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Aug 25, 2014 - We propose that human mitochondrial DNA from public waste streams may ... by the hypervariable region of the human mitochondrial genome...
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Sequencing Human Mitochondrial Hypervariable Region II as a Molecular Fingerprint for Environmental Waters Vikram Kapoor, Ronald W. DeBry, Dominic Boccelli, and David Wendell Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/es503189g • Publication Date (Web): 25 Aug 2014 Downloaded from http://pubs.acs.org on August 26, 2014

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Sequencing Human Mitochondrial Hypervariable Region

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II as a Molecular Fingerprint for Environmental Waters

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Vikram Kapoor1, Ronald W. DeBry2, Dominic L. Boccelli1 and David Wendell1*

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Cincinnati, Cincinnati, Ohio 45221, USA

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Keywords. Fecal source tracking, human mitochondrial DNA, high-throughput sequencing,

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genetic barcode, cluster analysis, population diversity

Department of Biomedical, Chemical, and Environmental Engineering, University of

Department of Biological Sciences, University of Cincinnati, Cincinnati, Ohio 45221, USA

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ABSTRACT

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To protect environmental water from human fecal contamination, authorities must be able to

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unambiguously identify the source of the contamination. Current identification methods focus

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on tracking fecal bacteria associated with the human gut, but many of these bacterial indicators

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also thrive in the environment and in other mammalian hosts. Mitochondrial DNA could solve

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this problem by serving as a human-specific marker for fecal contamination. Here we show that

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the human mitochondrial hypervariable region II can function as a molecular fingerprint for

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human contamination in an urban watershed impacted by combined sewer overflows. We present

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high-throughput sequencing analysis of hypervariable region II for spatial resolution of the

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contaminated sites and assessment of the population diversity of the impacting regions. We

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propose that human mitochondrial DNA from public waste streams may serve as a tool for

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identifying waste sources definitively, analyzing population diversity, and conducting other

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anthropological investigations.

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INTRODUCTION

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For over a century, the standard indicator for environmental water contamination has been fecal

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bacteria and associated microbial metagenomes. However, many recent investigations1-3 have

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recognized the limitations of microbial-based source tracking because bacteria survive in

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alternative hosts4 and the environment5, making the spatial and temporal components ambiguous.

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Chemical signatures associated with human waste, such as caffeine, sterols, detergents and

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personal care products, have also been used in source tracking studies6, 7; however, issues with

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persistence and detection sensitivity limits their use as reliable identification tools7. Thus, due to

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the uncertainty of present microbial/chemical source identification markers, there remains a need

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for a sensitive and unambiguous indicator of human waste in environmental water.

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Mitochondrial DNA (mtDNA), which contains species-specific sequences, can be used as a

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direct marker of human contamination by identifying human waste directly through its own

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discharged eukaryotic cells1-3, 8, 9.

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Human mtDNA has become a useful tool in a variety of scientific disciplines including

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criminal forensics9, 10, paleoanthropology11-16, population genetics17, 18, and more recently as part

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of cancer and degenerative disease investigations19, 20. Global sequencing efforts associated with

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distinct populations have provided improved phylogenetic resolution of the human mtDNA

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hypervariable regions as a genetic anthropological barcode. Additionally, human mtDNA has

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been recently used as an identifying mechanism in contaminated environmental waters through

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species-specific variation in mitochondrial NADH dehydrogenase, discerning human fecal waste

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from animal sources1, 2. In this investigation we apply the specificity afforded by the

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hypervariable region of the human mitochondrial genome to evaluate anthropogenic inputs to

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environmental water in a manner similar to molecular rRNA-based speciation used in microbial

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source tracking, microbial diversity investigations and bacterial metagenomic fingerprinting21, 22.

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Numerous sources of human contamination can be found in environmental waters. Major

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fecal sources include combined sewer overflows (CSO), sanitary sewer overflows (SSO),

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household sewage treatment systems, and agriculture/urban runoff 6, 23. Human fecal waste

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contains a large amount of exfoliated epithelial cells24, 25, which in turn contain thousands of

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mitochondrial genomic copies26, making mtDNA a robust molecular target for impacted

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environmental water. Additional sources of human mtDNA in the environment can include

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sloughed skin and hair26, 27 found in waters used for swimming, canoeing and other recreational

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activities8, 28.

