APPLIED AND ENVIRONMENTAL MICROBIOLOGY, Oct. 2011, p. 6972–6981
Copyright © 2011, American Society for Microbiology. All Rights Reserved.
Vol. 77, No. 19
Lachnospiraceae and Bacteroidales Alternative Fecal Indicators Reveal
Chronic Human Sewage Contamination in an Urban Harbor?†
Ryan J. Newton,1Jessica L. VandeWalle,1Mark A. Borchardt,2
Marc H. Gorelick,3,4and Sandra L. McLellan1*
Great Lakes WATER Institute, School of Freshwater Sciences, 600 E. Greenfield Ave, Milwaukee, Wisconsin 532041;
USDA Agricultural Research Service, 2615 East 29th St., Marshfield, Wisconsin 544492; Department of Pediatrics,
Medical College of Wisconsin, Milwaukee, Wisconsin3; and Children’s Research Institute, Milwaukee, Wisconsin4
Received 13 May 2011/Accepted 25 July 2011
The complexity of fecal microbial communities and overlap among human and other animal sources have
made it difficult to identify source-specific fecal indicator bacteria. However, the advent of next-generation
sequencing technologies now provides increased sequencing power to resolve microbial community composi-
tion within and among environments. These data can be mined for information on source-specific phylotypes
and/or assemblages of phylotypes (i.e., microbial signatures). We report the development of a new genetic
marker for human fecal contamination identified through microbial pyrotag sequence analysis of the V6 region
of the 16S rRNA gene. Sequence analysis of 37 sewage samples and comparison with database sequences
revealed a human-associated phylotype within the Lachnospiraceae family, which was closely related to the
genus Blautia. This phylotype, termed Lachno2, was on average the second most abundant fecal bacterial
phylotype in sewage influent samples from Milwaukee, WI. We developed a quantitative PCR (qPCR) assay for
Lachno2 and used it along with the qPCR-based assays for human Bacteroidales (based on the HF183 genetic
marker), total Bacteroidales spp., and enterococci and the conventional Escherichia coli and enterococci plate
count assays to examine the prevalence of fecal and human fecal pollution in Milwaukee’s harbor. Both the
conventional fecal indicators and the human-associated indicators revealed chronic fecal pollution in the
harbor, with significant increases following heavy rain events and combined sewer overflows. The two human-
associated genetic marker abundances were tightly correlated in the harbor, a strong indication they target the
same source (i.e., human sewage). Human adenoviruses were routinely detected under all conditions in the
harbor, and the probability of their occurrence increased by 154% for every 10-fold increase in the human
indicator concentration. Both Lachno2 and human Bacteroidales increased specificity to detect sewage com-
pared to general indicators, and the relationship to a human pathogen group suggests that the use of these
alternative indicators will improve assessments for human health risks in urban waters.
Fecal pollution in urban waterways is a major impairment to
water quality in cities across the United States (56), and wa-
terborne disease risk remains a significant public health issue
(3). There are numerous pathways by which urban waterways
may become contaminated. Combined sewer overflows (CSOs)
and sanitary sewer overflows (SSOs) garner the most attention,
as these events introduce nearly 4 trillion liters of untreated
sewage into the nation’s waterways each year (57). However,
less conspicuous routes such as stormwater drainage (1, 33,
41), upstream agricultural inputs (24), runoff from large im-
pervious city surfaces (17, 41), and leaking sanitary sewers (40,
41, 45) may also deliver significant amounts of fecal pollution
to waterways. These multiple modes of fecal pollution trans-
port result in a variety of pollution source contributors, includ-
ing humans, pets, urban wildlife, and agricultural animals. In
order to mitigate or prevent future pollution events, it will be
important to identify both the environmental conditions that
promote pollution and the organisms contributing to the fecal
Typically, fecal pollution is assessed by measuring culturable
levels of fecal coliforms, Escherichia coli, or enterococci (51),
which have been found to correlate with health risks to swim-
mers (14, 37). However, these general indicators are less useful
for investigating the source of fecal pollution because of their
lack of host specificity (5, 18), a nonspecific relationship with
human pathogens (31, 41, 42), and the ability of the indicators
to persist and/or reproduce in nature (6, 8, 20, 60). As a result
of these issues, several alternative fecal pollution detection
assays have been developed (7, 12, 16), including many that
were designed to detect and quantify human-derived sources
(7, 21, 24, 32, 46, 48). Most of these indicators rely upon
identifying a taxonomically narrow set of bacteria (e.g., a single
species). Despite the relevance and increasing use of bacteri-
ally based human fecal indicators, each of these methods is
incapable of discriminating between humans and at least one
other animal source (48). In addition, a significant association
between these novel indicators and the presence of human
pathogens in the environment has yet to be established, al-
though this is partly due to a lack of studies examining these
relationships (31, 58).
Next-generation sequencing techniques make it possible to
characterize a large portion of the single gene diversity of a
* Corresponding author. Mailing address: Great Lakes WATER Insti-
tute, School of Freshwater Sciences, 600 E. Greenfield Ave., Milwaukee,
WI 53204. Phone: (414) 382-1747. Fax: (414) 382-1705. E-mail: mclellan
† Supplemental material for this article may be found at http://aem
?Published ahead of print on 29 July 2011.
microbial community. The human microbiome project is at the
forefront of a wave of studies characterizing 16S rRNA gene
microbial diversity from a vast number of environments (34,
53). With this in mind, it was recently suggested that leveraging
the microbiome projects and applying new sequencing tech-
nologies (e.g., 454, Illumina, SOLiD, Ion Torrent) could iden-
tify many new source-specific targets and/or redefine ap-
proaches for tracking fecal pollution sources through the use of
multitaxon signatures (29). In this previous study, the authors
noted that the bacterial family Lachnospiraceae and the well-
studied Bacteroidales order were particularly abundant in sew-
age and many individual human fecal samples, making these
groups prime targets for identifying new source-associated fe-
cal markers and/or microbial signatures.
