Assay-for-estimating-total-bacterial-load-relative-qPCR-normalisation-of-bacterial-load-with-associated-clinical-implications_2015_Diagnostic-Microbio (1)

Assay for estimating total bacterial load: relative qPCR normalisation of
bacterial load with associated clinical implications
Ivan Brukner a,c,
⁎, Yves Longtin a,c
, Matthew Oughton a,c
, Vincenzo Forgetta b
, Andre Dascal a,c
a
Medical Faculty, McGill University, Montreal, Quebec, Canada
b
Lady Davis Institute, Montreal, Quebec, Canada
c
SMBD-Jewish General Hospital, Montreal, Quebec, Canada
a b s t r a c ta r t i c l e i n f o
Article history:
Received 4 February 2015
Received in revised form 3 April 2015
Accepted 20 April 2015
Available online xxxx
Keywords:
Clinical diagnostics
Relative bacterial load in clinical sample
Total bacterial load
Stool samples
qPCR assay
Normalisation
Relative microorganism abundance is a parameter describing biodiversity, referring to how common a bacterial
species is within the total bacterial flora. Anal, rectal, skin, mucal, and respiratory swabs are typical clinical sam-
ples where knowledge of relative bacterial abundance might make distinction between asymptomatic carriers
and symptomatic cases. Assays trying to measure total bacterial load are usually based on the amplification of
universal segments of 16S rRNA genes. Previous assays were not adoptable to “direct” PCR protocols, and/or
they were not compatible with hydrolysis-based detection. Using the latest summary of universal 16S sequence
motifs present in literature and testing our design with 500 liquid and 50 formed stool samples, we illustrate the
performance characteristics of a new 16S quantitative PCR (qPCR) assay, which addresses well-known technical
problems, including a) positive priming reaction in the absence of intended target due to self-priming and/or
mispriming of unintended targets; b) amplification bias due to nonoptimal primer/probe coverage; and c) too
large amplicons for clinical qPCR. Stool swabs ranked into bins of different bacterial loads show significant corre-
lation with threshold cycle values of our new assay. To the best of our knowledge, this is the first description of
qPCR assay measuring individual differences of total bacterial load present in human stool.
© 2015 Elsevier Inc. All rights reserved.
1. Introduction
The biochemical behaviour of complex bacterial populations is de-
termined by the identity of constituents living in this mix and their rel-
ative abundance. One of the most explored strategies for defining
bacterial load, using molecular biology techniques, is based on sequence
databases of 16S rRNA genes (Jumpstart Consortium Human
Microbiome Project Data Generation Working G, 2012). Other genetic
loci have been explored (Santos and Ochman, 2004) but have not
achieved widespread acceptance. In spite of exponential growth of se-
quencing data (Arboleya et al., 2012), there is a limited engineering po-
tential for designing qPCR assay with universal 16S rRNA primers and
probes, determined at least in part by the inherent diversity of 16S
rRNA genes (Callbeck et al., 2013; Ghyselinck et al., 2013; Haas et al.,
2011; Lyra et al., 2012). Accurate analytical characterisation of microbi-
ologically complex clinical samples will be dependent on the accuracy of
these “universal” bacterial qPCR assays.
Species-specific qPCR in combination with universal bacterial 16S
rRNA qPCR assay could offer estimation of relative abundance of bacte-
rial species in the context of complex microbial communities (Gosalbes
et al., 2012). Typically, the engineering strategy of such qPCR 16S rRNA
assays is based on designing primers and probe in the region of common
sequence identity of 16S rRNA genes. Primers that are sufficiently “uni-
versal” are critical to the development of a good assay.
