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Determination of specific types and relative levels of QPCRinhibitors in environmental water samples usingexcitationeemission matrix spectroscopy and PARAFAC

Jennifer Gentry-Shields*, Angela Wang, Rose M. Cory, Jill R. Stewart

Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina, Chapel Hill,

NC 27599-7431, USA

a r t i c l e i n f o

Article history:

Received 3 December 2012

Received in revised form

20 March 2013

Accepted 22 March 2013

Available online 3 April 2013

Keywords:

Water quality

qPCR

PCR inhibition

Humic substances

DOM

* Corresponding author. 313 Schaub Hall, 4fax: þ1 919 513 0014.

E-mail addresses: jen_shields@ncsu.edu (stewart@unc.edu (J.R. Stewart).0043-1354/$ e see front matter ª 2013 Elsevhttp://dx.doi.org/10.1016/j.watres.2013.03.049

a b s t r a c t

Assays that utilize PCR offer powerful tools to detect pathogens and other microorganisms

in environmental samples. However, PCR inhibitors present in nucleic acid extractions can

increase a sample’s limit of detection, skew calculated marker concentrations, or cause

false-negative results. It would be advantageous to predict which samples contain various

types and levels of PCR inhibitors, especially the humic and fulvic acids that are frequently

cited as PCR inhibitors in natural water samples. This study investigated the relationships

between quantitative PCR (qPCR) inhibition and the humic and fulvic content of dissolved

organic matter (DOM), as well as several other measures of DOM quantity and quality, in

water samples. QPCR inhibition was also compared to water quality parameters, precipi-

tation levels, and land use adjacent to the sampling location. Results indicate that qPCR

inhibition in the tested water samples was correlated to several humic substance-like,

DOM components, most notably terrestrially-derived, humic-like DOM and microbially-

derived, fulvic-like DOM. No correlation was found between qPCR inhibition and water

quality parameters or land use, but a relationship was noted between inhibition and

antecedent rainfall. This study suggests that certain fractions of humic substances are

responsible for PCR inhibition from temperate, freshwater systems. PARAFAC modeling of

excitationeemission matrix spectroscopy provides insight on the components of the DOM

pool that impact qPCR success and may be useful in evaluating methods to remove PCR

inhibitors present in samples.

ª 2013 Elsevier Ltd. All rights reserved.

1. Introduction quantitative, and amenable to automation (Santo Domingo

Methods for water quality monitoring are transitioning from

traditionally culture-based to molecular assays using host-

specific PCR assays. The reasons for this transition include

avoiding cultivation, which can be slow, biased, and expen-

sive; and the potential for molecular methods to be sensitive,

00 Dan Allen Drive, Cam

J. Gentry-Shields), wanga

ier Ltd. All rights reserved

et al., 2007). Additionally, multiple assays aimed at different

targets, such as for multiple pathogens, can be performed

using the same nucleic acid extractions, and the extracts can

be preserved for potential improvements in assay sensitivity

and diversity (Santo Domingo et al., 2007). Quantitative, real-

time PCR (qPCR) assays are especially promising, as they

pus Box 7642, Raleigh, NC 27695, USA. Tel.: þ1 919 515 3558;

116@gmail.com (A. Wang), rmcory@email.unc.edu (R.M. Cory), jill.

.

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 3 4 6 7e3 4 7 63468

potentially allow for increased sensitivity, quantification,

speed, and automation. However, the application of these

methods can be hampered by the presence of PCR inhibitors

present in samples, which can cause a shift, or increase, in the

quantification cycle (Cq), defined as the number of tempera-

ture cycles at which the target nucleic acid has sufficiently

been amplified to reach a defined threshold (i.e., the more

nucleic acid target available in the sample, the lower the Cq

value) (Shain and Clemens, 2008). This increase in Cq can in-

crease the sample limit of detection much higher than a

laboratory-obtained method detection limit, skew calculated

marker concentrations, or result in false-negatives.

Because PCR inhibitors can be co-concentrated with mo-

lecular targets when concentrating large volumes of water for

molecular methods (Abbaszadegan et al., 1993), several

methods have been evaluated to remove or reduce inhibition.

Adjuvants such as T34 gene protein, polyvinylpyrrolidone,

and bovine serum albumin can be added directly to the PCR

reaction to reduce inhibition (Fuhrman et al., 2005; Kreader,

1996; Monpoeho et al., 2000). Removal of inhibitors has been

reported with the use of density gradient centrifugation using

cesium chloride (Leff et al., 1995; Ogram et al., 1987), hex-

adecyltrimethylammonium bromide (Cho et al., 1996), poly-

vinylpolypyrrolidone (Frostegard et al., 1999; Zhou et al., 1996),

gel electrophoresis (Zhou et al., 1996), and Sephadex G-100

and G-200 columns (Abbaszadegan et al., 1993; Miller et al.,

1999). Variable inhibitor removal efficiencies have also been

reported for different DNA extraction/purification methods

(Martin-Laurent et al., 2001; Widjojoatmodjo et al., 1992).