Here we examined harbor water from Milwaukee, WI, for
fecal contamination with conventional and alternative indica-
tors and used pyrosequencing to characterize the harbor mi-
crobial community during dry weather, rain, and combined
sewer overflow events. We hypothesized that human fecal pol-
lution, including human pathogens, was entering the harbor
outside of sewer overflow scenarios. We used pyrosequencing
data to identify and then develop a new quantitative PCR
(qPCR) assay for a small subset of Lachnospiraceae phylotypes
that were highly abundant in sewage influent and prevalent in
human fecal communities. We then leveraged both our own
and publicly available data sets to further examine the speci-
ficity of the previously described total Bacteroidales spp. (13),
human Bacteroidales (7, 21), and our new qPCR assay. Finally,
adenovirus counts taken concurrently with our human fecal
indicator data allowed us to assess how these markers related
directly to the presence of human pathogens in the environ-
MATERIALS AND METHODS
Sample collection and DNA extraction for bacteria. Thirty-seven wastewater
treatment plant (WWTP) influent samples were taken from two major facilities
servicing metropolitan Milwaukee, WI. Samples were collected from both the
Jones Island (300 millions of gallons per day [MGD] maximum flow) and South
Shore (250 MGD maximum flow) treatment plants on 20 April 2005; 18 April, 21
August, 16 October, 20 November, and 11 December 2007; 17 March, 1 April, 8
April, 28 May, 11 June, 10 July, 8 October, and 10 December 2008; and 31
March, 22 April, 13 May, and 5 August 2009: an additional Jones Island sample
was collected on 21 August 2008. Samples were collected to a total volume of 1
liter from 24-h flow-weighted influent beginning at 6 a.m. on the preceding day
to 6 a.m. on the stated collection day. The 24-hour samples were then filtered
(100 ml or until filter clogging) through a 0.45-?m-pore-size mixed cellulose
esters filter (Millipore, Billerica, MA) and frozen at ?80°C until further pro-
Surface samples from water in Milwaukee’s harbor were collected at a point of
confluence for the Milwaukee, Menomonee, and Kinnickinnic Rivers (i.e., the
harbor channel) but prior to discharge outside the breakwall into Lake Michigan
(see the work of Mueller-Spitz et al.  for coordinate details). Samples were
collected on 7 April, 1 May, 5 and 19 June, 17 and 27 July, 7, 9, 20, 21, and 22
August, 11 and 28 September, and 2 October 2007 and on 11 April, 20 May, 9,
10, 11, 13, 16, 17, and 24 June, 1 and 8 July, and 5 September 2008. Lake
Michigan surface water samples were collected at a station 3.2 km east of
Milwaukee’s harbor on 23 June and 5 August 2010 and at a station 2.7 km from
shore but 4.3 km north of Milwaukee’s harbor on 5 August 2010. These three
samples serve as uncontaminated controls for the harbor samples. For each of
these harbor and lake samples, three surface water samples were collected,
mixed, and subsampled into a 1-liter bottle. Each 1-liter bottle was stored on ice
until being returned to the lab (within 3 h), and subsequently 200 ml (harbor) or
400 ml (lake) was filtered (no prefilter) onto a 0.22-?m-pore-size mixed cellulose
esters filter (47-mm diameter; Millipore, Billerica, MA). Filters were folded and
placed in 2-ml screw-cap tubes and then stored at ?80°C until further processing.
To extract DNA from samples, frozen sample filters were removed from the
freezer and immediately crushed into small pieces in the tube by using a sterile
spatula. The frozen filter pieces were added to a tube containing a bead-beating
matrix and buffers according to the standard protocol for the Fast DNA spin kit
for soil (MP Biomedicals, Solon, OH). DNA extractions were further carried out
according to the manufacturers’ instructions. Samples sent for pyrosequencing
were further purified using the MO BIO PowerClean DNA cleanup kit (MO
BIO Laboratories Inc., Carlsbad, CA). DNA concentrations were measured
using the NanoDrop ND-1000 (Thermo Fisher Scientific Inc., Pittsburgh, PA).
454 pyrosequencing and 16S rRNA gene data set analysis. Three 16S rRNA
gene 454 pyrosequencing data sets were queried for the presence of specific
fecal-indicator bacteria. All data sets have been processed and stored as part of
the VAMPS database (http://vamps.mbl.edu). The first data set consisted of the
37 WWTP influent samples described above. Of these samples, eight (Jones
Island and South Shore samples from 20 April 2005 and 18 April, 21 August, and
11 December 2007) were sequenced and described previously (29). The remain-
ing 29 samples were processed in the same manner as part of this study, with the
V6 hypervariable region of the 16S rRNA gene being amplified, sequenced using
a Roche genome sequencer GS-FLX, trimmed, quality controlled, and aligned
(29). The amplification and quality control process for this 37-sample data set
resulted in 1,062,510 bacterial sequence reads for analysis. The second data set
consisted of 48 human fecal samples yielding 1,202,874 bacterial sequences for
analysis. These samples were collected and deposited by Dethlefsen et al. (11)
from five individuals before, during, and after treatment with antibiotics and by
Turnbaugh et al. (52) from 11 families of lean and obese twins and their mothers;
sample and sequence details can be obtained from their respective publications.
The third data set, collected and deposited by Shanks et al. (47), consisted of 30
adult beef cattle fecal samples taken from cows at six different feed operations.
The pyrosequencing data from these fecal samples resulted in 620,611 quality
sequences for analysis. Sample and sequence details are described in the work of
Shanks et al. (47). All described in silico analyses of the previously deposited data
sets were performed directly for the present study.