The 16S rRNA PCR assay having the highest historical success for
referencing both PCR amplification and probe hybridization (Inglis
et al., 2012) over the last decade (Bemer et al., 2014; Benítez-Páez
et al., 2013; Bonilla et al., 2011; Choi et al., 2014; Cyplik et al., 2011;
Hardick et al., 2012; Insa et al., 2012; Nadkarni et al., 2002; Zemanick
et al., 2010) was developed by Nadkarni et al. (2002). The assay design
was based on an early discovery (Ruff-Roberts et al., 1994; Whiteley and
Bailey, 2000) of conserved sequence regions, close to positions 338, 541,
and 886, located in the Escherichia coli rRNA gene (Ghyselinck et al.,
2013; Morotomi et al., 2011). Substantial work has been performed to
refine this design over the last decade (Liu et al., 2012). This assay yields
a qPCR product of 466 nucleotides (Bartosch et al., 2004; Liu et al., 2012;
Yu et al., 2005). Although this primer–probe set has excellent coverage,
with matching efficiency (86–94%) over Ribosomal Database Project
(Cole et al., 2009), it remains suboptimal for routine qPCR-based clinical
diagnostics (Sikora et al., 2010) due to multiple factors (Cole et al., 2009;
Sikora et al., 2010): a) PCR amplicons should be shorter (~200 bp long)
to accommodate sheared genomic DNA in clinical specimens, as well as
to allow higher amplification efficacy; b) primer/probe universality
should be measured using highly diverse microbiota samples and (if
existing) ranking correlation with alternative quantitative methods
should be established; and c) the amplification bias of primer sets
should be characterised for all explored targets on the relative scale
Diagnostic Microbiology and Infectious Disease xxx (2015) xxx–xxx
⁎ Corresponding author. Tel.: +1-514-8038-782 (mobile), +1-514-3408-222x4801;
fax: +1-888-7805-003.
E-mail addresses: ibrukner@jgh.mcgill.ca, ibrukner@gmail.com (I. Brukner).
http://dx.doi.org/10.1016/j.diagmicrobio.2015.04.005
0732-8893/© 2015 Elsevier Inc. All rights reserved.
Contents lists available at ScienceDirect
Diagnostic Microbiology and Infectious Disease
journal homepage: www.elsevier.com/locate/diagmicrobio
Please cite this article as: Brukner I, et al, Assay for estimating total bacterial load: relative qPCR normalisation of bacterial load with associated
clinical implications, Diagn Microbiol Infect Dis (2015), http://dx.doi.org/10.1016/j.diagmicrobio.2015.04.005

using standard referenced rRNA gene as controls. All these unresolved
issues are preventing applicability of present 16S rRNA assays in quan-
titative diagnostic protocols.
The novelty of 16S rRNA qPCR assay described herein covers changes
in a) assay design, b) assay validation, and c) assay applicability. Assay
design was adapted to satisfy the needs for shorter qPCR (Liu et al.,
2012). These “direct” PCR assays (no nucleic acid isolation) are saving
significant turnaround time, obtaining final laboratory results faster.
Assay validation was done using well-documented conserved 16S
rRNA gene segments, surrounding V3 region of 16S rRNA gene
(Clifford et al., 2012). Primer candidates, listed in Supplementary
Table 2, are tested to satisfy 2 criteria: a) no presence of signal in the
negative control and b) preservation of same limits of detection
(LoDs), as previously reported by equivalent assays (Clifford et al.,
2012; Liu et al., 2012; Nadkarni et al., 2002). As a result, a new reverse
primer was discovered, completely eliminating “previously reported
contamination-like priming” in the absence of unintended targets
(Clifford et al., 2012; Huys et al., 2008; Liu et al., 2012; Mori et al.,
2014; Nadkarni et al., 2002). The novel hybridization probe was suc-
cessfully added for the first time, thus allowing assay compatibility
with hybridization-based diagnostic qPCR chemistry (TaqMan like).
This chemistry is presented in the majority of molecular in vitro diag-
nostic (IVD) clinical assays today. Overall, these changes are allowing
new applicability: semiquantitative estimations of total bacterial load
present in complex bacterial flora could be recorded and reported on a
routine basis, for the first time.
2. Materials and methods
The performance of new assay was tested on 500 liquid and 50
solid stool specimens, using crude “stool lysate” (glass beads lysis proto-
col, see below). For precise analytical quantification, a subset of stool
samples (n = 50) was chosen, and total nucleic acid was isolated.
