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. 2016 Oct 5:6:34645.
doi: 10.1038/srep34645.

A comprehensive insight into functional profiles of free-living microbial community responses to a toxic Akashiwo sanguinea bloom

Caiyun Yang  1   2 , Yi Li  2   3 , Yanyan Zhou  2 , Xueqian Lei  2 , Wei Zheng  2 , Yun Tian  2 , Joy D Van Nostrand  4 , Zhili He  4 , Liyou Wu  4 , Jizhong Zhou  4   5   6 , Tianling Zheng  2
Affiliations

A comprehensive insight into functional profiles of free-living microbial community responses to a toxic Akashiwo sanguinea bloom

Caiyun Yang et al. Sci Rep. .

Abstract

Phytoplankton blooms are a worldwide problem and can greatly affect ecological processes in aquatic systems, but its impacts on the functional potential of microbial communities are limited. In this study, a high-throughput microarray-based technology (GeoChip) was used to profile the functional potential of free-living microbes from the Xiamen Sea Area in response to a 2011 Akashiwo sanguinea bloom. The bloom altered the overall community functional structure. Genes that were significantly (p < 0.05) increased during the bloom included carbon degradation genes and genes involved in nitrogen (N) and/or phosphorus (P) limitation stress. Such significantly changed genes were well explained by chosen environmental factors (COD, nitrite-N, nitrate-N, dissolved inorganic phosphorus, chlorophyll-a and algal density). Overall results suggested that this bloom might enhance the microbial converting of nitrate to N2 and ammonia nitrogen, decrease P removal from seawater, activate the glyoxylate cycle, and reduce infection activity of bacteriophage. This study presents new information on the relationship of algae to other microbes in aquatic systems, and provides new insights into our understanding of ecological impacts of phytoplankton blooms.

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Figures

Figure 1
Figure 1. Location of sampling sites.
This map was created based on ArcGIS (Version 10, http://www.esri.com/software/arcgis/arcgis-for-desktop) by CYY.
Figure 2
Figure 2
(a) Shannon diversity and (b) Simpson evenness of total functional genes for bloom and control samples on day 1, 3, 4 and 5. Day 5 was bloom peak 2 (BP2). *p < 0.05; the error bars represent standard errors.
Figure 3
Figure 3. Detrended correspondence analysis of functional genes.
Figure 4
Figure 4
Hierarchical cluster analysis of functional genes from probes with significantly different abundance between bloom and control samples (a). Red indicates signal intensities above background (black), while green indicates signal intensities below background. Brighter red or green coloring indicates higher or lower signal intensities. Two major groups were observed. (b) The sum of signal intensities for genes from bloom and control sites in groups 1 and 2, (c) and the number of genes detected from each gene category in groups 1 and 2.
Figure 5
Figure 5
CCA of significantly changed gene data with selected environmental variables (a) and partial CCA-based variation partitioning analysis (VPA) (b). The selected environmental variables include chemical oxygen demand (COD), nitrite nitrogen (N2), nitrate nitrogen (N3), dissolved inorganic phosphorus (DIP), chlorophyll a (CHLa) and total algal density (TALG).
Figure 6
Figure 6. A subset of significantly changed genes in the bloom compared to the control site.
*0.01 ≤ P < 0.05; **p < 0.01.
See this image and copyright information in PMC

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