotrichia, Actinomyces, Fusobacterium, Campylobacter, Rothia, and Kingella were the most predominant at the genus level in the CFM group. Gemellaceae was the most predominant at the family level and Pelomonas, Gemella, Porphyromonas, and Streptococcus were dominant at the genus level in the Ctrl group (Fig. 2D-E).

According to the Pruzansky-Kaban classification, CFM patients were divided into Type I-IIA and Type IIB-III groups, patients with or without temporomandibular joint (TMJ) dysfunction. The difference in the salivary microbiome of both groups was compared to explore the relationship between the disease severity and microbial community structure. The top three genera with the highest average abundance were Prevotella (0.16), Neisseria (0.09), and Leptotrichia (0.07) in Type I-IIA and Neisseria (0.23), Haemophilus (0.09), and Prevotella (0.06) in Type IIB-III. The top 3 statistically significantly differential genera between the two groups included Faecalibacterium (0.0001, 0.0006, P = 0.001), Neisseria (0.09, 0.23, P = 0.004), and Porphyromonas (0.01, 0.05, P = 0.009) (Fig. 2F-G).

Functional Comparison of the Salivary Microbiome Between CFM and Ctrl Groups

The distribution of gene numbers between the two groups is displayed in Fig. S2-A, which revealed a significant difference between CFM and Ctrl (P = 0.016). Hierarchical clustering analysis was performed to evaluate the similarities and differences in gene abundances of all samples and the results showed that CFM and Ctrl groups had a high intra-group similarity (Fig. S2-B).

KEGG annotations of amino acid sequences obtained from the two groups are shown in Fig S2-C, with metabolism-associated genes accounting for the most significant proportion. Carbohydrate metabolism was annotated to 77,286 genes, making it the most functionally rich category, followed by amino acid metabolism, with 63,843 annotated genes, and Metabolism of cofactors and vitamins, Energy metabolism, Nucleotide metabolism, and Glycan biosynthesis and metabolism, with relatively abundant genes. Other metabolic pathways with relatively abundant genes included Genetic Information Processing, Environmental Information Processing, and Cellular Processes.

Based on the annotation of differentially-expressed KEGG ORTHOLOGY (KO) between CFM and Ctrl groups at level 3, the top 30 KO relative abundances were selected for clustering information based on the P-value to draw their functional heatmaps (Fig. 3A), revealing significant differences in functional abundance between the two groups.

Thirty-four functional pathways differed significantly between the two groups, and the top 10 functional pathways with the highest abundance were selected to generate the boxplots (Fig. 3B). Functional pathways with significant differences in the CFM group were Porphyrin and chlorophyll metabolism (0.013), Sulfur metabolism (0.003), Biotin metabolism (0.042), Histidine metabolism (0.040), Plant-pathogen interaction (0.019), Lysine degradation (0.005), Tryptophan metabolism (0.031), Salmonella infection (0.015), and Fluid shear stress and atherosclerosis (0.034). Biofilm formation-Escherichia coli (0.003) had a higher functional abundance and statistical significance in the Ctrl group. Functional biomarkers with significant differences between groups based on an LDA score of 2 are shown in Fig. 3C.

A correlation analysis was conducted to determine the relationship between high abundance bacteria and functions (Fig. 3D). Leptotrichia was significantly negatively correlated with Histidine metabolism (rs = -0.94, P = 0.017) and Tryptophan metabolism (rs = -0.94, P = 0.017). Neisseria was positively correlated with Tryptophan metabolism (rs = 0.71, P = 0.136), Lysine degradation (rs = 0.77, P = 0.103), and Histidine metabolism (rs = 0.49, P = 0.356) and negatively correlated with Sulfur metabolism (rs = -0.66, P = 0.175).

