Cohort Profile: ZOE 2.0—A Community-Based Genetic Epidemiologic Study of Early Childhood Oral Health

Divaris, Kimon; Slade, Gary D.; Zandoná, Andréa G. Ferreira; Preisser, John S.; Ginnis, Jeannie; Simancas‐Pallares, Miguel; Agler, Cary S.; Shrestha, Poojan; Karhade, Deepti Shroff; Ribeiro, Apoena de Aguiar; Cho, Hunyong; Gu, Yu; Meyer, Beau D; Joshi, Ashwini; Azcárate-Peril, M. Andrea; Basta, Patricia V.; Wu, Di; North, Kari E.

doi:10.3390/ijerph17218056

Cited by 23 publications

(32 citation statements)

References 38 publications

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“…The initial study population comprised 300 preschool-age children attending public preschool (Head Start) centers in North Carolina, participants of a community-based, cross-sectional, epidemiologic study (ZOE 2.0) of early childhood oral health (Divaris and Joshi 2020; Divaris et al 2020). Children were between 36 and 71 mo old.…”

Section: Methodsmentioning

confidence: 99%

“…Children underwent comprehensive dental examinations recording dental caries experience using International Caries Detection and Classification System (ICDAS) criteria by trained and calibrated examiners between August 2016 and February 2019. Detailed descriptions of the clinical examination (Ginnis et al 2019), biofilm collection, and analysis protocols (Divaris et al 2019), as well as the parent study’s cohort profile (Divaris et al 2020), have been reported in detail in previous publications.…”

Section: Methodsmentioning

confidence: 99%

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Metabolomics Insights in Early Childhood Caries

et al. 2021

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Dental caries is characterized by a dysbiotic shift at the biofilm–tooth surface interface, yet comprehensive biochemical characterizations of the biofilm are scant. We used metabolomics to identify biochemical features of the supragingival biofilm associated with early childhood caries (ECC) prevalence and severity. The study’s analytical sample comprised 289 children ages 3 to 5 (51% with ECC) who attended public preschools in North Carolina and were enrolled in a community-based cross-sectional study of early childhood oral health. Clinical examinations were conducted by calibrated examiners in community locations using International Caries Detection and Classification System (ICDAS) criteria. Supragingival plaque collected from the facial/buccal surfaces of all primary teeth in the upper-left quadrant was analyzed using ultra-performance liquid chromatography–tandem mass spectrometry. Associations between individual metabolites and 18 clinical traits (based on different ECC definitions and sets of tooth surfaces) were quantified using Brownian distance correlations (dCor) and linear regression modeling of log2-transformed values, applying a false discovery rate multiple testing correction. A tree-based pipeline optimization tool (TPOT)–machine learning process was used to identify the best-fitting ECC classification metabolite model. There were 503 named metabolites identified, including microbial, host, and exogenous biochemicals. Most significant ECC-metabolite associations were positive (i.e., upregulations/enrichments). The localized ECC case definition (ICDAS ≥1 caries experience within the surfaces from which plaque was collected) had the strongest correlation with the metabolome (dCor P = 8 × 10−3). Sixteen metabolites were significantly associated with ECC after multiple testing correction, including fucose ( P = 3.0 × 10−6) and N-acetylneuraminate (p = 6.8 × 10−6) with higher ECC prevalence, as well as catechin ( P = 4.7 × 10−6) and epicatechin ( P = 2.9 × 10−6) with lower. Catechin, epicatechin, imidazole propionate, fucose, 9,10-DiHOME, and N-acetylneuraminate were among the top 15 metabolites in terms of ECC classification importance in the automated TPOT model. These supragingival biofilm metabolite findings provide novel insights in ECC biology and can serve as the basis for the development of measures of disease activity or risk assessment.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Metabolomics Insights in Early Childhood Caries

et al. 2021

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“…To understand the distributional characteristics of metatranscriptomics data, in Section 3 we introduced data collected from a genetic-epidemiologic study of early childhood oral health (the "ZOE 2.0" study) [27,28] wherein oral microbial metatranscriptomics data were generated for approximately 300 children aged 3-5 years. We also introduced a gut microbial transcriptome dataset to study inflammatory bowel diseases [7].…”

Section: Outlinementioning

confidence: 99%

Comprehensive evaluation of methods for differential expression analysis of metatranscriptomics data