Another option for minimizing the effect of inhibition in-

cludes dilution of the inhibited sample (Cao et al., 2012;

Haugland et al., 2005). However, many of these methods are

costly, labor-intensive, lengthy, or can result in significant, or

even complete, loss of DNA during recovery procedures

(Ijzerman et al., 1997; Kuske et al., 1998; More et al., 1994; Zhou

et al., 1996).

Because none of these methods has proven fully effective

at removing inhibitors, amplification efficiency controls for

PCR have been developed to detect levels of inhibition present

in samples (Wilson, 1997). These controls are added directly to

samples pre- or post-extraction to provide a convenient and

sensitive test for inhibition, and may also allow for a correc-

tion in measurement variability (Haugland et al., 2005). Inhi-

bition controls can be either external controls or internal

amplification controls (including competitive or non-

competitive controls). A commonly utilized exogenous DNA

control recommended by the U.S. Environmental Protection

Agency (EPA) is salmon sperm testes DNA (USEPA, 2010),

which has been used in a recent study to quantify PCR inhi-

bition (Cao et al., 2012).

Although studies have attempted to quantify the level of

PCR inhibition present in DNA or RNA extracts, characteriza-

tion of the types and levels of PCR inhibitors present in sam-

ples is more difficult. Commonly cited inhibitors of PCR in

natural waters include humic substances, including humic

and fulvic acids that are present in all natural waters

(Abbaszadegan et al., 1993; Kreader, 1996; Tsai and Olson,

1992; Watson and Blackwell, 2000; Wilson, 1997). Humic sub-

stances are able to cause inhibition by inhibiting the enzy-

matic activity of the DNA polymerase or by binding to the DNA

template, preventing it from being amplified (Opel et al., 2009;

Sutlovic et al., 2008). Because humic substances in natural

aquatic environments consist of many different organic

molecules in diverse physical associations, it is difficult to

measure the exact concentrations of different humic or fulvic

acids within the humic superstructure. Thus the concentra-

tions of humic substances have traditionally been estimated

using the concentration of all dissolved organicmatter (DOM),

given that humic substances are major constituents (>50%) of

DOM in freshwater environments (McKnight et al., 2003).

The concentration of DOM is commonly estimated using

dissolved organic carbon (DOC) measurements, as all organic

compounds are on average 50% carbon by mass. Although

DOC is a useful parameter for estimating the total concen-

tration of organic molecules comprising DOM, it cannot pro-

vide information about DOM chemical composition, including

the structures or the abundance of functional groups. Because

it is likely that only a subset of organic molecules within the

DOM pool are involved in PCR inhibition, measures of DOC

alone may not be useful for predicting PCR inhibition.

Several analytical approaches have been developed to

study the chemical composition of DOM, but recent advances

in optical methods to understand the light-absorbing fraction

of DOM has significantly advanced knowledge of DOM source

and reactivity. One of these methods, excitationeemission

matrix (EEM) spectroscopy, has been used to characterize

DOM in natural waters (Coble et al., 1990) and municipal

wastewater treatment sludge (Chen et al., 2003). EEM spec-

troscopy measures emission spectra across a range of exci-

tation wavelengths, resulting in a landscape surface defined

by the fluorescence intensity at pairs of excitation and emis-

sion wavelengths (Chen et al., 2003). The EEM signals of DOM

samples have been delineated into regions, corresponding to

likely relative importance of humic acid, fulvic acid, aromatic

amino acids, and microbial by-products (Hudson et al., 2007).

Because EEM spectroscopy can detect these differences in

DOM composition, it has been investigated as a tool to predict

the level of PCR inhibition in sample concentrates from bio-

solids (Rock et al., 2010). Rock et al. (2010) investigated

whether EEM profiling (quantification of the area under peaks

in different EEM regions presented above) of biosolids samples

was to linked to levels of PCR inhibition. The authors found

that EEM profiling could be used to predict the level of inhi-

bition in biosolid samples. However, recent studies have

determined that the “peak picking”method used by Rock et al.

(2010) can obscure patterns within a dataset because the EEM

spectra of DOMcontainmany broad and overlapping emission

curves (Murphy et al., 2011). Understanding the underlying

variability captured in an EEMhas been significantly advanced

through parallel factor analysis (PARAFAC), a statistical

modeling approach that separates a dataset of EEMs into

mathematically and chemically independent components.

