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      Environments and Hosts Structure the Bacterial Microbiomes of Fungus-Gardening Ants and their Symbiotic Fungus Gardens

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          Abstract

          The fungus gardening-ant system is considered a complex, multi-tiered symbiosis, as it is composed of ants, their fungus, and microorganisms associated with either ants or fungus. We examine the bacterial microbiome of Trachymyrmex septentrionalis and Mycetomoellerius turrifex ants and their symbiotic fungus gardens, using 16S rRNA Illumina sequencing, over a region spanning approximately 350 km (east and central Texas). Typically, microorganisms can be acquired from a parent colony (vertical transmission) or from the environment (horizontal transmission). Because the symbiosis is characterized by co-dispersal of the ants and fungus, elements of both ant and fungus garden microbiome could be characterized by vertical transmission. The goals of this study were to explore how both the ant and fungus garden bacterial microbiome are acquired. The main findings were that different mechanisms appear to explain the structure the microbiomes of ants and their symbiotic fungus gardens. Ant associated microbiomes had a strong host ant signature, which could be indicative of vertical inheritance of the ant associated bacterial microbiome or an unknown mechanism of active uptake or screening. On the other hand, the bacterial microbiome of the fungus garden was more complex in that some bacterial taxa appear to be structured by the ant host species, whereas others by fungal lineage or the environment (geographic region). Thus bacteria in fungus gardens appear to be acquired both horizontally and vertically.

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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

            The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

            SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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              Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

                Author and article information

                Contributors
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                Journal
                Microbial Ecology
                Microb Ecol
                Springer Science and Business Media LLC
                0095-3628
                1432-184X
                August 2023
                November 07 2022
                August 2023
                : 86
                : 2
                : 1374-1392
                Article
                10.1007/s00248-022-02138-x
                36344828
                93cc496f-360b-44ae-9afc-10b783b5398b
                © 2023

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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