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We track and characterize human waste in environmental waters by targeting the human

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mitochondrial hypervariable region II (HVRII). Like previous human evolution studies12-16, the

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large number of single-nucleotide polymorphisms (SNPs) present in HVRII can be used as an

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identifying mechanism since these SNPs have varying allelic frequencies among populations14, 29,

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30

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water sample sites, since the waste impact is related to the humans contributing fecal waste near

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the sites (CSOs/SSOs; overland runoff) and persistence of human mtDNA from upstream

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sources. To determine the HVRII sequence diversity on a large scale, we have used high-

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throughput sequencing technology for characterizing human mtDNA HVRII variation found in

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water samples taken from an urban creek system (Duck Creek Watershed, Cincinnati OH)

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impacted by municipal CSOs and other human activities. We next used the HVRII sequences to

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extract haplotypes and assign mitochondrial haplogroups based on the Phylotree database31.

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Furthermore, we compared the population diversity obtained through HVRII-derived

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haplogrouping to the U.S. federal census data (by race) for the neighborhoods bordering the

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watershed.

. We have applied this allelic frequency specificity as a unique “barcode” to identify impacted

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MATERIALS AND METHODS

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Study area and sampling sites. The Duck Creek Watershed feeds a National and State Scenic

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River (Little Miami River) but has limited aquatic and riparian habitat32 and as a result of CSO

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overflows, has been shown to significantly impact the Little Miami River with human fecal

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contamination2. Initially, ten sampling points were selected within the Duck Creek watershed.

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The sites were chosen based on proximity to CSO’s, traditional municipal sampling sites and

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potential impact from human fecal pollution from sewage overflow and watershed runoff. The

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sampling sites were identified and assessed for the presence of human mitochondrial DNA

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through PCR-based detection as described in previous work2. Out of the 10 sites, five (Sites 1, 3,

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8, 9 and 10; Figure 1 and Table S1) were chosen for further analysis based on the consistent

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abundance of human mitochondrial DNA throughout the sampling period (Oct 2011 - Jul 2012).

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Sample processing, PCR and sequencing. Five river sites were selected within the Duck Creek

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Watershed representing different degrees of anthropogenic influence. Water sample collection

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and DNA extraction was performed as described earlier2. PCR assay targeting the mitochondrial

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hypervariable region II (422 bp) was carried out using the primers33 HVRII-F (5'-

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GGTCTATCACCCTATTAACCAC -3') and HVRII-R (5'-CTGTTAAAAGTGCATACCGCC -

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3') linked to the site-specific barcodes (Table S2). For all PCR assays, water DNA extracts (5

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µL) were used as templates in a final reaction volume of 50 µL using the OneTaq master mix

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(New England Biolabs, Ipswich, MA) with 200 nM each of the forward and reverse primer in a

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GeneAmp® PCR System 9700 thermal cycler (Applied Biosystems, Green Island, NY) under

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the following cycling conditions: initial denaturation of 30 s at 94 ºC, followed by 35 cycles of

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15 s at 94 ºC, 20 s at 56 ºC and 30 s at 68 ºC, and final extension step of 5 min at 68 ºC. All PCR

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products were purified using MinElute PCR Purification kit (Qiagen, Valencia, CA) and

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quantified using a NanoDrop 1000 Spectrophotometer (Thermo Scientific, Wilmington, DE).

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Controls containing no template DNA were used to check for cross contamination. Additionally,

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PCR inhibition was tested in water DNA extracts by using 10-fold dilutions of each DNA

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extract. The amplification products from the same sampling event were pooled in an equimolar

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ratio to conduct multiplexed sequencing using the Ion Torrent Personal Genome Machine (PGM)

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system (Life Technologies, San Francisco, CA). Sequencing of each pooled library was

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performed on the PGM system using a 314 chip v2 with the Ion PGM Template OT2 400 kit and

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Ion PGM Sequencing 400 kit according to the manufacturer's protocol. The HVRII sequence of

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the operator was also determined through Sanger sequencing and confirmed that it did not

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contribute to experimental data.

