Oral Microbiome Geography: Micron-Scale Habitat and Niche

Welch, Jessica L. Mark; Ramírez-Puebla, Shamayim T.; Borisy, Gary G.

doi:10.1016/j.chom.2020.07.009

Cited by 135 publications

(103 citation statements)

References 41 publications

(97 reference statements)

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“…Our M-GWAS analysis found 84%-85% concordant association signals shared by tongue dorsum and salivary microbiome, with all genome-wide significant associations in one niche ( Fig. 5 ; p < 5 × 10 −8 ) were also at least nominally significant in the other niche ( p < 0.05), consistent with our and previous findings that tongue dorsum and salivary microbiome communities exhibited high levels of similarity 44,45 , especially in micron-scale structure of oral niches 38,46 . Not only an independent cohort but also different niches (tongue dorsum and saliva) in the oral cavity corroborated the robust and replicable host-microbe association results.…”

Section: Discussionsupporting

confidence: 90%

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Metagenome-genome-wide association studies reveal human genetic impact on the oral microbiome

Liu

Tong

Zhu

et al. 2021

Preprint

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The oral microbiota contains billions of microbial cells, which could contribute to diseases in a number of body sites. Challenged by eating, drinking and dental hygiene on a daily basis, the oral microbiota is regarded as highly dynamic. Here, we report significant human genomic associations with the oral metagenome from more than 1,915 individuals, for both the tongue dorsum and saliva. Five genetic loci, APPL2, SLC2A9 and MGST1 associated with tongue dorsum, LOC102723769-OR11H1-POTEH and MTRNR2L1-LOC105371703-MIR4522 associated with salivary microbial features, reached study-wide significance (p < 3.16 * 10-11). Further analyses confirmed 6 genome-wide significant loci shared between tongue dorsum and saliva. For example, the dental caries pathogen Prevotella melaninogenica associated with MARK2-RCOR2; the periodontitis bacteria Treponema associated with CCL26-CCL24 and Porphyromonas associated with CSMD1 at both niches. Human genetics account for at least 10% of oral microbiome differences between individuals. Machine learning models showed that polygenetic risk score dominated over oral microbiome in predicting predisposing risk of dental diseases such as dental calculus and gingival bleeding. These findings indicate that human genetic differences are one explanation for a stable or recurrent oral microbiome in each individual.

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Section: Discussionsupporting

confidence: 90%

“…The saliva may appear more dynamic than the tongue dorsum, and the microbiome composition involves multiple niche in the oral cavity 38 . We next tried M-GWAS analysis for the saliva microbiome.…”

Section: Resultsmentioning

confidence: 99%

Metagenome-genome-wide association studies reveal human genetic impact on the oral microbiome

Liu

Tong

Zhu

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…It has been well documented that in dental caries, environmental perturbation alters the balance of the oral microbiota and eventually leads to a predominance of cariogenic bacteria, resulting in sustained demineralization of tooth hard tissue (Kidd and Fejerskov, 2013). Evidence has recently emerged that the oral microbiome may depend on age, oral dentition, diet and geography (Crielaard et al, 2011;Teng et al, 2015;Hansen et al, 2018;Dashper et al, 2019;Mark Welch et al, 2020). Effectively reducing dental caries burden requires a better understanding of its determinants.…”

Section: Discussionmentioning

confidence: 99%

Geographic Variation Did Not Affect the Predictive Power of Salivary Microbiota for Caries in Children With Mixed Dentition

Huang

Guo

et al. 2021

Front. Cell. Infect. Microbiol.