Cloning and phylogenetic analysis. In order to obtain a number of relatively
long 16S rRNA gene sequences (?800 bp) containing the Lachno2 V6 region
(see Table S1 in the supplemental material for the sequence), from which we
could design a qPCR assay for Lachno2, we cloned and sequenced rRNA genes
from Milwaukee sewage samples using taxon-specific primers. Sample DNA was
amplified using a mixture of forward primers, one targeting the Clostridium
coccoides group (CcocF ) and a newly designed primer, BF-063 (5?-AAG
TGA CGG TAC CTG AAT AA-3?), targeting sequences closely related to the
C. coccoides group that also contained the Lachno2 V6 region. The universal
1492R primer was used as the reverse primer. For each sample, four PCRs using
the CcocF and 1492R primer set were pooled with one reaction using the BF-063
and 1492R primer set prior to PCR cleanup. This ratio was determined based on
the number of exact matches to each primer among sequences contained in the
Ribosomal Database Project (RDP) (9). PCR products were purified using
Qiagen PCR purification kit (Qiagen Inc., Valencia, CA). Products were then
cloned into the pCR2.1 vector by using the TOPO TA cloning kit (Invitrogen,
Carlsbad, CA). Sequencing was carried out from the 1492R primer by using the
ABI BigDye Terminator Kit (Applied Biosystems, Foster City, CA) on an ABI
Prism 3700xi (Applied Biosystems, Foster City, CA). Sequences were trimmed
for quality using PHRED (15). All quality sequences were analyzed for chimeras
using Mallard (4). Sequences flagged by Mallard were analyzed using Chimera
Check (9) and removed if determined to be chimeric.
A number of cloned sequences were identified as having the Lachno2 V6
sequence. Thirty-two of these sequences that spanned the diversity of clones
containing the Lachno2 region and several near full-length sequences from
isolates closely related to the clones were initially aligned using the FAST
_ALIGNER ARB tool (27) before the alignment was heuristically adjusted using
primary and secondary rRNA structure as a guide. After alignment, a mask
excluding all gaps and trimming sequences to an equal length was applied. The
resulting final alignment of 738 positions was used in neighbor-joining phyloge-
netic analysis in ARB with 1,000 bootstrap calculations.
Culture-based fecal indicator enumeration. Harbor water samples were ana-
lyzed using the U.S. Environmental Protection Agency methods for enterococci
and E. coli enumeration. To enumerate these fecal indicators, samples were
filtered through a 0.45-?m-pore-size nitrocellulose filter (47-mm diameter; Mil-
lipore, Billerica, MA), placed on modified mTEC (55) and MEI (54) agars,
incubated for 24 h at 41°C (enterococci) or 44.5°C (E. coli), and counted for
CFU. We note here that the method for E. coli enumeration differs from EPA
Method 1603 (55) in that there was not a primary incubation at 35°C for 2 h. The
volume of water filtered for each sample varied depending on the expected level
VOL. 77, 2011HUMAN FECAL POLLUTION IN LAKE MICHIGAN 6973
Lachno2 qPCR design. In ARB, a PT server was constructed consisting of the
sequenced sewage clones and several closely related bacterial taxa (including 2 to
4 sequences each from Lactococcus, Bacillus, Clostridium perfringens, Pseudomo-
nas, Bacteroides, and Arcobacter). The probe design function was used to query
the PT server for potential primer/probe regions of variability between the
Lachno2 sequences and our database containing our clones and previously pub-
lished sequences (Silva database , November 2009). Potential probe/primer
sequences were obtained from the queries and rematched to the database to
check for specificity. The Primer Express software (Applied Biosystems by Life
Technologies) was used to analyze primers and probes for potential primer-
dimer interactions and hairpins. The Lachno2 forward primer is 5?-TTC GCA
AGA ATG AAA CTC AAA G-3?, the reverse complement of the universal C.
coccoides primer Ccoc-R (28); the Lachno2 reverse primer is 5?-AAG GAA
AGA TCC GGT TAA GGA TC-3?; and the probe sequence is 5?-(6-carboxy-
fluorescein [6-FAM])-ACC AAG TCT TGA CAT CCG-(minor groove binder
In silico qPCR assay analysis. Sequences matching the primers/probe for the
Lachno2 qPCR assay and two previously designed fecal marker qPCR assays,
total Bacteroidales spp. (13) and human Bacteroidales (7, 21), were identified with
the RDP probe match feature allowing for one mismatch per primer/probe and
matching only sequences from the RDP database where the quality criterion was
set to good and length was ?1,200 bp (59). All sequences matching these criteria
for each primer/probe set (i.e., qPCR assay) were downloaded as an alignment
from RDP. The V6 region from each sequence in the alignment (each qPCR
assay has a separate alignment) was then extracted and used as a query against
samples from the three V6 pyrosequencing data sets. Exact matches between the
query V6 sequences for each qPCR assay and the V6 sequences in each sample
in the three data sets were identified. The abundance of the matched V6 se-
quences in each sample for each assay were summed and divided by the total
number of bacterial V6 sequences obtained in each sample, thereby providing a
relative abundance calculation for each qPCR assay in each sample.
Quantitative PCR analyses. Quantitative PCR for all assays was carried out
using an ABI StepOne real-time PCR system with TaqMan hydrolysis probe
chemistry. Primer, probe, and target sequences for the total Bacteroidales spp.
and enterococcal assays can be found in the work of Dick and Field (13) and of
Ludwig and Schleifer (26), respectively. The qPCR assay for human Bacteroidales
followed methods previously published by Kildare et al. (21), with the exception
that we used the HF183 forward primer first reported by Bernhard and Field (7).
Standard curves were generated during each run and consisted of a linearized
plasmid containing the target sequence. Standard curves were run with DNA
serially diluted from 1.5 ? 106to 1.5 ? 101copies/reaction. Standards were run
in triplicate, and each sample was run in duplicate in a final volume of 25 ?l with
a final concentration of 1 ?M for each primer, 80 nM for the probe, 5 ?l of DNA
diluted to 4 ng/?l, and 12.5 ?l of an Applied Biosystems TaqMan master mix.