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Bioinformatics analyses. All PGM sequences were sorted according to barcodes and grouped

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under their respective sites. To compensate for potential sequencing errors, sequences having an

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average quality under 2010 (as derived from the automated analysis carried out by the Torrent

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Suite Software version 3.6), having unidentified bases (Ns), or being shorter than 300 bp were

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discarded. The quality-filtered sequences were then aligned to the revised Cambridge Reference

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Sequence (rCRS)34 for human mitochondrial DNA (NC_012920.1| Homo sapiens

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mitochondrion, complete genome); and analyzed with the Torrent Suite Software version 3.6

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(Life Technologies) using the plug-in VariantCallerForMtDNA version 3.0. The output of the

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variant caller is presented in tabular format, as a list of variations to the rCRS along with variant

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frequency values (Tables S3-S5). Additionally, the FASTQ files provided via the Ion Torrent

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server were exported to CLC Genomics Workbench Version 6.5 (CLC Bio, Cambridge, MA)

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and aligned to the rCRS, after which the Quality-based Variant Detection was called to detect

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insertions and deletions (indels) as well as SNPs with reference to the rCRS. CLC Workbench

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variant caller analysis parameters used in this study are given in Tables S6 and S7. A control

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DNA sample of HVRII sequence with known variants, which had been previously determined by

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conventional Sanger sequencing, was included during the analysis. The mitochondrial genome

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databases, including MITOMAP35, mtDB36, EMPOP37 and Phylotree31 were referred to validate

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the occurrence of detected variants. Sequences were submitted to MITOMASTER version Beta

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138 to extract haplotypes and assign mitochondrial haplogroups according to the sequence motifs

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present in HVRII. MITOMASTER performs variant calling relative to the rCRS, haplotyping

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based on Phylotree, and variant annotation based on Mitomap.

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Spatial analysis. Hierarchical cluster analysis (HCA) was used to classify the five sampling sites

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(1, 3, 8, 9 and 10) into spatial associations using the frequency distribution of SNPs obtained for

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three events - October 2011 (Set A; wet weather), March 2012 (Set B; dry weather) and July

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2012 (Set C; dry weather) respectively (Tables S3-S5) for a total of 15 data sets labeled as 1A,

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3A, etc., where 1 is the site and A refers to the event. HCA is an exploratory pattern detection

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method that partitions all cases into unique groups. Prior to HCA, the normality of the SNP

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frequency distribution (sorted by sampling location; frequency cutoff = 5%) was verified by

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analyzing the histograms and by applying the Shapiro–Wilk test. The combination of Euclidean

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distances as a similarity-dissimilarity measure and the Ward's method as a linkage algorithm was

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then applied to obtain the case clusters. The data matrix used for classification has the dimension

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of 15 (sampling points) X 25 (SNP frequencies), resulting in a total of 375 data points. Using this

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approach, it was possible to reduce the large number of HVRII sequences to 15 site-specific data

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sets.

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RESULTS

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Sequence and variant detection of HVRII for study sites. The Ion Torrent PGM system was

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used to examine the sequence diversity of HVRII amplicons as recently reported39-41. In total,

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more than 10,000,000 sequence reads were retrieved with a mean output exceeding 200,000 per

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pooled set, which were then filtered and grouped according to their respective sites. The absolute

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number of HVRII sequence read output per site was consistent with our previous human mtDNA

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– qPCR results for the watershed2. SNPs were detected using the Torrent Suite plug-in

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VariantCallerForMtDNA version 3.0, which applies a TMAP Smith–Waterman alignment

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optimization42 and outputs the variant allele frequency (%). Concurrently, sequences were

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analyzed using CLC Genomics Workbench Version 6.5 that employed the Neighborhood Quality

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Standard (NQS) algorithm43 to detect insertions and deletions (indels) and validate the SNPs

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detected via the variant caller. Indeed, SNP and indel analysis produced no significant

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differences from variant caller data supporting the reproducibility of the results by alternative

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computational methods. The HVRII sequence of the operator was compared to the sample

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sequence databases to check for cross contamination and produced no exact matches against the

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database reads.

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HVRII DNA from five sampling events was sequenced and screened producing an

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average read length of approximately 300 bp and a mean output of 20,000 sequences per site for

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a particular sampling event. Of this, a 270 bp portion from base position 51 to 320 was used for

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variant detection since these SNPs have been well documented35. A total of 31 distinct SNPs

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were detected of which 30 are present in MITOMAP - database of mtDNA Control Region

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Sequence Variants35. The relative distribution of SNPs (with frequency > 5%) for each site over

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the annual sampling period is presented in Figure 2. We observed some SNPs that were common

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to all sites with varying frequencies, while other SNPs were site-specific, allowing each

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sampling location to have a unique human mtDNA signature in the form SNP allelic frequencies.

