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      Transcriptome characterization of Pocillopora grandis transplanted into reefs with different health conditions: potential stress indicators at the holobiont level

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          Abstract

          ABSTRACT Understanding the potential of coral to adapt to environmental stressors that cause coral bleaching is urgent. The molecular responses of the coral holobiont to such stress conditions determine the success of symbiosis. Therefore, by characterizing molecular stress responses at the holobiont level, we can develop better tools to diagnose its health and resilience. However, only some genomic-scale resources are available for reef-building corals from the Eastern Tropical Pacific. This study aimed to perform a transcriptomic characterization of the Pocillopora grandis holobiont following transplantation into environments with different health conditions in Colima, Mexico. Healthy specimens of two color morphotypes (green and brown) were collected in Carrizales, a reef in good condition. Coral fragments from the two morphotypes were relocated within the source location (local transplant stress). In contrast, similar fragments were translocated into another reef with a poorer health state, La Boquita. After 24 h, the transplanted fragments were collected, and RNA-seq was performed with the Illumina system. De novo transcriptome assembly, functional annotation, identification of co-expression modules, and enrichment analysis of molecular pathways were performed. The analysis of rRNA LSU, rRNA SSU, and COI sequences confirmed the coral species, whereas analysis of Rubisco, psbA, and psaA transcripts revealed that the dominant endosymbiont was Durusdinium sp. Gene expression patterns observed across samples suggest that the transplantation to a reef with a poorer state of health affected processes such as photosynthesis, calcium homeostasis, and immune response. The transcriptomic indicators proposed here are valuable for further studies examining the adaptation of Pocillopora corals to global changes.

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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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              WGCNA: an R package for weighted correlation network analysis

              Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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                Author and article information

                Journal
                lajar
                Latin american journal of aquatic research
                Lat. Am. J. Aquat. Res.
                Pontificia Universidad Católica de Valparaíso. Facultad de Recursos Naturales. Escuela de Ciencias del Mar (Valparaíso, , Chile )
                0718-560X
                March 2024
                : 52
                : 1
                : 119-149
                Affiliations
                [3] Ensenada Baja California orgnameUniversidad Autónoma de Baja California orgdiv1Instituto de Investigaciones orgdiv2Laboratorio de Ecología y Biología del Desarrollo Mexico
                [5] Ensenada Baja California orgnameCentro de Investigación Científica y de Educación Superior de Ensenada orgdiv1Departamento de Ecología Marina México
                [4] Ensenada Baja California orgnameUniversidad Autónoma de Baja California orgdiv1Instituto de Investigaciones Oceanológicas Mexico
                [2] Manzanillo orgnameUniversidad de Colima orgdiv1Facultad de Ciencias Marinas Mexico
                [1] Ensenada Baja California orgnameCentro de Investigación Científica y de Educación Superior de Ensenada orgdiv1Departamento de Biotecnología Marina orgdiv2Laboratorio de Genómica Funcional México
                Article
                S0718-560X2024000100119 S0718-560X(24)05200100119
                10.3856/vol52-issue1-fulltext-2991
                405edc46-2c1e-4fd8-ae91-612f5272f9d8

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

                History
                : 17 October 2022
                : 29 September 2023
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 146, Pages: 31
                Product

                SciELO Chile

                Categories
                Research Articles

                symbiosis,Pocillopora grandis,coral holobiont,stress indicators,co-expression,RNA-Seq

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