Both crude lysate and nucleic acid isolation protocols were done
in parallel. Comparative evaluation with Nadkarni's assay (Zemanick
et al., 2010) was done on a subset of samples (n = 20). Correlation
analysis between quantification of total anaerobic bacteria by classical
serial dilution–plating count methods versus a 16S rRNA qPCR assay
was performed.
2.1. Stool samples
Our sample study group was represented by 500 liquid submitted
for detection of Clostridium difficile and 50 formed stools used for quan-
titative stool culture, collected from January 2014 to October 2014. For
measurements of wet stool mass used in the PCR assay, 49 stool samples
(from the collection of 550) were chosen by simple random sampling
method and processed as described below.
2.2. Stool swabbing and wet mass measurements
BBL Culture Swabs (BD, Franklin Lakes, New Jersey, USA) for collec-
tion and transport system, LQ Stuart (n = 49), were uniquely labelled
and measured before use, with an analytical precision of b1 mg. After
stool swabbing (full protocol presented in GeneOhm™ Cdiff Assay Man-
ual (Anon, 2014a) and in sample processing segment of methods),
swabs were closed and remeasured. The difference in mass, before
and after swabbing, was recorded for each sample.
2.3. Sample processing
Swabs were transferred into 1 mL of TE buffer (Tris-EDTA, 100X So-
lution [Molecular Biology], Fisher BioReagents) and mixed by vortexing.
For quantitative anaerobic culture, the resulting solutions were serially
diluted 100- to 10,000-fold. Fifty microliters of each dilution was plated
on Columbia blood agar and incubated 2 days under anaerobic
conditions at 37 °C, submitted to colony counts. For crude lysate direct
qPCR, the working protocol was as described in BD GeneOhm™ Cdiff
Assay Manual (Anon, 2014a). Briefly, approximately 15 mg of stool ma-
terial was diluted in 1 mL of TE buffer and vortexed for 5 min. Twenty
microliters of material was transferred into 100 μL of TE buffer contain-
ing 50-μL volume of glass beads (0.1 mm, Cell Disruption media; Scien-
tific Industries, New York, NY, USA). After vortexing for 5 min and
heating at 95 °C for 5 min, the tube was cooled down, and 3 μL was
added directly into the PCR assay. For isolation of nucleic acids from
stool, the same material equivalent was submitted to EazyMag
(Biomerieux, Saint-Laurent, Quebec, Canada) (Anon, 2014b), following
stool isolation protocol (100 μL input, 100 μL elute output). Ten-fold se-
rial dilutions of nucleic acid elute were used to illustrate qPCR efficacy
performance of different primer sets.
2.4. Primers and probes for qPCR
Nadkarni's 16S universal assay was performed (Nadkarni et al.,
2002), using subsequent published modifications (Liu et al., 2012;
Wang and Qian, 2009), based on compilation of new sequence data:
Forward variant 2: CCTAYGGGRBGCASCAG;
Forward variant 3: CCTACGGGDGGCWGCA;
Reverse variant 2; GGACTACHVGGGTWTCTAAT;
Reverse variant 3: GGACTACHVGGGTMTCTAAATC;
and (516 probe) TGCCAGCAGCCGCGGTAATAC.
Our 16S qPCR assay originated from work described by Clifford et al.
