BACKGROUND
scientists today are studying bacterial communities from diverse habitats, hosts, and health conditions based on the 16 s rrna gene  <cit> .
to date, most studies have focused on qualitative characterization based on the relative abundances of community bacterial groups  <cit> ; however, quantitative characterization—i.e., measurement of the total bacterial load—provides valuable and complementary information when combined with these qualitative data  <cit> .
traditional culture-based approaches for quantifying bacterial load are inherently limited for assessing the complex bacterial communities that exist in many clinical and environmental samples.
likewise, standard culture-based methods are ineffective for quantifying many fastidious and uncultivable bacterial species  <cit> .
among culture-independent approaches, quantitative real-time pcr  is currently best suited for measuring bacterial load, because of its intrinsic quantitative capability, ease of use, and flexibility in assay design  <cit> .
using the qpcr platform, we can design an assay capable of concurrently detecting and quantifying all unique bacteria that constitutes a complex community.
furthermore, by utilizing 16 s rrna gene as the target of a broad coverage qpcr assay, results from the qpcr evaluation can be easily combined with 16 s rrna gene-based qualitative characterization to fully describe the community of interest.
in the current paper, we present our design and validation of a broad-coverage quantitative real-time pcr assay—bactquant—for quantifying 16 s rrna gene copy number and estimating bacterial load.
to accomplish this, we have employed a novel nucleotide distribution-based approach to effectively summarize a large 16 s rrna gene sequence dataset for qpcr assay design.
we further addressed a general limitation of the qpcr platform—the normalization of in-run quantitative standards using fluorimetric or spectrometric methods—by developing an alternative qpcr-based method for quantifying plasmid standards.
lastly, we have complemented standard qpcr assay validation following miqe guideline  <cit>  with extensive in silico analysis using > <dig>  16 s rrna gene sequences from the ribosomal database project  <cit> .
methods
design of 16 s rrna gene quantitative real-time pcr assay
pre-aligned 16 s rrna gene sequences  were downloaded from the core set of the greengenes database  <cit> .
the alignment was analyzed to generate an output of nucleotide distribution—i.e., the summary of allele frequency at each nucleotide position in the 16 s rrna gene multiple sequence alignment file—and diversity score using a 3% gap-filter setting and the simpson’s diversity index, respectively.
assay design
the nucleotide distribution was examined to identify a conserved 500 bp region for assay design.
in designing the assays, we applied the following rules: 1) primer sequences cannot have more than three degenerate bases and 2) the probe sequence cannot have any degenerate bases.
the primer tm was calculated using salt adjusted calculation from the online tool oligocalc  <cit>  and the probe tm was calculated using the primer probe test tool for taqman® mgb quantification from the primer express® software for real-time pcr version  <dig>   .
computational analysis of assay specificity and coverage
a. specificity analysis.
specificity check was performed in genbank using megablast against human, mouse, and fungal sequences from the nucleotide collection   <cit> .
b. collection and identification of bacterial 16 s rrna gene sequence eligible for in silico coverage analysis.
all 16 s rrna gene sequence data used in the in silico coverage analysis were downloaded from the ribosomal database project  release  <dig> update  <dig>  <cit> .
briefly, all bacterial 16 s rrna gene sequences that were of “good” quality and had a length of 1200 bp or greater were extracted from rdp, along with taxonomic metadata and sequence ids.
additionally, the escherichia coli position data was kindly provided by staff at the rdp.
the downloaded sequences were filtered based on e. coli position.
only sequences with data present in the qpcr assay amplicon of interest were considered to be eligible for sequence matching for the particular qpcr assay.
numerical and taxonomic coverage analysis was performed for the bactquant assay and a published qpcr assay  <cit>  by developing a web service for the rdp probe match tool for sequence matching.
c. overview of sequence matching analysis for determining assay coverage.
all sequence matching for the in silico coverage analysis was performed using two conditions: a) perfect match of full-length primer and probe sequences and b) perfect match of full-length probe sequence and the last  <dig> nucleotides of primer sequences at the 3´ end.
for each sequence matching condition, the in silico coverage analysis was performed at three taxonomic levels: phylum, genus, and species, as well as for all sequences eligible for sequence matching.
the remaining taxonomic levels were omitted due to the large amounts of missing and inconsistent data.
details of in silico coverage analyses are as follows:
d. numerical coverage analysis.
at each analysis level, unique operational taxonomic unit , i.e., each unique taxonomic group ranging from unique phyla to unique species, containing at least one sequence that is a sequence match  for all three components of the assay of interest were identified using the following requirement: .
the in silico coverage analysis was performed in a stepwise fashion, beginning with all eligible sequences, then proceeding to analysis at the species-, genus-, and phylum-level.
at each step, the taxonomic identification of each sequence was generated by concatenation of relevant taxonomic data .
the sequence ids were used in lieu of a taxonomic identification for the first analysis step, which included all eligible sequences.
the stepwise numerical coverage analysis was performed as follows: all eligible sequences underwent sequence matching with all three components of the assays of interest using a select matching condition .
the sequence ids of matched sequences were assigned and binned as assay perfect match sequence ids.
for this first analysis step, the numerical coverage was calculated using the total number of sequences with assay perfect match sequence ids as the numerator and the total number of eligible sequences as the denominator.
next, at the species-level, all sequences assigned as assay perfect match sequence ids were dereplicated based on the concatenated phylum-genus- species taxonomic identifications.
species-level numerical coverage was then calculated using the total number of dereplicated taxonomic identifications as the numerator.
denominator was calculated using the dereplicated phylum-genus- species taxonomic identifications from all eligible sequences.
as a result of the logic of this analysis pipeline, a species  was considered an assay sequence match and thus “covered”, when at least one assay perfect match sequence id was in the species group.
the numerical coverage analysis was repeated on the genus-level using the dereplicated phylum-genus taxonomic identifications from the assay perfect match sequence ids bin  and from all eligible sequences , and lastly, on the phylum-level using phylum taxonomic identifications.
to facilitate calculation of assay coverage, two ambiguous phyla, “bacteria insertia sedis” and “unclassified bacteria” were excluded from the phylum-level analysis.
sequences with genus, species, and strain names containing “unclassified” were included in the numerical coverage analyses due to their high abundance.
e. taxonomic coverage analysis.
the in silico taxonomic coverage analysis was performed to generate a detailed output consisting of the taxonomic identifications that were covered or “uncovered”  at multiple taxonomic levels.
a step-wise approach was again utilized for this analysis, beginning with all eligible sequences, performed as follows: first, the assay perfect match sequence ids were subtracted from the sequence ids from all eligible sequences, with the resultant sequences assigned and binned as assay non-perfect match sequence ids.
next, on the species-level, the phylum-genus- species taxonomic identifications of all eligible sequences was first dereplicated, from which the “covered” species taxonomic identifications were subtracted.
species-level taxonomic coverage was then presented as a list of concatenated taxonomic identification of the covered and uncovered species.
this was repeated with the genus- and phylum-level taxonomic identifications for genus- and phylum-level taxonomic coverage analyses.
output of taxonomic identifications from analysis using all eligible sequences was not presented in this manuscript due to its extensive size but is available in additional file 1: figure s  <dig>
f. assay comparison using results from the in silico analyses.
results from the in silico analyses were summarized for assay comparison as follows: the numerical coverage for the bactquantand published qpcr assays were calculated at three taxonomic levels, as well as for all eligible sequences using both sequence matching conditions and presented as both the numerator and denominator, and percent covered calculated as the numerator divided by the denominator.
this was presented in table <dig>  additional comparison of the taxonomic coverage was performed by superimposing the genus-level numerical coverage of the bactquant assay for each phylum onto a maximum parsimony phylogenetic tree.
construction of the phylum-level phylogenetic tree was performed using mega <dig> with representative full-length 16 s rrna gene sequences from each of the  <dig> phyla analyzed  <cit> .
in addition, each phylum was annotated as not covered or poorly covered by the published qpcr assay if the phylum was uncovered or if >50% of the genera within the phylum were uncovered, respectively.
a list of the uncovered genera by phylum for the bactquant assay was also generated.
comparison results using the stringent and relaxed criterion were presented in figure <dig> and additional file 2: figure s <dig>  respectively.
the in silico analysis was performed using two sequence matching conditions.
