Patterns in the Composition of Microbial Communities from a Subtropical River: Effects of Environmental, Spatial and Temporal Factors

<div><p>Microbes are key components of aquatic ecosystems and play crucial roles in global biogeochemical cycles. However, the spatiotemporal dynamics of planktonic microbial community composition in riverine ecosystems are still poorly understood. In this study, we used denaturing gradient gel electrophoresis of PCR-amplified 16S and 18S rRNA gene fragments and multivariate statistical methods to explore the spatiotemporal patterns and driving factors of planktonic bacterial and microbial eukaryotic communities in the subtropical Jiulong River, southeast China. Both bacterial and microbial eukaryotic communities varied significantly in time and were spatially structured according to upper stream, middle-lower stream and estuary. Among all the environmental factors measured, water temperature, conductivity, PO₄-P and TN/TP were best related to the spatiotemporal distribution of bacterial community, while water temperature, conductivity, NO_x-N and transparency were closest related to the variation of eukaryotic community. Variation partitioning, based on partial RDA, revealed that environmental factors played the most important roles in structuring the microbial assemblages by explaining 11.3% of bacterial variation and 17.5% of eukaryotic variation. However, pure spatial factors (6.5% for bacteria and 9.6% for eukaryotes) and temporal factors (3.3% for bacteria and 5.5% for eukaryotes) also explained some variation in microbial distribution, thus inherent spatial and temporal variation of microbial assemblages should be considered when assessing the impact of environmental factors on microbial communities.</p> </div

RDA ordination showing the microbial community composition in relation to significant environmental variables.

<p>The environmental variables were significantly related to the variation of microbial community composition (<i>P</i> < 0.05). The numbers indicate the sampling sites, which were collected in dry (△) and wet (▼) seasons, respectively.</p

Variation partitioning between environmental, spatial and temporal variables.

<p>A = the pure temporal explanation; B = the temporal explanation that is shared by the environmental explanation; C = pure environmental explanation; D = the environmental explanation that is shared by the spatial explanation; E = pure spatial explanation.</p

Location of sampling sites in the Jiulong River Watershed.

<p>Location of sampling sites in the Jiulong River Watershed.</p

PCA plots showing the resemblance of environmental characteristics of sampling sites along the Jiulong River.

<p>The numbers indicate the sampling sites, which were collected in dry (△) and wet (▼) seasons, respectively. </p

Age-depth curve and sedimentation rate for Xingyun lake core. Radiocarbon dates are listed in Table 1.

<p>Age-depth curve and sedimentation rate for Xingyun lake core. Radiocarbon dates are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102167#pone-0102167-t001" target="_blank">Table 1</a>.</p

The monsoonal system in China and the location of Xingyun Lake (Based on Wu [19]).

<p>The monsoonal system in China and the location of Xingyun Lake (Based on Wu <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102167#pone.0102167-Wu1" target="_blank">[19]</a>).</p

Radiocarbon dating in Xingyun Lake sediment core and age model.

<p>Radiocarbon dating in Xingyun Lake sediment core and age model.</p

Genome-wide DNA methylation patterns in CD4+ T cells from Chinese Han patients with rheumatoid arthritis

<p><i>Introduction</i>: Rheumatoid arthritis (RA) is an autoimmune disease that causes chronic inflammation of the joints. Recent evidence indicated the epigenetic changes may contribute to the pathogenesis of RA.</p> <p><i>Method</i>: To understand the extent and nature of dysregulated DNA methylation in RA CD4T cells, we performed a genome-wide DNA methylation study in CD4 + T cells in 12 RA patients compared to 12 matched normal healthy controls. Cytosine methylation status was quantified with Illumina methylation 450K microarray.</p> <p><i>Result</i>: The DNA methylation profiling showed 383 hyper- and 785 hypo-methylated genes in the CD4 + T cells of the RA patients (<i>p</i> < 3.4 × 10⁻⁷). Gene ontology analysis indicated transcript alternative splicing and protein modification mediated by DNA methylation might play an important role in the pathogenesis of RA. In addition, the result showed that human leukocyte antigen (HLA) region including <i>HLA-DRB6</i>, <i>HLA-DQA1</i> and <i>HLA-E</i> was frequently hypomethylated, but <i>HLA-DQB1</i> hypermethylated in CpG island region and hypomethylated in CpG shelf region in RA patients. Outside the MHC region, <i>HDAC4</i>, <i>NXN</i>, <i>TBCD</i> and <i>TMEM61</i> were the most hypermethylated genes, while <i>ITIH3</i>, <i>TCN2</i>, <i>PRDM16</i>, <i>SLC1A5</i> and <i>GALNT9</i> are the most hypomethylated genes.</p> <p><i>Conclusion</i>: Genome-wide DNA methylation profile revealed significant DNA methylation change in CD4 + T cells from patients with RA.</p