(2012). The new components of our assay are a) new hybridization
probe; b) new reverse primer; and c) new PCR conditions, thus allowing
TaqMan-like chemistry and preventing self-priming/mispriming
events, even at low PCR annealing temperatures. The details of the
primer/probe sequence motifs are summarised on Fig. 3. The amplicon
spans 330–528 bp region of rRNA gene, with the reverse primer over-
lapping with the hybridization probe region of Nadkarni's assay. Clifford
assay was originally designed to work with fluorescent intercalating dye
but produced significant product in assays lacking template (Clifford
et al., 2012). Whilst exploring in silico primer coverage in this region,
we realised that nonstringent search criteria including 3–4 permissive
mismatches in primer binding region, with 3–4 nucleotides long region
at 3′ end, characterised by 100% sequence identity, might generate a
functional/applicable assay. Therefore, we gradually modified the
primers (by systematic trimming the 3′ region) until negative controls
produced no detectable PCR product even at 50 °C PCR annealing tem-
peratures, whilst still preserving similar LoDs compared with previous
assays. In addition, the probe was adjusted, resulting in the shortest
16S universal rRNA PCR assay reported so far. Controls for qPCR inhibi-
tion were performed in all reactions, as previously described (Brukner et al.,
2013). An AT clamp/tag was added at the 5′ end of forward and reverse
primers to increase relative fluorescence unit (RFU) signal (Afonina et al.,
2007). The exact sequence of our primers and probes is presented herein,
whilst relative positions versus E. coli referenced genome are presented in
Fig. 3: Forward P: 5-AATAAATCATAAACTCCTACGGGAGGCAGCAGT-3; re-
verse P: 5-AATAAATCATAACCTAGCTATTACCGCGGCTGCT-3; and probe:
5-/56-FAM/CGGCTAACTMCGTGCCAG/3IABkFQ/-3.
2.5. PCR assay and cycling conditions
Three microliters of nucleic acid elute or crude lysis material was
added into 17 μL of total volume mix (QuantiNova Probe PCR master;
Qiagen, Toronto, Ontario, Canada), following concentration of primer/
probe and cycling condition recommendations from the manufacturer
(Anon, 2014c), except that annealing temperature was significantly
lower at 50 °C. The Cp values were calculated using default parameters
of software provided by the real-time PCR instrument manufacturers
(LC 480 II; Roche). Results were available at 2.5 hours after assembly
of the PCR.
2 I. Brukner et al. / Diagnostic Microbiology and Infectious Disease xxx (2015) xxx–xxx

3. Results
The quantity of stool material attached to the swab during “typical”
swabbing process had an average value and SD of 16.5 (±9.6) mg
(Table 1). The values present on Table 1 inherently reflect variability
in swabbing techniques, superimposed over variations of the stool con-
sistency and/or water content.
Using average stool mass associated with swabbing (Table 1) and
factor of sample dilutions (GeneOhm™ C. diff Assay Manual), we calcu-
lated that approximately 10 μg of faecal material was entering into a sin-
gle qPCR. Mass variability between samples is presented on Fig. 1A on
x-axis, whilst the total bacterial load, as measured by our assay, is pre-
sented using Ct values on y-axis. The 2 variables produced linear corre-
lation coefficient of R2
= −0.04, P = 0.59, indicating that there is no
discernable relationship between fluctuations of these variables. Obvi-
ously, variations in total bacterial load are superimposed on mass varia-
tions and are influenced by multiple additional factors not accounted for
in our analysis; for instance, effects of antibiotics and other medications
and concurrent infections are amongst the strongest determinants of
gastrointestinal flora (Song et al., 2013).
The results of the qPCR assay could be affected by differences in sam-
ple preparation or processing. The first protocol tested by us was
Fig. 1. Comparison of bacterial load measurement techniques. (A) Small variation of input in stool sample mass during regular swabbing is presented on x-axis (variable mass of
stool entering sample buffer), and corresponding Ct values of our 16S rRNA universal qPCR assay, presented on y-axis (R2
= −0.04, P = 0.59, from linear regression [blue line]; grey
area reflects 95% confidence interval of the smooth). Data do not show any detectable correlation. (B) Ct values from quick lysis of stool (using glass beads, vortexing, and heating at
95 °C for 5 min) presented on x-axis versus Ct values after DNA was isolated from the same mass equivalent of input material, using EazyMag (Biomerieux) total nucleic acid isolation
procedure, presented on y-axis (R2
= 0.80, P = 6.4e-8, from linear regression [blue line]; grey area reflects 95% confidence interval of the smooth). (C) Boxplots of number of anaerobic
bacteria in liquid stool sample per mass equivalent of 10 μg of stool are presented as interindividual binned total bacterial counts, measured by bacterial counting plate method (bins on
x-axis) versus Ct values of our 16S rRNA PCR assay (same mass equivalent of 10 μg of stool) done with fast stool swab lysis protocol (for details, see Materials and methods section), on
y-axis (Ct values above 18 have low bacterial load and should be considered as samples with strongly reduced micro flora). Wilcoxon test showed the following P values for binned bac-
terial counts versus Ct values: 0–102
versus 102
–104
, P = 1.111e-05; 0–102
versus 104
–106
, P = 3.837e-05; and 102
–104
versus 104
–106
, P = 0.1467. Individual points are also plotted.