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The variation in site specific SNP frequencies could be the result of several factors including

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limited sample size, daily population changes related to employment (for a comparison of site

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populations see Table S8), changes in sampling time and storm runoff volumes during wet

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weather events, or a combination of these variables. Variants 73G and 263G were detected in all

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samples with high frequency (> 40%). Variants 143A and 236C were detected only at site 10

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during the entire sampling period. This is expected since site 10 is on a separate tributary of

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Duck creek and is not influenced by influx of water from any other site. Site 1 is downstream of

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site 3, however some SNPs were detected at site 1 but not 3 (151T, 182T, 185T, 235G, 239C).

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This may be due to the influx of water between the two sites (confluence with Little Duck Creek;

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see Figure 1) as well as additional inputs from runoff and other CSOs. Interestingly, some of the

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variants common to site 1 but not 3 (151T, 182T, 185T) are present at sites 8, 9 and 10 as well.

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The rCRS reference sequence contains a track of seven cytosines from positions 303 to

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30934. Length heteroplasmy has been known to occur in this stretch due to C insertions that can

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create C-stretches of eight or more Cs44, 45. An additional C was found in most of the samples,

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while sampling site 9 presented two additional Cs in this region. Many samples were also found

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to have an additional cytosine with respect to the rCRS in the cytosine tract 311–315. The

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frequency of one additional C in the 311–315 region ranges from 20% to 70% within the sample

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sets, while the frequency of two additional Cs is between 7% and 60%. Sequences with three

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additional Cs in the 311–315 track were the most rare and were found only at site 9 with less

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than 5% frequency.

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Spatial variability of sites using SNP allelic frequencies. Because the sampling locations are

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impacted by upstream conditions (e.g., CSOs) and immediate surroundings, the frequencies of

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the HVRII SNP alleles at each sampling location should provide a “fingerprint” specific to each

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location. Using the allelic frequencies generated for each site, we applied cluster analysis to

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distinguish environmental water obtained from individual sample locations within the watershed.

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The dendrogram of the location pattern resulting from the HCA of HVRII sequence SNP data

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from the period of Oct 2011–Jul 2012 is presented in Figure 3, illustrating distinct site clusters.

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The sampling sites were grouped into three main clusters based on their specific HVRII SNP

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signature. Cluster 1 was formed by site 3; cluster 2 by sites 8, 9 and 10; and cluster 3, site 1. It

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can be seen that cluster 1 is characterized by the highest linkage distance to the other clusters.

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Clusters 2 and 3 are linked at a shorter distance and are together linked to Cluster 1 at a higher

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distance. Note that Cluster 1 corresponds to the middle catchment of the Duck Creek Watershed;

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cluster 3 is the lower catchment; while cluster 2 formed by sites 8, 9, and 10 is located in the

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upper catchment. Sites 8 and 9 are directly linked to each other since they are on the same

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section of river without influence of water influx from other CSO sources. Interestingly, site 1

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clusters most closely to itself and 8, 9 and 10, despite 3 being between them. It is also interesting

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to note that the sites are most self-similar despite the time between sampling events and the

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differences in weather (wet weather for set A and dry weather for set B and C). These results

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support the applicability of HVRII sequence analysis as a metagenomics tool for human

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contamination sources in environmental water and provides a mechanism for spatial

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classification of sites based on human mitochondrial variable region SNP allelic frequencies, or

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‘HVR fingerprint’.

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We further compared the Euclidean distances between the five different sites based upon

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the observed SNP frequency “fingerprints” (Figure S1). For all five locations, the smallest

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Euclidean distance generally occurred when performing a self-comparison of the SNP frequency

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fingerprints. Similar to the clustering results (Figure 3), sites 8, 9 and 10 tended to be most

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similar with each other. Additionally, when looking at the results from sites 1 and 3, the SNP

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frequencies are more closely related with sites 8, 9 and 10 than with each other, even though site

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1 is just downstream of site 3. The expectation is that sites immediately up/down-stream of each

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other would be most similar in terms of SNP frequencies, even with possible degradation of the

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mtDNA. To investigate the observed differences between sites 1 and 3, an existing Storm Water

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Management Model (SWMM) of the combined sanitary/storm water system (provided by the

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Metropolitan Sewer District of Greater Cincinnati) was used to identify the local regions that

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contribute to the combined systems that could impact the receiving streams (see Figure S2). Site