*the difference in number of sequences eligible for in silico evaluation is due to the difference in primer lengths and locations of the two assays.
quantification and normalization of cloned plasmid standards
overview
to obtain accurately quantified plasmid standards for validation the bactquant assay, a  <dig> copies/μl plasmid stock was quantified using a qpcr assay targeting portion of the vector using the second derivative maximum analysis algorithm on the lightcyler platform.
the resultant crossing point value  is used in plasmid normalization.
the details are as follows:
generation of normalized 16 s rrna gene plasmid standards
amplification of the full 16 s rrna gene was performed using e. coli genomic dna as the template and 16 s rrna gene primers 27 f and 1492r as previously described  <cit> .
visualization of pcr amplicon was performed using gel electrophoresis with sybr 2% agarose gel.
the resultant pcr amplicons were immediately used as the target gene insert with the topo® ta cloning® kit   following the manufacturer’s instructions.
the resultant propagated cloned plasmids were purified using the qiaprep spin miniprep kit .
sequence verification of the purified plasmids containing the 16 s rrna gene insert was performed with capillary electrophoresis using bigdye® terminator v <dig>  cycle sequencing kit on the  <dig> genetic analyzer platform .
quantification of the cloned plasmids was performed by analyzing three 10-fold dilutions using the vector qpcr assay.
normalization was performed using the dilution factor 2Δcp, where Δcp =  <dig> – .
pan-bacterial qpcr assay optimization and initial specificity check
assay optimization
using the normalized plasmid standards, different primer and probe titrations were tested on the on the 7900ht real time pcr system  and evaluated based on reaction efficiency and assay dynamic range for 10 μl and 5 μl reaction volumes.
for 10 μl and 5 μl reactions, the optimized conditions included 1 μl of template into 9 μl and 4 μl of reaction mix, respectively, with the final reaction containing  <dig>  μm of each forward and reverse primer,  <dig> nm the taqman® probe, 1x platinum® quantitative pcr supermix-udg w⁄;rox  and molecular-grade water.
irrespective of reaction volume, each experiment included an in-run standard curve  and no-template controls performed in triplicate.
amplification and real-time fluorescence detections were performed on the 7900ht real time pcr system  using the following pcr conditions: 3 min at 50°c for ung treatment, 10 min at 95°c for taq activation, 15 s at 95°c for denaturation and 1 min at 60°c for annealing and extension x  <dig> cycles.
cycle threshold value  for each 16 s qpcr reaction were obtained using a manual ct threshold of  <dig>  and automatic baseline in the sequence detection systems v <dig>  software .
initial specificity check against human and fungal genomic dna
using the optimized assay condition, the newly designed assay was tested against 1 ng, 100 pg, and 10 pg of human genomic dna , c. albicans genomic dna , the normalized plasmid standards in triplicate reactions.
laboratory analysis of assay performance using diverse bacterial genomic dna
to assess our assay performance against diverse bacteria, we tested our assay against a diverse collection of bacterial genomic dna to determine the assay efficiency and correlation coefficients.
the details are as follows:
bacterial strains
arsenophonus nasoniae atcc  <dig> , budvicia aquatica atcc  <dig>  buttiauxella gaviniae atcc  <dig>  cedecea davisae atcc  <dig> , cellvibrio gilvus atcc <dig>  citrobacter freundii atcc  <dig>  clostridium difficile atcc  <dig>  cronobacter aerogenes atcc  <dig>  ewingella americana atcc  <dig> , edwardsiella tarda atcc  <dig>  escherichia vulneris atcc  <dig>  hafnia alvei atcc  <dig>  ewingella americana atcc  <dig> , klebsiella oxytoca atcc  <dig>  kluyvera ascorbata atcc  <dig>  leclericia adecarboxylata atcc  <dig>  leminorella richardii atcc  <dig>  moellerella wisconsensis atcc  <dig>  morganella morganii atcc  <dig>  obesumbacterium proteus atcc  <dig>  pantoea agglomerans atcc  <dig>  photorhabdus asymbiotica atcc  <dig>  plesiomonas shigelloides atcc  <dig>  pragia fontium atcc  <dig>  proteus mirabilis atcc  <dig> , providencia rustigianii atcc  <dig>  pseudomonas aeruginosa atcc  <dig>  pseudomonas andersonii atcc baa- <dig>  pseudomonas anguilliseptica atcc  <dig>  pseudomonas azotofixans atcc baa- <dig>  pseudomonas fragi atcc  <dig>  pseudomonas lundensis atcc  <dig>  pseudomonas luteola atcc  <dig>  pseudomonas mendocina atcc  <dig>  pseudomonas monteilii atcc  <dig>  pseudomonas mosselii atcc baa- <dig>  pseudomonas otitidis atcc baa- <dig>  pseudomonas pseudoalcaligenes atcc  <dig>  psuedomonas putida atcc  <dig>  pseudomonas stutzeri atcc  <dig>  pseudomonas taetrolens atcc  <dig>  rahnella aquatilus atcc  <dig>  raoultella ornithinolytica atcc  <dig> , shigella dysenteriae atcc  <dig>  salmonella enterica atcc  <dig>  serratia liquefaciens atcc  <dig>  tatumella ptyseos atcc  <dig>  trabulsiella guamensis atcc  <dig>  yersinia enterocolitica atcc  <dig>  and yokenella regensburgei atcc  <dig> were obtained from the american type culture collection .
bacterial propagation and enrichment were performed under the appropriate condition for each bacterial strain following atcc recommendations.
extraction of bacterial genomic dna
extraction using the enriched broth was performed using zr fungal/bacterial dna minipreptm  following the manufacturer’s instruction.
elution of the purified genomic dna was performed using 100 μl of 1x te buffer.
other sources of bacterial genomic dna
genomic dna from bacteroides fragilis atcc  <dig> , bacteorides ureolyticus gs- <dig> atcc  <dig>  borrelia burgdorferi strain b <dig> atcc  <dig> , campylobacter jejuni atcc  <dig> , chlaymydia trachomatis atcc vr-348b , chlamydophila pneumoniae atcc vr-1360d , fusobacterium nucleatum atcc  <dig> , pectobacterium atrosepticum atcc baa- <dig> , pseudomonas syringae atcc  <dig> , streptomyces violaceoruber atcc baa- <dig> , thermus thermophilus atcc baa- <dig> , treponema denticola atcc  <dig> , and vibrio cholera atcc  <dig> were obtained from the american type culture collection.
dna from mycobacterium avium, subsp.
avium, mycobacetrium abscessus, mycobacterium bovis, mycobacterium chelonae, mycobacterium gastri, mycobacterium gordonae, mycobacterium fortuitum, mycobacterium kansasii, mycobacterium marinum, mycobacterium nonchromogenicum, mycobacterium phlei, mycobacterium smegmatis, mycobacterium vaccae, and mycobacterium xenopi were kindly provided by national taiwan university, taipei, taiwan.
dna from clinical isolates of acinetobacter baumannii, klebsiella pneumoniae, burkholderia pseudomallei, coxiella burnetti, enterobacter cloacae, enterococcus faecium, escherichia coli, francisella tularensis, haemophilus influenzae, legionella pneumophila, listeria monocytogenes, moraxella catarrhalis, neisseria gonorrhoeae, pseudomonas aeruginosa, salmonella enterica subsp.
enterica serovar gallinarum, staphylococcus arlettae, staphylococcus capitis, staphylococcus cohnii, staphylococcus epidermidis, staphylococcus equorum, staphylococcus hominis, staphylococcus haemolyticus, staphylococcus kloosii, staphylococcus lugdunensis, staphylococcus saprophyticus, staphyloccocus xylosus, streptococcus agalactiae, streptococcus pneumoniae, and viridans streptococcus and were kindly provided by a project supported by nih/niaid u01ai <dig> at the translational genomics research institute, flagstaff, az, usa.
experimental design
for sensitivity and efficiency analysis, bacterial genomic dna from each species was analyzed in three 10-fold serial dilutions in triplicate reactions using the optimized 16 s qpcr conditions as described above.
data analysis
for each species tested, reaction efficiency and correlation coefficient were calculated using the data from tests against three 10-fold serial dilutions and presented in table <dig>  sequence comparison analysis was performed by aligning the assay primer and probe sequences with 16 s rrna gene sequences of the five uncovered species: borrelia burgdorferi , cellvibrio gilvus , escherichia vulneris , chlamydia trachomatis , and chlamydophila pneumoniae  in seqman®.
amplification profile of the five uncovered species were annotated with results from the sequence comparison and presented in additional file 3: figure s 3a-e.