CFM is the second most common craniofacial malformation after cleft lip and palate, with an incidence rate ranging from 1 in 3,500 to 1 in 5,600 live births ^{25, 26}. Based on CFM characteristics, including abnormal oral anatomical structure, underdeveloped mandible, oblique occlusal plane, and frequent co-occurrence with OSAS ^{27, 28}, we hypothesized that these patients are at a higher risk of oral microbial dysbiosis (OMD). According to previous literature, OMD is associated with alveolar bone resorption, tooth loosening ^29,30, chewing dysfunction, and aggravating occlusal plane tilt and can affect long-term orthodontic or orthognathic treatment, all of which may affect the treatment and improvement of CFM patients ^31,32. However, there are no reports on the distribution characteristics of the oral microbiota in CFM patients. Therefore, the present study employed 16S rRNA sequencing to compare and investigate the structural features of the oral microbiota in CFM patients and healthy controls and identified numerous bacterial taxa with statistical differences. Furthermore, metagenomic sequencing identified functional abundance and differences in the oral microbiome in CFM samples. These data allowed a comprehensive analysis of bacterial diversity in CFM samples.

The standard oral microbiota protects against potential pathogens by occupying distinct ecological niches without causing harm ³³. A disruption in microbial homeostasis leads to the transformation of the original microbial community structure into a dysbiotic pathogenic state, decreasing pH in the oral cavity. Consequently, the acidic environment exacerbates the proliferation of pathogenic bacteria, leading to inflammation and tissue damage ³⁴. The observed pathogenicity is not only caused by individual bacterial species but also by alterations in community structure that are closely linked to various systemic and oral diseases. Some systemic diseases related to microbiota dysbiosis include osteoporosis, cancer, cardiovascular diseases, gastrointestinal diseases, and diabetes ^{35,36,37, 38}. For oral diseases, an alteration in microbial composition results in elevated levels of pathogenic bacteria, thereby worsening immune-mediated inflammatory responses. Moreover, this leads to elevated levels of specific cytokines and inflammatory mediators, which contribute to the degradation of alveolar bone and periodontal tissues, resulting in alveolar bone resorption, ultimately impacting masticatory function and aesthetic appearance. Microbiota dysbiosis in the oral cavity can result in dental caries, pulpitis, periodontitis, and head and neck tumors ^{39, 40,41, 42, 43,38}.

Individuals with craniofacial disorders at birth, which impact the growth and function of teeth and jaws, are frequently at a heightened risk of experiencing poor oral health conditions ⁴⁴. This heightened risk can be attributed to challenges in effectively cleaning structurally abnormal oral cavities and the continuous exposure of dental enamel and gum tissue to the oral environment due to deformities. Consequently, these factors may facilitate the formation and accumulation of plaque, ultimately increasing the vulnerability to oral diseases.

The clinical manifestations and treatment methods of CFM may have a potential mutual influence on OMD. The clinical manifestations include an underdeveloped mandible and an inclined occlusal plane. Additionally, patients with CFM exhibit a higher incidence of poorly developed teeth (ranging from 6.7–33.3%) and delayed tooth eruption (ranging from 20.5–54.3%) compared with normal individuals, whose respective incidence rates are 4.5–13.3% and 3.4–4.3%. Variations in tooth morphology and increased interdental spacing are frequently in CFM ^45,46,28. The above-mentioned anatomical abnormalities have been recognized as potential factors contributing to OMD and demonstrate interconnected relationships ⁴⁷. Furthermore, a previous study found that the prevalence of OSAS among patients with CFM ranged from 7–67% ⁴⁸. Other studies reported notable alterations in the composition and metabolomic features of the oral microbiota in pediatric patients with OSAS ^49,50,51. This phenomenon was attributed to recurrent episodes of upper airway collapse, reduced nasal airflow, snoring, and mouth breathing during sleep ⁵², which resulted in compromised oral self-cleaning capabilities, salivary gland dysfunction ^{53, 54,55,56}, reduced blood oxygen content, and alterations in airway humidity, temperature, and pH due to diminished nasal airflow ⁵⁷. Additionally, the oral cavity of patients with OSAS was directly exposed to dust, particles, and various airborne microorganisms, ultimately disrupting the microbial balance, leading to increased secretion of pro-inflammatory cytokines, mediating respiratory inflammation and edema, and inducing or exacerbating the occurrence of OSAS ⁵⁸.