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9 is heavily influenced by the population in Kennedy Heights, while Site 10 is influenced by

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Kennedy Heights, Pleasant Ridge and parts of Oakley. Site 8 is directly downstream of 9 and

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further influenced by Madisonville. These three sites all have commonality with Kennedy

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Heights and the demographics within that region. Site 3, located downstream of the confluence

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of Sites 8 and 10, is additionally impacted by the Linwood region, which increases the

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differences between Sites 3, and 8, 9 and 10. Finally, Site 1, located further downstream than

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Site 3 after the confluence of two additional tributaries, continues to show additional differences

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from Site 3 (as expected) but was observed to be more similar with Sites 8, 9 and 10, which was

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not expected. When assessing the regional impacts, the tributary that merges with the flow

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passing Site 3, is additionally impacted by the Madisonville area that would strengthen the SNP

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frequency impact from the similar demographics in the Madisonville and Kennedy Heights

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region thereby strengthening the similarity with the sites upstream of Site 3. These different

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flow paths may also explain why SNPs 151T, 182T and 185T were observed at Site 1 (as well as

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Sites 8, 9 and 10) but not Site 3.

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Population diversity assessment using haplogroup classification. Distinct mitochondrial

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haplogroups have arisen from mutation during human evolution and largely follow the migration

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of Homo sapiens from specific geographical regions36, 46, 47. These paleoanthropological

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haplogroups can also be assigned to race based on the frequency of observation as a means of

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investigating population diversity46, 47. Consequently, we sought to use our human mtDNA

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sequences to extract haplotypes and classify them into haplogroups by comparing them to the

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Phylotree database31. The mitochondrial sequences from sets A (wet weather) and B (dry

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weather) were compared and assigned to haplogroups based on the differences in HVRII

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sequence mutations with respect to the rCRS. We observed abundant diversity from haplogroup

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data of HVRII amplicons at all sites, which is consistent with the clear indication of human

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contamination in the creeks2. Although the sequences were obtained from an equimolar pool of

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the HVRII amplicons, the relative composition of the haplogroups varied considerably across the

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different sampling sites (Figure 4). The most salient features of the haplogroup distribution in the

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clustered sequences were the relatively high frequencies of haplogroup H (30-50%). Haplogroup

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H includes the rCRS and is typically characterized by variant 73A in HVRII; most of the other

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haplogroups are characterized by 73G. Haplogroup L was also relatively abundant, but with

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large sample-to-sample variation.

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To further explore the applicability of our HVRII-derived haplogroup data to local

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population diversity as defined by 2010 U.S. census data48, several mitochondrial databases and

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studies were consulted to assign haplogroups to the general population groups35-37, 46, 47. From

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these, we used the Wallace47 haplogroup classification according to which L0, L1, L2, L3, L4,

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L5, L6 were assigned as 'African American'; H, HV, J, K, P, S, T, U, V as 'White'; B, D, E, F, G,

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M, R, W as 'Asian'; and A, C, X represented 'American Indian'; while all other, less numerous

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haplogroups were designated to an 'other' category. Figure 5 presents the comparative analysis of

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the population data obtained through the two strategies - HVRII-derived population groups viz-a-

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viz the census data for population (by race). Classification of the mtDNA haplogroups showed

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20% African mtDNA, 59% European mtDNA, and 12% Asian/American Indian mtDNA.

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According to census data, 62% self-declared as White, 32% as African American and 2% as

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Asian. There was a strong correlation between the federal census data and the mitochondrial

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haplogroups as an indicator of population composition (Pearson product-moment correlation

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coefficient, r = 0.97) demonstrating the suitability of human mitochondrial sequences to infer the

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population structure of the neighborhoods impacting the watershed. One important deviation

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from the census data was the significantly larger percentage of Asian/American Indian mtDNA

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detected (Figure 5). This discrepancy could be the result of several factors: coarse haplogroup

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assignments, proximity to the creek CSO input or underrepresentation in the census data. The

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latter of these three possibilities presents the opportunity that HVR sequencing directly from

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waste streams or impacted water may provide a more accurate means of deducing population

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diversity since human waste disposal is a personal necessity while census response is not. We

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suggest future epidemiological studies that employ HVR sequencing methods from waste

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streams that may provide complementary population diversity information as well as additional

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insight unavailable to voluntary census response data collection.