*no 16 s rrna gene sequence available in the ribosomal database project.
laboratory quantitative assay validation using pure plasmid standards and mixed templates
assay quantitative validation
for the assay quantitative validation, we followed the minimum information for publication of quantitative real-time pcr experiments, or the miqe guidelines whenever applicable  <cit> .
the miqe guidelines were complemented with additional tests to determine assay performance in the presence of background fungal and human genomic dna.
in our experimental design, we included seven template conditions: plasmid standards alone and plasmid standards with  <dig>  ng c. albicans genomic dna  and with  <dig>  ng, 1 ng, 5 ng, and 10 ng of human genomic dna per reaction in 10 μl reactions and plasmid standards alone in 5 μl reactions.
for each condition assessed, three qpcr runs were performed to assess reproducibility, or inter-run variability.
in each run, three replicate standard curves were tested across the 384-well plate to assess repeatability, or intra-run variability.
all reactions were performed in triplicates.
data analysis
using the data generated, the following assay parameters were calculated: 1) inter-run assay coefficient of variation  for copy number and ct value, 2) average intra-run assay cov for copy number and ct. value, 3) assay dynamic range, 4) average reaction efficiency, and 5) correlation coefficient .
the limit of detection was not defined for the pure plasmid standards experiments due to variability in reagent contamination.
at each plasmid standard concentration, the ct standard deviation across all standard curves over three runs was divided by the mean ct value across all standard curves over three runs to obtain the inter-run assay cov.
the cov from each standard curve from each run  were used to calculate the average and the standard deviation of the intra-run cov.
linear regression of each standard curve across the full dynamic range was performed to obtain the slope and correlation coefficient values.
the slope was used to calculate the reaction efficiency using efficiency = 10- <dig>  of note, for each triplicate reaction with ct standard deviation > <dig> , the triplicates were compared and if a clear outlier was present , the outlier well was excluded from analysis.
amplification profiles of the seven conditions tested were annotated and presented in figure2a-b and additional file 4: figure s 4a-e. results from laboratory quantitative validation using all conditions tested were summarized in table <dig>  detailed results of inter- and intra-run coefficient of variation for ct value and copy number were presented for all conditions tested in figure <dig> and additional file 5: supplemental file 1a-c using scattered plots generated with the vegan package in r  <cit> .
bacteria-to-human ratio calculations
calculations were performed using the following copy number and genome size estimates: the average bacterial 16 s rrna gene copy number per genome was estimated to be  <dig>  copies as calculated by rrndb  <cit>   and the average human 18 s rrna gene copy number per genome was estimated to be  <dig> copies  <cit> .
the diploid human genome was estimated to be  <dig>  mb  <cit>  or the mass equivalent of  <dig>  mb/ =  <dig>  pg per diploid human genome  <cit> .
RESULTS
assay design and initial specificity check
using our 16 s rrna gene nucleotide distribution output, we identified a conserved 500 bp region for assay design.
within this region, we selected three highly conserved sub-regions abutting v3-v <dig> for the design of a taqman® quantitative real-time pcr  assay .
degenerate bases were incorporated strategically in the primer sequence to increase the unique 16 s rrna gene sequence types matching the qpcr assay.
no degeneracies were permitted in the taqman® probe sequence .
initial in silico specificity analysis using megablast showed that the probe is a perfect match against human and c. albicans ribosomal dna, due to its highly conserved nature, but the primers were specific and screening using human and c. albicans genomic dna did not show non-specific amplification.
in silico analysis of assay coverage using 16 s rrna gene sequences from  <dig> bacterial phyla
numerical and taxonomic in silico coverage analyses at the phylum, genus, and species levels were performed using 16 s rrna gene sequences from the ribosomal database project  as sequence matching targets.
a total of  <dig> , <dig> 16 s rrna gene sequences were downloaded from rdp.
of these,  <dig>  sequences were determined to be eligible for sequence match comparison based on sequence availability in the e. coli region of the bactquant assay amplicon.
the in silico coverage analyses was performed based on perfect match of full-length primer and probe sequences  and perfect match with full-length probe sequence and the last  <dig> nucleotides of primer sequences at the 3′ end .
using the stringent criterion, in silico numerical coverage analysis showed that  <dig> of the  <dig> bacterial phyla evaluated were covered by the bactquant assay.
the three uncovered phyla being candidate phylum od <dig>  candidate phylum tm <dig>  and chlorobi .
among most of the  <dig> covered phyla, more than 90% of the genera in each phylum were covered by the bactquant assay.
the covered phyla included many that are common in the human microbiome, such as tenericutes , firmicutes , proteobacteria , bacteroidetes , actinobacteria , and fusobacteria .
only three covered phyla had lower than 90% genus-level coverage, which were deferribacteres , spirochaetes , and chlamydiae  .
on the genus- and species-levels,  <dig>  genera  and  <dig>  species  had at least one perfect match using the stringent criterion.
this improved to  <dig>  genera  and  <dig>  species  when the relaxed criterion was applied .
using the same relaxed criterion,  <dig>  or 84% of all eligible sequences were perfect matches with the bactquant assay .
detailed taxonomic information on the covered and uncovered otus for the bactquant assay can be found in additional file 5: supplemental file  <dig>  additional file 6: supplemental file  <dig>
during our in silico validation, a previously published qpcr assay was identified, which was used as a published reference for comparison  <cit> .
the in silico comparison showed that the bactquant assay covers more otus irrespective of the criterion applied .
based on the stringent criterion, the published assay has  <dig> additional uncovered phyla in comparison to bactquant; these were: candidate phylum op <dig>  aquificae, caldiserica, thermodesulfoacteria, thermotogae, dictyoglomi, deinococcus-thermus, lentisphaerae, chlamydiae, and candidate phylum op <dig> .
applying the relaxed criterion added two phyla, aquificae and lentisphaerae, to those covered by the published assay .
the genus-level coverage of the published assay was also low, with fewer than 50% genus-level coverage in six of its covered phyla.
for cyanobacteria, planctomycetes, synergistetes, and verrucomicrobia, only a single genus was covered by the published assay .
in all, the bactquant assay covered an additional  <dig> genera and  <dig>  species than the published assay, or the equivalent of 15% more genera, species, and total unique sequences than the published assay .
detailed taxonomic information on the covered and uncovered otus for the published qpcr assay can be found in additional file 7: supplemental files  <dig>  additional file 8: supplemental files  <dig>
laboratory analysis of assay performance using diverse bacterial genomic dna
laboratory evaluation of the bactquant assay showed 100% sensitivity against  <dig> species identified as perfect matches from the in silico coverage analysis.
the laboratory evaluation was performed using genomic dna from  <dig> unique species encompassing eight phyla: actinobacteria , bacteroidetes , deinococcus-thermus , firmicutes , fusobacteria , proteobacteria , chlamydiae , and spirochaetes .
overall, evaluation using genomic dna from the  <dig> in silico perfect match species demonstrated r2-value of > <dig>  and amplification efficiencies of  <dig> to 120% .
laboratory evaluation against the five in silico uncovered species showed variable assay amplification profiles and efficiencies.
of these five species, chlamydia trachomatis, chlamydophila pneumoniae, and cellvibrio gilvus were identified as uncovered irrespective of in silico analysis criterion.
however, while c. trachomatis and c. pneumoniae showed strongly inhibited amplification profile, c. gilvus amplified successfully with a r2-value of > <dig>  and an amplification efficiency of 92% .
two other species, borrelia burgdorferi and escherichia vulneris, which were uncovered only when using the stringent criterion, also showed successful amplification with a r2-value of > <dig>  and 90% and 93% reaction efficiency, respectively .
comparison of the assay and bacterial sequences showed that c. trachomatis and c. pneumoniae shared a single mismatch in the center of the probe sequence, whereas c. gilvus had a mismatch on the 3′ end of the probe.
the mismatch in b. burgdorferi and e. vulneris was a single base difference in 5′ end of the reverse and the forward primer, respectively .
these findings strongly suggest the location of the sequence mismatch is an important determinant of amplification outcome.
