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      Analysis of the Gut Mycobiome in Adult Patients with Type 1 and Type 2 Diabetes Using Next-Generation Sequencing (NGS) with Increased Sensitivity—Pilot Study

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          Abstract

          The studies on microbiome in the human digestive tract indicate that fungi could also be one of the external factors affecting development of diabetes. The aim of this study was to evaluate the quantitative and qualitative mycobiome composition in the colon of the adults with type 1 (T1D), n = 26 and type 2 (T2D) diabetes, n = 24 compared to the control group, n = 26. The gut mycobiome was characterized in the stool samples using the analysis of the whole internal transcribed spacer (ITS) region of the fungal rDNA gene cluster by next-generation sequencing (NGS) with increased sensitivity. At the L2 (phylum) level, Basidiomycota fungi were predominant in all 3 study groups. Group T1D presented significantly lower number of Ascomycota compared to the T2D group, and at the L6 (genus) level, the T1D group presented significantly lower number of Saccharomyces genus compared to control and T2D groups. In the T1D group, a significant positive correlation between total cholesterol and low-density lipoprotein cholesterol (LDL-C) levels and fungi of the genus Saccharomyces, and in the T2D group, a negative correlation between the total cholesterol level and Malassezia genus was found. The obtained results seem to be a good foundation to extend the analysis of the relationship between individual genera and species of fungi and the parameters determining the metabolism of carbohydrates and lipids in the human body.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            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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              G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences

              G*Power (Erdfelder, Faul, & Buchner, 1996) was designed as a general stand-alone power analysis program for statistical tests commonly used in social and behavioral research. G*Power 3 is a major extension of, and improvement over, the previous versions. It runs on widely used computer platforms (i.e., Windows XP, Windows Vista, and Mac OS X 10.4) and covers many different statistical tests of the t, F, and chi2 test families. In addition, it includes power analyses for z tests and some exact tests. G*Power 3 provides improved effect size calculators and graphic options, supports both distribution-based and design-based input modes, and offers all types of power analyses in which users might be interested. Like its predecessors, G*Power 3 is free.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Nutrients
                Nutrients
                nutrients
                Nutrients
                MDPI
                2072-6643
                25 March 2021
                April 2021
                : 13
                : 4
                : 1066
                Affiliations
                [1 ]Department of Molecular Medical Microbiology, Chair of Microbiology, Faculty of Medicine, Jagiellonian University Medical College, 18 Czysta Street, 31-121 Krakow, Poland; dominika.salamon@ 123456uj.edu.pl (D.S.); agnieszka.sroka@ 123456uj.edu.pl (A.S.-O.)
                [2 ]Center for Experimental and Innovative Medicine, University of Agriculture, Krakow1c Rędzina Street, 30-248 Krakow, Poland; artur.gurgul@ 123456urk.edu.pl (A.G.); zbigniew.arent@ 123456urk.edu.pl (Z.A.)
                [3 ]Department of Metabolic Diseases, Faculty of Medicine, Jagiellonian University Medical College, 2 Jakubowskiego Street, 30-688 Krakow, Poland; magdalena.szopa@ 123456uj.edu.pl (M.S.); maciej.malecki@ 123456uj.edu.pl (M.T.M.)
                [4 ]Department of Metabolic Diseases, University Hospital, 2 Jakubowskiego Street, 30-688 Krakow, Poland
                [5 ]Department of Epidemiology of Infections, Chair of Microbiology, Faculty of Medicine, Jagiellonian University Medical College, 31-121 Krakow, Poland; malgorzata.bulanda@ 123456uj.edu.pl
                Author notes
                [* ]Correspondence: tomasz.gosiewski@ 123456uj.edu.pl ; Tel.: +48-12-633-25-67
                Author information
                https://orcid.org/0000-0002-5974-5520
                https://orcid.org/0000-0001-5979-144X
                https://orcid.org/0000-0003-4725-5943
                Article
                nutrients-13-01066
                10.3390/nu13041066
                8064496
                33806027
                35ca2241-feff-491c-8ad4-9a7048e07df4
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 10 February 2021
                : 22 March 2021
                Categories
                Article

                Nutrition & Dietetics
                diabetes,gut mycobiome,next-generation sequencing (ngs)
                Nutrition & Dietetics
                diabetes, gut mycobiome, next-generation sequencing (ngs)

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