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DISCUSSION

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Several ribo-typing investigations have attempted to associate the human intestinal microflora

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with bacterial metagenomes found in environmental waters21, 41; however, there is significant

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variation in microbial species composition within and between individuals. Moreover, the

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microbial communities might replicate after discharge in water making it difficult to differentiate

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bacteria associated with fecal contamination events. Conversely, mitochondrial sequences

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represent a direct marker of human waste since they are derived from the host cells, which in

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turn enable the mtDNA HVRs to define inter-individual variation and population dynamics

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contributing to the impacted water.

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We investigated the occurrence of HVRII allelic frequencies of human mtDNA derived

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from water samples taken within an impacted urban creek system. We used SNPs within the

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human HVRII region to form site-specific genetic barcodes (HVR fingerprint) for evaluating

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anthropogenic watershed inputs. Human mtDNA is readily available in public waste streams and

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impacted environmental waters, allowing this approach to be more broadly applied as a

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metagenomics tool for studying human population diversity, waste source tracking and other

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anthropological investigations.

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Water samples taken from the impacted watershed contained mitochondrial genome copy

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equivalents ranging from a few 100 to several 100,000 human mtDNA2. As a result of the large

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amount of mtDNA, and abundant diversity, it was impractical to isolate and sequence full

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mitochondrial genomes from our environmental water samples. However, the analysis of small

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mtDNA regions that have maximal discriminative power has proven useful in past

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anthropological studies14-17 ; Krings et al.15 determined 340 bp of the mtDNA HVRII from the

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Neandertal type specimen to better estimate the relationship of the Neandertal mtDNA to the

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contemporary human mtDNA gene pool, an approach adapted in this investigation.

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Altogether, the HCA approach (Figure 3) combined with site-specific frequency

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distribution of SNPs (Figure 2) represents a unique classification for environmental waters that

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was both location and human community specific. The HVR fingerprint specificity was

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surprising considering the mixed nature of municipal sewage, variation in CSO discharge with

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weather and temporal separation of sampling events. The molecular fingerprinting strategy

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described here may be further adapted to analyze additional human mtDNA genes either by

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impacted environmental water or directly though public wastewater. This may provide a

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significant resource for local community mtDNA genetics, and could be used to examine the

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association of human disease and aging with mtDNA genes49-51. To this end, it has been reported

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that mitochondrial gene mutations might predispose individuals to diseases like diabetes,

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Alzheimer’s and Parkinson’s47, 49, 50; however, the true impact of these mutations on human

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health remains to be determined. Studies involving the correlative analysis of mtDNA variation

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of human mitochondrial sequences found in human-impacted environments may provide a direct

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route to examine the prevalence of these diseases in the local population.

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With respect to the mtDNA analysis, we applied a high-throughput sequencing strategy

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to analyze human mitochondrial HVRII DNA obtained from an urban creek system at different

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time points accounting for spatial-temporal resolution of human contamination in an urban

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watershed. The use of barcoded primers allowed multiplexed sample sequencing and enabled the

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identification of collective HVRII SNP frequencies that were site specific. Although our study

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was confined to analysis of HVRII region of human mtDNA in a limited number of geographic

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locations, a wealth of information can be obtained through other mtDNA genomic targets,

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particularly regions associated with aging and cancer47, 50, 51.

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ASSOCIATED CONTENT

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Supporting Information. Tables of the sampling sites, molecular barcodes used for multiplexed

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sequencing, HVRII SNP frequency data and bioinformatics analysis parameters for CLC

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Genomics Workbench; and figures for Euclidean distance between sites and GIS map of Duck

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Creek Watershed showing combined sewer lines. This material is available free of charge via the

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Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

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Corresponding Author

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*E-mail: [email protected]

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ACKNOWLEDGMENTS

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We thank R. Ravi, E. Wurtzler and N. Punuru for assistance in the laboratory and C. Smith for

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help in sample collection. This research was supported by the Metropolitan Sewer District of

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Greater Cincinnati and a URC Graduate Research Fellowship from the University of Cincinnati

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(Cincinnati, OH).