furthermore, it supports that the bactquant assay’s coverage in laboratory application is likely greater than determined by the in silico analyses.
laboratory quantitative assay validation using pure plasmid standards and mixed templates
to fully characterize the assay quantitative profile, the bactquant assay was tested using different reaction volumes and against both pure and mixed templates containing bacterial and human rrna gene targets.
laboratory evaluation using pure plasmid standards in 10 μl and 5 μl reaction volumes showed excellent amplification profiles, with an assay dynamic range of 102– <dig> 16 s rrna gene copies per reaction .
for the 10 μl reactions, the inter- and intra-run coefficients of variance  ranged from  <dig> – <dig> % and  <dig> – <dig> % for ct values and from  <dig> – <dig> % and  <dig> – <dig> % for copy number, respectively .
the inter- and intra-run cov was comparable for the different reaction volumes, except for the higher cov in 5 μl reactions containing more than  <dig> plasmid copies .
this suggests that the 5 μl reaction volumes may be better suited for samples with low amounts of bacterial dna.
establishment of the limit of detection  for the bactquant assay using pure plasmid standards was not attempted because it was affected by the level of contaminants in reagents, as previously reported  <cit> .
further laboratory evaluations using mixed templates showed that the ratio of bacteria-to-human dna ratio determined the assay dynamic range of the bactquant assay .
experiments using seven tenfold dilutions of plasmid standards with  <dig>  ng and 1 ng human gdna showed that the assay dynamic range was unchanged from pure plasmid standard.
however, experiments using 5 ng and 10 ng of human gdna showed narrower assay dynamic ranges of  <dig> -  <dig> and  <dig> -  <dig> 16 s rrna gene copies per reaction, respectively.
based on this result, the lods for 10 μl reactions using templates containing 5 ng and 10 ng of human gdna were estimated to be a bacteria-to-human ribosomal gene copy ratio of 500: <dig> and 1000: <dig>  respectively.
this could be further simplified to a bacteria-to-human ribosomal gene copy ratio of 1: <dig>  from a genomic equivalent perspective, the lod of the bactquant assay was approximately at a bacteria-to-human ratio of 127: <dig>
discussion
we designed and evaluated a new expanded-coverage bacterial quantitative real-time pcr assay targeting the 16 s rrna gene.
to accomplish this, we curated a set of high-quality 16 s rrna gene sequences for assay design and evaluated the coverage of our primers and as a union .
in addition, we improved the quantitative capacity of our assay using a cloned plasmid standard.
our computational and laboratory analyses showed that bactquant had superior in silico taxonomic coverage while retaining favorable in vitro performance.
as would be expected, the diverse gene sequences targeted by bactquant have resulted in variable reaction efficiencies.
nevertheless, laboratory evaluation showed 100% sensitivity against perfect match species from the in silico analysis.
to allow researchers to determine whether bactquant covers key organisms in their target community, we provided additional detailed otu coverage information in the supplemental files.
we have applied the logic that an otu was covered if it contained at least one perfect match sequence in the in silico analysis.
16 s rrna gene sequences with ambiguous or degenerate bases at the primer and probe sites were considered non-perfect matches, thus making our coverage estimates more conservative.
lastly, although we prohibited the use of a degenerate probe to maximize our assay’s quantitative ability, this approach may permit detection of specific taxa such as chlamydia spp .
and chlamydophila spp.
for most studies, the desired measurement of bacterial load is the number of cells rather than 16 s rrna gene copy number; however, the 16 s rrna gene copy number varies among bacterial species and even among strains  <cit> .
the range of copy number is estimated at one to  <dig>  with most non-spore forming species having fewer than  <dig> copies per genome  <cit> .
we use the average 16 s rrna gene copy number per genome from rrndb in our genomic equivalent estimation, but alternative approaches are possible.
this, combined with logarithmic growth of bacteria, suggest that using estimated average copy number could be sufficient.
the in silico analysis was an important component of our validation of bactquant against diverse bacterial sequence types, even though sequence matching is not a perfect predictor of laboratory performance  <cit> .
many factors are known to affect reaction efficiency, such as oligonucleotide thermodynamics, the type of pcr master mix used, and the template dna extraction method.
concentration of background nontarget genomic dna is another factor that can affect the quantitative parameters rrna gene-based assays  <cit> .
the interference of background human dna with bactquant dynamic range reported in this paper was most likely due to cross-reactivity of human dna with the probe, which targets a region conserved even among eukaryotic organisms, including in the human 18 s rrna gene.
this may be overcome by using an intercalating reporter dye in place of a fluorescent probe as a qpcr reporter mechanism; however, the loss of tertiary-level of specificity is a potential concern in direct application of an intercalating dye assay to specimens containing high amounts of nontarget dna.
exogenous bacterial dna, particularly from biologically synthesized reagents such as taq dna polymerase are a known limitation for analyzing samples with low bacterial load  <cit> .
recently, this issue has received renewed attention due to increased usage of next-generation sequencing and the frequent data contamination from exogenous bacterial dna.
several methods have been evaluated for removing bacterial contaminants from taq dna polymerase, including uv irradiation  <cit> , dnase i treatment, and ultrafiltration  <cit> .
the level of e. coli contamination in taq dna polymerase has been estimated at  <dig> to  <dig> genome equivalents of bacterial dna per unit of enzyme  <cit> .
this is consistent with the lowest amount of contamination we have observed in our experiments, which were  <dig> and  <dig> copies of 16 s rrna gene in 5 μl and 10 μl reactions, respectively.
the ubiquity of bacterial dna also makes the determination of assay specificity challenging.
our use of qpcr-quantified plasmid standards addressed a major limitation in the preparation of qpcr quantification standards.
the conventional approach of quantifying bacterial genomic dna or plasmid standards necessitates converting mass  to copy number  and can introduce substantial error.
thus far, we have also successfully applied bactquant to a diverse range of clinical specimens, including swab eluents, surgical specimens, and respiratory specimens, but we did not present these findings in this paper.
to fully understand the likelihood of false negative results due to interference from human dna or bactquant’s limit of detection will require additional evaluations.
CONCLUSIONS
in summary, we have developed and evaluated a new broad-coverage qpcr assay—bactquant—for bacterial detection and quantification that showed concurrently improved assay coverage and favorable quantitative parameters.
laboratory tests showed that in vitro performance was even better than predicted in the in silico analysis; however, our approach of evaluating assay coverage by considering the primer and probe sequences as a single unit is appropriate and necessary.
in addition, when employing a copy number estimation method, such as qpcr, the quantification of standards is critical for accurate template quantification.
thus, our approach of quantifying plasmid standards using the intrinsic property of real-time pcr is another important step for any absolute quantification experiments using qpcr.
competing interests
the authors have declared that no competing interests exist.
authors’ contributions
cml contributed to the overall study design, the acquisition, analysis, and interpretation of data, and drafting the manuscript, ma contributed to the bioinformatics portion of the study design and its implementation, sk participated in bioinformatics analysis and assay design, prh and yth both contributed to the acquisition and interpretation of laboratory data, pk conceived of the study and contributed to the overall study design, lbp contributed to the overall study design and helped to draft the manuscript.
all authors read and approved the final manuscript.
supplementary material
additional file  <dig>
figure s <dig>  figure s <dig> containing the in silico coverage analysis using the relaxed criteria.
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additional file  <dig>
figure s2a-e. standard curve amplification plots using mixed templates.
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additional file  <dig>
figure s3a-e. amplification plots of the non-perfect match targets, including c. trachomatis, c. pneumoniae, c. gilvus, b. burgdorferi, and e. vulneris.
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additional file  <dig>
figure s4a-e. coefficient of variance  distribution across assay dynamic range for mixed templates.
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additional file  <dig>
supplemental file  <dig>  detailed results for bactquant using the stringent criteria.
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additional file  <dig>
supplemental file  <dig>  detailed results for bactquant using the relaxed criteria.
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additional file  <dig>
supplemental file  <dig>  detailed results for published assay using the stringent criteria.
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additional file  <dig>
supplemental file  <dig>  detailed results from published assay using the relaxed criteria.