Each component represents a single fluorophore or a group of

strongly co-varying fluorophores, providing a “fingerprint” of

DOM and its humic substance-like and amino acid-like fluo-

rescing components (Stedmon and Bro, 2008; Stedmon et al.,

2003). Use of PARAFAC modeling of EEMs may be able to pro-

vide a clearer indication of the types and levels of specific

DOM components, including humic and fulvic acids, that

influence levels of PCR inhibition in environmental samples.

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 3 4 6 7e3 4 7 6 3469

The ability to predict the types and levels of PCR inhibitors,

especially the humic and fulvic acids, present in environ-

mental water samples would be advantageous because it

could allow for a more targeted strategy for sample analysis

(Gibson et al., 2012). A targeted strategy for inhibition could aid

in minimizing the number of false-negative results and in-

crease the degree of accuracy of molecular methods, which

has important public health implications. QPCR inhibition

makes interpreting the public health risk of pathogens or

microbial indicators in environmental samples problematic.

Often when pathogens are present, the concentrations are

typically low, and co-occurring inhibition can result in an

underestimation of health risk. Thus, predicting PCR inhibi-

tion is valuable for molecular methods, including qPCR, to

contribute to a more accurate assessment of health risk.

The objective of this study was to investigate relationships

between the level of qPCR inhibition present in environmental

water samples as measured by a commonly utilized exoge-

nous DNA control, salmon sperm testes DNA, and the con-

centrations of fluorescent DOM components detected by

PARAFAC analysis of EEMs. In addition, the level of qPCR in-

hibition detected in samples was compared to the total con-

centration of DOC (a measure of DOM), three measures of

DOM source (specific ultraviolet absorbance, slope ratio, and

fluorescence index). Because the chemical composition of

DOM (and thus the likely moieties contributing to inhibition)

vary with physical and watershed parameters (Wang, 2011),

the level of inhibition present in samples was also compared

to land use, development intensity, rainfall, and water quality

parameters (temperature, dissolved oxygen, conductivity,

total dissolved solids, pH, and turbidity) to determine if these

factors would impact qPCR inhibition.

2. Materials and methods

2.1. Study sites and sample collection

Environmental water samples were collected from 15 sites on

Morgan Creek, New Hope Creek, and Northeast Creek tribu-

taries within the B. Everett Jordan Lake catchment in North

Carolina, U.S. Sites were selected in this temperate, fresh-

water system to represent a range of land use, levels of

impervious surface, and watershed area (Gentry-Shields

et al., 2012).

Land use characteristics and level of impervious surfaces

within each site were determined using the 2005 Multi-

Resolution Land Characterization Consortium’s National

Land Cover Data and are described in Rowny and Stewart

(2012). Based on land use characterizations utilized in previ-

ous studies (DiDonato et al., 2009; Holland et al., 2004) and the

distribution of impervious surface cover between sites in this

study, sites were classified into either low (<8% impervious

cover) or high (>8% impervious cover) levels of development

intensity.

Samples were collected from each site twice a month at

approximately the same time of day during background dry

weather conditions for one year between April 2010 and

March 2011. Samples were not collected if precipitation in

excess of 2.5 cm occurred in the 72-hr period preceding

planned collection; sampling was delayed until these condi-

tions were met. Samples were also collected at four time

points during three rain events occurring in September 2010,

November 2010, and January 2011. Rain eventswere defined as

at least three days without appreciable rainfall followed by a

rainfall event more than 2 h in duration with at least 1.0 inch

of precipitation. The first sample was taken immediately

before the rain started andwas used as a baseline sample. The

second samplewas takenwithin an hour after the first sample

to capture the initial surface runoff. The third sample was

collected 2e6 h after the second, depending on the severity of

the rain event, to collect peak discharge. The fourth sample

was collected 2e12 h after the third sample during the end of

the rain event. The four samples were taken to coordinate

with the baseline, rising limb, peak, and falling limb of the

hydrograph. Hourly precipitation data was obtained from the

National Oceanic and Atmospheric Administration’s U.S.

Climate Reference Network (http://www.ncdc.noaa.gov/crn/)

Durham station. Antecedent precipitation was calculated for

each sampling time-point by summing the preceding 2, 24,

and 48 hourly observations. Streamflowdatawas available for

7 of the 15 sites, and was collected as described previously

(Gentry-Shields et al., 2012).

During dry weather sampling events, two 1-L grab samples

were collected in sterilized polypropylene bottles formicrobial

analyses, including qPCR inhibition characterization, and one

40-ml sample was collected in a sterile amber glass vial for

DOM characterization from each of the sites. At the time of

sampling, a YSI Professional Pro was used to record water

temperature, dissolved oxygen, conductivity, total dissolved

solids, and pH.Water sampleswere immediately placed on ice

and transported to the laboratory for processing within 6 h of

collection. A bench-topHach 2100Nnephelometerwas used to

determine turbidity once the samples had been returned to

the lab.