Each boxplot depicts the range of binned data where the internal line is the median value and the boundaries of the box are the first and third quartiles. Whisker extends from edge of box
to 1.5 times the interquartile range.
3I. Brukner et al. / Diagnostic Microbiology and Infectious Disease xxx (2015) xxx–xxx

composed of standard nucleic isolation procedure before qPCR is done,
whilst the second protocol was adopted to fit rapid bacterial lysis,
followed by direct qPCR. On Fig. 1B, one can see that Ct values obtained
by isolation of nucleic acid from stool (first protocol), versus fast lysis
(second) protocol, do correlate significantly (r2
= 0.87).
An essential question was if Ct values of 16S rRNA qPCR accurately
reflect total bacterial load. Since anaerobic bacterial population com-
prises at least 90% of total stool bacterial flora (Clarke, 1974; Zemanick
et al., 2010), we used anaerobic culture plating methods (serial dilutions
of diluted stool material) to arbitrarily rank samples into 3 groups:
a) low load (1–102
CFU/mL), b) medium (102
–104
CFU/mL), and
c) high load (104
–106
CFU/mL or more). The results presented
(Fig. 1C) show that low bacterial load is presented between Ct 18 and
22 and is distinct from Ct values reflecting high and medium bacterial
loads. Detecting opportunistic pathogens, present in the context of bac-
terial flora with high or medium versus low bacterial loads, could have
totally different clinical meaning.
The primer/probe design of our assay is presented on Fig. 3. The
“room” for probe design was generated on the expense of short reverse
primer, which is equivalent to hybridization probe binding region of
Nadkarni et al. (2002), thus allowing shorter amplicon length. The per-
formance of new assay was compared with Nadkarni's assay, using the
same samples and corresponding dilutions (see Materials and methods
section and Fig. 2). The correlation amongst Ct values was close to 0.9.
Note that Nadkarni's assay is not applicable for fast lysis protocol, whilst
Clifford assay cannot be used for TaqMan-based chemistry. Disadvan-
tages of previously referenced assays are as follows: a) Nadkarni et al
assay and later modifications (Liu et al., 2012) together with Clifford
et al. (2012) produce late “masking” Ct values, which are higher than
Ct = 30 (±2) (see Fig. 2A right and referenced work (Clifford et al.,
2012; Liu et al., 2012)) and b) amplification failure rate using fast lysis
protocol was higher for longer qPCR amplicon (Nadkarni et al) com-
pared to our assay.
The distribution of Ct values of our 16S qPCR assay over 500 stool
samples is presented in Fig. 4. Sporadic testing of negative samples en-
ters into category of “low total bacterial load”, as validated by culture
plating method, indicating that “no Ct” values are characterised by bac-
terial flora with very low load. Considering that “average” stool has 1012
bacteria/g, the average number of bacteria entering direct qPCR assay is
approximately 107
. Considering individual samples, the clinical variabil-
ity of total bacterial load per swab deviates from average numbers by
3 log10 values, as presented in Fig. 4. These deviations could have signif-
icant clinical meaning, especially in the cases where antibiotic- and/or
infection-induced changes in microbiota could be relevant for diagnosis
and therapy.
