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      BONCAT-FACS-Seq reveals the active fraction of a biocrust community undergoing a wet-up event

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          Abstract

          Determining which microorganisms are active within soil communities remains a major technical endeavor in microbial ecology research. One promising method to accomplish this is coupling bioorthogonal non-canonical amino acid tagging (BONCAT) with fluorescence activated cell sorting (FACS) which sorts cells based on whether or not they are producing new proteins. Combined with shotgun metagenomic sequencing (Seq), we apply this method to profile the diversity and potential functional capabilities of both active and inactive microorganisms in a biocrust community after being resuscitated by a simulated rain event. We find that BONCAT-FACS-Seq is capable of discerning the pools of active and inactive microorganisms, especially within hours of applying the BONCAT probe. The active and inactive components of the biocrust community differed in species richness and composition at both 4 and 21 h after the wetting event. The active fraction of the biocrust community is marked by taxa commonly observed in other biocrust communities, many of which play important roles in species interactions and nutrient transformations. Among these, 11 families within the Firmicutes are enriched in the active fraction, supporting previous reports indicating that the Firmicutes are key early responders to biocrust wetting. We highlight the apparent inactivity of many Actinobacteria and Proteobacteria through 21 h after wetting, and note that members of the Chitinophagaceae, enriched in the active fraction, may play important ecological roles following wetting. Based on the enrichment of COGs in the active fraction, predation by phage and other bacterial members, as well as scavenging and recycling of labile nutrients, appear to be important ecological processes soon after wetting. To our knowledge, this is the first time BONCAT-FACS-Seq has been applied to biocrust samples, and therefore we discuss the potential advantages and shortcomings of coupling metagenomics to BONCAT to intact soil communities such as biocrust. In all, by pairing BONCAT-FACS and metagenomics, we are capable of highlighting the taxa and potential functions that typifies the microbes actively responding to a rain event.

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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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              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

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                26 June 2023
                2023
                : 14
                : 1176751
                Affiliations
                [1] 1Intercollege Graduate Degree Program in Ecology, Huck Institutes of the Life Sciences, The Pennsylvania State University , University Park, PA, United States
                [2] 2Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory , Berkeley, CA, United States
                [3] 3Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology , Thuwal, Saudi Arabia
                [4] 4Lawrence Berkeley National Laboratory, DOE Joint Genome Institute , Berkeley, CA, United States
                [5] 5Department of Ecosystem Science and Management, The Pennsylvania State University , University Park, PA, United States
                Author notes

                Edited by: Sonia Chamizo, University of Almería, Spain

                Reviewed by: Capucine Baubin, University of Colorado Boulder, United States; Nicholas J. Reichart, Pacific Northwest National Laboratory (DOE), United States

                *Correspondence: Estelle Couradeau, efc5279@ 123456psu.edu
                Article
                10.3389/fmicb.2023.1176751
                10330726
                37434715
                d0489d25-fc76-4a33-a01e-bb2b3dd8b22e
                Copyright © 2023 Trexler, Van Goethem, Goudeau, Nath, Malmstrom, Northen and Couradeau.

                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
                : 28 February 2023
                : 05 June 2023
                Page count
                Figures: 6, Tables: 1, Equations: 0, References: 78, Pages: 14, Words: 10700
                Funding
                Funded by: U.S. Department of Energy, doi 10.13039/100000015;
                TN, EC, and MV gratefully acknowledge funding from the Office of Science Early Career Research Program, Office of Biological and Environmental Research, of the U. S. Department of Energy under contract number DE-AC02-05CH11231. Research was conducted by the U.S. Department of Energy Joint Genome Institute ( https://ror.org/04xm1d337), a DOE Office of Science User Facility, was supported by the Office of Science of the U.S. Department of Energy operated under Contract No. DE-AC02-05CH11231.
                Categories
                Microbiology
                Original Research
                Custom metadata
                Terrestrial Microbiology

                Microbiology & Virology
                boncat,biocrust,soil metagenomics,active microorganisms,soil wetting
                Microbiology & Virology
                boncat, biocrust, soil metagenomics, active microorganisms, soil wetting

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