A High-Throughput DNA-Sequencing Approach for Determining

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A High-Throughput DNA Sequencing Approach to Determine Sources of Fecal Bacteria in a Lake Superior Estuary Clairessa M Brown, Christopher Staley, Ping Wang, Brent Dalzell, Chan Lan Chun, and Michael J. Sadowsky Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b01353 • Publication Date (Web): 22 Jun 2017 Downloaded from http://pubs.acs.org on June 25, 2017

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Environmental Science & Technology

A High-Throughput DNA Sequencing Approach to Determine Sources of Fecal Bacteria in a Lake Superior Estuary

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Clairessa M. Brown1, Christopher Staley1, Ping Wang1, Brent Dalzell2, Chan Lan Chun3, and

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Michael J. Sadowsky1,*

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BioTechnology Institute, University of Minnesota, St. Paul, MN, 2Department of Soil, Water, Climate, University of Minnesota, St. Paul, MN, and 3Natural Resources Research Institute and Department of Civil Engineering, University of Minnesota Duluth, Duluth MN

*Address Correspondences to: Dr. Michael J. Sadowsky, 1479 Gortner Ave., 140 Gortner Labs, BioTechnology Institute, University of Minnesota. St. Paul, MN 55108 USA; Email: [email protected]; Phone (612) 624-2706

Keywords: next-generation sequencing, high-throughput sequencing, microbial source tracking, fecal pollution, Lake Superior, bacterial community structure

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Abstract

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Current microbial source tracking (MST) methods, employed to determine sources of fecal

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contamination in waterways, use molecular markers targeting host-associated bacteria in animal

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or human feces. However, there is a lack of knowledge about fecal microbiome composition in

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several animals and imperfect marker specificity and sensitivity. To overcome these issues, a

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community-based MST method has been developed. Here, we describe a study done in the Lake

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Superior-St. Louis River estuary using SourceTracker, a program that calculates the source

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contribution to an environment. High-throughput DNA sequencing of microbiota from a diverse

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collection of fecal samples obtained from 11 types of animals (wild, agricultural, and

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domesticated) and treated effluent (n=233), was used to generate a fecal library to perform

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community-based MST. Analysis of 319 fecal and environmental samples revealed that the

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community compositions in water and fecal samples were significantly different, allowing for

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determination of the presence of fecal inputs and identification of specific sources.

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SourceTracker results indicated that fecal bacterial inputs into the Lake Superior estuary were

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primarily attributed to wastewater effluent, and to a lesser extent geese and gull wastes. These

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results suggest that a community-based MST method may be another useful tool to determine

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sources of aquatic fecal bacteria.

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1. Introduction Microbial source tracking methods (MST) rely on the use of specific microorganisms or

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bacterial profiles, thought to be host-associated (e.g. human, ruminant, bird), to determine

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sources of fecal bacteria in the environment 1. The intestinal tracts of different animals harbor

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distinct bacterial communities that vary in the presence and abundance of specific taxa 2. This

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allows for the determination of different fecal sources by using unique bacterial profiles seen

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within different animals’ fecal microbiomes.

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Currently, the most common MST methods involve the use of quantitative PCR (qPCR)

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and molecular markers (primers) that mainly target the 16S rRNA genes of presumptively host-

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associated microorganisms. The majority of these markers target members of the genus

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Bacteroides. However, developing qPCR markers that are both sensitive and specific has proven

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to be challenging in several cases 3–6.

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The introduction of high-throughput DNA sequencing (HTS) technology has allowed

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increased use of culture-independent methods to rapidly assess bacterial community structure in

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several different environments 2,7–10. Community-based MST methods involve the creation of a

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library of operational taxonomic units (OTUs) present in feces from different animal types,

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featuring source-specific microbial profiles. These libraries, referred to as fecal taxon libraries

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(FTL), are compared with bacterial community profiles from environmental samples to find

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shared taxa or OTUs between the two sample types.

