Linearity and sensitivity for each Microbial DNA qPCR Array was determined using synthetic templates over a 6 log serial dilution ranging from 1 copy to 1 million copies. The following are representative results for all the qPCR assays. [A] shows the real-time amplification curves of the KPC antibiotic resistance gene qPCR assay. In [B], a standard curve was prepared that shows that the primer efficiency equals 103% (calculated from slope = –3.3236) and the correlation coefficient is 0.9983, indicating optimum performance for the KPC qPCR assay. All Microbial DNA qPCR Assays have primer efficiencies between 80–120% and correlation coefficients (R)>0.995.
This chart demonstrates the difference between the limit of detection (LOD) and the lower limit of quantification (LLOQ). The LOD is defined as the lowest concentration at which 95% of the positive samples are detected, whereas the LLOQ is the lowest concentration that falls within the linear range of a standard curve. LOD depends upon the precision of the assay, and requires at least 40 replicates for determination of a positive sample. For the Microbial DNA qPCR Assays, LLOQ is sufficient to determine assay sensitivity.
This chart shows the distribution of LLOQ for all Microbial DNA qPCR Assays. 93% of all Microbial DNA qPCR Assays have a LLOQ of <100 gene copies.
This chart shows the distribution of LLOQ for Microbial DNA qPCR Assays for antibiotic resistance gene detection. 95% of all antibiotic resistance gene assays have a LLOQ of <100 gene copies.
This chart shows the distribution of LLOQ for microbial identification Microbial DNA qPCR Assays. 92% of all microbial identification assays have a LLOQ of <100 gene copies.
This chart shows the distribution of LLOQ for Microbial DNA qPCR Assays for virulence factor gene detection. 97% of all virulence factor gene assays have a LLOQ of <100 gene copies.
To determine the specificity of Microbial DNA qPCR Assays, each assay was tested against 119 genomic DNA samples from different bacteria and fungi. To facilitate testing, genomic DNA from different microbial species was pooled (10 different genomic DNA samples per pool) and each assay was tested against the different pools. None of the pools contained DNA from the same genus, to facilitate identification of cross-reacting species. Each pool contained the equivalent of 2000 genome copies for each microbial species. In addition, each assay was tested against human, mouse, and rat genomic DNA. A representative example for Streptococcus pyogenes is shown. The assay for Streptococcus pyogenes gave a CT of 26.9 and 26.6 for the Staphylococcus/Streptococcus pool and complete pool [A]. Both pools contained genomic DNA for Streptococcus pyogenes. To determine which genomic DNA was detected by the Streptococcus pyogenes assay, each individual genomic DNA comprising the Staphylococcus/Streptococcus pool was tested separately [B]. Only Streptococcus pyogenes genomic DNA gave an acceptable CT call (26.8) while the others gave a CT>35. Most of the assays were specific as they did not detect unintended targets. For assays that detected other species, the list of detected targets along with in silico predictions are given in the specifications sheet.
To ensure that Microbial DNA qPCR Assays performed comparably in a complex sample, where there may be up to a thousand different microbial species, each assay was tested using stool, tooth plaque, and sputum samples. For each sample, synthetic template targets were spiked in and the CT was compared to synthetic template alone. PCR was performed using several sample types, which included pooled synthetic template targets alone, stool, stool plus pooled synthetic template targets, plaque, plaque plus pooled synthetic template targets, sputum, and sputum plus pooled synthetic template targets. If the CT<35 in stool, plaque, or sputum samples alone, then ΔCT was calculated (i.e., CTstool + pooled synthetic template targets – CTpooled synthetic template targets). This calculation was performed for all the assays. For each assay, the ΔCT<3, indicating that a complex metagenomic background does not affect the performance of each Microbial DNA qPCR Assay.
To verify the specificity of the Antibiotic Resistance Genes Microbial DNA qPCR Array (cat no. BAID-1901Z) results from Klebsiella pneumoniae isolates, pyrosequencing assays were designed to detect for the presence and sequences of SHV-156G, SHV-156D, SHV-238G240E, SHV-238S240K, SHV-238S240E, SHV-238G240K, ermB, mefA, tetA, tetB,CTX-M-1 Group, CTX-M-2 Group, AAC(6′)-lb-cr and aadA1. For each Klebsiella pneumoniae isolate, results from the Antibiotic Resistance Genes Microbial DNA qPCR Array were confirmed by pyrosequencing. Representative pyrograms for [A] SHV-156G, [B] SHV-238/240, [C] KPC and [D] CTX-M-1 group are shown. For SHV variants, the Antibiotic Resistance Gene Microbial DNA qPCR Array was able to reliably distinguish single nucleotide polymorphisms occurring at different sites.
