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      Gene expression dynamics of natural assemblages of heterotrophic flagellates during bacterivory

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          Abstract

          Background

          Marine heterotrophic flagellates (HF) are dominant bacterivores in the ocean, where they represent the trophic link between bacteria and higher trophic levels and participate in the recycling of inorganic nutrients for regenerated primary production. Studying their activity and function in the ecosystem is challenging since most of the HFs in the ocean are still uncultured. In the present work, we investigated gene expression of natural HF communities during bacterivory in four unamended seawater incubations.

          Results

          The most abundant species growing in our incubations belonged to the taxonomic groups MAST-4, MAST-7, Chrysophyceae, and Telonemia. Gene expression dynamics were similar between incubations and could be divided into three states based on microbial counts, each state displaying distinct expression patterns. The analysis of samples where HF growth was highest revealed some highly expressed genes that could be related to bacterivory. Using available genomic and transcriptomic references, we identified 25 species growing in our incubations and used those to compare the expression levels of these specific genes.

          Conclusions

          Our results indicate that several peptidases, together with some glycoside hydrolases and glycosyltransferases, are more expressed in phagotrophic than in phototrophic species, and thus could be used to infer the process of bacterivory in natural assemblages.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s40168-023-01571-5.

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          Most cited references91

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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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            Trimmomatic: a flexible trimmer for Illumina sequence data

            Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Contributors
                obiol@icm.csic.es
                ramonm@icm.csic.es
                Journal
                Microbiome
                Microbiome
                Microbiome
                BioMed Central (London )
                2049-2618
                15 June 2023
                15 June 2023
                2023
                : 11
                : 134
                Affiliations
                [1 ]GRID grid.418218.6, ISNI 0000 0004 1793 765X, Department of Marine Biology and Oceanography, , Institut de Ciències del Mar (ICM-CSIC), ; Passeig Marítim de la Barceloneta 37-49, Barcelona, Catalonia 08003 Spain
                [2 ]GRID grid.418095.1, ISNI 0000 0001 1015 3316, Institute of Parasitology, Biology Centre, Czech Academy of Sciences, ; České Budějovice, Czech Republic
                [3 ]GRID grid.14509.39, ISNI 0000 0001 2166 4904, Faculty of Science, , University of South Bohemia, ; České Budějovice, Czech Republic
                Article
                1571
                10.1186/s40168-023-01571-5
                10268365
                37322519
                fe48bf14-bdea-4dac-81c8-2dea849ae8fa
                © The Author(s) 2023

                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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 15 November 2022
                : 12 May 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004837, Ministerio de Ciencia e Innovación;
                Award ID: ALLFLAGS (CTM2016-75083-R)
                Award ID: “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S)
                Award ID: DIVAS (PID2019-108457RB-I00)
                Award ID: DIVAS (PID2019-108457RB-I00)
                Award ID: ALLFLAGS (CTM2016-75083-R)
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000780, European Commission;
                Award ID: SINGEK (H2020-MSCA-ITN-2015-675752)
                Award ID: MSCA-IF SMART (CZ.02.2.69/0.0/0.0/20_079/0017809)
                Award ID: SINGEK (H2020-MSCA-ITN-2015-675752)
                Award Recipient :
                Funded by: Consejo Superior de Investigaciones Cientificas (CSIC)
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2023

                bacterivory,heterotrophic flagellates,metatranscriptomics,functional genes,unamended incubations,phagocytosis,peptidases,glycosidases

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