Amplification consisted of the following cycles: 50°C for 2 min followed by 95°C
for 10 min, and 40 cycles of denaturing at 95°C for 15 s followed by a combined
annealing-elongation step at 60°C for 1 min. The amplification efficiency of the
standard curves for the newly designed Lachno2 assay was 1.98. In previous
studies, on the same river/harbor system examined here, we found inhibition in
?1% of more than 350 water samples following dilution of extracted DNA to 4
ng/?l (41), the dilution also carried out in this study. Inhibition assays as de-
scribed by Shanks et al. (48) were carried out for all samples using the Lachno2
qPCR assay, and no inhibition was detected.
For all qPCR assays, the data are reported as copy numbers per 100 ml of
original sample water, which was calculated by taking into account the original
water volume sampled, the resulting volume following a DNA extraction, the
volume of extracted DNA entering the qPCRs, and the relationship of the qPCR
standard curve to the fluorescence product of the qPCR amplification in each
sample. Standard Pearson product-moment correlation coefficients (r) were cal-
culated for the comparison of qPCR-based fecal indicator data. Log transfor-
mations were carried out on all data prior to use in Pearson’s r calculations.
Sample collection, DNA extraction, and enumeration of adenovirus. Sample
collection end dates for sewage samples analyzed for viruses were 18 May, 8
June, 13 July, 10 August, 14 September, 12 October, 9 November, and 14
December 2009 and 11 January 2010; harbor sample collection dates are listed
above. Adenovirus concentrations in sewage were measured from 4-liter, 7-day
flow-weighted composite samples provided by the Milwaukee Metropolitan Sew-
erage District from the South Shore WWTP. The entire 4-liter volume was
concentrated by polyethylene glycol flocculation (23) to 2- to 4-ml final concen-
trated sample volumes (FCSVs). Adenoviruses in harbor channel water were
collected by continuous pumping of 100 to 200 liters through a glass wool filter
(23) while the sampling boat made a transect along the width of the channel.
Glass wool filters were eluted with 3% beef extract (wt/vol) containing 0.5 M
glycine (pH 9.5), and the eluent was additionally concentrated by polyethylene
glycol to a 2-ml FCSV. DNA was extracted from a 280-?l FCSV with the
QIAamp DNA blood minikit and buffer AVL (Qiagen, Valencia, CA). qPCR
was performed on a LightCycler 480 instrument (Roche Diagnostics, Mannheim,
Germany) using the LightCycler 480 probes master kit (Roche Diagnostics). Six
microliters of extracted DNA was added to 14 ?l of master mix for a final
reaction volume of 20 ?l. Harbor water adenoviruses were quantified using the
primers 5?-GGA CGC CTC GGA GTA CCT GA-3? and 5?-CGC TGI GAC CIG
TCT GTG G-3? and the TaqMan 5?-CAC CGA TAC GTA CTT CAG CCT
GGG T-3? probe designed by Cromeans et al. (10), which primarily target
serotypes 2, 5, 6, 40, and 41. Sewage adenovirus analysis, conducted later, relied
on the primers and probes developed by Kuo et al. (22), which more broadly
target adenovirus subgroups A, B, C, D, and F. Subgroups C and F include
serotypes 2, 5, 6, 40, and 41. Thermocycling began with 95°C for 10 min followed
by 45 cycles of 15 s at 94°C and 1 min at 60°C. All samples were checked for PCR
inhibition following previously described methods for detecting hepatitis G virus
cDNA seeded into qPCR mixtures containing sample DNA (23). The detection
limit was one genomic copy of the viruses per 20-?l qPCR volume. A subsample
from the sewage and harbor FCSVs was used for DNA extraction and qPCR for
the human fecal markers as described above.
A logistic regression for the relationship between the sum abundance of the
human fecal indicators (sum copies per ml in each sample of Lachno2 and
human Bacteroidales) from qPCR data and adenovirus presence in the harbor
water samples was carried out using the glm() function with a binomial distri-
bution family specified in the R statistics package (R Development Core Team,
2007). The logistic regression was set up so that the adenovirus data were the
Nucleotide sequence accession numbers. 16S rRNA gene clone sequences
identified in this study have GenBank nucleotide sequence accession numbers
JF826248 to JF826279. The 16S rRNA gene pyrotag sequences generated for this
study are available through the Visualization and Analysis of Microbial Popula-
tion Structures (VAMPS) database (www.vamps.mbl.edu/).
Identification of a ubiquitous Lachnospiraceae phylotype in
human feces. Sequence analysis of 37 sewage influent samples
revealed that the most common feces-associated phylotypes
belonged to the taxonomic group Lachnospiraceae. A single
Lachnospiraceae-associated phylotype, termed Lachno2, was
the second most abundant human feces-associated phylotype
in the sewage samples (mean relative abundance, 0.3%) (Fig.
1). The most abundant sewage feces-associated phylotype did
not show specificity between human and cattle fecal samples
(data not shown). Lachno2 was also abundant in a data set of
human fecal samples collected in studies by Dethlefsen et al.
(11) and Turnbaugh et al. (52) (Fig. 1) but was not detected in
cattle fecal samples (47). The sample variability for Lachno2
among all sewage samples, which are comprised of a flow-
weighted composite of ?1.1 million people, was much less
(?20-fold) than the sample variability among individual hu-
man fecal samples.