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REFERENCES

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24. Kamra, A.; et al. Exfoliated colonic epithelial cells: surrogate targets for evaluation of bioactive food components in cancer prevention. J. Nutr. 2005, 135 (11), 2719-2722. 25. Albaugh, G. P.; et al. Isolation of exfoliated colonic epithelial cells, a novel, non‐invasive approach to the study of cellular markers. Int. J. Cancer 1992, 52 (3), 347-350. 26. Andreasson, H.; Gyllensten, U.; Allen, M. Real-time DNA quantification of nuclear and mitochondrial DNA in forensic analysis. Biotechniques 2002, 33 (2), 402-411. 27. Higuchi, R.; von Beroldingen, C. H.; Sensabaugh, G. F.; Erlich, H. A. DNA typing from single hairs. Nature 1988, 332 (6164), 543-546. 28. Soller, J. A.; Schoen, M. E.; Bartrand, T.; Ravenscroft, J. E.; Ashbolt, N. J. Estimated human health risks from exposure to recreational waters impacted by human and nonhuman sources of faecal contamination. Water Res. 2010, 44 (16), 4674-4691. 29. Parsons, T. J.; et al. A high observed substitution rate in the human mitochondrial DNA control region. Nature Genet. 1997, 15, 363-368. 30. Meyer, S.; Weiss, G.; von Haeseler, A. Pattern of nucleotide substitution and rate heterogeneity in the hypervariable regions I and II of human mtDNA. Genetics 1999, 152 (3), 1103-1110. 31. van Oven, M.; Kayser, M. Updated comprehensive phylogenetic tree of global human mitochondrial DNA variation. Hum. Mutat. 2009, 30 (2), E386-E394.

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32. U. S. Environmental Protection Agency (2009) Biological and Water Quality Study of the Lower Little Miami River and Selected Tributaries. OHIO EPA Technical Report EAS/2009-10-06. 33. Hutter, G.; et al. Use of polymorphisms in the noncoding region of the human mitochondrial genome to identify potential contamination of human leukemia-lymphoma cell lines. Hematol. J. 2004, 5 (1), 61-68. 34. Andrews, R. M.; et al. Reanalysis and revision of the Cambridge reference sequence for human mitochondrial DNA. Nature Genet. 1999, 23 (2), 147-147. 35. Brandon, M. C.; et al. MITOMAP: a human mitochondrial genome database—2004 update. Nucleic Acids Res. 2005, 33 (suppl 1), D611-D613. 36. Ingman, M.; Gyllensten, U. mtDB: Human Mitochondrial Genome Database, a resource for population genetics and medical sciences. Nucleic Acids Res. 2006, 34 (suppl 1), D749-D751. 37. Parson, W.; Dür, A. EMPOP—a forensic mtDNA database. Forensic Sci. Int. Genet. 2007, 1 (2), 88-92. 38. Brandon, M. C.; et al. MITOMASTER: a bioinformatics tool for the analysis of mitochondrial DNA sequences. Hum. Mutat. 2009, 30 (1), 1-6. 39. Parson, W.; et al. Evaluation of next generation mtGenome sequencing using the Ion Torrent Personal Genome Machine (PGM). Forensic Sci. Int. Genet. 2013, 7 (5), 543549. 40. Seo, S. B.; et al. Single nucleotide polymorphism typing with massively parallel sequencing for human identification. Int. J. Legal Med. 2013, 127 (6), 1079-1086.

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41. Whiteley, A. S.; et al. Microbial 16S rRNA Ion Tag and community metagenome sequencing using the Ion Torrent (PGM) Platform. J. Microbiol. Methods 2012, 91 (1), 80-88. 42. Li, H.; Homer, N. A survey of sequence alignment algorithms for next-generation sequencing. Brief. Bioinform. 2010, 11 (5), 473-483. 43. Altshuler, D.; et al. An SNP map of the human genome generated by reduced representation shotgun sequencing. Nature, 2000, 407 (6803), 513-516. 44. Li, M.; et al. Detecting heteroplasmy from high-throughput sequencing of complete human mitochondrial DNA genomes. Am. J. Hum. Genet. 2010, 87 (2), 237-249. 45. Stewart, J.; et al. Length variation in HV2 of the human mitochondrial DNA control region. J. Forensic Sci. 2001, 46 (4), 862-870. 46. Lee, C.; Măndoiu, I. I.; Nelson, C. E. Inferring ethnicity from mitochondrial DNA sequence. BMC Proceedings. 2011, 5 (2), S11; BioMed Central Ltd. 47. Wallace, D. C.; Brown, M. D.; Lott, M. T. Mitochondrial DNA variation in human evolution and disease. Gene 1999, 238 (1), 211-230. 48. Cincinnati Census Data Website; http://www.cincinnati-oh.gov/planning/reportsdata/census-demographics. 49. Taylor, R. W.; Turnbull, D. M. Mitochondrial DNA mutations in human disease. Nature Rev. Genet. 2005, 6 (5), 389-402. 50. Wallace, D. C. Mitochondrial genetics: a paradigm for aging and degenerative diseases? Science 1992, 256 (5057), 628-632. 51. Shen, E. Z.; et al. Mitoflash frequency in early adulthood predicts lifespan in Caenorhabditis elegans. Nature 2014, 508 (7494), 128-132.

