Enumerating known taste and odor organisms in the Dan River

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Chapter 3: Characterizing the microbial diversity and community structure of the Dan River system

Abstract

River ecosystems across the US and globally face numerous stressors that impact both ecological function and water quality. In rivers, microorganisms regulate critical ecosystem services, like the geochemical cycling of carbon and nitrogen, and disturbances to microbial diversity can negatively impact their function. New tools to analyze microbiomes have developed in the last few decades, but little of this work has been focused on river systems. To address this issue, monthly water, sediment, and periphyton samples were collected yearly from the Smith and Dan Rivers. Additionally, patterns of total microbial diversity and community structure were analyzed to determine when and where major ecological shifts in the river microbiome occurred. This study focused on the locational and temporal patterns of -diversity, -diversity, and relative abundances of bacterial and fungal taxa within multiple river habitats that provide new insight into freshwater microbial ecology. Microbial – and -diversity and the makeup of total microbial communities were primarily dependent on their habitat—water, sediment, or periphyton. Within the river habitats, -diversity was largely dependent on habitat and season, where -diversity was dependent on habitat, season, and river reach. In each habitat, Betaproteobacteria and Ascomycota had the highest relative abundance, where shifts within taxonomic groups were observed by season and river reach. Overall, results from the study offer new insights into the ecological patterns of river bacteria and fungi.

Introduction

Rivers are dynamic ecosystems made up of multiple habitats—including water, sediment, periphyton, and plants—that harbor communities of microorganisms. Microbial communities perform numerous ecological functions in river ecosystems, most importantly as drivers to the biogeochemical cycling of nutrients (Meybeck, 2003). Important examples include nitrifiers like Nitrosomonas (Cebron et al., 2003) and Flavobacteriaceae, which transforms river organic matter (McBride, 2014). While river microbiota has been the subject of a considerable amount of study, much of this work has been particularly related to eutrophication and pathogens (Meyer, 1994; Paerl & Pinckney, 1996; Benham et al., 2006; Fries et al., 2008). However, the total biodiversity (microbial or otherwise) of an ecosystem can also be an important component of understanding the overall ecology and resiliency of that system. Diversity indicates the stability on an ecosystem and its ability to recover ecosystem services following an ecological disturbance (Oliver et al., 2015). In rivers, microbial communities experience constant physical disturbance from streamflow, and it is important to monitor the constant changes of diversity across space and time to understand where and when ecosystem services like nutrient cycling can be most affected (Giller et al., 2004). Furthermore, diversity can change in multiple ways, which are commonly considered independently in ecological research. The two most commonly analyzed components of diversity, are -diversity, which describes how many different species are in a community (i.e., richness) and how related they may be (Lozupone & Knight, 2008), while -diversity considers how changes across samples or communities adds additional diversity to the whole community (Koleff et al., 2003; Lozupone & Knight, 2008). Until recently, characterizing the total diversity of microbial communities has been difficult because of their taxonomic complexity (Lozupone & Knight, 2008). However, with the shift towards DNA-based approaches to identifying and quantifying microorganisms present in environmental samples, next generation sequencing (NGS) provides a powerful approach to profiling microbial communities with high resolution (Shokralla et al., 2012). NGS has been most widely used to explore the microbiomes of soil, marine, and other host systems (Sun et al., 2017; Fuhrman et al., 2015; Shreiner et al., 2015). In contrast, much less research focused on factors controlling patterns of diversity in the river microbiome has been conducted using more detailed NGS methods. early NGS studies conducted on the river microbiome indicate that the composition of the river microbiome can be variable. However, Proteobacteria ( – and -) and Bacteroidetes are typically the dominant bacterial phyla (Savio et al., 2015; Staley et al., 2015; Zwart et al., 2002), along with Ascomycota and Basidiomycota being the dominant fungal phyla in water (Pietryczuk et al., 2017; Liu et al., 2015). By location, diversity typically increases from upstream to downstream (Besemer et al., 2013; Chen et al., 2018). Abiotic factors such as nutrients, pH, temperature, organic matter, and flow rate have been found to correlated with changes in microbial community structure and diversity (Staley et al., 2013; Lindstorm et al., 2015; Pietryczuk et al., 2017; Liu et al., 2013). Human activity can also change microbial community structure and reduce diversity, including mining activity (Yergeau et al. 2012) and river damning (Chen et al. 2018). Temporal differences in microbial community structure have also been observed, but complex interactions between the microbiome and the environment makes understanding the causes of temporal changes difficult (Henriques et al., 2006; Staley et al., 2013). Seasonal patterns occur, but results have been inconsistent. For instance, de Oliveira Margis (2015) reported that – and -diversity of the Sinos River in Brazil decreased from winter to summer, while Staley et al. (2015) observed an increase in -diversity from early to late summer in the Upper Mississippi River. Finally, Garcia-Armisen et al. (2014) found higher -diversity in the fall and winter months than the spring and summer on the Zenne River in Brussels. It is important to note that most studies on river microbiomes have been focused only on bacteria because NGS methods for identifying other microorganisms have only been developed more recently. Therefore, it is uncertain if fungi or protists undergo similar patterns space and time. Furthermore, it is important to continue examining locational and temporal changes in microbial diversity not only in river water, but also focus on the other habitats that make up river systems, like periphyton. The objective of this study was to explore the locational and temporal patterns of microbiomes in the Dan River watershed across multiple sites, habitat types, and seasons. The research was designed to answer: What are the major patterns of bacterial and fungal -diversity in the rivers How does microbial -diversity change among the habitat types and with changes in time and space? What taxonomic groups dominate and change within the microbiomes of the Dan River ecosystem? A primary hypothesis is that the different habitat types sampled during this study—water, sediment, and periphyton—would be the biggest determinant of both microbial – and – diversity, with sediment being the most diverse habitat due to its static nature, pore spaces, and nutrient availability. It was also hypothesized that water column would have the most variable community structure, as it is continuously flowing downstream and changing with the different runoff it receives. Because both the Dan and Smith Rivers have different headwater sources, microbial community structure was also expected to vary between the two rivers. Finally, seasonal changes in -diversity were expected to change, with warmer months harboring lower diversity in each of the habitat types than cooler months as observed in other studies.