Development of a qPCR assay for Lachno2. Clone libraries
targeting the C. coccoides group (28), which contains the Lach-
nospiraceae family, were constructed from Milwaukee WWTP
samples. A total of 115 unique clones of the Lachno2 phylo-
type were identified. The longer sequence information pro-
vided by the 500- to 1,000-bp clone sequences revealed that the
Lachno2 phylotype is most closely related to the isolate Blautia
wexlerae WAL 14507, a Gram-positive fecal anaerobe, and
several near-full-length clones termed Ruminococcus obeum
(Fig. 2). Analysis of these clone sequences with the RDP clas-
sifier (59) suggested each belonged to the genus Blautia, as all
sequences were classified as Blautia with 100% confidence.
Sequence identity calculations demonstrated that clones rep-
6974NEWTON ET AL.APPL. ENVIRON. MICROBIOL.
resenting the Lachno2 phylotype had relatively little sequence
variation (mean sequence identity ? 98.6%).
Primers and a probe were designed to target Lachno2 in a
qPCR assay (see Materials and Methods). An RDP probe
match analysis (0 mismatches for each primer/probe) against
the public databases suggested high specificity for our
Lachno2-based qPCR assay, as 696 of 706 matched sequences
(?1,200 bp) were classified as Blautia. Of the 706 sequences,
there were 19 unique V6 regions (see Table S1 in the supple-
mental material), which indicated that the qPCR assay tar-
geted a slightly broader group than the unique Lachno2 phy-
lotype. Based on the recovery of these phylotypes in the
curated RDP data set (RDP criteria: ?1,200 bp, quality ?
good, and “both” isolates and uncultured sequences), all but
two of these phylotypes were recovered from human fecal
content. Of the two nonhuman phylotypes, one was recovered
from African elephant feces and the other from a mouse in-
testinal tract. Of the 19 qPCR-targeted phylotypes, the
FIG. 1. Bar plot and summary box plot of the Lachno2 phylotype relative abundance in 48 human fecal samples and 37 Milwaukee sewage
influent samples. Boxes illustrate the 25th-, 50th-, and 75th-percentile data. Whiskers indicate the 10th- and 90th-percentile data. Outliers are
depicted with open circles. The samples included in the human fecal content plot are from Dethlefsen et al. (11) (bars 1 to 15) and Turnbaugh
et al. (52) (bars 16 to 48). The order of paired Jones Island (JI) and South Shore (SS) treatment plant influent samples included in the Milwaukee
sewage influent plot are as follows: 20 April 2005; 18 April, 21 August, 16 October, 20 November, and 11 December 2007; 17 March, 1 April, 8
April, 28 May, 11 June, 10 July, 21 August (JI only), 8 October, and 10 December 2008; and 31 March, 22 April, 13 May, and 5 August 2009.
FIG. 2. Unrooted consensus phylogram from neighbor-joining phylogenetic analysis depicting clone sequences containing the Lachno2 phy-
lotype and several close relatives. Bacillus subtilis (AB042061) and Bacillus pumilus (AY456263) were used as an outgroup. Only nodes obtaining
50% confidence during bootstrapping are labeled. GenBank accession numbers are listed in parentheses next to the isolate or clone name. The
scale bar indicates 1% sequence divergence.
VOL. 77, 2011 HUMAN FECAL POLLUTION IN LAKE MICHIGAN6975
Lachno2 phylotype was the dominant phylotype in sewage
samples (median ? 88% of the recovered V6 sequences from
the 19 phylotypes among 37 samples). Extending the allowed
mismatches to one mismatch per primer/probe broadened the
assay detection to 892 (882 classified as Blautia) total se-
quences and 66 unique V6 sequences (see Table S1 in the
In silico analysis of occurrence for total Bacteroidales spp.,
human Bacteroidales, and Lachno2 PCR targets in human,
cattle, and sewage samples. In silico analysis, allowing one
mismatch for each primer/probe sequence, revealed that se-
quences targeted by the total Bacteroidales spp. assay were
highly variable and in some cases very abundant in individual
fecal samples (up to 57.3% and 40.9% of total bacterial se-
quences in human and cattle fecal samples, respectively). In
sewage, total Bacteroidales spp. were also highly abundant but
were more evenly distributed among samples (up to 5.8% of
total bacterial sequences) (Fig. 3). Likewise, the human Bac-
teroidales and Lachno2 assays targeted sequences that were
abundant in the human fecal (up to 16.6% and 18.6% of total
bacterial sequences, respectively) and sewage (up to 2.1% and
0.9%, respectively) data sets, but neither of these human-as-
sociated assays targeted abundant sequences in the cattle data
set (Fig. 3). Of the five cow fecal samples containing Lachno2-
related sequences, the highest percentage of Lachno2 observed
was 0.02%, and all of the Lachno2 sequences associated with
the cattle fecal samples were from clones harboring a mis-
match in at least two of the three Lachno2 primers/probe (data
Measurement of total and human-associated fecal indica-
tors in Milwaukee’s harbor. Conventional culture-based fecal
indicators, E. coli and enterococci, and a qPCR-based fecal
indicator for total Bacteroidales spp. (13) were measured in the
channel of Milwaukee’s harbor on 26 dates during late spring
and summer in 2007 and 2008. The sample dates included dry
periods (?0.5 in. of rain within 48 h), rain events (?0.5 in. rain
within 48 h), CSO events, and post-CSO periods (within 5
days). In all cases, the fecal indicators revealed that fecal pol-
lution was chronic in Milwaukee’s harbor during the spring/
summer period (Fig. 4). Levels on a quarter of the dry period
dates exceeded the U.S. EPA criterion for safe recreational
waters (235 CFU/100 ml) for E. coli, and levels on seven of
nine dates sampled during rain events also exceeded these
criteria. Both the E. coli and enterococci fecal indicators in-
creased significantly from dry to rain events (t test, P value ?
0.05) and often showed 20- to 50-fold increases during CSOs,
a period of sewage discharge into the harbor area (Fig. 4).