In addition to clinical manifestations, various factors during the treatment process of CFM may interact with OMD. Surgery is the most widely accepted treatment for characteristic deformity in CFM to enhance both aesthetic and functional outcomes, mainly through mandibular elongation and dental functional improvement ⁵⁹. The most significant surgeries include mandibular distraction osteogenesis (MDO) and orthodontics or orthognathic surgery. MDO is usually performed during the mixed dentition period, during which children have more significant potential for skeletal growth. MDO helps lengthen the affected mandible, create the space of an open bite, induce the descent of the maxillary occlusal plane, and improve the occlusal plane and facial symmetry of the patient ^{60, 61}. During the MDO process, the active growth of the alveolar bone on the affected side is a significant determinant in the downward movement of the maxillary occlusal plane, thereby significantly influencing the maintenance of occlusal plane stability ^{62, 63, 64, 65, 66}. Orthodontics or orthognathic surgery is usually performed as a supplementary treatment for patients with poor occlusion and appearance in the permanent dentition stage ⁶⁷. At this stage of treatment, oral anatomical factors, particularly the alveolar bone, are critical in dental implantation, orthodontic procedures, periodontal therapy, and oral functional restoration ^{68, 69, 70,71, 72,73}. In summary, many factors may interact with OMD in patients with CFM. However, whether CFM is associated with OMD remains elusive, An association between the two is necessary to optimize the current treatment sequence. The current study compared the oral microbiota structure and function between CFM patients and normal individuals and confirmed the existence of OMD in patients with CFM.

Regarding the selection of samples and sampling time points, previous studies have shown that saliva is the most suitable sample for analyzing the composition of oral biofilm microbiota, with minimal harm to the host ^74,74,75. The oral microbiota in young children exhibits notable alterations in composition and diversity, ultimately stabilizing around the age of 2 ⁷⁶. Therefore, children in the mixed dentition period were selected as the study subjects, and saliva was used as the study sample.

The Shannon dilution curve demonstrated the rationality of the amount of sequenced data, excellent sequencing quality, and reliable research results. The CFM group exhibited more extraordinary species richness and evenness than the Ctrl group. Beta diversity analysis indicated significant differences in microbial community structure between the two groups; between-group differences were more significant than within-group differences. The phyla with statistical differences and high relative abundance were Actinobacteriota and Firmicutes in the CFM and Ctrl groups, respectively. These are resident bacteria in the oral cavity, with some genera classified as opportunistic pathogenic bacteria ^77,78.

The dominant differential species in the CFM group included Corynebacterium, Capnocytophaga, Rothia, Actinomyces, Prevotella, Campylobacter, Leptotrichia, and Fusobacterium, which are resident microbial communities in the oral cavity and play crucial roles in maintaining microecological balance. Certain species may exhibit pathogenicity when their abundance increase or the host’s immune system is compromised.

Capnocytophaga, a genus of facultative anaerobic bacteria, includes certain species identified as opportunistic pathogens in the human subgingival sulcus and dental plaque ^79,80. Previous studies reported a correlation between the increased abundance of Capnocytophaga and prepubertal periodontitis ^81,82, gingivitis ⁸³, and oral cancer ^{84, 85}.

Corynebacterium is primarily aerobic and typically non-pathogenic but sometimes opportunistically invades tissues (through wounds) or leads to infections such as granulomatous lymphadenitis, pneumonia, pharyngitis, skin infections, and endocarditis in immunocompromised hosts ^{86, 87, 88}. An increased abundance of Corynebacterium is also linked to a reduced likelihood of developing head and neck squamous cell carcinoma, suggesting potential implications for cancer prevention strategies ⁸⁹.

Actinomyces are facultative anaerobes that exhibit optimal growth in anaerobic environments, serve as opportunistic pathogens in the oral cavity, and are particularly prevalent in the gingiva. The metabolic activities of Actinomyces, contributing significantly to acid-base equilibrium, include metabolizing sugar into acid and amino acids into acid and ammonia. Additionally, Actinomyces are implicated in the generation of hydrogen sulfide and methyl mercaptan, which have been linked to oral malodor, dental caries, and periodontal disease ^{90, 91}. Besides, excessive abundance of Actinomyces can lead to various complications, such as dental surgery infection, oral abscess ⁹², and oral tumors ⁹³.

