Multiomics Analysis Reveals Significant Disparities in the Oral Microbiota and Metabolites Between Pregnant Women with and without Periodontitis

Min Zhao,1 Changyi Yang,1 Linhong Zhu,1 Xiaoqian Guo,1 Haiyan Ma,2 Yuanhao Luo,3 Qi Wang,4 Juanjuan Chen4 1Department of Periodontology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, 750004, People’s Republic of China; 2School of Stomatology, Ningxia Medical University, Yinchuan, Ningxia, 750004, People’s Republic of China; 3The Second Clinical Medical School, Lanzhou University, Lanzhou, Gansu, 730030, People’s Republic of China; 4Cuiying Biomedical Research Center, Lanzhou University Second Hospital, Lanzhou, Gansu, 730030, People’s Republic of ChinaCorrespondence: Juanjuan Chen; Qi Wang, Email [email protected]; [email protected]: Our study investigated the disparities and correlations between oral microbiota and metabolites in pregnant patients with and without periodontitis.Methods: Subgingival plaque samples from all subjects were collected for shotgun metagenomic sequencing and broad-target metabolomics analysis.Results: Forty pathogens, including Porphyromonas gingivalis, Fusobacterium nucleatum, Eubacterium saphenum, Gemella morbillorum, Tannerella forsythia, Streptococcus anginosus group, Selenomonas sputigena etc, were significantly enriched in pregnant patients with periodontitis (PPP). Conversely, symbiotic species such as Morococcus cerebrosus, Streptococcus vestibularis, S. salivarius, S. mitis, and S. pneumoniae were significantly more abundant in healthy controls (HCs). A total of 87 predicted functional modules (PFMs) exhibited significant differences between the two groups; eight PFMs showed high enrichment in PPP with involvement of PPP-enriched species within these pathways. The remaining 79 PFMs encompassing ribonucleotide biosynthesis, carbohydrate, and amino acid metabolism were highly abundant in HCs. For oral microbial metabolome, a total of 105 metabolites related to 150 KEGG pathways displayed significant differences between the two groups. Pathways such as pyruvate metabolism, folate biosynthesis, vascular smooth muscle contraction, and AMPK/mTOR signaling pathway along with their associated metabolites were found to be enriched in PPP, while carbohydrate metabolism predominated among HCs. Spearman’s rank correlation analysis revealed significant positive associations between species enriched in PPP and metabolites enriched in PPP, but significant negative associations between species enriched in PPP and metabolites enriched in HCs.Discussion: Our findings provide potential biomarkers for distinguishing periodontitis during pregnancy while offering valuable insights into mechanisms exploration and clinical intervention.Keywords: Pregnancy-induced periodontitis, oral microbiota composition, predicted functional modules, microbial metabolites profiling, biomarkers identification

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SHILAP Revista de lepidopterología
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2024-10-01
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Statistical and Computational Modeling
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article

Multiomics Analysis Reveals Significant Disparities in the Oral Microbiota and Metabolites Between Pregnant Women with and without Periodontitis

SHILAP Revista de lepidopterología
Statistical and Computational Modeling
article

Multiomics Analysis Reveals Significant Disparities in the Oral Microbiota and Metabolites Between Pregnant Women with and without Periodontitis

article en

Abstract

Min Zhao,1 Changyi Yang,1 Linhong Zhu,1 Xiaoqian Guo,1 Haiyan Ma,2 Yuanhao Luo,3 Qi Wang,4 Juanjuan Chen4 1Department of Periodontology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, 750004, People’s Republic of China; 2School of Stomatology, Ningxia Medical University, Yinchuan, Ningxia, 750004, People’s Republic of China; 3The Second Clinical Medical School, Lanzhou University, Lanzhou, Gansu, 730030, People’s Republic of China; 4Cuiying Biomedical Research Center, Lanzhou University Second Hospital, Lanzhou, Gansu, 730030, People’s Republic of ChinaCorrespondence: Juanjuan Chen; Qi Wang, Email [email protected]; [email protected]: Our study investigated the disparities and correlations between oral microbiota and metabolites in pregnant patients with and without periodontitis.Methods: Subgingival plaque samples from all subjects were collected for shotgun metagenomic sequencing and broad-target metabolomics analysis.Results: Forty pathogens, including Porphyromonas gingivalis, Fusobacterium nucleatum, Eubacterium saphenum, Gemella morbillorum, Tannerella forsythia, Streptococcus anginosus group, Selenomonas sputigena etc, were significantly enriched in pregnant patients with periodontitis (PPP). Conversely, symbiotic species such as Morococcus cerebrosus, Streptococcus vestibularis, S. salivarius, S. mitis, and S. pneumoniae were significantly more abundant in healthy controls (HCs). A total of 87 predicted functional modules (PFMs) exhibited significant differences between the two groups; eight PFMs showed high enrichment in PPP with involvement of PPP-enriched species within these pathways. The remaining 79 PFMs encompassing ribonucleotide biosynthesis, carbohydrate, and amino acid metabolism were highly abundant in HCs. For oral microbial metabolome, a total of 105 metabolites related to 150 KEGG pathways displayed significant differences between the two groups. Pathways such as pyruvate metabolism, folate biosynthesis, vascular smooth muscle contraction, and AMPK/mTOR signaling pathway along with their associated metabolites were found to be enriched in PPP, while carbohydrate metabolism predominated among HCs. Spearman’s rank correlation analysis revealed significant positive associations between species enriched in PPP and metabolites enriched in PPP, but significant negative associations between species enriched in PPP and metabolites enriched in HCs.Discussion: Our findings provide potential biomarkers for distinguishing periodontitis during pregnancy while offering valuable insights into mechanisms exploration and clinical intervention.Keywords: Pregnancy-induced periodontitis, oral microbiota composition, predicted functional modules, microbial metabolites profiling, biomarkers identification

SHILAP Revista de lepidopterología
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Statistical and Computational Modeling
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