During rain events, water samples were collected in the

same manner as during the dry-weather events, with the

exception that water quality parameters were determined at

the laboratory rather than in the field due to logistical con-

straints. An additional 500 ml water sample was collected in a

sterile polypropylene bottle for recording the water tempera-

ture on-site using a digital thermometer. This sample was

sealed, placed on ice, and transported to the laboratory to be

tested with a YSI Professional Plus to determine the other

water quality parameters.

2.2. Determination of qPCR inhibition level

Half of the dry weather samples (the first time-point each

month) and one-quarter of the rain event samples (the second

time-point of each rain event corresponding the rising limb of

the hydrograph) were measured for inhibition (198 samples).

Two-hundred ml water from each site was filtered onto

47-mm diameter, 0.45-mm pore-size polycarbonate filters

(Millipore, Bedford, MA) using a four-place filtration manifold

and vacuum pump assembly with filter funnels (Millipore), as

suggested by Haugland et al. (2005). Filters were transferred

into sterile 2-ml screw-cap tubes and stored at �80 �C until

DNA extraction. Bacterial DNA was extracted from filters

within 2 weeks of concentration using theMoBio Powersoil kit

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 3 4 6 7e3 4 7 63470

(MoBio, Carlsbad, CA) to elute a final concentrated DNA sam-

ple in 100-ml nuclease-free sterile water. The DNA samples

were stored at �80 �C until further processing.

QPCR inhibition was evaluated by spiking a known amount

of an exogenous DNA control, salmon sperm testes DNA

(SKETA; courtesy Rachel Noble, UNC), to each bacterial DNA

concentrate and a blank at the end of the extraction step.

SKETA was added to each sample and control at a final con-

centration of 20 ng per 100 ml total extracted DNA. The amount

of SKETA DNA detected in each sample was determined using

qPCR as described by Haugland et al. (2005). Primer and probe

sequences for SKETA were SketaF2: 50-GGTTTCCGCAGCTGGGfor the forward primer; SketaR3: 50-CCGAGCCGTCCTGGTCTAfor the reverse primer; and SketaP2: 50-6FAM-AGTCG-

CAGGCGGCCACCGT-BHQ-1 for the probe. The qPCR reaction

mixture contained 2 ml of sample, each primer at a concen-

tration of 500 nM, the probe at a concentration of 120 nM,

12.5 ml of 2X PCR buffer, and nuclease-free water for a total

reaction mixture of 25 ml (Quantitect Probe PCR kit). The re-

action mixture was assayed on a Cepheid SmartCycler (Sun-

nyvale, CA) using the following conditions: (i) 2 min at 50 �C,(ii) 15 min at 95 �C, (iii) 45 cycles of 1 s at 95 �C and 1 min at

50 �C. All amplification reactionswere carried out in duplicate.

A duplicate 5-point standard curve of SKETA DNA was used

for quantification. For this, SKETA DNA was serially diluted to

give a range of DNA from 10 ng to 0.05 ng of SKETA DNA per ml

(R2 ¼ 0.997).

QPCR sample reactions with a Cq value of 1.8 higher than

that of SKETA were considered inhibited, based on a standard

deviation of 0.99 for the SKETA control. The level of qPCR in-

hibition in each sample was calculated as the difference be-

tween the calculated concentrations of SKETA DNA in the

sample and control divided by the control concentration, and

was reported as percent inhibited. There is no standard

method for reporting inhibition levels within samples. The

results of this research are reported as differences in ng of

DNA rather than raw Cq values in order to (1) present linear

data, as opposed to the logarithmic data given by Cq values,

and (2) account for differences in the amount of SKETA added

to samples in PCR runs occurring weeks apart.

2.3. DOM characterization and quantification

All of the dry weather samples and all of the rain event

samples except those collected in February and March 2011

were analyzed for DOM (401 samples). Water samples were

collected in clean, fluorescent DOM-free, 40-ml amber glass

vials. After sample collection, water samples were filtered

through a pre-rinsed Millex 0.2-mm or 0.22-mm syringe filter

(Millipore) within 2 h or were refrigerated until analysis. The

syringe was rinsed with laboratory-grade water between

samples. Following filtration, samples were stored in clean,

fluorescent DOM-free, amber 40-ml vials. Once filtered,

absorbance and fluorescence spectra were determined as

described in Section 2.3.2. After absorbance and fluorescence

were measured, samples were transferred to 24-ml clear glass

vials, acidified using hydrochloric acid to bring samples to a

pH of approximately 2e3 (to remove inorganic carbon), and

refrigerated until dissolved organic carbon (DOC) content was

measured.

2.3.1. DOC quantificationDOC was measured using a Shimadzu TOC 5000 Analyzer

(Columbia, MD), which analyzes organic carbon using the

High Temperature Combustion Method (Eaton et al., 1998).

