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      A longitudinal field study of commercial honey bees shows that non-native probiotics do not rescue antibiotic treatment, and are generally not beneficial

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          Abstract

          Probiotics are widely used in agriculture including commercial beekeeping, but there is little evidence supporting their effectiveness. Antibiotic treatments can greatly distort the gut microbiome, reducing its protective abilities and facilitating the growth of antibiotic resistant pathogens. Commercial beekeepers regularly apply antibiotics to combat bacterial infections, often followed by an application of non-native probiotics advertised to ease the impact of antibiotic-induced gut dysbiosis. We tested whether probiotics affect the gut microbiome or disease prevalence, or rescue the negative effects of antibiotic induced gut dysbiosis. We found no difference in the gut microbiome or disease markers by probiotic application or antibiotic recovery associated with probiotic treatment. A colony-level application of the antibiotics oxytetracycline and tylosin produced an immediate decrease in gut microbiome size, and over the longer-term, very different and persistent dysbiotic effects on the composition and membership of the hindgut microbiome. Our results demonstrate the lack of probiotic effect or antibiotic rescue, detail the duration and character of dysbiotic states resulting from different antibiotics, and highlight the importance of the gut microbiome for honeybee health.

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities.

            mothur aims to be a comprehensive software package that allows users to use a single piece of software to analyze community sequence data. It builds upon previous tools to provide a flexible and powerful software package for analyzing sequencing data. As a case study, we used mothur to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the alpha and beta diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments. This analysis of more than 222,000 sequences was completed in less than 2 h with a laptop computer.
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              UCHIME improves sensitivity and speed of chimera detection

              Motivation: Chimeric DNA sequences often form during polymerase chain reaction amplification, especially when sequencing single regions (e.g. 16S rRNA or fungal Internal Transcribed Spacer) to assess diversity or compare populations. Undetected chimeras may be misinterpreted as novel species, causing inflated estimates of diversity and spurious inferences of differences between populations. Detection and removal of chimeras is therefore of critical importance in such experiments. Results: We describe UCHIME, a new program that detects chimeric sequences with two or more segments. UCHIME either uses a database of chimera-free sequences or detects chimeras de novo by exploiting abundance data. UCHIME has better sensitivity than ChimeraSlayer (previously the most sensitive database method), especially with short, noisy sequences. In testing on artificial bacterial communities with known composition, UCHIME de novo sensitivity is shown to be comparable to Perseus. UCHIME is >100× faster than Perseus and >1000× faster than ChimeraSlayer. Contact: robert@drive5.com Availability: Source, binaries and data: http://drive5.com/uchime. Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                kirk.anderson@usda.gov
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                23 January 2024
                23 January 2024
                2024
                : 14
                : 1954
                Affiliations
                [1 ]GRID grid.512827.b, ISNI 0000 0000 8931 265X, USDA-ARS Carl Hayden Bee Research Center, ; 2000 E. Allen Rd, Tucson, AZ 85719 USA
                [2 ]Department of Entomology and Center for Insect Science, University of Arizona, ( https://ror.org/03m2x1q45) Tucson, AZ 85721 USA
                [3 ]ScientificBeekeeping.com, Grass Valley, CA USA
                Article
                52118
                10.1038/s41598-024-52118-z
                10806037
                38263184
                8e8dbf86-a9ca-4c3b-8fac-7ef16f9973e9
                © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 31 October 2023
                : 14 January 2024
                Funding
                Funded by: ScientificBeekeeping.com
                Funded by: FundRef http://dx.doi.org/10.13039/100007917, Agricultural Research Service;
                Award ID: 2022-21000-021-00D
                Award ID: 2022-21000-021-00D
                Award ID: 2022-21000-021-00D
                Award ID: 2022-21000-021-00D
                Award ID: 2022-21000-021-00D
                Award ID: 2022-21000-021-00D
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                microbial ecology,microbiome,antimicrobials
                Uncategorized
                microbial ecology, microbiome, antimicrobials

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