In order to illustrate the importance of working with small qPCR
amplicons described in our assay, we used examples of recently Food
and Drug Administration (FDA)–approved direct qPCR assay, intended
LOR ALOD Average efficiency (SD) r2
Nadkarni and modifications* 10E3-10E8 1000 97% (2%) >0.994
Our assay 10E4-10E8 1000 94% (4%) >0.961
Fig. 2. Typical RFU signal versus PCR cycle diagram: The 10× serial dilutions of DNA isolated from stool done by referenced qPCR assays (Liu et al., 2012; Nadkarni et al., 2002) (right) and
our assay (left): excellent correlation for both assays is in the range of 12–24 Ct units. Table below: LOR = linear operative range of the assay is in the range 108
–103
bacterial genome
equivalents; ALOD = average LoD is 103
; r2
= correlation coefficient. Note that 10 stool samples were pooled to get average “microbiota” diversity representation.
Fig. 3. Schematic representation of our, Clifford et al. (2012), Nadkarni et al. (2002), and BacQuant (modified Nadkarni) (Liu et al., 2012) 16S universal rRNA PCR assays, including primers
as well as probe sequences (boxed, if applicable) and degenerate nucleotides (bold underline). Positions are defined in reference to NC00193.3 (4166659…4168200; E. coli str K-12, subst.
MG1655). Note that the Clifford assay has no hybridization probe and is not compatible with hybridization-based probe chemistry.

for IVD assays of C. difficile, like BD GeneOhm™ C. difficile (REF 441400
Manual (Anon, 2014a)) or Focus Dx C. difficile PCR (Anon, 2014d). Al-
though both assays are not quantitative in nature, they have the poten-
tial to be converted into quantitative assays. These assays are based on
direct use of diluted stool material in the reaction mix with the promise
of rapid turnaround time (Bergeron, 2008). However, they are not com-
patible with any present form of 16S rRNA qPCR described so far. There-
fore, without proper development of 16S universal qPCR assay, they will
remain qualitative.
4. Discussion
The protocol for fast, semiquantitative estimation of relative bacteri-
al load in “microbiologically complex flora” is offered, with particular
discussion of clinical advantages that such data presentation might
offer. Due to the “promiscuity” of primer(s) for priming nonintended
targets or contamination with minor quantities of 16S rRNA gene, previ-
ously reported 16S rRNA assays typically produced positive results in
later qPCR cycles (Clifford et al., 2012; Liu et al., 2012; Nadkarni et al.,
2002; Philipp et al., 2010). Our assay did not have this issue (see Fig. 2
and negative controls). The use of culture of serial dilutions from highly
diverse microbiological samples (individual stools) as a method for
evaluating accuracy of a 16S qPCR method for quantification was, to
our knowledge, not previously documented.
Our newly designed PCR assay (Fig. 3) is an evolved version of
the one described by Clifford et al. (2012). They used intercalating
fluorescence dye detection. This approach generated problem of
nonspecific qPCR signal (Clifford et al., 2012; Vandesompele et al.,
2002), and it is not compatible with TaqMan probe chemistry.
The new reverse primer, developed by us, completely eliminates
mispriming of nonintended targets whilst allowing room for
hybridization-like probe. It is compatible with fast direct clinical
qPCRs, promoted by the new generation of molecular diagnostic com-
panies (GeneOhm and Focus Dx). A potential application of our new
assay allows confident reporting of negative results.
Comparative analytical measurements of LoDs and operative range
were done on the DNA isolated from stool samples. The results reflect
the average bacterial numbers present in the stool. We noticed that “av-
erage” LoD of our assay is still in the same region of 1000 bacteria
(Fig. 2), similar to the assay of Nadkarni et al. (2002) and other modified
versions (Liu et al., 2012), validated in the context of 10 ng or less of
human DNA. When comparative study was performed using crude ly-
sate of stool swabs, the shorter amplicon has clear advantage. Although
our results are semiquantitative in nature, they allow detection of sam-
ples having high and/or medium versus low total bacterial loads with
significant confidence (0–102
versus 104
–106
, P = 3.837e-05) and
(102
–104
versus 104
–106
, P = 0.1467, respectively; see Fig. 1C).