Organic carbon calibration standards were prepared fresh

from a working solution of 21.25 mg potassium hydrogen

phthalate in 100-ml laboratory-grade water. Standards and

blanks were placed between every 6e7 samples to check the

reliability of the instrument.

2.3.2. Absorbance and fluorescence measurementsThe UVeVisible absorbance spectrum of each sample was

measured between 200 and 700 nm in increments of 1 nm

using a Hewlett Packard 8452A Diode Array Spectrophotom-

eter in order to obtain the spectral slope ratio (SR) and the

specific ultraviolet absorbance (SUVA). Sample absorbance

spectra were made in reference to DI water, and decadic

absorbance values were reported as a function of absorbance

wavelength. The SR was calculated for each sample based on

the absorbance spectrum following the procedures of Helms

et al. (2008). SR is a unitless value, correlated to the molecu-

lar weight of DOM, and can offer insight into the sources of

DOM (Chen et al., 2011; Helms et al., 2008). Microbially derived

DOM has a smaller average molecular weight and thus has a

higher SR compared to terrestrially derived DOM (Helms et al.,

2008). The SUVA was reported as the decadic absorbance at

254 nm in a 1-cm cuvette divided by the DOC concentration

(mg/L carbon). SUVA is strongly, positively correlated to the

average aromatic carbon content of the DOMmeasured by 13C

NMR (Cory et al., 2007; Weishaar et al., 2003), and thus SUVA is

a proxy for the aromaticity and source (i.e., terrestrial vs.

microbial) of DOM (Weishaar et al., 2003). High SUVA values

indicate more humic DOM derived from terrestrial sources

and low SUVA values indicate a lower humic content corre-

sponding to a DOM fraction that is microbially derived.

Excitationeemission matrices (EEMs) were collected using a

Fluorolog-321 spectrofluorometer (HORIBA Jobin Yvon Inc,

Edison, NJ) equipped with a charge-coupled device (CCD) de-

tector. Samples were placed in the same 1-cm quartz cuvette

used to measure absorbance intensities. The EEMs were

collected by measuring fluorescence intensity over excitation

wavelengths of 240e450 nm in increments of 5 nm using the

CCD to capture anemission spectrumover the range of 320e550

in increments using a range of integration times and wave-

length bins optimized for maximum dynamic range for each

sample. The optimum dynamic range for each sample was

determined from the UVeVis absorbance of the sample, which

was measured prior to the EEM. Dynamic range was chosen to

maximize detection of emission at low excitation wavelength

without saturating the CCD detector at high excitation wave-

length. The EEMs were corrected for inner-filter effects and for

instrument-specific excitation and emission corrections in

Matlab version 7.7 (MathWorks, Natick, MA) (Cory et al., 2010).

The fluorescence index (FI) was calculated as the ratio of

emission intensity at 470 nm over the emission intensity at

520 nm at an excitation wavelength of 370 nm (Cory et al.,

2010; McKnight et al., 2001) from the corrected EEM. FI is a

qualitativemeasure of the relative proportion of the terrestrial

and microbial organic matter, and is less resolved than a full

EEM (Cory et al., 2010; McKnight et al., 2001).

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 3 4 6 7e3 4 7 6 3471

2.3.3. PARAFACParallel Factor Analysis (PARAFAC) was used to decompose

fluorescence signals in the dataset of EEMs into unique com-

ponents that can be used to characterize and quantify changes

in DOM fluorescence. Due to the inherent heterogeneity of

DOM, each component likely represents a spectrumof groupsof

similarly fluorescing constituents (Stedmon et al., 2003). PAR-

AFAC uses an alternating least squares algorithm to minimize

the sum of squared residuals in a three way model (Stedmon

and Bro, 2008; Stedmon et al., 2003). The data is decomposed

into a set of tri-linear terms and a residual array (Equation (1)).

xijk ¼XF

f¼1

aibfjckf þ εijk; i ¼ 1; :; I; j ¼ 1; :; k ¼ 1; :;K; (1)

Equation (1). PARAFAC Equation.

In Equation (1), xijk is the fluorescence intensity of emission

of sample i at the jth emission wavelength and the kth exci-

tation wavelength for a PARAFAC model with F number of

components. The terms a, b, and c represent the concentra-

tion, emission spectra, and excitation spectra, respectively, of

the different fluorophores. The aif term is directly proportional

to the concentration of the fth analyte of the ith sample, while

bjf and ckf are scaled estimates of the emission and excitation

spectra at wavelengths j and k respectively for the fth analyte.

The eijk term represents any unexplained signal that can be

from residual noise and un-modeled variability.