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Several community-based MST studies 7,8,11–13,14 have utilized the SourceTracker

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program 15 which accepts quality-controlled sequence data, along with a list of samples either

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identified as a potential fecal source or an environmental sink. SourceTracker uses a Bayesian


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statistical approach to determine the percentage of the bacterial community, and the probability,

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that a potential fecal source contributed to an environment 15.

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While SourceTracker results include the standard deviation for an estimation of error,

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higher confidence in the Bayesian model used to predict sources can be achieved by running

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SourceTracker multiple times, in a bootstrap-like manner, and measuring the uncertainty of the

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model using the relative standard deviation (RSD) 13. An important consideration with

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community-based MST methods is determining the appropriate library size of samples for each

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source. This can be established by using statistical power analysis, which determines whether the

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sample size is large enough to be able to detect significant differences between sample groups.

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The Dirichlet multinomial distribution can be used to model data during power analysis, such

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that Type II error due to overdispersion is alleviated 16. This approach has been successfully used

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to model microbial community analysis data 17.

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Lake Superior, the largest freshwater lake in the world by area, is used for recreational

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and commercial purposes. Fecal bacterial inputs onto beaches in the Lake Superior harbor area,

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Park Point, and the St. Louis River estuary, measured by E. coli concentrations, have been found

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to be seasonally impacted by waterfowl 18. Results obtained using qPCR in previous research

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have also indicated that the largest point source contributor of fecal-associated bacteria to the

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Lake Superior-Duluth Harbor was treated wastewater effluent, likely originating from two local

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wastewater treatment plants (WWTP) 19,20.

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In this study, we examined potential sources of fecal bacterial inputs into the Duluth-

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Superior Harbor and St. Louis River in the Lake Superior watershed employing a community-

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based MST approach using Illumina DNA sequencing data and SourceTracker analyses. To

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achieve this goal, water samples were collected over two years, from seven different sites in the


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Duluth-Superior Harbor and St. Louis River estuary in Duluth, MN, USA along with fecal

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samples from 11 different types of animal sources and treated wastewater effluent. Samples were

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sequenced and high-quality HTS data from animal and effluent samples were used to create a

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FTL for community-based MST. Power analyses, done using a Dirichlet multinomial distribution

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to model 16S community data, were used to determine whether an appropriate library size had

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been obtained for the entire FTL. Multiple SourceTracker runs allowed fecal sources to be

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identified, along with RSD values which allowed for an estimation of confidence to be assigned

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to the predicted proportions of fecal sources. Results of this study show that a community-based

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MST approach, with an appropriately-sized FTL, could potentially be used as a tool to allow

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watershed managers to confidently predict sources of fecal inputs into waterways.

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2.1 Sample collection. Triplicate, two-liter water samples were collected just below the

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surface from seven sites in the Lake Superior-St. Louis estuary during summer and fall 2014 and

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the summer of 2015 (Fig. 1). These sites included the: St. Louis River site 1 in 2014 and St.

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Louis River site 2 in 2015, Western Lake Superior Sanitary District (WLSSD) outfall, Rice’s

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Point, Brewery Creek storm drain, Southworth Marsh storm drain, and Minnesota Point Beach

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sites in both years (Table S1). The St. Louis River site in 2014 changed due to accessibility.

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Water was sampled on July 23, August 20, and November 5 in 2014. In 2015, water was

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sampled on June 16 and July 27. All water samples were transported on ice to the lab, and stored

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at 4°C for less than 24 hours prior to filtration.

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2. Methods

Fecal samples were collected from 11 different animal types from a variety of sources including farms, via wildlife managers, and from personal pet donations across Minnesota.


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Several grams of feces were collected from individual chickens, cows, production turkeys,

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swine, beavers, gulls, Canada geese, wild and domesticated rabbits, deer, cats, and dogs.