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additional file  <dig>
table s <dig>  base distribution output used in primer and probe design, with the bolded base signifying the selected base and incorporation of more than one allele at a given nucleotide position was accomplished using degenerate bases.
the alignment position information in the base distribution file contains many gaps as a result from the sequence alignment and differs from the e. coli region information from table  <dig>
click here for file
acknowledgements
we thank lindsey watson , jordan buchhagen , jolene bowers , and tania contente-cuomo  at translational genomics research institute for their assistance with different aspects of the laboratory work presented in this manuscript.
funding
this work was supported by the national institutes of health , the department of defense , and the tgen foundation.
the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

 scientists today are studying bacterial communities from diverse habitats, hosts, and health conditions based on the 16 s rrna gene. to date, most studies have focused on qualitative characterization based on the relative abundances of community bacterial groups; however, quantitative characterization—i.e., measurement of the total bacterial load—provides valuable and complementary information when combined with these qualitative data. traditional culture-based approaches for quantifying bacterial load are inherently limited for assessing the complex bacterial communities that exist in many clinical and environmental samples. likewise, standard culture-based methods are ineffective for quantifying many fastidious and uncultivable bacterial species. among culture-independent approaches, quantitative real-time pcr is currently best suited for measuring bacterial load, because of its intrinsic quantitative capability, ease of use, and flexibility in assay design. using the qpcr platform, we can design an assay capable of concurrently detecting and quantifying all unique bacteria that constitutes a complex community. furthermore, by utilizing 16 s rrna gene as the target of a broad coverage qpcr assay, results from the qpcr evaluation can be easily combined with 16 s rrna gene-based qualitative characterization to fully describe the community of interest. in the current paper, we present our design and validation of a broad-coverage quantitative real-time pcr assay—bactquant—for quantifying 16 s rrna gene copy number and estimating bacterial load. to accomplish this, we have employed a novel nucleotide distribution-based approach to effectively summarize a large 16 s rrna gene sequence dataset for qpcr assay design. we further addressed a general limitation of the qpcr platform—the normalization of in-run quantitative standards using fluorimetric or spectrometric methods—by developing an alternative qpcr-based method for quantifying plasmid standards. lastly, we have complemented standard qpcr assay validation following miqe guideline with extensive in silico analysis using > 16 s rrna gene sequences from the ribosomal database project. methods design of 16 s rrna gene quantitative real-time pcr assay pre-aligned 16 s rrna gene sequences were downloaded from the core set of the greengenes database. the alignment was analyzed to generate an output of nucleotide distribution—i.e., the summary of allele frequency at each nucleotide position in the 16 s rrna gene multiple sequence alignment file—and diversity score using a 3% gap-filter setting and the simpson’s diversity index, respectively. assay design the nucleotide distribution was examined to identify a conserved 500 bp region for assay design. in designing the assays, we applied the following rules: 1) primer sequences cannot have more than three degenerate bases and 2) the probe sequence cannot have any degenerate bases. the primer tm was calculated using salt adjusted calculation from the online tool oligocalc and the probe tm was calculated using the primer probe test tool for taqman® mgb quantification from the primer express® software for real-time pcr version. computational analysis of assay specificity and coverage a. specificity analysis. specificity check was performed in genbank using megablast against human, mouse, and fungal sequences from the nucleotide collection. b. collection and identification of bacterial 16 s rrna gene sequence eligible for in silico coverage analysis. all 16 s rrna gene sequence data used in the in silico coverage analysis were downloaded from the ribosomal database project release update. briefly, all bacterial 16 s rrna gene sequences that were of “good” quality and had a length of 1200 bp or greater were extracted from rdp, along with taxonomic metadata and sequence ids. additionally, the escherichia coli position data was kindly provided by staff at the rdp. the downloaded sequences were filtered based on e. coli position. only sequences with data present in the qpcr assay amplicon of interest were considered to be eligible for sequence matching for the particular qpcr assay. numerical and taxonomic coverage analysis was performed for the bactquant assay and a published qpcr assay by developing a web service for the rdp probe match tool for sequence matching. c. overview of sequence matching analysis for determining assay coverage. all sequence matching for the in silico coverage analysis was performed using two conditions: a) perfect match of full-length primer and probe sequences and b) perfect match of full-length probe sequence and the last nucleotides of primer sequences at the 3´ end. for each sequence matching condition, the in silico coverage analysis was performed at three taxonomic levels: phylum, genus, and species, as well as for all sequences eligible for sequence matching. the remaining taxonomic levels were omitted due to the large amounts of missing and inconsistent data. details of in silico coverage analyses are as follows: d. numerical coverage analysis. at each analysis level, unique operational taxonomic unit, i.e., each unique taxonomic group ranging from unique phyla to unique species, containing at least one sequence that is a sequence match for all three components of the assay of interest were identified using the following requirement:. the in silico coverage analysis was performed in a stepwise fashion, beginning with all eligible sequences, then proceeding to analysis at the species-, genus-, and phylum-level. at each step, the taxonomic identification of each sequence was generated by concatenation of relevant taxonomic data. the sequence ids were used in lieu of a taxonomic identification for the first analysis step, which included all eligible sequences. the stepwise numerical coverage analysis was performed as follows: all eligible sequences underwent sequence matching with all three components of the assays of interest using a select matching condition. the sequence ids of matched sequences were assigned and binned as assay perfect match sequence ids. for this first analysis step, the numerical coverage was calculated using the total number of sequences with assay perfect match sequence ids as the numerator and the total number of eligible sequences as the denominator. next, at the species-level, all sequences assigned as assay perfect match sequence ids were dereplicated based on the concatenated phylum-genus- species taxonomic identifications. species-level numerical coverage was then calculated using the total number of dereplicated taxonomic identifications as the numerator. denominator was calculated using the dereplicated phylum-genus- species taxonomic identifications from all eligible sequences. as a result of the logic of this analysis pipeline, a species was considered an assay sequence match and thus “covered”, when at least one assay perfect match sequence id was in the species group. the numerical coverage analysis was repeated on the genus-level using the dereplicated phylum-genus taxonomic identifications from the assay perfect match sequence ids bin and from all eligible sequences, and lastly, on the phylum-level using phylum taxonomic identifications. to facilitate calculation of assay coverage, two ambiguous phyla, “bacteria insertia sedis” and “unclassified bacteria” were excluded from the phylum-level analysis. sequences with genus, species, and strain names containing “unclassified” were included in the numerical coverage analyses due to their high abundance. e. taxonomic coverage analysis. the in silico taxonomic coverage analysis was performed to generate a detailed output consisting of the taxonomic identifications that were covered or “uncovered” at multiple taxonomic levels. a step-wise approach was again utilized for this analysis, beginning with all eligible sequences, performed as follows: first, the assay perfect match sequence ids were subtracted from the sequence ids from all eligible sequences, with the resultant sequences assigned and binned as assay non-perfect match sequence ids. next, on the species-level, the phylum-genus- species taxonomic identifications of all eligible sequences was first dereplicated, from which the “covered” species taxonomic identifications were subtracted. species-level taxonomic coverage was then presented as a list of concatenated taxonomic identification of the covered and uncovered species. this was repeated with the genus- and phylum-level taxonomic identifications for genus- and phylum-level taxonomic coverage analyses. output of taxonomic identifications from analysis using all eligible sequences was not presented in this manuscript due to its extensive size but is available in additional file 1: figure s f. assay comparison using results from the in silico analyses. results from the in silico analyses were summarized for assay comparison as follows: the numerical coverage for the bactquantand published qpcr assays were calculated at three taxonomic levels, as well as for all eligible sequences using both sequence matching conditions and presented as both the numerator and denominator, and percent covered calculated as the numerator divided by the denominator. this was presented in table additional comparison of the taxonomic coverage was performed by superimposing the genus-level numerical coverage of the bactquant assay for each phylum onto a maximum parsimony