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Methods
Study Design

The data presented here are derived from the same sample set collected in Chapter 2. Full details of sampling design are described in sections 2.2.2 and 2.2.3. Briefly, monthly sampling from August 2016 to September 2017 was conducted in response to two drinking water T&O events that previously affected municipalities along the Dan River. Since the sources of these events were unknown, the biological methods for this study focused not on specific T&O causing organisms (covered in Chapter 2), but on analyzing changes in total microbial diversity across space and time as a potential indicator of major ecological changes in the rivers. Eleven sites along the Smith (Sites 1-5) and Dan (Sites 6, 7, and 9-12), as well as one unique industrial culvert site (Site 8), were chosen in the upper Dan River watershed in Virginia and North Carolina. At each site, sediment and water samples were collected. At sites with adequate boat access, periphyton samples from longitudinal transects were also collected. The samples were stored on ice for transport back to Virginia Tech laboratories for analysis.

Characterizing microbial diversity and community structure

Bacterial and fungal communities from all samples were characterized using targeted amplicon sequencing. Upon returning to the lab, field samples were processed for storage prior to DNA extraction. For water samples, 600 mL was vacuum filtered through 0.45 m filters. The filters were then stored at -80 C until total DNA was extracted using the DNeasy PowerWater DNA isolation kit (Qiagen, Hilden, Germany). For periphyton, 10 mL of periphyton suspended in river water was also filtered using 0.45 m filters and stored at -80 C until DNA was extracted with the DNeasy PowerWater kit. Aliquots of the sediment samples were kept at -80 C until DNA was extracted from 0.25 g sediment using DNeasy PowerSoil DNA isolation kits (Qiagen, Hilden, Germany). All extracted DNA was kept at -20 C for storage prior to amplification. Bacterial community structure was characterized by amplifying the V4 region of the 16S rRNA gene through polymerase chain reaction (PCR). Samples were amplified in triplicate using the 515F and 806R primer pair (Caporaso et al., 2012) with adapters for Illumina sequencing and unique barcode sequence for multiplexing samples. Fungal community structure was characterized by amplifying the ITS1 region of the ITS rRNA gene through PCR using the ITS1F and ITS2 primer pair (Bellemain et al., 2010). For both 16S and ITS amplicons, the PCR reaction mixture included 12.5 L 2X AccuStart II PCR ToughMix (Quantabio, Beverly, MA, USA), 10.5 L PCR-grade water (Qiagen, Hilden, Germany), 0.5 L of each 10 M primer (Invitrogen, Carlsbad, CA, USA), and 1 L of DNA template. The PCR reaction mixture was then amplified on a thermal cycler (Bio-Rad, Hercules, CA) at 94 C for 5 min, 35 cycles of 94 C for 45 s, 50 C for 45 s, 72 C for 90 s, and finally, 72 C for 10 min. The PCR products were then visualized on agarose gels through gel electrophoresis and purified using the MagJET NGS Cleanup and Size Selection Kit (Thermo Scientific Inc., Waltham, MA, USA). In total, 448 samples were divided into five separate 16S amplicon pools and three separate ITS amplicon pools. DNA concentrations of amplicon pools from each sample were quantified using a Qubit 2.0 fluorometer (Invitrogen, Carlsbad, CA, USA). Samples were pooled in equimolar ratios for sequencing. The pooled sample was then sent to the Virginia Tech Biocomplexity Institute in Blacksburg, VA, to be sequenced on the Illumina MiSeq platform with 250 base-pair paired ends.

