Intestinal microbiota composition and efficacy of iron supplementation in Peruvian children.

OBJECTIVE: Despite repeated public health interventions, anemia prevalence among children remains a concern. We use an evolutionary medicine perspective to examine the intestinal microbiome as a pathway underlying the efficacy of iron-sulfate treatment. This study explores whether gut microbiota composition differs between anemic children who respond and do not respond to treatment at baseline and posttreatment and if specific microbiota taxa remain associated with response to iron supplementation after controlling for relevant inflammatory and pathogenic variables. METHODS: Data come from 49 pre-school-aged anemic children living in San Juan de Lurigancho, Lima, Peru. We tested for differences in alpha and beta diversity using QIIME 2 and performed differential abundance testing in DESeq2 in R. We ran multivariate regression models to assess associations between abundance of specific taxa and response while controlling for relevant variables in Stata 17. RESULTS: While we found no evidence for gut microbiota diversity associated with child response to iron treatment, we observed several differential abundance patterns between responders and non-responders at both timepoints. Additionally, we present support for a nonzero relationship between lower relative abundance of Barnesiellaceae and response to iron supplementation in samples collected before and after treatment. CONCLUSION: While larger studies and more specific approaches are needed to understand the relationship between microbes and anemia in an epidemiological context, this study suggests that investigating nutritional status and pathogen exposure is key to better understanding the gut microbiome and impact of iron fortification.

Longitudinal Analysis of Canine Oral Microbiome Using Whole Genome Sequencing in Aging Companion Dogs.

Aged companion dogs have a high prevalence of periodontal disease and canine cognitive dysfunction syndrome (CCDS) and the two disorders are correlated. Similarly, periodontal disease and Alzheimer's Disease are correlated in people. However, little is known about the oral microbiota of aging dogs. The goal of this project was to characterize the longitudinal changes in oral microbiota in aged dogs. Oral swabs were taken from ten senior client-owned dogs on 2-3 occasions spanning 24 months and they underwent whole genome shotgun (WGS) sequencing. Cognitive status was established at each sampling time. A statistically significant increase in alpha diversity for bacterial and fungal species was observed between the first and last study visits. Bacteroidetes and proteobacteria were the most abundant bacterial phyla. Porphyromonas gulae was the most abundant bacterial species (11.6% of total reads). The species Lactobacillus gasseri had a statistically significant increase in relative abundance with age whereas Leptotrichia sp. oral taxon 212 had a statistically significant positive longitudinal association with cognition score. There is an increased fungal and bacterial alpha diversity in aging dogs over time and nearly universal oral dysbiosis. The role of the oral microbiota, particularly Leptotrichia and P. gulae and P. gingivalis, in aging and CCDS warrants further investigation.

BZINB Model-Based Pathway Analysis and Module Identification Facilitates Integration of Microbiome and Metabolome Data.

Integration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome) employ simple correlation-based network analyses; however, these methods are not always well-suited for microbiome analyses because they do not accommodate the excess zeros typically present in these data. In this paper, we introduce a bivariate zero-inflated negative binomial (BZINB) model-based network and module analysis method that addresses this limitation and improves microbiome-metabolome correlation-based model fitting by accommodating excess zeros. We use real and simulated data based on a multi-omics study of childhood oral health (ZOE 2.0; investigating early childhood dental caries, ECC) and find that the accuracy of the BZINB model-based correlation method is superior compared to Spearman's rank and Pearson correlations in terms of approximating the underlying relationships between microbial taxa and metabolites. The new method, BZINB-iMMPath, facilitates the construction of metabolite-species and species-species correlation networks using BZINB and identifies modules of (i.e., correlated) species by combining BZINB and similarity-based clustering. Perturbations in correlation networks and modules can be efficiently tested between groups (i.e., healthy and diseased study participants). Upon application of the new method in the ZOE 2.0 study microbiome-metabolome data, we identify that several biologically-relevant correlations of ECC-associated microbial taxa with carbohydrate metabolites differ between healthy and dental caries-affected participants. In sum, we find that the BZINB model is a useful alternative to Spearman or Pearson correlations for estimating the underlying correlation of zero-inflated bivariate count data and thus is suitable for integrative analyses of multi-omics data such as those encountered in microbiome and metabolome studies.

BZINB model-based pathway analysis and module identification facilitates integration of microbiome and metabolome data.