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Dental caries is one of the most prevalent chronic oral diseases, affecting approximately half of children worldwide. The microbial composition of dental caries may depend on age, oral health, diet, and geography, yet the effect of geography on these microbiomes is largely underexplored. Here, we profiled and compared saliva microbiota from 130 individuals aged 6 to 8 years old, representing both healthy children (H group) and children with caries-affected (C group) from two geographical regions of China: a northern city (Qingdao group) and a southern city (Guangzhou group). First, the saliva microbiota exhibited profound differences in diversity and composition between the C and H groups. The caries microbiota featured a lower alpha diversity and more variable community structure than the healthy microbiota. Furthermore, the relative abundance of several genera (e.g., Lactobacillus, Gemella, Cryptobacterium and Mitsuokella) was significantly higher in the C group than in the H group (p<0.05). Next, geography dominated over disease status in shaping salivary microbiota, and a wide array of salivary bacteria was highly predictive of the individuals’ city of origin. Finally, we built a universal diagnostic model based on 14 bacterial species, which can diagnose caries with 87% (AUC=86.00%) and 85% (AUC=91.02%) accuracy within each city and 83% accuracy across cities (AUC=92.17%). Although the detection rate of Streptococcus mutans in populations is not very high, it could be regarded as a single biomarker to diagnose caries with decent accuracy. These findings demonstrated that despite the large effect size of geography, a universal model based on salivary microbiota has the potential to diagnose caries across the Chinese child population.

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“…4C ) ( 58 ). Further, though it lacks experimental evidence for negative associations underlying CRC-enriched microbes, there have been numerous investigations about the competitive relationship among taxa during growth of biofilm ( 58 – 61 ). For example, Porphyromonas gingivalis , another known biofilm-forming partner of F. nucleatum , showed a negative correlation with P. micra ( 58 ).…”

Section: Resultsmentioning

confidence: 99%

Metagenomic Analysis of Common Intestinal Diseases Reveals Relationships among Microbial Signatures and Powers Multidisease Diagnostic Models

Jiang

Luo

et al. 2021

mSystems

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Common intestinal diseases such as Crohn’s disease (CD), ulcerative colitis (UC), and colorectal cancer (CRC) share clinical symptoms and altered gut microbes, necessitating cross-disease comparisons and the use of multidisease models. Here, we performed meta-analyses on 13 fecal metagenome data sets of the three diseases. We identified 87 species and 65 pathway markers that were consistently changed in multiple data sets of the same diseases. According to their overall trends, we grouped the disease-enriched marker species into disease-specific and disease-common clusters and revealed their distinct phylogenetic relationships; species in the CD-specific cluster were phylogenetically related, while those in the CRC-specific cluster were more distant. Strikingly, UC-specific species were phylogenetically closer to CRC, likely because UC patients have higher risk of CRC. Consistent with their phylogenetic relationships, marker species had similar within-cluster and different between-cluster metabolic preferences. A portion of marker species and pathways correlated with an indicator of leaky gut, suggesting a link between gut dysbiosis and human-derived contents. Marker species showed more coordinated changes and tighter inner-connections in cases than the controls, suggesting that the diseased gut may represent a stressed environment and pose stronger selection on gut microbes. With the marker species and pathways, we constructed four high-performance (including multidisease) models with an area under the receiver operating characteristic curve (AUROC) of 0.87 and true-positive rates up to 90%, and explained their putative clinical applications. We identified consistent microbial alterations in common intestinal diseases, revealed metabolic capacities and the relationships among marker bacteria in distinct states, and supported the feasibility of metagenome-derived multidisease diagnosis. IMPORTANCE Gut microbes have been identified as potential markers in distinguishing patients from controls in colorectal cancer, ulcerative colitis, and Crohn’s disease individually, whereas there lacks a systematic analysis to investigate the exclusive microbial shifts of these enteropathies with similar clinical symptoms. Our meta-analysis and cross-disease comparisons identified consistent microbial alterations in each enteropathy, revealed microbial ecosystems among marker bacteria in distinct states, and demonstrated the necessity and feasibility of metagenome-based multidisease classifications. To the best of our knowledge, this is the first study to construct multiclass models for these common intestinal diseases.

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Oral Microbiome Geography: Micron-Scale Habitat and Niche

Cited by 135 publications

References 41 publications

Metagenome-genome-wide association studies reveal human genetic impact on the oral microbiome

Metagenome-genome-wide association studies reveal human genetic impact on the oral microbiome

Geographic Variation Did Not Affect the Predictive Power of Salivary Microbiota for Caries in Children With Mixed Dentition

Metagenomic Analysis of Common Intestinal Diseases Reveals Relationships among Microbial Signatures and Powers Multidisease Diagnostic Models

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