One should note that our assay operates at lower annealing temper-
atures (50 °C) than typically used but still does not have any mispriming
issues. The lower annealing temperature has a beneficial effect on
“primer coverage”, since hybridization and primer elongation per-
formed at lower temperatures tolerate more mismatches in primer
binding region. Increases in permissive mismatches would affect primer
coverage making it almost identical between different 16S PCR assays. It
would be useful for bioinformatic analysis of primer design to include
probability of self-priming and priming amongst nonintended targets,
which can be validated in vitro. Also, in light of presented data, primer
coverage reports should include options covering up to 4 permissive
mismatches, keeping 3′ end of primers with short (n = 4 nt) but max-
imal sequence identity with intended targets.
Assay reoptimisation (and revalidation) seems inevitable, as new se-
quencing data emerge. Additionally, amplification bias over the most
common intestinal bacteria has to be measured, in order to satisfy in-
dustrial regulatory requirements for the reagent reactivity. That will
be done following the Minimum Information for Publication of Quanti-
tative Real-Time PCR Experiments guidelines (Bustin et al., 2009) but
using relative reference control in each measurement. Previous mea-
surements of PCR efficacy for multiplicity of targets (16S rRNA genes)
Table 1
Mass and Ct of repetitive swabs taken from a single stool sample (intrasample variability)
and amongst different stool samples (intersample variability), as measured by total
change in mass per swab, before and after swabbing.
a) Intrasample variability Mean stool mass (n = 49), mg 13.0
SD of stool mass (n = 49), mg 5.5
b) Intersample variability Mean stool mass (n = 49), mg 16.5
SD of stool mass (n = 49), mg 9.6
c) tcdB Ct variability Mean Ct (n = 5), cycles 24.4
SD of Ct, cycles 0.12
For estimation of Ct variability, we used Ct values of an in-house assay for C. difficile tcdB
(toxin B gene).
Fig. 4. Distribution (y-axis) of Ct values (x-axis) from 500 liquid stool swabs using our 16S qPCR assay. Note that the x-axis covers inherent variability in total bacterial load amongst dif-
ferent stool samples over a 210
(1024-fold) range, which is divided in our arbitrary scale on “low” (Ct N 18) covering low bacterial load (0–102
colonies in serial dilution–plate counting
experiments); “medium” bacterial load (102
–104
colonies: Ct = 15–18), and “high” bacterial load (104
and more, Ct b 15).
5I. Brukner et al. / Diagnostic Microbiology and Infectious Disease xxx (2015) xxx–xxx

do not illustrate amplification bias of primers (Liu et al., 2012) when
multiple similar targets are present in the same reaction tube.
Recently, new Qiagen qPCR C. difficile assays was FDA approved of-
fering absolute quantitative values of ToxB concentration. It remains
to be validated how absolute versus relative quantification will improve
clinical value of laboratory data. Some researchers (Dionne et al., 2013)
had found relation between clinical disease status and absolute quantity
of pathogen (Leslie et al., 2012), but the exact correlation between Ct
values and clinical disease severity is still unclear. Relative quantifica-
tion of bacteria from samples with diverse and complex flora is not
part of clinical laboratory routine, but current technological conditions
now permit the possibility to measure relative abundance of bacteria
and exploring its clinical impact.
Acknowledgments
This work evolved through constant intellectual challenge from
R&D team from Focus Diagnostics USA. The authors want to thank
McGill Commercialisation office (Nadia Nour) and Alain Dumont
for preserving continual applicability focus during developmental
phase of the project. We also thank our colleagues in the Department
of Diagnostic Medicine at the Jewish General Hospital (Dr Elizabeth
MacNamara, Stacy, Coleen, Dipika, and Kellee) for their valued organisa-
tion and technical assistance.
Appendix A. Supplementary data
Supplementary data to this article can be found online at http://dx.
doi.org/10.1016/j.diagmicrobio.2015.04.005.
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