phylogenetic tree. construction of the phylum-level phylogenetic tree was performed using mega with representative full-length 16 s rrna gene sequences from each of the phyla analyzed. in addition, each phylum was annotated as not covered or poorly covered by the published qpcr assay if the phylum was uncovered or if >50% of the genera within the phylum were uncovered, respectively. a list of the uncovered genera by phylum for the bactquant assay was also generated. comparison results using the stringent and relaxed criterion were presented in figure and additional file 2: figure s respectively. the in silico analysis was performed using two sequence matching conditions. *the difference in number of sequences eligible for in silico evaluation is due to the difference in primer lengths and locations of the two assays. quantification and normalization of cloned plasmid standards overview to obtain accurately quantified plasmid standards for validation the bactquant assay, a copies/μl plasmid stock was quantified using a qpcr assay targeting portion of the vector using the second derivative maximum analysis algorithm on the lightcyler platform. the resultant crossing point value is used in plasmid normalization. the details are as follows: generation of normalized 16 s rrna gene plasmid standards amplification of the full 16 s rrna gene was performed using e. coli genomic dna as the template and 16 s rrna gene primers 27 f and 1492r as previously described. visualization of pcr amplicon was performed using gel electrophoresis with sybr 2% agarose gel. the resultant pcr amplicons were immediately used as the target gene insert with the topo® ta cloning® kit following the manufacturer’s instructions. the resultant propagated cloned plasmids were purified using the qiaprep spin miniprep kit. sequence verification of the purified plasmids containing the 16 s rrna gene insert was performed with capillary electrophoresis using bigdye® terminator v cycle sequencing kit on the genetic analyzer platform. quantification of the cloned plasmids was performed by analyzing three 10-fold dilutions using the vector qpcr assay. normalization was performed using the dilution factor 2Δcp, where Δcp =  –. pan-bacterial qpcr assay optimization and initial specificity check assay optimization using the normalized plasmid standards, different primer and probe titrations were tested on the on the 7900ht real time pcr system and evaluated based on reaction efficiency and assay dynamic range for 10 μl and 5 μl reaction volumes. for 10 μl and 5 μl reactions, the optimized conditions included 1 μl of template into 9 μl and 4 μl of reaction mix, respectively, with the final reaction containing  μm of each forward and reverse primer, nm the taqman® probe, 1x platinum® quantitative pcr supermix-udg w⁄;rox and molecular-grade water. irrespective of reaction volume, each experiment included an in-run standard curve and no-template controls performed in triplicate. amplification and real-time fluorescence detections were performed on the 7900ht real time pcr system using the following pcr conditions: 3 min at 50°c for ung treatment, 10 min at 95°c for taq activation, 15 s at 95°c for denaturation and 1 min at 60°c for annealing and extension x cycles. cycle threshold value for each 16 s qpcr reaction were obtained using a manual ct threshold of and automatic baseline in the sequence detection systems v software. initial specificity check against human and fungal genomic dna using the optimized assay condition, the newly designed assay was tested against 1 ng, 100 pg, and 10 pg of human genomic dna, c. albicans genomic dna, the normalized plasmid standards in triplicate reactions. laboratory analysis of assay performance using diverse bacterial genomic dna to assess our assay performance against diverse bacteria, we tested our assay against a diverse collection of bacterial genomic dna to determine the assay efficiency and correlation coefficients. the details are as follows: bacterial strains arsenophonus nasoniae atcc, budvicia aquatica atcc buttiauxella gaviniae atcc cedecea davisae atcc, cellvibrio gilvus atcc citrobacter freundii atcc clostridium difficile atcc cronobacter aerogenes atcc ewingella americana atcc, edwardsiella tarda atcc escherichia vulneris atcc hafnia alvei atcc ewingella americana atcc, klebsiella oxytoca atcc kluyvera ascorbata atcc leclericia adecarboxylata atcc leminorella richardii atcc moellerella wisconsensis atcc morganella morganii atcc obesumbacterium proteus atcc pantoea agglomerans atcc photorhabdus asymbiotica atcc plesiomonas shigelloides atcc pragia fontium atcc proteus mirabilis atcc, providencia rustigianii atcc pseudomonas aeruginosa atcc pseudomonas andersonii atcc baa- pseudomonas anguilliseptica atcc pseudomonas azotofixans atcc baa- pseudomonas fragi atcc pseudomonas lundensis atcc pseudomonas luteola atcc pseudomonas mendocina atcc pseudomonas monteilii atcc pseudomonas mosselii atcc baa- pseudomonas otitidis atcc baa- pseudomonas pseudoalcaligenes atcc psuedomonas putida atcc pseudomonas stutzeri atcc pseudomonas taetrolens atcc rahnella aquatilus atcc raoultella ornithinolytica atcc, shigella dysenteriae atcc salmonella enterica atcc serratia liquefaciens atcc tatumella ptyseos atcc trabulsiella guamensis atcc yersinia enterocolitica atcc and yokenella regensburgei atcc were obtained from the american type culture collection. bacterial propagation and enrichment were performed under the appropriate condition for each bacterial strain following atcc recommendations. extraction of bacterial genomic dna extraction using the enriched broth was performed using zr fungal/bacterial dna minipreptm following the manufacturer’s instruction. elution of the purified genomic dna was performed using 100 μl of 1x te buffer. other sources of bacterial genomic dna genomic dna from bacteroides fragilis atcc, bacteorides ureolyticus gs- atcc borrelia burgdorferi strain b atcc, campylobacter jejuni atcc, chlaymydia trachomatis atcc vr-348b, chlamydophila pneumoniae atcc vr-1360d, fusobacterium nucleatum atcc, pectobacterium atrosepticum atcc baa-, pseudomonas syringae atcc, streptomyces violaceoruber atcc baa-, thermus thermophilus atcc baa-, treponema denticola atcc, and vibrio cholera atcc were obtained from the american type culture collection. dna from mycobacterium avium, subsp. avium, mycobacetrium abscessus, mycobacterium bovis, mycobacterium chelonae, mycobacterium gastri, mycobacterium gordonae, mycobacterium fortuitum, mycobacterium kansasii, mycobacterium marinum, mycobacterium nonchromogenicum, mycobacterium phlei, mycobacterium smegmatis, mycobacterium vaccae, and mycobacterium xenopi were kindly provided by national taiwan university, taipei, taiwan. dna from clinical isolates of acinetobacter baumannii, klebsiella pneumoniae, burkholderia pseudomallei, coxiella burnetti, enterobacter cloacae, enterococcus faecium, escherichia coli, francisella tularensis, haemophilus influenzae, legionella pneumophila, listeria monocytogenes, moraxella catarrhalis, neisseria gonorrhoeae, pseudomonas aeruginosa, salmonella enterica subsp. enterica serovar gallinarum, staphylococcus arlettae, staphylococcus capitis, staphylococcus cohnii, staphylococcus epidermidis, staphylococcus equorum, staphylococcus hominis, staphylococcus haemolyticus, staphylococcus kloosii, staphylococcus lugdunensis, staphylococcus saprophyticus, staphyloccocus xylosus, streptococcus agalactiae, streptococcus pneumoniae, and viridans streptococcus and were kindly provided by a project supported by nih/niaid u01ai at the translational genomics research institute, flagstaff, az, usa. experimental design for sensitivity and efficiency analysis, bacterial genomic dna from each species was analyzed in three 10-fold serial dilutions in triplicate reactions using the optimized 16 s qpcr conditions as described above. data analysis for each species tested, reaction efficiency and correlation coefficient were calculated using the data from tests against three 10-fold serial dilutions and presented in table sequence comparison analysis was performed by aligning the assay primer and probe sequences with 16 s rrna gene sequences of the five uncovered species: borrelia burgdorferi, cellvibrio gilvus, escherichia vulneris, chlamydia trachomatis, and chlamydophila pneumoniae in seqman®. amplification profile of the five uncovered species were annotated with results from the sequence comparison and presented in additional file 3: figure s 3a-e. *no 16 s rrna gene sequence available in the ribosomal database project. laboratory quantitative assay validation using pure plasmid standards and mixed templates assay quantitative validation for the assay quantitative validation, we followed the minimum information for publication of quantitative real-time pcr experiments, or the miqe guidelines whenever applicable. the miqe guidelines were complemented with additional tests to determine assay performance in the presence of background fungal and human genomic dna. in our experimental design, we included seven template conditions: plasmid standards alone and plasmid standards with  ng c. albicans genomic dna and with  ng, 1 ng, 5 ng, and 10 ng of human genomic dna per reaction in 10 μl reactions and plasmid standards alone in 5 μl reactions. for each condition assessed, three qpcr runs were performed to assess reproducibility, or inter-run variability. in each run, three replicate standard curves were tested across the 384-well plate to assess repeatability, or intra-run variability. all reactions were performed in triplicates. data analysis using the data generated, the following assay parameters were calculated: 1) inter-run assay coefficient of variation for copy number and ct value, 2) average intra-run assay cov for copy number and ct. value, 3) assay dynamic range, 4) average reaction efficiency, and 5) correlation coefficient. the limit of detection was not defined for the pure plasmid standards experiments due to variability in reagent contamination. at each plasmid standard concentration, the ct standard deviation across all standard curves over three runs was divided by the mean ct value across all standard curves over three runs to obtain the inter-run assay cov. the cov from each standard curve from each run were used to calculate the average and the standard deviation of the intra-run cov. linear regression of each standard curve across the full dynamic range was performed to obtain the slope and correlation coefficient values. the slope was used to calculate the reaction efficiency using efficiency = 10- of note, for each triplicate reaction with ct standard deviation >, the triplicates were compared and if a clear outlier was present, the outlier well was excluded from analysis. amplification profiles of the seven conditions tested were annotated and presented in figure2a-b and additional file 4: figure s 4a-e. results from laboratory quantitative validation using all conditions tested were summarized in table detailed results of inter- and intra-run coefficient of variation for ct value and copy number were presented for all conditions tested in figure and additional file 5: supplemental file 1a-c using scattered plots generated with the vegan package in r. bacteria-to-human ratio calculations calculations were performed using the following copy number and genome size estimates: the average bacterial 16 s rrna gene copy number per genome was estimated to be copies as calculated by rrndb and the average human 18 s rrna gene copy number per genome was estimated to be copies. the diploid human genome was estimated to be  mb or the mass equivalent of  mb/ =   pg per diploid human genome.  