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      Novel Interactions Between Phytoplankton and Bacteria Shape Microbial Seasonal Dynamics in Coastal Ocean Waters

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          Abstract

          Trophic interactions between marine phytoplankton and heterotrophic bacteria are at the base of the biogeochemical carbon cycling in the ocean. However, the specific interactions taking place between phytoplankton and bacterial taxa remain largely unexplored, particularly out of phytoplankton blooming events. Here, we applied network analysis to a 3.5-year time-series dataset to assess the specific associations between different phytoplankton and bacterial taxa along the seasonal scale, distinguishing between free-living and particle-attached bacteria. Using a newly developed network post-analysis technique we removed bacteria-phytoplankton correlations that were primarily driven by environmental parameters, to detect potential biotic interactions. Our results indicate that phytoplankton dynamics may be a strong driver of the inter-annual variability in bacterial community composition. We found the highest abundance of specific bacteria-phytoplankton associations in the particle-attached fraction, indicating a tighter bacteria-phytoplankton association than in the free-living fraction. In the particle-associated fraction we unveiled novel potential associations such as the one between Planctomycetes taxa and the diatom Leptocylindrus spp. Consistent correlations were also found between free-living bacterial taxa and different diatoms, including novel associations such as those between SAR11 with Naviculales diatom order, and between Actinobacteria and Cylindrotheca spp. We also confirmed previously known associations between Rhodobacteraceae and Thalassiosira spp. Our results expand our view on bacteria-phytoplankton associations, suggesting that taxa-specific interactions may largely impact the seasonal dynamics of heterotrophic bacterial communities.

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          Most cited references85

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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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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              Statistical significance for genomewide studies.

              With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genomewide data set are tested against some null hypothesis, where a number of features are expected to be significant. Here we propose an approach to measuring statistical significance in these genomewide studies based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q value is associated with each tested feature. The q value is similar to the well known p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.
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                Author and article information

                Journal
                Frontiers in Marine Science
                Front. Mar. Sci.
                Frontiers Media SA
                2296-7745
                July 22 2022
                July 22 2022
                : 9
                Article
                10.3389/fmars.2022.901201
                7a656384-97f8-4256-ab4c-bbbfa836c4c4
                © 2022

                Free to read

                https://creativecommons.org/licenses/by/4.0/

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