1 Supporting Information 2 3 Manuscript title: 4 5 Effectiveness of qPCR permutations, internal controls and dilution as means for minimizing the impact of inhibition while measuring Enterococcus in environmental waters 6 7 Running Head: Enterococcus qPCR inhibition 8 9 10 Authors: 11 Yiping Caoa,*, John F. Griffitha, Samuel Dorevitchb, Stephen B. Weisberga 12 13 14 15 16 17 a Southern California Coastal Water Research Project, 3535 Harbor Blvd, Suite 110, Costa Mesa, CA 92626 b University of Illinois at Chicago School of Public Health; Division of Environmental and Occupational Health Sciences; Institute for Environmental Science and Policy; 2121 W. Taylor St, Chicago, IL 60612 18 19 The Supporting Information contains five tables, one figure, and two appendices. 20 1 21 Materials and Methods 22 Direct determination of Enterococcus inhibition by serial dilution. Our findings utilized the 23 definition of “true” Enterococcus target assay inhibition as ΔCt between neighboring 5-fold 24 dilutions one cycle less than expected (i.e., 1.32 = log25 -1). This definition assumed a perfect 25 amplification efficiency of 2, which is higher than that measured (>1.9) for the Enterococcus 26 assays. An amplification efficiency of 1.9 only increases the expected ΔCt from a 5-fold dilution 27 by 0.18 cycles. Moreover, the measured amplification efficiencies were not statistically different 28 between the five qPCR methods or between simplex and duplex within each method, indicating 29 the same efficiency can be used for all assays. Therefore, it was scientifically more conservative 30 and logistically simpler to use the log25 as the expected ΔCt from a 5-fold dilution. We also 31 assumed a natural variability of 0.5 cycles for qPCR replicates, allowing a total of 1 cycle 32 deviation from the log25 expected ΔCt. This is slightly higher than that estimated by standard 33 deviation, which ranged 0.14 to 0.42 cycles for the 22 standard curve runs during this study. 34 However, when inhibition analysis was repeated using a more stringent rule (i.e., 1.82 = log25 - 35 2x0.25 instead of 1.32 cycles), similar results regarding relative susceptibility to inhibition, 36 performance of ICs and dilution were obtained (data not shown). 37 38 Table S1. Thermal conditions for the five qPCR methods. qPCR method Initial holding TaqRegular TaqFast TaqFastfast TaqEnviron ScorpionN 50oC, 120s; 95oC, 600s same as in TaqRegular 95oC, 20s same as in TaqRegular 95oC, 120s Thermal cycling (# of cycles) 95oC, 15s; 60oC, 60s (40) 95oC, 5s; 60oC, 30s (40) 95oC, 5s; 62oC, 43s (45) 39 40 2 41 42 Table S2. Primer and probe sequences. All sequences for the ScorpionN method are proprietary and not listed here. Assay Primer and probe sequences (5'→3') Enterococccus For, GAGAAATTCCAAACGAACTTG; rev, CAGTGCTCTACCTCCATCATT; probe, FAMTGGTTCTCTCCGAAATAGCTTTAGGGCTA-TAMRA Sketa For, GGTTTCCGCAGCTGGG; rev*, CCGAGCCGTCCTGGTC; probe, FAM-AGTCGCAGGCGGCCACCGT-TAMRA UCP Probe, VIC-CCTGCCGTCTCGTGCTCCTCA-TAMRA 43 44 45 46 * This is the Sketa22 reverse primer (as in EPA Method A, USEPA 2010), which eliminates the two 3'-terminal bases of the former Sketa2 reverse primer (Haugland et al. 2005. Water Res 39, 559–568). 47 48 3 49 Results 50 Table S3. Standard curves for all qPCR assays * qPCRMethod Assay/Target TaqRegular Enterococcus Enterococcus Sketa UCP TaqFast Enterococcus Enterococcus Sketa UCP TaqFastfast Enterococcus Enterococcus Sketa UCP TaqEnviron Enterococcus Enterococcus Sketa UCP † ScorpionN Enterococcus Enterococcus Enterococcus Sketa IAC Duplex no UCP UCP no UCP no UCP no no IAC SSC - Standard Curve Equation Y = -3.47 X + 39.11 Y = -3.39 X + 38.8 Y = -3.44 X + 15.32 Y = -3.88 X + 39.05 Y = -3.58 X + 41.82 Y = -3.48 X + 42.38 Y = -3.44 X + 15.45 Y = -3.92 X + 39.47 Y = -3.6 X + 40.48 Y = -3.59 X + 40.36 Y = -3.37 X + 15.15 Y = -4.56 X + 43.33 Y = -3.37 X + 40.85 Y = -3.52 X + 43.22 Y = -3.37 X + 15.3 Y = -3.31 X + 38.99 Y = -3.43 X + 40.97 Y = -3.57 X + 41.74 Y = -3.52 X + 41.28 Y = -3.36 X + 25.46 Y = -3.4 X + 44.61 R2 1.00 1.00 1.00 0.99 1.00 0.99 1.00 0.98 1.00 1.00 1.00 0.98 0.99 0.98 1.00 0.98 1.00 0.99 1.00 1.00 0.99 E 1.94 1.97 1.95 1.81 1.90 1.94 1.95 1.80 1.90 1.90 1.98 1.66 1.98 1.92 1.98 1.99 1.96 1.91 1.92 1.99 1.97 51 52 53 54 55 56 * Standard curves (8 point, triplicate) were run as 5-fold serial dilution from 1.57 x 10^6 cells per filter for Enterococcus, 5-fold serial dilution from 1 ng (Taqman) or 10ng (ScorpionN) per reaction for Sekta, 2-fold serial dilution from 1600 copies per reaction for UCP and IAC. Higher concentrations were used for Sketa in ScorpionN than in Taqman assays because the former had lower analytical sensitivity. 