Fusobacterium, a genus of obligate anaerobic bacteria, has been associated with various human diseases, including periodontal disease, oral, head, and neck infections, colorectal cancer, and localized skin ulcers ^{94, 95, 96, 97}. Identical to Prevotella and Porphyromonas, Fusobacterium can degrade nitrogenous compounds into short-chain fatty acids, sulfide compounds, and ammonia, which are cytotoxic and can induce tissue inflammation by modulating immune responses ⁹⁸ and even promote cell apoptosis ⁹⁹. These processes all contribute to the initiation and progression of periodontal diseases. Prevotella can grow under anaerobic conditions and cause acidification. An increased abundance of Prevotella is related to the development of periodontal disease, abscesses, and oral tumors ^{100, 101,102}. Our data showed that Porphyromonas was highly abundant in the Ctrl group and Type IIB-III patients.

Leptotrichia is an anaerobic Gram-negative bacteria that forms part of the bacterial biofilm of the oral cavity. Its increased abundance has been reported to be associated with inflammatory bowel disease, cancer, and adenomatous polyps ^103,104.

Campylobacter grows best in microaerobic environments and can cause gastrointestinal infections ¹⁰⁵. Certain species of Rothia, a Gram-positive bacterium, are opportunistic pathogens in the oral cavity and pharynx that sometimes cause septicemia, endocarditis, and other severe infections ^{106, 107, 108}.

Prior research has demonstrated that elevated abundance of Streptococcus mutans and Porphyromonas gingivalis is implicated in periodontal disease, dental caries, and certain systemic illnesses. S. mutans is a Gram-positive and anaerobic acid-producing microorganism that can synthesize extracellular polymers of glucan. It also produces acid and thrives in acidic environments, ultimately causing tooth decay and the dissolution of hydroxyapatite in the enamel and dentin. P. gingivalis can ferment sugar and produce acid, which facilitates microbial adhesion to tooth surfaces and enamel demineralization ^{109,110,111,112,113}. Furthermore, P. gingivalis and S. mutans have been sporadically detected in the saliva of individuals with good oral health, suggesting that they may also form part of the resident oral flora. The present study found that the average abundance of S. mutans in CFM and Ctrl groups was 0.002 and 0.0002, respectively, with no statistical significance. The average abundance of P. gingivalis was higher in the Ctrl group than in the CFM group, with no statistical significance.

Alterations in in oral bacterial composition have been demonstrated in patients with OSAS and obesity ^{114, 115}. To determine whether OBD in CFM patients is mainly affected by OSAS, we performed a Spearman correlation analysis to ensure the relationship between CFM Pruzansky-Kaban type, OAHI, SpO2, BMI, and oral microbial structure¹¹⁶. The results showed that the relative abundance of Neisseria and Porphyromonas increased with the severity of CFM deformity. Excessively high levels of Neisseria may lead to abnormal activation of the immune system causing cellular inflammation; the inflammatory state of the oral cavity further promotes the colonization of Neisseria^117,118. Porphyromonas is a strictly anaerobic Gram-negative rod bacteria that has been shown to be a cause of periodontitis and pulpitis. In this study, the abundance of Staphylococcus increased with the decrease of SpO2, suggesting that it is a facultative anaerobic organism. Partial species such as S. aureus and S. epidermidis are opportunistic pathogens resident in the oral cavity and known to cause oral mucositis and gingivitis. In addition, there was no relevance between OAHI, BMI, and bacteria structure. Collectively, our correlation analysis results and those of previous studies suggest a decrease in the diversity of the oral microbiota in individuals with moderate to severe OSAS¹¹⁹. In contrast, our study found a significant increase in the richness and evenness of the oral microbiota in patients with CFM, suggesting a potential link between oral microbiome diversity and CFM. The relationship between CFM severity and specific genera and corresponding species needs to be further studied.