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Figure Legends

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Figure 1. Locations of the sampling sites in the Duck Creek Watershed in Cincinnati, Ohio. The

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sites are marked as green circles in the map while the red circles represents CSO locations. The

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boundary of the watershed is shown in the inset map of the state of Ohio. Sites 1, 3, 8, 9 and 10

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were used for HVRII sequence analysis due to the consistent abundance of human mitochondrial

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DNA at these sites. Sites 1 and 3 are located on Duck Creek at river mile 2.0 and 3.4. Sites 8 and

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9 are located on Deerfield Creek, near CSO 556, since this overflow had the highest number of

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annual overflow events and largest ever-volumetric contribution to the CSO total overflow. Site

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10 is located on Upper Duck Creek close to CSO 68, which had the second highest contribution

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to the CSO total overflow. Site 1 is downstream of site 3 with the additional influx of water

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coming form Little Duck Creek. Site 8 is directly downstream of site 9. Site 10 is on a separate

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section of the creek and is not influenced by influx of water from any other site, while site 3 is

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influenced by water coming from sites 8, 9 and 10.

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Figure 2. Heat map demonstrating the occurrence and variant frequency for SNPs detected in

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human mitochondrial HVRII region (position 51 - 320 bp relative to rCRS) for all sampling sites

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at three distinct times (A = October 2011; B = March 2012; C = July 2012). Variant frequency is

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defined as the number of reads having a SNP divided by the total reads in the sample. All

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variants with a frequency greater than 5% are reported. It can be seen that variants 73G and

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263G occurred at all sites with frequency greater than 40%.

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Figure 3. Dendrogram (left) from the HCA of SNP frequency data for the study sites (right)

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obtained from the period of Oct 2011-Jul 2012. The site-specific datasets are grouped into three

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clusters. Cluster 1 (orange background) is formed by 3C, 3B, 3A; cluster 2 (purple background)

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is formed by 10A, 10C, 9A, 9B, 8B, 9C, 10B, 8C, 8A; and cluster 3 (grey background) is formed

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by 1C, 1B, 1A. The study sites are marked as green circles in the map while the red circles

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represents CSO locations.

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Figure 4. Bar charts showing the haplogroup distribution of Set A and B for all sequences longer

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than 300 bp derived from Ion Torrent Sequencing of HVRII amplicons. The sequences were

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annotated using MITOMASTER version Beta 1 that performs variant calling relative to the

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rCRS, haplotyping based on Phylotree, and variant annotation based on Mitomap. The relative

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composition of the haplogroups varied considerably across the different sampling sites for both

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the sets. However, the haplogroup H was most abundant for all the sites followed by haplogroup

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L.

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Figure 5. Pie charts demonstrating the population racial diversity in Duck Creek Watershed

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obtained through (a) annotation of HVRII sequences (October 2011) into haplogroups, and (b)

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2010 population census data (by race). (c) Comparison of site-specific distribution of population

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according to races obtained through HVRII annotation and census data 2010 respectively.

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Census data was obtained for the Cincinnati neighborhood approximations of Duck Creek

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Watershed region which included Linwood, Oakley, Madisonville and Pleasant Ridge census

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tracts. Haplogroups were divided into races according to mitochondrial databases Phylotree

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(White = H, HV, J, K, P, S, T, U, V; African American = L0, L1, L2, L3, L4, L5, L6; American

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Indian = A, C, X; Asian = B, D, E, F, G, R, W; and Others).

Figure 1

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Figure 2

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Figure 3

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A

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B

100%

100%

Others

Others

HV

80%

HV

80%

R 60%

U N

40%

K J

20%

T

Percent sequences

Percent sequences

T

M

R 60%

U N

40%

K J

20%

M

L H

0% Site 1

Site 3

Site 8

Site 9

Site 10

L H

0% Site 1

Figure 4

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Site 8

Site 9

Site 10

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Figure 5

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