assay design and initial specificity check using our 16 s rrna gene nucleotide distribution output, we identified a conserved 500 bp region for assay design. within this region, we selected three highly conserved sub-regions abutting v3-v for the design of a taqman® quantitative real-time pcr assay. degenerate bases were incorporated strategically in the primer sequence to increase the unique 16 s rrna gene sequence types matching the qpcr assay. no degeneracies were permitted in the taqman® probe sequence. initial in silico specificity analysis using megablast showed that the probe is a perfect match against human and c. albicans ribosomal dna, due to its highly conserved nature, but the primers were specific and screening using human and c. albicans genomic dna did not show non-specific amplification. in silico analysis of assay coverage using 16 s rrna gene sequences from bacterial phyla numerical and taxonomic in silico coverage analyses at the phylum, genus, and species levels were performed using 16 s rrna gene sequences from the ribosomal database project as sequence matching targets. a total of, 16 s rrna gene sequences were downloaded from rdp. of these, sequences were determined to be eligible for sequence match comparison based on sequence availability in the e. coli region of the bactquant assay amplicon. the in silico coverage analyses was performed based on perfect match of full-length primer and probe sequences and perfect match with full-length probe sequence and the last nucleotides of primer sequences at the 3′ end. using the stringent criterion, in silico numerical coverage analysis showed that of the bacterial phyla evaluated were covered by the bactquant assay. the three uncovered phyla being candidate phylum od candidate phylum tm and chlorobi. among most of the covered phyla, more than 90% of the genera in each phylum were covered by the bactquant assay. the covered phyla included many that are common in the human microbiome, such as tenericutes, firmicutes, proteobacteria, bacteroidetes, actinobacteria, and fusobacteria. only three covered phyla had lower than 90% genus-level coverage, which were deferribacteres, spirochaetes, and chlamydiae. on the genus- and species-levels, genera and species had at least one perfect match using the stringent criterion. this improved to genera and species when the relaxed criterion was applied. using the same relaxed criterion, or 84% of all eligible sequences were perfect matches with the bactquant assay. detailed taxonomic information on the covered and uncovered otus for the bactquant assay can be found in additional file 5: supplemental file additional file 6: supplemental file during our in silico validation, a previously published qpcr assay was identified, which was used as a published reference for comparison. the in silico comparison showed that the bactquant assay covers more otus irrespective of the criterion applied. based on the stringent criterion, the published assay has additional uncovered phyla in comparison to bactquant; these were: candidate phylum op aquificae, caldiserica, thermodesulfoacteria, thermotogae, dictyoglomi, deinococcus-thermus, lentisphaerae, chlamydiae, and candidate phylum op. applying the relaxed criterion added two phyla, aquificae and lentisphaerae, to those covered by the published assay. the genus-level coverage of the published assay was also low, with fewer than 50% genus-level coverage in six of its covered phyla. for cyanobacteria, planctomycetes, synergistetes, and verrucomicrobia, only a single genus was covered by the published assay. in all, the bactquant assay covered an additional genera and species than the published assay, or the equivalent of 15% more genera, species, and total unique sequences than the published assay. detailed taxonomic information on the covered and uncovered otus for the published qpcr assay can be found in additional file 7: supplemental files additional file 8: supplemental files laboratory analysis of assay performance using diverse bacterial genomic dna laboratory evaluation of the bactquant assay showed 100% sensitivity against species identified as perfect matches from the in silico coverage analysis. the laboratory evaluation was performed using genomic dna from unique species encompassing eight phyla: actinobacteria, bacteroidetes, deinococcus-thermus, firmicutes, fusobacteria, proteobacteria, chlamydiae, and spirochaetes. overall, evaluation using genomic dna from the in silico perfect match species demonstrated r2-value of > and amplification efficiencies of to 120%. laboratory evaluation against the five in silico uncovered species showed variable assay amplification profiles and efficiencies. of these five species, chlamydia trachomatis, chlamydophila pneumoniae, and cellvibrio gilvus were identified as uncovered irrespective of in silico analysis criterion. however, while c. trachomatis and c. pneumoniae showed strongly inhibited amplification profile, c. gilvus amplified successfully with a r2-value of > and an amplification efficiency of 92%. two other species, borrelia burgdorferi and escherichia vulneris, which were uncovered only when using the stringent criterion, also showed successful amplification with a r2-value of > and 90% and 93% reaction efficiency, respectively. comparison of the assay and bacterial sequences showed that c. trachomatis and c. pneumoniae shared a single mismatch in the center of the probe sequence, whereas c. gilvus had a mismatch on the 3′ end of the probe. the mismatch in b. burgdorferi and e. vulneris was a single base difference in 5′ end of the reverse and the forward primer, respectively. these findings strongly suggest the location of the sequence mismatch is an important determinant of amplification outcome. furthermore, it supports that the bactquant assay’s coverage in laboratory application is likely greater than determined by the in silico analyses. laboratory quantitative assay validation using pure plasmid standards and mixed templates to fully characterize the assay quantitative profile, the bactquant assay was tested using different reaction volumes and against both pure and mixed templates containing bacterial and human rrna gene targets. laboratory evaluation using pure plasmid standards in 10 μl and 5 μl reaction volumes showed excellent amplification profiles, with an assay dynamic range of 102– 16 s rrna gene copies per reaction. for the 10 μl reactions, the inter- and intra-run coefficients of variance ranged from – % and – % for ct values and from – % and – % for copy number, respectively. the inter- and intra-run cov was comparable for the different reaction volumes, except for the higher cov in 5 μl reactions containing more than plasmid copies. this suggests that the 5 μl reaction volumes may be better suited for samples with low amounts of bacterial dna. establishment of the limit of detection for the bactquant assay using pure plasmid standards was not attempted because it was affected by the level of contaminants in reagents, as previously reported. further laboratory evaluations using mixed templates showed that the ratio of bacteria-to-human dna ratio determined the assay dynamic range of the bactquant assay. experiments using seven tenfold dilutions of plasmid standards with  ng and 1 ng human gdna showed that the assay dynamic range was unchanged from pure plasmid standard. however, experiments using 5 ng and 10 ng of human gdna showed narrower assay dynamic ranges of - and - 16 s rrna gene copies per reaction, respectively. based on this result, the lods for 10 μl reactions using templates containing 5 ng and 10 ng of human gdna were estimated to be a bacteria-to-human ribosomal gene copy ratio of 500: and 1000: respectively. this could be further simplified to a bacteria-to-human ribosomal gene copy ratio of 1: from a genomic equivalent perspective, the lod of the bactquant assay was approximately at a bacteria-to-human ratio of 127: discussion we designed and evaluated a new expanded-coverage bacterial quantitative real-time pcr assay targeting the 16 s rrna gene. to accomplish this, we curated a set of high-quality 16 s rrna gene sequences for assay design and evaluated the coverage of our primers and as a union. in addition, we improved the quantitative capacity of our assay using a cloned plasmid standard. our computational and laboratory analyses showed that bactquant had superior in silico taxonomic coverage while retaining favorable in vitro performance. as would be expected, the diverse gene sequences targeted by bactquant have resulted in variable reaction efficiencies. nevertheless, laboratory evaluation showed 100% sensitivity against perfect match species from the in silico analysis. to allow researchers to determine whether bactquant covers key organisms in their target community, we provided additional detailed otu coverage information in the supplemental files. we have applied the logic that an otu was covered if it contained at least one perfect match sequence in the in silico analysis. 