There were on significant differences between the CFM and Ctrl groups in Cellular Processes, Environmental Information Processing, Genetic Information Processing, Human Diseases, Metabolism, and Organismal Systems. At level 3, pathway activity of Porphyrin and chlorophyll metabolism, Sulfur metabolism, Biotin metabolism, Histidine metabolism, Plant − pathogen interaction, Lysine degradation, Tryptophan metabolism, Salmonella infection, Fluid shear stress, and atherosclerosis was significantly increased in the CFM group than in the Ctrl group.

Tryptophan, an essential amino acid absorbed through the intestinal epithelium, is utilized for protein synthesis, and approximately 10–20% of it is further metabolized by bacteria ^{120, 121, 122}. The metabolism of free tryptophan in the oral cavity involves three pathways: (1) Indole pathway: This pathway involves the direct conversion of tryptophan into indole and its derivatives by periodontal plaque microorganisms, including Fusobacterium, Prevotella, and Porphyromonas. These metabolites form part of the developmental mechanisms of periodontitis and halitosis¹²³. (2) 5-Hydroxytryptamine (5-HT) pathway: Tryptophan is converted into 5-hydroxytryptophan (5-HTP) via the catalysis of tryptophan hydroxylase (TPH), and then metabolized to generate 5-hydroxytryptamine (5-HT) ¹²⁴. 5-HT is a key neurotransmitter involved in regulating central nervous system functions. Moreover, 5-HT interacts with receptors on various immune cells, such as T cells, macrophages, and dendritic cells, triggering inflammatory and immune regulatory responses^125,126. (3) Kynurenine metabolic pathway: More than 95% of free tryptophan is metabolized via this pathway. This pathway is driven by indoleamine 2,3-dioxygenase 1 (IDO) and tryptophan 2,3-dioxygenase (TDO), and leads to the production of kynurenine (Kyn) and the associated downstream products such as quinolinic acid, niacin, nicotinamide adenine dinucleotide, and kynurenic acid ¹²⁷. IDO, a key rate-limiting enzyme involved in this pathway, contributes to the formation of inflammation and immunosuppression. The activation of IDO can enhance the establishment of an immunosuppressive microenvironment, thereby inhibiting the progression of the anti-tumor immune response. IDO expression is significantly elevated in melanoma, colon cancer, and lung cancer tissues, and this high expression is inversely related to tumor prognosis^{128, 129,130}. Furthermore, several tryptophan metabolites may also modulate diverse biological processes ¹³¹.

In the study by Balci et al. (2021), we discovered that patients with stage III grade B periodontitis have elevated levels of tryptophan in their saliva compared to healthy controls. Moreover, the level of tryptophan can be used for probing bleeding and serve as a plaque index¹³². Furthermore, 5-HT, another metabolite of tryptophan, and its precursor 5-HTP, regulate bone metabolism ^133,134. Evidence from animal studies have demonstrated that 5-HTP can promote osteoclastogenesis and aggravate alveolar bone resorption ¹³⁵.

Sulfur metabolism has been implicated in the regulation of energy metabolism in humans. Sulfur, one of the most prevalent bio-elements, is involved in diverse processes including cell signaling, free radical detoxification, enhancement of structural support, and energy production^{136, 137, 138, 139, 140}. The organic and inorganic sulfur levels in the body are primarily influenced by the consumption of amino acids such as methionine, B-complex vitamins, and trace elements ^141,142. Sulfur-metabolizing bacteria have been shown to modulate sulfur metabolism. Representative ones include Streptococcus, Prevotella, and Fusobacterium ^{143,144,145,146,147,148,149}. Hydrogen sulfide (H₂S), sulfite, thiosulfate, and sulfate are the main sulfur metabolism products. As an essential product, physiological levels of H₂S induce anti-inflammatory effects, inhibit invasive pathogens, and help alleviate tissue damage^{150,151,152,153,154}. In contrast, excessively high concentrations of H₂S cause toxicity in animals are, mainly in the cardiovascular system, the central nervous system, and the energy metabolism process. Specifically, in the energy metabolism process, H₂S inhibits cytochrome c oxidase (COX) activity within the mitochondrial electron transport chain (ETC)¹⁵⁵. This triggers DNA damage, disrupts the intestinal mucus bilayer, promotes inflammation, and contributes to colorectal cancer ¹⁵⁶.