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Triplicate, two-liter samples of treated wastewater effluent were collected from WLSSD during

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every sampling event.

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2.2 Sample processing and DNA extraction. Fecal samples were transported at 4°C (on

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ice) and stored at -20°C until DNA could be extracted. All triplicate 2L environmental water

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samples were pre-filtered through 5µm nitrocellulose filters (Millipore-Sigma, St. Louis, MO) to

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remove debris and larger microorganisms and processed as previously described 21. The

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environmental water was subsequently filtered through 0.45µm, and then 0.22µm, nitrocellulose

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filters (Millipore-Sigma, St. Louis, MO) to capture all bacteria. Filters were transferred into 50ml

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conical tubes containing 2ml of 0.01% sodium pyrophosphate buffer, pH 7.0 containing 0.2%

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Tween 20 (polyethylene glycol sorbitan monolaurate) and vortexed, twice, for 3 min at room

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temperature. Each conical tube held up to five filters from the same environmental site, and some

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of the 0.45µm and 0.22µm filters were placed in the same tube if space allowed. The supernatant,

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containing re-suspended cells from the filters, were transferred from the conical tubes to 1.5ml

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Eppendorf tubes and centrifuged for 3 min at 13,300×g. Cell pellets from 0.45µm and 0.22µm

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filters from the same sample were ultimately combined if the filters from the same sample were

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placed in different 50ml conical tubes. Samples were stored at -20°C until DNA was extracted.

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DNA from fecal and environmental water samples was extracted using the DNeasy PowerSoil

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DNA extraction kit (Qiagen, CA, USA), as per kit directions.

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2.3 PCR and DNA sequencing. PCR and DNA sequencing were performed at the

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University of Minnesota Genomics Center (St. Paul, MN, USA) using primers F784 (5’

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RGGATTAGATACCC 3’) 22 and 1046R (5’ CGACRRCCATGCANCACCT 3’) 23 , targeting


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the V5 and V6 regions of the 16S rRNA gene. Sequencing was done using the dual indexing

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method as previously described 24. Amplicons were paired-end sequenced on the Illumina HiSeq

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2000, HiSeq 2500 (150bp), and MiSeq (300bp) platforms (Illumina, San Diego, CA).

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Sequencing results can be accessed from GenBank under BioProject PRJNA377760.

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2.4 Processing of sequence data. All sequencing data obtained from the Illumina MiSeq

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platform runs were trimmed to 150 bp to match the run length obtained from Illumina HiSeq

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runs. All sequence processing was performed using QIIME versions 1.8.0 and 1.9.1 software 25.

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Illumina adapter contamination and low quality base regions were removed using Trimmomatic

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v. 3.2 26. Primers, homopolymers >8, and reads smaller than 75% of the amplicon length were

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removed using Pandaseq 27. This program was also used to concatenate reads using the fastq-join

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script 28. Chimeras were removed using UCHIME 6.1 29. Open reference OTUs were grouped

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using uclust, at 97% identity, and compared to the SILVA ver. 119 reference database 30,31 using

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the PyNAST alignment algorithm 25. Taxonomy was assigned utilizing the RDP Classifier, with

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an 80% bootstrap value 32. Singletons were removed from the dataset and all data that passed

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quality control were used for statistical analysis. The final dataset was comprised of DNA

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sequences from 20 beavers, 14 cats, 16 chickens, 32 cows, 19 deer, 17 dogs, 25 geese, 14 gulls,

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18 wild and domesticated rabbits (treated as a single source), 18 swine, 18 turkeys, and 22

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effluent samples.

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2.5 Statistical analyses. Statistical analyses were done using QIIME v. 1.8.0 25, RStudio

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v. 0.99.896, R v. 3.2.1 33 and mothur v. 1.34.0 34. After sequence processing, multiple rarefaction

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depths were evaluated by random sampling of sequences so that the number of observed taxa

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were close to the number of expected taxa. A final sequence depth of 25,000 was chosen for all

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subsequent statistical analysis. Bray Curtis dissimilarity 35 was calculated in mothur and was


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used in principal coordinates analysis (PCoA) and analysis of molecular variance (AMOVA) 36.