16 s rrna gene sequences with ambiguous or degenerate bases at the primer and probe sites were considered non-perfect matches, thus making our coverage estimates more conservative. lastly, although we prohibited the use of a degenerate probe to maximize our assay’s quantitative ability, this approach may permit detection of specific taxa such as chlamydia spp. and chlamydophila spp. for most studies, the desired measurement of bacterial load is the number of cells rather than 16 s rrna gene copy number; however, the 16 s rrna gene copy number varies among bacterial species and even among strains. the range of copy number is estimated at one to with most non-spore forming species having fewer than copies per genome. we use the average 16 s rrna gene copy number per genome from rrndb in our genomic equivalent estimation, but alternative approaches are possible. this, combined with logarithmic growth of bacteria, suggest that using estimated average copy number could be sufficient. the in silico analysis was an important component of our validation of bactquant against diverse bacterial sequence types, even though sequence matching is not a perfect predictor of laboratory performance. many factors are known to affect reaction efficiency, such as oligonucleotide thermodynamics, the type of pcr master mix used, and the template dna extraction method. concentration of background nontarget genomic dna is another factor that can affect the quantitative parameters rrna gene-based assays. the interference of background human dna with bactquant dynamic range reported in this paper was most likely due to cross-reactivity of human dna with the probe, which targets a region conserved even among eukaryotic organisms, including in the human 18 s rrna gene. this may be overcome by using an intercalating reporter dye in place of a fluorescent probe as a qpcr reporter mechanism; however, the loss of tertiary-level of specificity is a potential concern in direct application of an intercalating dye assay to specimens containing high amounts of nontarget dna. exogenous bacterial dna, particularly from biologically synthesized reagents such as taq dna polymerase are a known limitation for analyzing samples with low bacterial load. recently, this issue has received renewed attention due to increased usage of next-generation sequencing and the frequent data contamination from exogenous bacterial dna. several methods have been evaluated for removing bacterial contaminants from taq dna polymerase, including uv irradiation, dnase i treatment, and ultrafiltration. the level of e. coli contamination in taq dna polymerase has been estimated at to genome equivalents of bacterial dna per unit of enzyme. this is consistent with the lowest amount of contamination we have observed in our experiments, which were and copies of 16 s rrna gene in 5 μl and 10 μl reactions, respectively. the ubiquity of bacterial dna also makes the determination of assay specificity challenging. our use of qpcr-quantified plasmid standards addressed a major limitation in the preparation of qpcr quantification standards. the conventional approach of quantifying bacterial genomic dna or plasmid standards necessitates converting mass to copy number and can introduce substantial error. thus far, we have also successfully applied bactquant to a diverse range of clinical specimens, including swab eluents, surgical specimens, and respiratory specimens, but we did not present these findings in this paper. to fully understand the likelihood of false negative results due to interference from human dna or bactquant’s limit of detection will require additional evaluations.  in summary, we have developed and evaluated a new broad-coverage qpcr assay—bactquant—for bacterial detection and quantification that showed concurrently improved assay coverage and favorable quantitative parameters. laboratory tests showed that in vitro performance was even better than predicted in the in silico analysis; however, our approach of evaluating assay coverage by considering the primer and probe sequences as a single unit is appropriate and necessary. in addition, when employing a copy number estimation method, such as qpcr, the quantification of standards is critical for accurate template quantification. thus, our approach of quantifying plasmid standards using the intrinsic property of real-time pcr is another important step for any absolute quantification experiments using qpcr. competing interests the authors have declared that no competing interests exist. authors’ contributions cml contributed to the overall study design, the acquisition, analysis, and interpretation of data, and drafting the manuscript, ma contributed to the bioinformatics portion of the study design and its implementation, sk participated in bioinformatics analysis and assay design, prh and yth both contributed to the acquisition and interpretation of laboratory data, pk conceived of the study and contributed to the overall study design, lbp contributed to the overall study design and helped to draft the manuscript. all authors read and approved the final manuscript. supplementary material additional file figure s figure s containing the in silico coverage analysis using the relaxed criteria. click here for file additional file figure s2a-e. standard curve amplification plots using mixed templates. click here for file additional file figure s3a-e. amplification plots of the non-perfect match targets, including c. trachomatis, c. pneumoniae, c. gilvus, b. burgdorferi, and e. vulneris. click here for file additional file figure s4a-e. coefficient of variance distribution across assay dynamic range for mixed templates. click here for file additional file supplemental file detailed results for bactquant using the stringent criteria. click here for file additional file supplemental file detailed results for bactquant using the relaxed criteria. click here for file additional file supplemental file detailed results for published assay using the stringent criteria. click here for file additional file supplemental file detailed results from published assay using the relaxed criteria. click here for file additional file table s base distribution output used in primer and probe design, with the bolded base signifying the selected base and incorporation of more than one allele at a given nucleotide position was accomplished using degenerate bases. the alignment position information in the base distribution file contains many gaps as a result from the sequence alignment and differs from the e. coli region information from table click here for file acknowledgements we thank lindsey watson, jordan buchhagen, jolene bowers, and tania contente-cuomo at translational genomics research institute for their assistance with different aspects of the laboratory work presented in this manuscript. funding this work was supported by the national institutes of health, the department of defense, and the tgen foundation. the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
based on our analysis of a diverse collection of 16 s rrna gene sequences, we designed a broad-coverage quantitative real-time pcr assay—bactquant—for quantifying 16 s rrna gene copy number and estimating bacterial load. inter- and intra-run coefficient of variance was <3% and <16% for ct and copy number, respectively. bacterial load quantification is a critical component of bacterial community analysis, but a culture-independent method capable of detecting and quantifying diverse bacteria is needed. the bactquant assay offers significantly broader coverage than a previously reported universal bacterial quantification assay bactquant in vitro performance was better than the in silico predictions. of the bacterial species evaluated, amplification efficiencies ranged from to 120%, with r2-value of >, including species with sequence mismatches. the aligned core set of 16 s rrna gene sequences in the greengenes database were analyzed for assay design. cloned plasmid standards were generated and quantified using a qpcr-based approach. coverage analysis showed that 91% of the phyla, 96% of the genera, and >80% of the species analyzed contained at least one perfect sequence match to the bactquant assay. a bacterial taqman® qpcr assay targeting a 466 bp region in v3-v was designed. we further utilized in silico evaluation to complement laboratory-based qpcr characterization to validate bactquant. coverage analysis was performed computationally using > sequences and further evaluated following the minimum information for publication of quantitative real-time pcr experiments guidelines.
 bacterial load quantification is a critical component of bacterial community analysis, but a culture-independent method capable of detecting and quantifying diverse bacteria is needed. based on our analysis of a diverse collection of 16 s rrna gene sequences, we designed a broad-coverage quantitative real-time pcr assay—bactquant—for quantifying 16 s rrna gene copy number and estimating bacterial load. we further utilized in silico evaluation to complement laboratory-based qpcr characterization to validate bactquant. methods the aligned core set of 16 s rrna gene sequences in the greengenes database were analyzed for assay design. cloned plasmid standards were generated and quantified using a qpcr-based approach. coverage analysis was performed computationally using > sequences and further evaluated following the minimum information for publication of quantitative real-time pcr experiments guidelines.  a bacterial taqman® qpcr assay targeting a 466 bp region in v3-v was designed. coverage analysis showed that 91% of the phyla, 96% of the genera, and >80% of the species analyzed contained at least one perfect sequence match to the bactquant assay. of the bacterial species evaluated, amplification efficiencies ranged from to 120%, with r2-value of >, including species with sequence mismatches. inter- and intra-run coefficient of variance was <3% and <16% for ct and copy number, respectively.  the bactquant assay offers significantly broader coverage than a previously reported universal bacterial quantification assay bactquant in vitro performance was better than the in silico predictions. 