In order to interpret the PARAFAC scores as actual con-

centrations of the components in a sample, the identity of the

responsible fluorophore and subsequent second order cali-

bration would be needed (Stedmon and Bro, 2008). However,

this specific identification of fluorophores is not possible with

complex, heterogeneous mixtures such as DOM because

components likely represent a group of co-varying fluo-

rophores and the relationships between component spectra,

absorptivity, and fluorescence quantum yield are unknown

for each group. Therefore, Fmax values (the fluorescence of

each component at the respective excitation and emission

maximum of each component in each sample) and the rela-

tive % of fluorescent DOM (FDOM) components were used to

quantify and compare DOM components. While Fmax values

provide estimates of the relative concentration of each

component in a sample, it is important to note that compar-

ison of the relative concentrations between components de-

pends on their quantum efficiency and their response to

changes in the local molecular environment. Estimates for the

precision of the Fmax values of each component were obtained

from the triplicate analyses of each sample. The relative

percent of each FDOM component was calculated by dividing

the component Fmax value by the total FDOM.

The PARAFAC model was run and validated following

Stedmon and Bro (2008) using the DOMFluor Toolbox in Mat-

lab version 7.7. The PARAFAC model was developed using a

dataset of 401 samples, with 206 samples collected during dry

weather and 195 samples collected during rain events. A total

of 43 samples were identified as outliers during the PARAFAC

modeling procedure and were removed from the model,

leaving 358 total samples with 180 dry weather samples and

178 rain event samples.

A six component model was validated following the four-

way split-half analysis procedure of Stedmon and Bro (2008).

Themodel explained 99.8% of the variationwithin the dataset,

with variation explained by each component decreasing in

order from Component 1 to Component 6 (Fig. 1; Table 1).

2.4. Statistical analyses

Statistical operations were conducted in SAS 9.1 (SAS Inc.,

Cary, NC) statistical software with a level of significance set at

p < 0.05. The relationship of inhibition to level of antecedent

rainfall, discharge, level of development, level of impervious

surface, water quality parameters, DOC, FI, SUVA, SR, and

DOM component concentrations was determined using the

Spearman Rank Order Method. KruskaleWallis one-way

ANOVAs and ManneWhitney U tests were used to test the

significance of difference in sample inhibition between sea-

sons and land use development categories. The data was

evaluated in two sets - one with all sample data and one with

only inhibited samples to better determine the factors

responsible for increased qPCR inhibition.

3. Results

3.1. Complete dataset

Of the 198 samples forwhich inhibitionwasmeasured, 31 (16%)

exhibited some amount of inhibition, with >15% difference

between the concentration of SKETA detected in the control

and the sample. Twenty samples (10%) were inhibited to such

an extent that there was >50% difference, and 7 samples (4%)

were inhibited to such an extent that there was >90% differ-

ence (1-log) between the control and sample concentrations.

3.1.1. Correlation of inhibition to season and rainfall levelsQPCR inhibitionwas higher in spring and fall than in winter or

summer (Fig. 2). Using a KruskaleWallis one-way analysis of

variance (ANOVA) and subsequent ManneWhitney U test,

inhibition was significantly higher in spring than in winter

( p < 0.03).

A Spearman Rank analysis found inhibition to be nega-

tively correlated to antecedent rainfall levels occurring 24-hr

(r ¼ �0.27; p < 0.001) previous.

3.1.2. Correlation of inhibition to land use and water qualityparametersQPCR inhibition was similar across sites with different levels

of development, and a ManneWhitney U test found no dif-

ference between high or low development intensity sites.

Using Spearman Rank analyses, inhibition was not correlated

to land use, level of impervious surface, or watershed size.

Inhibition was not correlated to water quality parameters,

including temperature, pH, turbidity, total dissolved solids

(TDS), dissolved oxygen (DO), or conductivity.

3.1.3. Correlation of inhibition to DOM quantity and qualityUsing Spearman Rank analyses, inhibition was not correlated

to DOC, SUVA, SR, or FI. Inhibitionwas correlated to two of the

fluorescent DOM component concentrations (using relative

% values): a positive correlation was observed between

qPCR inhibition and %C2 (r ¼ 0.17; p < 0.04) and a negative

Fig. 1 e Fluorescence signatures of the six PARAFAC DOM components identified in the sample set. Contour plots of

components C1eC6 are ordered by decreasing percent of the variation explained.

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 3 4 6 7e3 4 7 63472

correlation was observed between qPCR inhibition and %C3

(r ¼ �0.18; p < 0.03).

3.2. Partial dataset consisting of only inhibited samples

The dataset limited to only samples with PCR inhibition

included 31 samples with >15% difference between the con-

centration of SKETA detected in the control and the sample.

3.2.1. Correlation of inhibition to season and rainfall levelsInhibition was significantly higher in the spring and fall than

in the winter and summer ( p < 0.05; Fig. 3). Using Spearman

Rank analyses, inhibition was not correlated to antecedent

rainfall levels or discharge.

