Sequencing Human Mitochondrial Hypervariable Region II as a

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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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Environmental Science & Technology

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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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ACS Paragon Plus Environment


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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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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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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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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

493

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

494

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

495

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

496

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

499

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

501

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

502

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.

505

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

507

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


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508

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

510

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

511

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

513

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

514

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

515

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

516

represents CSO locations.

517

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

519

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

520

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

521

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

522

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

523

L.

524

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

525

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

526

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

527

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

528

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

531

(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



Figure 3


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A


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


Site 3

Site 8

Site 9

Site 10


Figure 5


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TOC Graphic


Sequencing Human Mitochondrial Hypervariable Region II as a

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