H¹,

C²,

Bm³

et al. 2021

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Background: Measuring and understanding the function of the human microbiome is key for several aspects of health; however, the development of statistical methods specifically for the analysis of microbial gene expression (i.e., metatranscriptomics) is in its infancy. Many currently employed differential expression analysis methods have been designed for different data types and have not been evaluated in metatranscriptomics settings. To address this knowledge gap, we undertook a comprehensive evaluation and benchmarking of eight differential analysis methods for metatranscriptomics data. Results: We used a combination of real and simulated metatranscriptomics data to evaluate the performance (i.e., model fit, Type-I error, and statistical power) of eights methods: log-normal (LN), logistic-beta (LB), MAST, Kruskal-Wallis, two-part Kruskal-Wallis, DESeq2, and ANCOM-BC and metagenomeSeq. The simulation was informed by supragingival biofilm microbiome data from about 300 preschool-age children enrolled in a study of early childhood caries (ECC), whereas validations were sought in two additional datasets, including an ECC and an inflammatory bowel disease one. The LB test showed the highest power in both small and large sample sizes and reasonably controlled Type-I error. Contrarily, MAST was hampered by inflated Type-I error. Using LN and LB tests, we found that genes C8PHV7 and C8PEV7, harbored by the lactate-producing Campylobacter gracilis, had the strongest association with ECC. Conclusion: This comprehensive model evaluation findings offer practical guidance for the selection of appropriate methods for rigorous analyses of differential expression in metatranscriptomics data. Selection of an optimal method is likely to increase the possibility of detecting true signals while minimizing the chance of claiming false ones.

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“…In the following section, we describe the microbiome and metabolome data used for the new method development and application, alongside the three contributing studies. ZOE 2.0 study data ZOE 2.0 is a community-based molecular epidemiologic study of early childhood oral health in North Carolina (11,12). The study collected clinical information on preschool-age children's (ages 3-5) dental cavities (i.e., referred to early childhood caries or ECC) (13) and supragingival biofilm samples from a sample of over 6,000 children (14).…”

Section: Cohort and Data Descriptionmentioning

confidence: 99%

“…The study collected clinical information on preschool-age children's (ages 3-5) dental cavities (i.e., referred to early childhood caries or ECC) (13) and supragingival biofilm samples from a sample of over 6,000 children (14). A subset of participants' biofilm samples underwent metagenomics, metatranscriptomics, and metabolomics analyses, under the umbrella Trans-Omics for Precision Dentistry and Early Childhood Caries or TOPDECC (accession: phs002232.v1.p1) (11). As such, metagenomics (i.e., shotgun whole genome sequencing or WGS), metatranscriptomics (i.e., RNA-seq), and global metabolomics data (i.e., ultra-performance liquid chromatography-tandem mass spectrometry) (5,15,16) from supragingival biofilm samples of ~300 children, paired with clinical information on ECC are available.…”

Section: Cohort and Data Descriptionmentioning

confidence: 99%

Improved Metabolite Prediction Using Microbiome Data-Based Elastic Net Models

Xie

Cho

Lin

et al. 2021

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data are still scant. While many studies generate microbiome data, they lack matched metabolomics data or have considerable missing proportions of metabolites. Since metabolomics is key to understanding microbial and general biological activities, the possibility of imputing individual metabolites or inferring metabolomics pathways from microbial taxonomy or metagenomics is intriguing. Importantly, current metabolomics profiling methods such as the HMP Unified Metabolic Analysis Network (HUMAnN) have unknown accuracy and are limited in their ability to predict individual metabolites. To address this gap, we developed a novel metabolite prediction method, and we present its application and evaluation in an oral microbiome study. We developed ENVIM based on the Elastic Net Model (ENM) to predict metabolites using micorbiome data. ENVIM introduces an extra step to ENM to consider variable importance scores and thus achieve better prediction power. We investigate the metabolite prediction performance of ENVIM using metagenomic and metatranscriptomic data in a supragingival biofilm multi-omics dataset of 297 children ages 3-5 who were participants of a community-based study of early childhood oral health (ZOE 2.0) in North Carolina, United States. We further validate ENVIM in two additional publicly available multi-omics datasets generated from studies of gut health and vagina health. We select gene-family sets based on variable importance scores and modify the existing ENM strategy used in the MelonnPan prediction software to accommodate the unique features of microbiome and metabolome data. We evaluate metagenomic and metatranscriptomic predictors and compare the prediction performance of ENVIM to the standard ENM employed in MelonnPan. The newly-developed ENVIM method showed superior metabolite predictive accuracy than MelonnPan using metatranscriptomics data only, metagenomics data only, or both of these two. Both methods perform better prediction using gut or vagina microbiome data than using oral microbiome data for the samples' corresponding metabolites. The top predictable compounds have been reported in all these three datasets from three different body sites. Enrichment of prediction some contributing species has been detected.

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Cohort Profile: ZOE 2.0—A Community-Based Genetic Epidemiologic Study of Early Childhood Oral Health

Cited by 23 publications

References 38 publications

Metabolomics Insights in Early Childhood Caries

Metabolomics Insights in Early Childhood Caries

Comprehensive evaluation of methods for differential expression analysis of metatranscriptomics data

Improved Metabolite Prediction Using Microbiome Data-Based Elastic Net Models

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