3.2.2. Correlation of inhibition to land use and water qualityparametersQPCR inhibition detected in samples at sites with different

levels of development were similar, and a KruskaleWallis

Table 1 e Fluorescence characteristics of excitationeemissionmodel.

Component Excitation/Emission(nm/nm)

EEM region(Coble, 1996)

Likely sou

C1 (250) 325/425 A&M Fulvic aci

C2 250/500 A&C Humic ac

C3 250/410 A Fulvic aci

C4 260 (370)/445 A&C Fulvic aci

C5 280/350 B&T Protein

C6 (280) 350/380 Wastewa

a DOM ¼ dissolved organic matter.

Table excerpted from (Wang, 2011).

ANOVA found no difference between inhibition in samples

from areas with high or low levels of development. Using

Spearman Rank analyses, inhibition was not correlated to

land use, level of impervious surface, or watershed size.

Using Spearman Rank analyses, inhibition was not corre-

lated to any water quality parameters.

3.2.3. Correlation of inhibition to DOM quantity and qualitySpearman Rank analyses determined that inhibition was not

correlated to DOC, FI, SUVA or SR, but qPCR inhibition was

positively correlated to the concentration of C4 (r ¼ 0.43;

p < 0.02) and negatively correlated to %C3 (r ¼ �0.49; p < 0.01).

4. Discussion

The purpose of this study was to investigate relationships be-

tween the level of qPCR inhibition present in environmental

water samples, as measured by SKETA, and the concentrations

matrix (EEM) spectroscopy components from PARAFAC

rce Description

d Mixed: humic DOMa associated with biological production

of autochthonous sources

id Terrestrial: ubiquitously observed

d Mixed: more likely to be associated with microbially-derived

precursor material

d Mixed: more likely to be associated with terrestrial DOM

Amino acid (tryptophan-like)

ter Wastewater DOM

Fig. 2 e Means (middle lines) and standard errors for qPCR

inhibition by season for full dataset.

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 3 4 6 7e3 4 7 6 3473

of fluorescent DOM (FDOM) components detected by PARAFAC

analysis of EEMs, as well as more general characterizations of

DOM. The results of this study demonstrated a positive corre-

lation between qPCR inhibition and FDOM components C2 and

C4. C2 and C4 are humic like, and humic substances have been

cited previously to cause PCR inhibition (Abbaszadegan et al.,

1993; Kreader, 1996; Tsai and Olson, 1992; Watson and

Blackwell, 2000; Wilson, 1997). Of these fractions of fluores-

cent DOM, C2 is most strongly associated with terrestrially

derived humic acids. In contrast, C4 ismore strongly associated

with the fulvic acid fraction of DOM, and is likely derived from

multiple sources of organic matter. These correlations agree

with the results of Rock et al. (2010), who found that humic

acid-like compounds (such as C2) and fulvic acid, microbial by-

product-like (such as C3) corresponded to increases in qPCR

inhibition better than other FDOM components examined.

In contrast to Rock et al. (2010), this study found a negative

correlation between qPCR inhibition and C3. C3 is primarily a

microbially derived, fulvic acid DOM component, associated

with biomolecules low in aromatic carbon (e.g., lipids, protein,

carbohydrates, and chitin). There are two possible reasons for

this negative correlation between qPCR inhibition and C3.

First, the inverse correlation may be due to the importance of

Fig. 3 e Means (middle lines) and standard errors for qPCR

inhibition by season for partial (inhibited samples only)

dataset.

terrestrially derived DOM components in influencing PCR in-

hibition levels. For example, as a “less terrestrial” component

(e.g., C3) increases relative to the total, the “more terrestrial”

components (e.g., C2) decrease as a percent of total fluores-

cence. If the more terrestrial-source components are pre-

dominately responsible for PCR inhibition, then as %C3

increases PCR inhibition would be expected to decrease.

Alternatively, the reason for the negative correlation between

qPCR inhibition and C3 may be related to the fact that mea-

sures of DOM quantity and quality were performed on the raw

water sample, whereas inhibition was measured in a

concentrated, DNA-extracted sample. The membrane filtra-

tion or DNA extraction methods may disproportionately

remove or concentrate various types of humic substance

complexes from environmental samples. The processing

methods used in this study may have removed some humic

substances, such as the C3 fulvic acid complexes, more

effectively than others, such as C2 or C4. If this is the case, the

processing methods may have removed the majority of qPCR

inhibitors from samples in which most of the qPCR inhibition

was due to C3. Consequently, the level of qPCR inhibition

would be expected to decrease as the fraction of C3 increases.