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Hierarchical clustering was calculated in the R package pvclust in RStudio 37 using the UPGMA

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method. Clustering was performed on a Bray Curtis dissimilarity matrix with 1,000 bootstrap

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iterations containing the averages of family-level taxa abundances from all sample types.

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Community-based MST was done using SourceTracker v. 1.0 15 through QIIME v. 1.9.0 25.

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While family-level taxa tables rarefied to a sequencing depth of 25,000 were used,

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SourceTracker was run with default parameters five independent times on the same FTL.

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Spearman rank correlations were performed in RStudio to determine the correlation between

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predicted source proportions obtained via SourceTracker and RSD values determined from the

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five independent runs of the SourceTracker program.

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RSD analysis was performed to estimate confidence in SourceTracker proportions and

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was calculated by using the average standard deviations of a sample across the five independent

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SourceTracker runs. This value was divided by the average predicted source proportions of that

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same sample obtained from the five independent SourceTracker runs.

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Power analyses were performed on all animal fecal samples to determine whether

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sufficient samples from each source type were present in the library to avoid increased statistical

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Type II error. Library size analysis was done by reducing the number of individuals within each

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source type (ranging from 14 to 32) and then running power analysis using the R HMP package

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to determine a minimum size of the library 16. If the source type did not have as many samples as

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required, all samples were used.

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3. Results 3.1 Bacterial community structure among water and fecal samples Water samples from seven different locations in the Duluth-Superior Harbor and St.

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Louis River estuary in Duluth, MN and animal fecal samples were collected during 2014 and

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2015. The V5 and V6 regions of the 16S rRNA gene were sequenced from 319 environmental

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lake water, fecal, and treated wastewater effluent samples yielding nearly 60 million reads. The

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233 fecal samples were obtained from 12 different domesticated, agricultural, and wild animal

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types, as well as wastewater effluent, and together the source sequences comprised the FTL used

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for community-based MST.

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The average sequencing coverage for water and feces was 99%, and ranged from 97% to

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100% (Table S2). Feces, on average, had lower diversity than did lake water (Table S2). Feces

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had, on average, a Shannon index of 4.2 and 1,672 OTUs that clustered at 97% similarity, while

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freshwater samples had an average Shannon index of 4.8 and 3,272 OTUs clustering at 97%

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similarity (Table S2).

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Hierarchical clustering showed that the environmental water and animal samples

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clustered separately, indicating the microbiota in feces is different from that found in water (Fig.

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2A). The most common microbiota found in environmental water communities included

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members of the families Sporichthyaceae, LD12, and Comamonadaceae (Fig. 2B). In contrast,

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members of the families Erysipelotrichaceae, Peptostreptococcaceae, and Ruminococcaceae

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were prevalent in the treated wastewater effluent samples, and animal fecal samples were

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dominated by members of the families Ruminococcaceae, Lachnospiraceae, and

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Peptostreptococcaceae (Fig. 2B). Nearly 30% of the sequencing reads from rabbits, swine, deer,

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cows, and beavers were classified as Ruminococcaceae and Lachnospiraceae. Cats, dogs, and


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chickens had nearly equal amounts of those families, as well as several other families, which

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explains why their samples clustered together (Fig. 2A). While geese had nearly equal amounts

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of Ruminococcaceae and Lachnospiraceae, which was similar to cats, dogs, and chickens,

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several of the families that make up the community structure were relatively different (Fig. 2A).

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Similar to what was found with hierarchical clustering, PCoA revealed that fecal and

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environmental water samples clustered separately (Fig. 3). AMOVA pairwise-comparisons

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identified significantly different (p

A High-Throughput DNA-Sequencing Approach for Determining

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