Likewise, the qPCR-based total Bacteroidales spp. fecal indi-
cator was detected in all samples, including dry-weather peri-
ods (minimum ? 1,641 copies/100 ml) and increased signifi-
cantly (t test, P value ? 0.05) during and after CSO events, in
some cases reaching more than 3 million copies per 100 ml of
harbor water (Fig. 4).
Human fecal pollution was detected by both the Lachno2
and human Bacteroidales qPCR assays in all harbor water sam-
ples tested, which included dry, rain, and CSO periods (Fig. 5A
and B). The human Bacteroidales and Lachno2 assays for hu-
man fecal contamination showed a strong correlation (Pear-
son’s r ? 0.97, P ? 0.01; non-CSO data, r ? 0.86, P ? 0.01) and
similar concentrations in all samples (Fig. 5B). Dramatic in-
creases, 100- to 1,000-fold, were observed for both human fecal
markers during CSO events. Elevated but not significant mean,
median, and maximum Lachno2 concentrations were observed
during rain events (?0.5 in. in 48 h) compared to nonrain
events (?0.5 in. in 48 h) (Fig. 5A). The average ratio for the
human Bacteroidales to total Bacteroidales spp. in sewage in-
fluent samples, a predominantly human fecal source, was
3.13% ? 0.96%. This ratio was also high in the harbor during
CSOs (3.81% ? 1.24%) but was significantly lower in the
non-CSO samples (1.68 ? 1.09%, t test P value ?0.05).
FIG. 3. Heat map illustrating the in silico-estimated relative abundance of sequences targeted by three qPCR assays, total Bacteroidales spp.
(13), human Bacteroidales (7, 21), and Lachno2 (this study) in each sample from the human fecal (11, 52), cow fecal (47), and WI sewage (reference
29 and this study) V6 pyrosequencing data sets. Each square represents a unique sample from the data set. The sequence targets of each qPCR
assay were identified by matching the primers/probe (allowing for one mismatch per primer/probe) from each assay to the RDP 16S rRNA gene
sequence database filtered by a quality criterion of good and a length criterion of ?1,200 bp (9). All sequences matching these criteria for each
qPCR assay were downloaded as an alignment from RDP, and then the V6 region from these aligned sequences was extracted. Each extracted V6
sequence was used as a query against the V6 data from samples in the three data sets: human fecal, cow fecal, and WI sewage. Exact matches to
the query V6 sequences were identified in every sample, and then the occurrences of the matched V6 sequences were summed in each sample.
The sum of these exact matches for each assay was divided by the total number of bacterial sequence reads in each sample to obtain a relative
abundance for the three qPCR assays. Heat map relative abundance scale bars are depicted next to their intended data sets. The dash symbol in
a sample square indicates that no target sequences were identified in the sample.
6976 NEWTON ET AL.APPL. ENVIRON. MICROBIOL.
The standard enterococcal qPCR assay for general fecal
contamination also was strongly correlated to the Lachno2
assay for human fecal contamination (Pearson’s r ? 0.91, P ?
0.01) when all data were included and when CSO-related sam-
ples were excluded (Pearson’s r ? 0.82, P ? 0.01). Comparing
the concentrations of these markers in the harbor also revealed
a pattern (Fig. 5C); the enterococcal assay generally exhibited
higher concentrations during the dry, rain, and post-CSO pe-
riods, whereas the Lachno2 assay exhibited in every case a
higher concentration during CSOs (Fig. 5C).
Three Lake Michigan samples collected ?3.2 km from the
harbor served as environmental negative controls for the
qPCR assays, as this distance from shore greatly reduces
the amount of fecal pollution present under all environmental
scenarios (i.e., dry, rain, CSO). In all three samples, the
Lachno2 and human Bacteroidales qPCR assays were below the
detection threshold for the assays (50 copies per 100 ml of
water), while the total Bacteroidales assay revealed levels
slightly above this detection threshold at 1.8 ? 102, 1.1 ? 103,
and 2.2 ? 102copies per 100 ml of water.
FIG. 4. Bar plot of CFU counts per 100 ml for enterococci (yellow) and E. coli (blue) on the left axis and of 16S rRNA gene copies per 100
ml from a total Bacteroidales (Bac) sp. qPCR assay (green) on the right axis. All samples were collected from Milwaukee harbor water in 2007
(A) and 2008 (B). Samples are shown in order and were collected on 17 and 27 July, 9 August, 1 May, 19 June, 6 and 7 August, 11 and 28
September, 2 October, 4 April, 20 August, 5 June, and 21 and 22 August 2007 (A) and 20 May, 24 June, 1 and 8 July, 5 September, 11 April, and
9, 10, 11, 13, 16, and 17 June 2008 (B). Sample dates are color coded (below x axis) by environmental conditions during sampling: blue, ?0.5 in.
rain over 48-h period prior to sampling; gray, ?0.5 in. rain over 48-h period prior to sampling; brown, CSO period; and tan, within 5 days post-CSO.
nd, not detected.
FIG. 5. (A) Box plot of the Lachno2 copies per 100 ml of harbor water as determined by qPCR. Boxes indicate the 25th, 50th, and 75th
percentiles and whiskers indicate the minimum and maximum data points. Four harbor environmental condition periods are depicted: ?0.5 in. rain
in 48 h (n ? 6), ?0.5 in. rain in 48 h (n ? 9), during CSO (n ? 7), and 5 days after CSO (n ? 5). (B) Scatter plot of Lachno2 copies per 100 ml
versus copies per 100 ml for the qPCR-based assay for human Bacteroidales (Bac) (B) and enterococci (C). One-to-one lines are depicted and all
data are plotted on a log scale. Each point is color coded by the environmental conditions represented in the box plot.
VOL. 77, 2011 HUMAN FECAL POLLUTION IN LAKE MICHIGAN 6977
Relating human fecal indicators to adenovirus. Adenovirus
abundance (genome copies per 100 ml) varied by 3 orders of
magnitude in sewage influent samples (Fig. 6A) and even with
limited data exhibited a trend toward much higher abundance
in spring and early summer than in other times of the year (Fig.
