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      Salivary Microbiome Differences in Prepubertal Children With and Without Adrenal Androgen Excess

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          Abstract

          Background

          Premature adrenarche is a condition of childhood adrenal androgen excess (AAE) in the absence of gonadotropin-dependent puberty, and has been linked to insulin resistance and progression to metabolic syndrome. Microbial dysbiosis is associated with progression of inflammatory states and chronic diseases. Here, we aimed to examine the salivary microbiomes of children with adrenal androgen excess (AAE) and assess the relationship with adrenal androgens and metabolic parameters.

          Methods

          In a prospective cross-sectional study of children with AAE and healthy controls, adrenal and metabolic parameters were characterized and salivary microbiome was profiled using V3-V4 16S rDNA gene amplicon sequencing.

          Results

          There was increased α-diversity in AAE (5 M, 15 F) compared to controls (3 M, 8 F), with positive correlation of 11OHA4, 11KA4, testosterone, androstenedione, DHEA, and DHEAS. Subanalyses showed increased α-diversity in both overweight/obese AAE and normal weight AAE compared to normal weight controls. Genus Peptostreptococcus, Veillonella, and Streptococcus salivarius were increased in normal weight AAE. Genus Prevotella, Abiotrophia, and Neisseria were increased in overweight/obese AAE.

          Conclusion

          These pilot data demonstrate differences in salivary microbiome profiles of children with and without AAE. Further studies are needed to assess the causal relationships between adrenal androgens, metabolic dysfunction, and salivary microbiome composition.

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          Most cited references59

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          Is Open Access

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            DADA2: High resolution sample inference from Illumina amplicon data

            We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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              Is Open Access

              phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data

              Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
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                Author and article information

                Journal
                0100714
                6400
                Pediatr Res
                Pediatr Res
                Pediatric research
                0031-3998
                1530-0447
                19 July 2021
                June 2022
                02 August 2021
                11 July 2022
                : 91
                : 7
                : 1797-1803
                Affiliations
                [1 ]Division of Pediatric Endocrinology, Diabetes and Metabolism, Columbia University Irving Medical Center, New York, New York 10032 USA
                [2 ]Department of Medicine and Microbiome & Pathogen Genomics Collaborative Center, Columbia University Irving Medical Center, New York, New York 10032 USA
                [3 ]Department of Pharmacology, University of Michigan, Ann Arbor, Michigan 48109 USA
                Author notes
                [* ]Corresponding Author: Sharon E. Oberfield, MD, Division of Pediatric Endocrinology, Diabetes and Metabolism, Columbia University Irving Medical Center, 622 West 168 Street, PH 17W – 307, New York, New York 10032, Tel: 212-305-6559, Fax: 212-305-4778, seo8@ 123456cumc.columbia.edu

                Author Contributions

                BKW, ACB, HP, RJA, SEO, and ACU made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data.

                BKW, ACB, HP, SEO, and ACU were involved in drafting the article or revising it critically for important intellectual content.

                RJA, SEO, and ACU provided final approval of the version to be published.

                Article
                NIHMS1722880
                10.1038/s41390-021-01661-w
                8807752
                34341500
                3b22ddd1-1f77-438f-b777-ccaae59f68c1

                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

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                Pediatrics
                Pediatrics

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