Biotin, also known as vitamin B7, vitamin H, or coenzyme R, plays a crucial role in metabolism by acting as a cofactor for various enzymes. The levels of biotin are influenced bydietary intake and bacterial activity. It has been suggested that deficiencies in the bacterial production of biotin affected the microbial community dynamics, host metabolic processes, and inflammatory responses^{157, 158,159}. Most Bacteroidetes and Proteobacteria phyla organisms are responsible for biotin biosynthesis ^160,161. This essential micronutrient, biotin, serves as a cofactor for a range of enzymes, including biotin-dependent carboxylases, decarboxylases, and transcarboxylases ¹⁶². For instance, it participates in various metabolic processes, including fatty acid synthesis, amino acid metabolism, and gluconeogenesis, by forming covalent bonds with lysine residues ^{163, 164}. Biotin exerts anti-inflammatory effects by inhibiting NF-κB activation, and its deficiency can lead to inflammation by stimulating the secretion of pro-inflammatory cytokines ^{165, 166}.

In the human digestive tract, histidine (His), an essential amino acid, plays a vital role for various gut bacteria. These bacteria break down histidine and use it as a nutrient source. This breakdown process produces several byproducts, including histamine, uric acid, glutamate, and imidazole propionate (IMP) ^167,168. Previous research has demonstrated that His and its metabolites exert diverse physiological effects on the human body. Increased levels of His have been associated with the inhibition of inflammation and oxidative stress. Overexpression of His decreases taste and olfactory sensitivity, ellicits symptoms such as headaches, weakness, drowsiness, nausea, and cognitive impairment ¹⁶⁹. Moreover, His and His-containing dipeptides (HIS-cd) were linked to the occurrence of inflammatory bowel diseases and ocular disorders ^{170,171, 172, 173}. Histamine, a significant immune modulator, affects the hypersensitivity reactions and chronic inflammatory processes ^174,175, as well as intestinal diseases, anxiety, and immune responses induced by dysbiosis of the gut microbiota ^176,177. Research in animals suggests that glutamate might help heal damage in the intestinal barrier by regulating the release of corticotropin-releasing factor (CRF) ¹⁷⁸. Excessive IMP damages the gut barrier and alters the levels of inflammatory cytokines ^179,180, disrupts insulin signaling via the mammalian target of rapamycin complex 1 (mTORC1) ¹⁶⁸.

Lysine is an essential amino acid which is degradated via two distinct pathways. The primary pathway involves the formation of saccharopine via ε-deamination within liver mitochondria, leading to the formation of acetyl-CoA^{181, 182}. Another pathway entails α-deamination or transamination to produce pipecolic acid (PA). Fusobacterium nucleatum, which is increased in CFM, has been demonstrated to ferment lysine ¹⁸³. Intestinal bacteria normally produce very low amounts of precursor molecules for protein-bound uremic toxins (PBUTs) like indole, para-cresol, and phenol. On the other hand, lysine acts as a diuretic, helping to flush out toxins from the body. Therefore, the marked elevation in lysine degradation hinders the effective removal of toxins^{184, 185}.