6A). The variation in adenovirus abundance contrasts with the
human fecal indicator (Lachno2 plus human Bacteroidales)
abundance, which was relatively stable across the sewage in-
fluent samples (maximum 2-fold variation in abundance) (Fig.
6A). Despite the difference in variability, there was a relation-
ship between the abundance of human fecal indicators and
adenovirus occurrence (Fig. 6B). A logistic regression model
exhibited a good fit (goodness of fit, P value ? 0.46, where P ?
0.05 indicates a good model fit) and indicated that the odds of
observing adenovirus in the harbor increased by 154% for
every 10-fold increase in the human indicator abundance.
In this study, we examined the prevalence of human fecal
waste in Milwaukee’s harbor. Previous attempts to identify
fecal pollution sources and assess risk from pollution events
have led to the development of human-associated fecal pollution
assays, many of which target the order Bacteroidales, a group of
fecal anaerobes (21, 24, 32, 46, 48). However, the exact specificity
and applicability of these assays in varied environments remain
unknown (48); thus, we sought to identify another human fecal
indicator that could complement existing genetic markers. Rely-
ing upon multiple taxa to create a human-specific indicator sig-
nature should improve source specificity and provide more con-
sistent results among environments given likely differences in
decay rates for various types of organisms.
The introduction of next-generation sequencing technology
has allowed the undertaking of large microbial community
sequencing projects (2, 29, 34, 39). These projects now provide
a resource from which we can examine the specificity of tens of
thousands of microbial phylotypes to a specific environmental
habitat(s). In a previous microbial community study of WWTP
influent, McLellan et al. (29) suggested that the Lachno-
spiraceae family would be an ideal bacterial group for fecal
source tracking because of its abundance in WWTP influent
samples. Closer examination of the microbial community data
here revealed that a single phylotype (Lachno2), which is
closely related to the genus Blautia (Fig. 3), was especially
abundant in both human fecal samples and Milwaukee sewage
samples but was not present in cattle fecal samples, a common
fecal pollution source in the harbor (Fig. 2). The identification
of database sequences and clone sequences from our own
libraries containing Lachno2 further confirmed that it be-
longed to a phylogenetically narrow group and would therefore
be an excellent candidate for a host-associated fecal indicator.
Lachno2 was consistently present, but its relative abundance
varied largely from human to human, which was in contrast to
the small relative abundance variation among the sewage sam-
ples (Fig. 2). Previous large sequencing efforts related to the
human microbiome have indicated immense variation in the
fecal communities among humans (38, 52), which has
prompted some researchers to suggest that a core fecal signa-
ture will be difficult to identify (52). The sewage influent sam-
ples in this study spanned a 3-year period and represented
annual, seasonal, and geographic (two WWTP service areas)
variation in metropolitan Milwaukee’s human population. The
smaller variation in the sewage influent samples, which repre-
sent a composite of up to 1.1 million human fecal communities,
suggests that the identification of core microbes in human fecal
content may be identified through examination of WWTP in-
fluent samples. Future studies that examine a larger number of
FIG. 6. (A) Scatter plot of the human feces-associated indicators human Bacteroidales (red circles) and Lachno2 (blue circles) versus
adenovirus as measured in sewage influent to South Shore WWTP in Milwaukee, WI. Plot point sample dates listed in order from most to least
abundant adenovirus are as follows: 13 July, 18 May, 8 June, 14 September, 12 October, 9 November, 10 August, and 14 December 2009 and 11
January 2010. Note that for visualization both axes are log scaled. (B) Scatter plot of human feces-associated indicators (human Bacteroidales plus
Lachno2) versus adenovirus genome as measured in Milwaukee’s harbor. Sample dates are color coded as follows: blue, ?0.5 in. rain; gray, ?0.5
in. rain; brown, CSO; and tan, ?5 days post-CSO. Points along the x axis did not contain measurable adenovirus. Note that both axes are log scaled
6978 NEWTON ET AL.APPL. ENVIRON. MICROBIOL.
human fecal communities in addition to WWTP influent sam-
ples from a large geographic range could provide the needed
insight to tease apart the core human fecal community.
We developed a qPCR assay for the Lachno2 phylotype and
made use of the pyrosequencing data sets to examine in silico
the specificity of this assay as well as previously defined total
and human Bacteroidales qPCR assays. This process revealed
that our assay likely targets more taxa than those associated
with the unique Lachno2 phylotype, but the range of phylo-
types remained phylogenetically narrow (98.9% of sequences
targeted were RDP classified as Blautia), were associated with
humans and not our comparison host, cattle (Fig. 3), and were
almost exclusively human associated in public 16S rRNA gene
databases. Our in silico analysis also revealed high person-to-
person variability in the relative abundance of the total and
human Bacteroidales assays in human fecal samples (Fig. 3).
Interestingly, the human Bacteroidales assay targeted se-
quences that were a significantly (P ? 0.001) larger part of the
community in the human fecal samples from the study of
Dethlefsen et al. (11) than from the study of Turnbaugh et al.
(52), while the Lachno2 marker exhibited the inverse of this
relationship (P ? 0.001) (Fig. 3). The complementary nature of
these two marker assays suggests that using them in tandem or
as part of a larger profile may provide a more consistent mea-
sure of human fecal contamination than using either on its
Fecal pollution of urban waterways is a major contributor to
waterborne illnesses in the United States and remains a wide-
spread problem for both coastal freshwater and marine eco-
systems (3, 56, 57). While simply detecting fecal pollution pro-
vides evidence of health risks, identifying the pollution source
is ultimately the information needed for effective remediation
efforts to take place that will significantly reduce risk. In Mil-
waukee, WI, major fecal pollution events in nearshore waters
occur each year during combined and sanitary sewer overflows.