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Data Analysis

Using the USEARCH pipeline (Edgar, 2010), raw forward and reverse sequencing reads were merged with at least 150 bp overlap and then filtered to retain only sequences with a minimum length of 200 bp and less than 0.5 expected errors per read. Sequences with >97% similarity were then clustered into operational taxonomic units (OTUs), and chimeric sequences were identified and removed using UCHIME (Edgar et al., 2011; Edgar, 2013). The RDP classifier (Wang et al., 2007) in QIIME (Caparoso et al., 2010) was then used to assign taxonomy to OTUs using the SILVA database (Quast et al., 2013) for bacteria and the UNITE database (Koljalg et al., 2013) for fungi. Before performing any statistical analyses, 16S sequences identified as chloroplast or mitochondria were removed. OTUs representing fewer than 0.01% of the total number of reads were also removed to prevent rare taxa or sequencing error from impacting downstream analyses (Bokulich et al., 2013). Finally, bacterial and fungal OTU tables were then rarified to 10,000 and 2,000 sequencing reads per sample, respectively, to normalize for sampling effort. and -diversity analyses were performed using the ‘vegan,’ ‘fossil,’ and ‘phyloseq’ packages in R in order to answer the primary research questions. -diversity compares the diversity of samples within a community, and -diversity compares the diversity of one community to other communities. To summarize the overall -diversity of both bacterial and fungal microbiomes, the Shannon Diversity Index was used to account for the richness and evenness of species detected in each sample. Shannon values are presented as level plots created in R using the ‘ggplot2’ package. For analysis, samples were separated into groups by habitat (water, sediment, periphyton), season (Fall, Spring, Summer, Winter) and river (Dan, Smith, and Culvert). One-way ANOVAs followed by Tukey’s HSD post-hoc tests were performed in R using the ‘dplyr’ package to find significant differences. To examine patterns of -diversity, Bray-Curtis dissimilarity matrices were calculated for all pairwise sample comparisons among bacteria and fungi separately. Patterns of similarity in community structure were visualized using principal coordinate analysis plots. Samples were separated into the same groups as described above for -diversity and an Adonis test followed by a permutational multivariate ANOVA test (PERMANOVA) using the ‘vegan’ package in R was used to test for significant -diversity patterns. Finally, to identify the major taxonomic groups in the bacteria and fungi samples, the top ten most abundant bacterial classes and the six most abundant fungal phyla were summarized using bar plots in R using the ‘ggplot2’ package.

Chapter 1: Introduction and Literature Review
1. River taste and odor issues
2. Biological sources of taste and odor
2.1 Actinomycetes
2.2 Fungi
2.3 Algae
3. Other river microbiota
4. Profiling methods
5. Brief introduction into the river microbiome
5.1 Bacteria
5.2 Fungi
6. Thesis overview
References
Chapter 2: Enumerating known taste and odor organisms in the Dan River
Abstract
1. Introduction
2. Methods
2.1 Study Design
2.2 Sampling Procedure
2.3 Sample Analysis
2.4 Data Analysis
3. Results
3.1 Taste and odor organism abundance across multiple habitats in the Dan River
3.2 Locational and temporal patterns of taste and odor organism abundance4. Discussion
References
Chapter 3: Characterizing the microbial diversity and community structure of the Dan River
Abstract
1. Introduction
2. Methods
2.1 Study Design
2.2 Characterizing microbial diversity and community structure
2.3 Data analysis
3. Results
3.1 Seasonal and locational changes of -diversity
3.2 Seasonal and locational patterns of -diversity in the Dan River
3.3 Taxonomic summary of microbial communities in the Dan River
4. Discussion

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Locational and temporal patterns in microorganisms potentially affecting water quality in the Dan River system

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