In this research, the saliva microbiome of patients with CFM was compared with that from controls by 16S rRNA and metagenomics sequencing. The results revealed significant differences in the diversity, composition, and functional profiles of oral microbial communities between CFM patients and controls in the mixed dentition stage. The increased abundance of bacterial genera in CFM patients primarily consists of opportunistic pathogens in the oral cavity. Previous research has linked the abnormal growth of these bacteria to various oral diseases, including halitosis, dental caries, periodontitis, pulpitis, oral infections, periodontal abscesses, and oral tumors. Furthermore, specific genera have been linked to occurrence of systemic diseases, including gastrointestinal infections, gastrointestinal tumors, pneumonia, pharyngitis, skin infections, sepsis, and endocarditis. Notably, Neisseria and Porphyromonas, were positively correlated with the severity of CFM, could stimulate cellular inflammation especially when their abundance was high, ultimately enhancing the development of oral inflammation. Pathogenic bacteria such as Prevotella and S. mutans were enriched in the CFM group. In contrast, P. gingivalis, a pathogenic bacteria, displayed higher abundance in the Ctrl. group, probably due to failure to exclude individuals with oral diseases in the Ctrl group. Several functional pathways were upregulated in patients with CFM compared to controls, particularly those related to the metabolism of amino acids and biotin. Notably, tryptophan metabolism, which significantly affects oral health, was also upregulated in the CFM group. Furthermore, pathways such as sulfur metabolism, biotin metabolism, and histidine metabolism were upregulated, which may increase the risk of various diseases.

This study has some limitations that should be discussed. The enrolled sample size was small primarily due to the rarity of CFM in craniofacial disease, with an incidence rate ranging from 1 in 3,500 to 1 in 5,600. In addition, variables such as dietary habits and geographical location were unaccounted for due to the small sample size, potentially limiting the generalizability of the study findings to other regions or ethnic groups. Moreover, since the 16S rRNA sequencing used in this study accurately detects bacteria at the genus level, species-level oral bacteria were not further discussed. Subsequent research should investigate the potential effects and mechanisms of the identified microbial biomarkers and to develop appropriate treatment strategies. Future investigations should also incorporate intervention strategies or explore metabolites to improve the efficacy.

CFMs significantly impact facial structure and function. Due to the complexity of these conditions, patients with CFM benefit most from a multidisciplinary treatment approach. This comprehensive care, involving a team of specialists, aims to restore both facial appearance and function. By analyzing the clinical manifestations and treatment strategies that may be related to OMD in CFM, we found that several genera were significantly increased, including Corynebacterium, Capnocytophaga, Rothia, Actinomyces, Prevotella, Campylobacter, Leptotrichia, and Fusobacterium in CFM patients. Neisseria and Porphyromona were positively correlated with the severity of CFM, whereas pathways that were upregulated predominantly included Tryptophan metabolism, Sulfur metabolism, Biotin metabolism, Histidine metabolism, and Lysine degradation. Nesseria promotes Tryptophan metabolism and Lysine degradation, while Leptotrichia decreases Histidine metabolism and Tryptophan metabolism. This study identified the key bacterial genera and functional changes associated with CFM. Furthermore, researchers should address the dysbiosis of oral microbiota in CFM patients to mitigate the persistence of pathogenic bacteria and their impact on oral health and long-term treatment outcomes.

Acknowledgments

The authors thank the patients diagnosed with CFM and control group volunteers who participated in our study. They also thank Ouyi Biotechnology Company Limited for its technical services and valuable suggestions for bioinformatics analysis.

Ethics approval and consent to participate

This study was approved by the ethical review board of the Plastic Surgery Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College (approval no. 2021-199) on Month 12, 2021. All participants voluntarily consented to participate in the study, providing informed consent. Their decision did not impact the treatment protocol.

Competing interests

The authors declare no competing interest.

Raw data:

The datasets supporting the conclusions of this article are available in the Bioproject database repository.

[unique persistent identifier and hyperlink to datasets of macrogenomic sequencing in https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1118211].

[unique persistent identifier and hyperlink to datasets of 16s rRNA sequencing in https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1118189].

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Structural and functional changes in the oral microbiome of the patients with craniofacial microsomia

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Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Materials and Methods

Subject selection

Sample Collection

16S rRNA Diversity Sequencing

DNA extraction and amplification

Library Construction and Sequencing

Bioinformatic Analysis

Statistical Analysis

Results

Baseline Demographic and Clinical Characteristics

Diversity, Composition, and Comparison of the Salivary Microbiome

Functional Comparison of the Salivary Microbiome Between CFM and Ctrl Groups

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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