During these periods, untreated sewage is discharged to Mil-
waukee’s rivers and subsequently the harbor and Lake Michi-
gan, thereby providing us an opportunity to use conventional
and alternative fecal indicators to compare known human fecal
contamination events with other times in which human fecal
pollution should not be present.
In this study, we found that fecal pollution is chronic in
Milwaukee’s harbor (Fig. 4). This persistent input into the
harbor suggests multiple delivery routes outside of reported
sewer overflows. In urban environments, it has been demon-
strated that human fecal pollution may enter receiving bodies
from stormwater runoff/outfall discharge, leaking sanitary
pipes, and illicit sanitary sewer connections (40, 41). Potential
routes of unrecognized sewage inputs have been documented
in the Milwaukee area (41), and our identification of signifi-
cantly increased human fecal indicators following rain events
provides further evidence that stormwater may be an impor-
tant source of human-derived fecal pollution.
As human fecal content is a likely source for human health
risks from fecal and/or sewage pollution, we focused on the
contribution of human fecal pollution to the “total” fecal pol-
lution in the harbor. Using the ratio of human Bacteroidales to
total Bacteroidales spp. as a proxy for relative human fecal
contribution to total fecal pollution (41), we observed that
CSO periods in the harbor had ratios similar to what was seen
for sewage, which suggests that untreated sewage is the main
source of fecal pollution during these periods. During the
non-CSO periods, although the human Bacteroidales compo-
nent of fecal contamination remained high, the ratio dropped
to roughly half of what is typically found in sewage, which
suggests that both human and nonhuman sources contribute to
the chronic fecal pollution in the harbor. Likewise, a compar-
ison of the Lachno2 and enterococcal qPCR assays revealed an
abundance ratio shift from a low to high Lachno2/enterococcus
ratio when switching from non-CSO to CSO periods (Fig. 5C).
As the enterococcal qPCR assay targets human and multiple
animal fecal sources, these data further provide evidence of a
decreased human fecal contribution to total fecal pollution in
the harbor during non-CSO events. Nonhuman fecal pollution
in urban environments can occur from multiple primary
sources, including birds, domestic pets, and urban wildlife (19,
25, 49, 50). Further efforts in Milwaukee’s urban environment
are needed to identify and quantify all of the contributing
Other studies have shown that the human Bacteroidales as-
say may detect bacteria from nonhuman mammal fecal mate-
rial, although this cross-reactivity appears fairly minor (48).
Excluding cattle, it is unknown whether the Lachno2 assay has
cross-reactivity for bacteria from nonhuman animal fecal ma-
terial. However, a tight correlation was observed between the
human Bacteroidales and Lachno2 assays in the harbor (Fig.
5C). It is unlikely that this tight correlation would exist unless
the markers had detected the same source microbial commu-
nity and this source was a major polluter of the harbor. It is
also unlikely that these two bacterial indicators, as members of
different phyla, have an identical host distribution; thus, we
suggest that their correlation in the harbor water is strong
evidence that the indicators specifically identify human sewage
in our system. Because there may be no single genetic marker
that is exclusive to a single source, a community approach
(characterizing a suite of markers) may be a very effective
alternative (29). This is especially true in light of the large
variability among human fecal communities (52). The tight
correlation of the Lachno2 and human Bacteroidales assays
suggests that these two markers are an excellent starting point
for development of this source-specific community approach to
human fecal pollution detection in surface waters.
Although human fecal indicators have revealed the presence
of human fecal pollution in surface waters, a strong link be-
tween these indicators and human pathogen presence has yet
to be established (58). Our examination of adenovirus, a group
commonly used as an index for the presence of human viruses
in water (31, 35), showed that a linear abundance relationship
is not present between the human indicators and virus abun-
dance in sewage; therefore, we did not expect to observe a
direct correlation in harbor water. Viral titers in sewage are
known to fluctuate with season (43, 44). We also observed
large fluctuations in adenovirus abundance in sewage across a
year, which contrasted the relative stability over time of the
bacterial human fecal markers (Fig. 6A). Further, different
ecological forcings upon the bacteria and viruses once in the
harbor waters may cause different retention times for each
group and thus also affect a potential linear relationship. Our
results did, however, suggest that it is likely to find human
adenovirus in the harbor when the bacterial human fecal indi-
VOL. 77, 2011HUMAN FECAL POLLUTION IN LAKE MICHIGAN6979
cator abundance is high (Fig. 6B). In fact, a logistic regression
model revealed that a 10-fold increase in human indicator
abundance in the harbor results in a 154% increase in the odds
of observing adenovirus; thus, it may be feasible to use a
bacterial human fecal signature to assess pathogen risk. The
abundance of these bacterial indicators, which is 2 to 4 orders
of magnitude greater then the abundance of human adenovirus
in sewage, provides increased sensitivity for detecting human
fecal pollution in freshwaters and makes these markers partic-
ularly suitable for tracking sewage contamination. These re-
sults also add credence to the hypothesis that tracking multiple
indicators and/or human pathogens may be required to ade-
quately assess human health risk from human fecal contami-
nation of surface waters.
We thank Stuart E. Jones for his insightful discussion and technical
assistance in applying a logistic regression model to the data, Steve
Corsi for providing sewage samples for adenovirus analysis, Beth Sauer
for her helpful comments on previous versions of the manuscript, and
three reviewers for their astute comments.
Funding for this work was provided by NOAA’s Oceans and
Human Health Initiative extramural grant program (grant no.
NA05NOS4781243), NIAID (grant no. 1 R21 AI076970-01A1), and
University of Wisconsin Sea Grant Institute under grants from the
National Sea Grant College Program, NOAA, the U.S. Department
of Commerce,andthe State
ofWisconsin (grant no.
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