Integration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome) employ simple correlation-based network analyses; however, these methods are not always well-suited for microbiome analyses because they do not accommodate the excess zeros typically present in these data. In this paper, we introduce a bivariate zero-inflated negative binomial (BZINB) model-based network and module analysis method that addresses this limitation and improves microbiome-metabolome correlation-based model fitting by accommodating excess zeros. We use real and simulated data based on a multi-omics study of childhood oral health (ZOE 2.0; investigating early childhood dental disease, ECC) and find that the accuracy of the BZINB model-based correlation method is superior compared to Spearman’s rank and Pearson correlations in terms of approximating the underlying relationships between microbial taxa and metabolites. The new method, BZINB-iMMPath facilitates the construction of metabolite-species and species-species correlation networks using BZINB and identifies modules of (i.e., correlated) species by combining BZINB and similarity-based clustering. Perturbations in correlation networks and modules can be efficiently tested between groups (i.e., healthy and diseased study participants). Upon application of the new method in the ZOE 2.0 study microbiome-metabolome data, we identify that several biologically-relevant correlations of ECC-associated microbial taxa with carbohydrate metabolites differ between healthy and dental caries-affected participants. In sum, we find that the BZINB model is a useful alternative to Spearman or Pearson correlations for estimating the underlying correlation of zero-inflated bivariate count data and thus is suitable for integrative analyses of multi-omics data such as those encountered in microbiome and metabolome studies.

ExpressHeart: Web Portal to Visualize Transcriptome Profiles of Non-Cardiomyocyte Cells.

Unveiling the molecular features in the heart is essential for the study of heart diseases. Non-cardiomyocytes (nonCMs) play critical roles in providing structural and mechanical support to the working myocardium. There is an increasing amount of single-cell RNA-sequencing (scRNA-seq) data characterizing the transcriptomic profiles of nonCM cells. However, no tool allows researchers to easily access the information. Thus, in this study, we develop an open-access web portal, ExpressHeart, to visualize scRNA-seq data of nonCMs from five laboratories encompassing three species. ExpressHeart enables comprehensive visualization of major cell types and subtypes in each study; visualizes gene expression in each cell type/subtype in various ways; and facilitates identifying cell-type-specific and species-specific marker genes. ExpressHeart also provides an interface to directly combine information across datasets, for example, generating lists of high confidence DEGs by taking the intersection across different datasets. Moreover, ExpressHeart performs comparisons across datasets. We show that some homolog genes (e.g., Mmp14 in mice and mmp14b in zebrafish) are expressed in different cell types between mice and zebrafish, suggesting different functions across species. We expect ExpressHeart to serve as a valuable portal for investigators, shedding light on the roles of genes on heart development in nonCM cells.

A Web-Based Rendering Application for Communicating Dental Conditions.

The importance of visual aids in communicating clinical examination findings or proposed treatments in dentistry cannot be overstated. Similarly, communicating dental research results with tooth surface-level precision is impractical without visual representations. Here, we present the development, deployment, and two real-life applications of a web-based data visualization informatics pipeline that converts tooth surface-level information to colorized, three-dimensional renderings. The core of the informatics pipeline focuses on texture (UV) mapping of a pre-existing model of the human primary dentition. The 88 individually segmented tooth surfaces receive independent inputs that are represented in colors and textures according to customizable user specifications. The web implementation SculptorHD, deployed on the Google Cloud Platform, can accommodate manually entered or spreadsheet-formatted tooth surface data and allows the customization of color palettes and thresholds, as well as surface textures (e.g., condition-free, caries lesions, stainless steel, or ceramic crowns). Its current implementation enabled the visualization and interpretation of clinical early childhood caries (ECC) subtypes using latent class analysis-derived caries experience summary data. As a demonstration of its potential clinical utility, the tool was also used to simulate the restorative treatment presentation of a severe ECC case, including the use of stainless steel and ceramic crowns. We expect that this publicly available web-based tool can aid clinicians and investigators deliver precise, visual presentations of dental conditions and proposed treatments. The creation of rapidly adjustable lifelike dental models, integrated to existing electronic health records and responsive to new clinical findings or planned for future work, is likely to boost two-way communication between clinicians and their patients.

An Automated Machine Learning Classifier for Early Childhood Caries.

Purpose: The purpose of the study was to develop and evaluate an automated machine learning algorithm (AutoML) for children's classification according to early childhood caries (ECC) status. Methods: Clinical, demographic, behavioral, and parent-reported oral health status information for a sample of 6,404 three- to five-year-old children (mean age equals 54 months) participating in an epidemiologic study of early childhood oral health in North Carolina was used. ECC prevalence (decayed, missing, and filled primary teeth surfaces [dmfs] score greater than zero, using an International Caries Detection and Assessment System score greater than or equal to three caries lesion detection threshold) was 54 percent. Ten sets of ECC predictors were evaluated for ECC classification accuracy (i.e., area under the ROC curve [AUC], sensitivity [Se], and positive predictive value [PPV]) using an AutoML deployment on Google Cloud, followed by internal validation and external replication. Results: A parsimonious model including two terms (i.e., children's age and parent-reported child oral health status: excellent/very good/good/fair/poor) had the highest AUC (0.74), Se (0.67), and PPV (0.64) scores and similar performance using an external National Health and Nutrition Examination Survey (NHANES) dataset (AUC equals 0.80, Se equals 0.73, PPV equals 0.49). Contrarily, a comprehensive model with 12 variables covering demographics (e.g., race/ethnicity, parental education), oral health behaviors, fluoride exposure, and dental home had worse performance (AUC equals 0.66, Se equals 0.54, PPV equals 0.61). Conclusions: Parsimonious automated machine learning early childhood caries classifiers, including single-item self-reports, can be valuable for ECC screening. The classifier can accommodate biological information that can help improve its performance in the future.