This hypothesis would suggest that not only do different

extraction methods remove varying levels of inhibitors, but

they may also remove varying types of qPCR inhibitors. Rock

et al. (2010) came to a similar conclusion; an extraction

method they examined was found to be less efficient at

removing aquatic/marine humic acids than other types of

humic material. Fractionation of humic substances during

extractions may have important implications for environ-

mental sample processing. One potentially useful application

would be to trace the different types of humic substances

through the concentration and extraction process using

PARAFAC in order to determine which processing methods

are best suited to removing specific humic substances. Future

studies should aim to explore the relationship between DOM

components and qPCR inhibition levels using various pro-

cedures in order to test this theory.

Because DOM was quantified pre-concentration and inhi-

bition was measured post-concentration/DNA extraction, it is

important to discuss whether DOM in the water samples

accurately represents the organic materials that are collected

on the polycarbonate filters. Aquatic humic substances are

amorphous, with molecular weights varying from 500 to

>10,000 (Thurman et al., 1982). It is likely that some portion of

the larger humic substances would concentrate onto the

membrane filter, and previous studies have reported that

membrane filtration can co-concentrate humic substances

(e.g., Abbaszadegan, et al., 1993; Guttman-Bass and Nasser,

1984; Sobsey and Hickey, 1985). In addition, humic acids

have a limited solubility at neutral pH, allowing them to form

precipitates and/or colloids at high concentrations (Straub

et al., 2005), and filter clogging, caused by particulate matter

(Farrah et al., 1976; Hunt and Wilson, 1986) or by humic sub-

stances themselves (Guttman-Bass and Nasser, 1984), can

contribute to the collection of humic substances onto mem-

brane filters. Specifically, the polycarbonate filters used in this

study are unable to sustain high loadings of particulatematter

(Hunt and Wilson, 1986), which could allow for the concen-

tration of humic substances onto filters.

wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 3 4 6 7e3 4 7 63474

QPCR inhibition was related to both rainfall and season-

ality. In the full dataset, inhibition was negatively correlated

to rainfall occurring 24 h prior to sample collection. This

negative correlation may be related to an increased %C3 in

samples following a rainfall event, as C3 was significantly

positively correlated with the level of rainfall occurring 24 h

prior (data not shown). QPCR inhibition varied seasonally and

was higher in spring and fall. This result is not surprising, as

the fluorescence signals associated with DOM components

tend to vary seasonally. Microbially-derived DOM tends to be

higher during the spring and summermonths, due to primary

productivity occurring during this period, and terrestrially-

derived DOM tends to be higher during the fall due to hydro-

logical processes as well as carbon inputs into the water

(e.g., falling leaves) (Jaffe et al., 2008).

Interestingly, qPCR inhibition was not correlated to any of

the general measures of DOM quality (SUVA, FI, SR), land use,

or water quality parameters. A lack of correlation to general

DOM quality measures may be due to the fact that these are

less resolved than the DOM components determined with

PARAFAC modeling of EEMs (Cory et al., 2010; McKnight et al.,

2001). A lack of correlation to land use may be due to the fact

that inhibition was correlated to both microbial-source DOM

components and terrestrial-source DOM components, which

were detected at both high and low development intensity

sites. Similarly, QPCR inhibition may not directly correlate to

water quality parameters as separate DOM components may

be influenced by water quality parameters in opposing ways.

In sum, these results indicate that predicting qPCR inhibition

is not straightforward. QPCR inhibition cannot be predicted

easily using general parameters, such as the occurrence or

levels of precipitation, land use, or common water quality

measures. Rather, the factors influencing qPCR inhibitionmay

be more specific. PARAFAC modeling of EEMs may provide

insight into the components of the DOM pool that impact

qPCR success and may prove useful in developing a targeted

strategy for inhibition to aid in minimizing the number of

false-negative results and increase the degree of accuracy of

molecular methods.

5. Conclusions

� QPCR inhibition is correlated to specific dissolved organic

matter components, predominantly terrestrially-derived,

humic-like DOM components and microbially-derived, ful-

vic-like DOM components.

� PARAFAC modeling of EEMs provides insight on the com-

ponents of the DOM pool that impact qPCR success andmay

be useful in evaluating processing methods to remove PCR

inhibitors present in samples.

� QPCR inhibition is not correlated to general measures of

DOM quality (SUVA, FI, SR), land use, or water quality

parameters.

� QPCR inhibition makes interpreting the public health risk

of pathogens or microbial indicators in environmental

samples problematic; a targeted strategy adjusting for the

types and levels of PCR inhibitors can help to minimize

false-negatives and increase the degree of accuracy of

molecular methods in these samples.

Acknowledgments

This work was supported by the Water Resources Research

Institute of the University of North Carolinawith funding from

the USGS and the North Carolina Urban Water Consortium,

project number 70252.

The authors would like to thank Rachel Noble at UNC

Institute of Marine Sciences for providing the salmon sperm

DNA and primers/probes for this study. We would also like to

thank Jakob Rowny for collection of the samples.

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