Dynamic microbial populations along the Cuyahoga River

<div><p>The study of the microbial communities has gained traction in recent years with the advent of next-generation sequencing with, or without, PCR-based amplification of the 16S ribosomal RNA region. Such studies have been applied to topics as diverse as human health and environmental ecology. Fewer studies have investigated taxa outside of bacteria, however. We present here data demonstrating the utility of studying taxa outside of bacteria including algae, diatoms, archaea and fungi. Here, we show how location along the Cuyahoga River as well as a transient rainfall event heavily influence the microbial composition. Our data reveal how individual OTUs vary between samples and how the patterns of OTU abundance can accurately predict sampling location. The clustering of samples reveals that these taxa are all sensitive to water conditions in unique ways and demonstrate that, for our dataset, algae was most distinctive between sample groups, surpassing bacteria. Diversity between sampling sites could allow studies investigating pollution or water quality to identify marker OTUs or patterns of OTU abundance as indicators to assess environmental conditions or the impact of human activity. We also directly compare data derived from primers amplifying distinct taxa and show that taxa besides bacteria are excellent indicators of water condition.</p></div

PCA analysis on evenly subsampled datasets.

<p>To compare how well each dataset distinguished sample groups, we subsampled each to the same number of reads per sample and performed PCA. The algae dataset was best able to separate the sample groups as shown by the k-means clustering analysis accuracy of 100% compared to the other datasets which had lower accuracies.</p

Algae and bacterial PCA analyses.

<p>PCA analysis of algal and bacterial datasets reveal that the four sample groups produce distinct sample groups. PC1 vs PC2 and PC2 vs PC3 are presented for both datasets.</p

PCA on PC1-3 from each dataset.

<p>Principal component analysis on PC1-3 from each dataset. This analysis determined which principal components organize samples from different datasets similarly. The plot shows that PC1 from the algae, bacteria, archaea and diatom datasets are similar and distinct from PC1 in the fungal dataset. PC2 and 3 are similar across all datasets as demonstrated by similar values for PC1, which in this analysis explains 63.8% of the total variance.</p

Combined OTU PCA analysis.

<p>PCA analysis of OTUs from each dataset combined. OTUs from each dataset were combined into a single dataset to allow comparison of taxa between datasets. PC1 vs PC2 and PC2 vs PC3 are presented.</p

Top ten OTUs by total read count across all samples per dataset.

<p>Top ten OTUs by total read count across all samples per dataset.</p