To determine the reproducibility of the Microbial DNA qPCR Array, both intra-individual and inter-individual variability was tested. In this experiment, 500 ng genomic DNA isolated from belt-filter presscake sewage sample was loaded onto the Antibiotic Resistance Genes Microbial DNA qPCR Array (cat. no. BAID-1901Z). To determine intra-individual variability, the same operator ran two different PCR arrays on different days with four technical repeats. To determine inter-individual variability, two different operators ran PCR arrays with four technical repeats. The results show low inter- and intra-individual variation of the qPCR array.
The vaginal microbiota is a key component influencing women’s urogenital health. To determine what changes in the vaginal microbiota occurs during bacterial vaginosis, the Vaginal Flora Microbial DNA qPCR Array (cat. no. BAID-1902Z), which detects up to 90 different microbial species, was used to test cervical swabs from healthy individuals and from patients with bacterial vaginosis. Genomic DNA from vaginal samples originating from three patients that tested negative for bacterial vaginosis, three patients that tested positive for Candida, three patients that tested positive for Garderella vaginalis, and one patient that tested positive for Trichomonas vaginalis by BD Affirm™ VPIII Microbial Identification Test were run on the Vaginal Flora Microbial DNA qPCR Array. Genomic DNA from ThinPrep samples were isolated using QIAGEN’s QIAamp MinElute Media Kit and 500 ng genomic DNA from each sample was analyzed. After the PCR run on a Roche LightCycler 480, raw CT values were exported to the Microbial DNA qPCR data analysis software. Positive (+) / negative (blank) / inconclusive (+/-) results for each microbial species were determined using the identification criteria. The results from the Vaginal Flora Microbial DNA qPCR Array were in concordance with the BD Affirm VPIII Microbial Identification Test.
To compare any differences in the vaginal microbiome between healthy women and women with bacterial vaginosis, each sample that tested positive for Gardnerella vaginalis using the Vaginal Flora Microbial DNA qPCR Array was compared to samples from healthy women (n=3). Fold-change in microbial species abundance was calculated by the ΔΔCT method using human genomic DNA to normalize. The results show that as the relative abundance of Gardnerella vaginalis increases, the abundance of the commensal species Lactobacillus crispatus decreases. Also, an increase in Gardnerella vaginalis was associated with an increase in other bacterial vaginosis-associated microbial species. This suggests that Lactobacillus crispatus protects the vagina from bacterial vaginosis-associated microbial species.
The human gut microbiota is known to act as a reservoir for antibiotic resistance genes, where they can be transferred horizontally to potential pathogenic bacteria. To detect the presence of antibiotic resistance genes from gut microbiota, stool samples from five healthy adults were collected and genomic DNA was isolated using QIAGEN’s QIAamp DNA Stool Mini Kit. 500 ng genomic DNA from each stool sample was analyzed for presence of antibiotic resistance genes using the Antibiotic Resistance Genes Microbial DNA qPCR Array (cat. no. BAID-1901Z). The Antibiotic Resistance Genes Microbial DNA qPCR Array contains assays for 83 antibiotic resistance genes, assays to identify methicillin-resistant Staphylococcus aureus, and control assays. ErmB, mefA, and tetA were found in all or most of the stool samples tested, showing that they may be highly prevalent in the gut. These antibiotic resistance genes have been reported to be isolated from bacterial strains originating from food, suggesting a possible source of origin. This highlights the importance of increased monitoring of antibiotic resistance reservoirs to identify potential sources of antibiotic-resistant bacteria.
Municipal biosolids generated by wastewater treatment plants are significant reservoirs for antibiotic resistance genes, since they originate from fecal microbiota. The end product from the treatment plants can either be disposed of in landfills or sold as fertilizer for agricultural use, where antibiotic-resistant bacteria may be reintroduced into the food supply. To determine the diversity of antibiotic resistance genes in municipal biosolids, genomic DNA from belt-filter presscake sewage samples was isolated and analyzed for the presence of antibiotic resistance genes using the Antibiotic Resistance Genes Microbial qPCR Array (cat. no. BAID-1901Z). Raw CT values were exported into the data analysis software and identification criteria was followed. Figure [A] shows the results from the sewage sample. There were 14 antibiotic resistance genes from different resistance classifications that were present in the metagenomic sample. In addition, there were genes that gave an inconclusive result. To determine the presence/absence of the antibiotic resistance genes from the inconclusive results, the “Determination of Inconclusive Microbial DNA qPCR Array/Assay Results” protocol was followed for SHV, ACT-1 group, MIR and LAT [B]. The verification protocol determined that these genes were present in the sewage sample. This highlights the importance of increased surveillance of antibiotic resistance reservoirs to identify potential sources of antibiotic-resistant bacteria that may affect the food supply.