Genome Sequence of Citrobacter freundii AMC0703, Isolated from the Intestinal Lumen of an 11-Year-Old Organ Donor.

Citrobacter freundii AMC0703 was isolated from the intestinal mucosa of an 11-year-old organ donor. Genome analysis revealed the presence of multiple factors potentially aiding in pathogenicity, including fimbriae, flagella, and genes encoding resistance to fluoroquinolones, cephamycin, fosfomycin, and aminocoumarin.

Genome Sequence of Eubacterium callanderi AMC0717, Isolated from the Colonic Mucosa of an 11-Year-Old Organ Donor.

Eubacterium callanderi AMC0717 was isolated from the mucosa of the transverse colon of an 11-year-old organ donor. This strain contains genes putatively encoding short-chain fatty acids (SCFAs), exopolysaccharide (EPS), and several B vitamins.

The Supragingival Biofilm in Early Childhood Caries: Clinical and Laboratory Protocols and Bioinformatics Pipelines Supporting Metagenomics, Metatranscriptomics, and Metabolomics Studies of the Oral Microbiome.

Early childhood caries (ECC) is a biofilm-mediated disease. Social, environmental, and behavioral determinants as well as innate susceptibility are major influences on its incidence; however, from a pathogenetic standpoint, the disease is defined and driven by oral dysbiosis. In other words, the disease occurs when the natural equilibrium between the host and its oral microbiome shifts toward states that promote demineralization at the biofilm-tooth surface interface. Thus, a comprehensive understanding of dental caries as a disease requires the characterization of both the composition and the function or metabolic activity of the supragingival biofilm according to well-defined clinical statuses. However, taxonomic and functional information of the supragingival biofilm is rarely available in clinical cohorts, and its collection presents unique challenges among very young children. This paper presents a protocol and pipelines available for the conduct of supragingival biofilm microbiome studies among children in the primary dentition, that has been designed in the context of a large-scale population-based genetic epidemiologic study of ECC. The protocol is being developed for the collection of two supragingival biofilm samples from the maxillary primary dentition, enabling downstream taxonomic (e.g., metagenomics) and functional (e.g., transcriptomics and metabolomics) analyses. The protocol is being implemented in the assembly of a pediatric precision medicine cohort comprising over 6000 participants to date, contributing social, environmental, behavioral, clinical, and biological data informing ECC and other oral health outcomes.

Dynamic evolution of pathogenicity revealed by sequencing and comparative genomics of 19 Pseudomonas syringae isolates.

Closely related pathogens may differ dramatically in host range, but the molecular, genetic, and evolutionary basis for these differences remains unclear. In many Gram- negative bacteria, including the phytopathogen Pseudomonas syringae, type III effectors (TTEs) are essential for pathogenicity, instrumental in structuring host range, and exhibit wide diversity between strains. To capture the dynamic nature of virulence gene repertoires across P. syringae, we screened 11 diverse strains for novel TTE families and coupled this nearly saturating screen with the sequencing and assembly of 14 phylogenetically diverse isolates from a broad collection of diseased host plants. TTE repertoires vary dramatically in size and content across all P. syringae clades; surprisingly few TTEs are conserved and present in all strains. Those that are likely provide basal requirements for pathogenicity. We demonstrate that functional divergence within one conserved locus, hopM1, leads to dramatic differences in pathogenicity, and we demonstrate that phylogenetics-informed mutagenesis can be used to identify functionally critical residues of TTEs. The dynamism of the TTE repertoire is mirrored by diversity in pathways affecting the synthesis of secreted phytotoxins, highlighting the likely role of both types of virulence factors in determination of host range. We used these 14 draft genome sequences, plus five additional genome sequences previously reported, to identify the core genome for P. syringae and we compared this core to that of two closely related non-pathogenic pseudomonad species. These data revealed the recent acquisition of a 1 Mb megaplasmid by a sub-clade of cucumber pathogens. This megaplasmid encodes a type IV secretion system and a diverse set of unknown proteins, which dramatically increases both the genomic content of these strains and the pan-genome of the species.