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      The nasal mycobiome of individuals with allergic rhinitis and asthma differs from that of healthy controls in composition, structure and function

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          Abstract

          Allergic rhinitis (AR) and asthma (AS) are two of the most common chronic respiratory diseases and a major public health concern. Multiple studies have demonstrated the role of the nasal bacteriome in AR and AS, but little is known about the airway mycobiome and its potential association to airway inflammatory diseases. Here we used the internal transcriber spacers (ITS) 1 and 2 and high-throughput sequencing to characterize the nasal mycobiome of 339 individuals with AR, AR with asthma (ARAS), AS and healthy controls (CT). Seven to ten of the 14 most abundant fungal genera ( Malassezia, Alternaria, Cladosporium, Penicillium, Wallemia, Rhodotorula, Sporobolomyces, Naganishia, Vishniacozyma , and Filobasidium) in the nasal cavity differed significantly ( p ≤ 0.049) between AS, AR or ARAS, and CT. However, none of the same genera varied significantly between the three respiratory disease groups. The nasal mycobiomes of AR and ARAS patients showed the highest intra-group diversity, while CT showed the lowest. Alpha-diversity indices of microbial richness and evenness only varied significantly ( p ≤ 0.024) between AR or ARAS and CT, while all disease groups showed significant differences ( p ≤ 0.0004) in microbial structure (i.e., beta-diversity indices) when compared to CT samples. Thirty metabolic pathways (PICRUSt2) were differentially abundant (Wald’s test) between AR or ARAS and CT patients, but only three of them associated with 5-aminoimidazole ribonucleotide (AIR) biosynthesis were over abundant (log2 Fold Change >0.75) in the ARAS group. AIR has been associated to fungal pathogenesis in plants. Spiec-Easi fungal networks varied among groups, but AR and ARAS showed more similar interactions among their members than with those in the CT mycobiome; this suggests chronic respiratory allergic diseases may disrupt fungal connectivity in the nasal cavity. This study contributes valuable fungal data and results to understand the relationships between the nasal mycobiome and allergy-related conditions. It demonstrates for the first time that the nasal mycobiota varies during health and allergic rhinitis (with and without comorbid asthma) and reveals specific taxa, metabolic pathways and fungal interactions that may relate to chronic airway disease.

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

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              Fitting Linear Mixed-Effects Models Usinglme4

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                Author and article information

                Contributors
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                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                17 December 2024
                2024
                : 15
                : 1464257
                Affiliations
                [1] 1Department of Biostatistics and Bioinformatics, Computational Biology Institute, Milken Institute School of Public Health, The George Washington University , Washington, DC, United States
                [2] 2CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Campus Agrário de Vairão , Vairão, Portugal
                [3] 3Departamento de Microbiología, Facultad de Ciencias de la Salud, Universidad de Talca , Talca, Chile
                [4] 4Centro de Ecología Integrativa, Universidad de Talca , Talca, Chile
                [5] 5Centro de Investigaciones Médicas, Escuela de Medicina, Universidad de Talca , Talca, Chile
                [6] 6Serviço de Imunologia Básica e Clínica, Departamento de Patologia, Faculdade de Medicina da Universidade do Porto , Porto, Portugal
                [7] 7Centro de Investigação em Tecnologias e Serviços de Saúde (CINTESIS@RISE), Faculdade de Medicina da Universidade do Porto , Porto, Portugal
                [8] 8Serviço de Imunoalergologia, Unidade Local de Saúde São João (ULS São João) , Porto, Portugal
                [9] 9UCIBIO, Research Unit on Applied Molecular Biosciences, Forensic Sciences Research Laboratory, University Institute of Health Sciences (1H-TOXRUN, IUCS-CESPU), Avenida Central de Gandra , Gandra, Portugal
                [10] 10Associate Laboratory i4HB—Institute for Health and Bioeconomy, University Institute of Health Sciences—CESPU, Avenida Central de Gandra , Gandra, Portugal
                Author notes

                Edited by: Alina Maria Holban, University of Bucharest, Romania

                Reviewed by: Richard George Douglas, The University of Auckland, New Zealand

                Elopy Sibanda, National University of Science and Technology, Zimbabwe

                Yu Sun, South China Agricultural University, China

                *Correspondence: Marcos Pérez-Losada, mlosada@ 123456gwu.edu
                Article
                10.3389/fmicb.2024.1464257
                11685215
                39741585
                ef7e43b2-0ccb-496e-ab34-a82260e727a8
                Copyright © 2024 Pérez-Losada, Castro-Nallar, García-Huidobro, Boechat, Delgado, Rama and Oliveira.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 14 July 2024
                : 17 October 2024
                Page count
                Figures: 5, Tables: 1, Equations: 0, References: 123, Pages: 14, Words: 10548
                Funding
                Funded by: Fundação para a Ciência e a Tecnologia, doi 10.13039/501100001871;
                Award ID: PTDC/ASP-PES/27953/2017
                Award ID: POCI-01-0145-FEDER-027953
                Funded by: European Social Fund, doi 10.13039/501100004895;
                Award ID: IF/00764/2013
                Funded by: European Regional Development Fund, doi 10.13039/501100008530;
                Funded by: FCT
                Funded by: Ministério da Educação e Ciência, doi 10.13039/501100003381;
                The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was co-funded by the EU via European Regional Development Fund (ERDF) and by national funds via the Fundação para a Ciência e a Tecnologia (FCT) and the project PTDC/ASP-PES/27953/2017—POCI-01-0145-FEDER-027953. MP-L was supported by the FCT under the “Programa Operacional Potencial Humano—Quadro de Referência Estratégico” Nacional funds from the European Social Fund and Portuguese “Ministério da Educação e Ciência” IF/00764/2013.
                Categories
                Microbiology
                Original Research
                Custom metadata
                Infectious Agents and Disease

                Microbiology & Virology
                allergy,asthma,its,mycobiome,nasal cavity,portugal,rhinitis
                Microbiology & Virology
                allergy, asthma, its, mycobiome, nasal cavity, portugal, rhinitis

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