57 58 † Standard curves for SSC were not done because SSC was supplied premixed with primer and probe and serial dilution of the DNA template therefore was not possible. 59 4 60 Table S4. Internal control Ct values for uninhibited reference materials. qPCRMethod TaqRegular TaqFast TaqFastfast TaqEnviron ScorpionN IC Sketa UCP Sketa UCP Sketa UCP Sketa UCP Sketa IAC SSC Average Ct ± standard deviation (n) across all plates 20.7 ± 1.0 (134 ) 32.8 ± 1.2 (135 ) 21.2 ± 0.6 (107 ) 33.6 ± 0.8 (108 ) 20.5 ± 1.1 (131 ) 35.0 ± 1.2 (132 ) 22.1 ± 0.7 (90 ) 33.2 ± 0.6 (90 ) 26.6 ± 1.1 (132 ) 34.2 ± 1.9 (128 ) 31.3 ± 1.4 (134 ) Average standard deviation for triplicates within each plate 0.06 0.39 0.06 0.47 0.06 0.50 0.09 0.27 0.09 0.44 0.27 61 62 63 64 Table S5. Enterococcus concentration (calibrator cell equivalent per 100ml) by qPCR from selected environmental water samples, based on qPCR screening results prior to inhibition analysis. Location Chicago Area Waterways System (Chicago, IL) Site ID BR CP LA NA SK CO EL Inland lakes in the greater Chicago BW area (Busse Woods, Lake Arlington, LAR Skokie Lagoon, Tampier Lake) SL TL North branch dam (Chicago, IL) NBD Fox River (Suburbs of Chicago, IL) FR Montrose Harbor (Chic ago, IL) MH Avalon Beach (Avalon, CA) A B C Newport Bay (Newport Beach, CA) BNB35 n 4 2 6 4 9 2 2 4 2 6 6 8 2 2 12 11 2 2 65 66 5 Min 1100 2100 4550 4000 3050 24000 2950 64 11000 0 26 325 7100 0 0 1554 1728 22885 Max 18000 7900 31000 41000 15000 51000 6800 2500 46500 120000 290 13000 15450 76 4949 30041 3089 36279 Median 2218 5000 9275 22500 6200 37500 4875 1260 28750 32023 80 1455 11275 38 1721 3408 2409 29582 67 68 Figure S1. Quantification of (A) ICA and SSC, (B) UCP, and (C) Sketa in presence of various concentrations of Enterococcus. Error bars represent standard deviation. 69 70 71 72 In order to examine the Enterococcus concentration range where IC quantification was unaffected, IC assays were run in presence of Enterococcus at the same eight Enterococcus concentrations as in the standard curves (i.e., 5-fold serial dilution from 1.57 x 106 to 20 cells per filter). This range varied with IC and sometimes by qPCR methods. 73 74 75 76 77 78 79 80 81 82 Specifically, IAC (panel A) experienced no or delayed amplification at the four highest Enterococcus concentrations in the ScorpionN qPCR method. SSC (panel A) experienced delayed amplification only at the highest Enterococcus concentration in the ScorpionN qPCR method. UCP (panel B) experienced no or delayed amplification at the three highest Enterococcus concentrations in all Taqman® qPCR methods except in TaqEnviron where UCP exhibited normal amplification at all eight Enterococcus concentrations tested. Interestingly, for Sketa (panel C) in all five qPCR methods, smaller Ct values for sketa (simplex assay) were observed when Enterococcus DNA were present in the same tube at the two highest concentrations, indicating potentially cross reaction of Sketa assay with Enterococcus genomic DNA. 83 84 6 85 86 87 88 89 90 7 91 92 Appendix S1. An example of using simulation to assess impact of Enterococcus qPCR inhibition in ambient water monitoring 93 This example assesses the impact of inhibition using hypothetic data. Because it may not be 94 practical to directly assess Enterococcus inhibition routinely through the serial dilution approach 95 (as in the present study), one may survey the beach sites for frequency of inhibition detected by a 96 given internal control. For example, if inhibition detected by Sketa (in ScorpionN) ranged from 97 8 to 30% for certain beach sites, the Enterococcus inhibition could be estimated to vary from 10 98 to 38% based on Bayes' theorum (see below). 99 A simple standard formula for the Bayes' theorem is P(A|B)=P(B|A) x P(A) / P(B). P(A) 100 is the unconditional probability of event A, and P(B) is the unconditional probability of 101 event B. P(A|B) is the conditional probability of event A given B (i.e., the probability of 102 event A occurs if B happened). P(B|A), similarly, is the conditional probability of event B 103 given A. 104 105 In this example, we have 106 P(A) = Probability of inhibition declared by the internal control Sketa (threshold=3.0 107 cycles) regardless if there is inhibition on the Enterococcus qPCR (ScorpionN). 108 P(A)=0.08~0.3is assumed in this example 109 P(B) = Probability of the Enterococcus ScorpionN qPCR being inhibited. P(B) is what 110 we are estimating. 111 P(A|B) = Conditional probability of Sketa correctly declaring inhibition if the 112 Enterococcus qPCR is inhibited. P(A|B)=0.37/(0.37+0.13) (Table 3). 113 P(B|A) = Conditional probability of the Enterococcus qPCR being inhibited if Sketa 114 declares inhibition. P(B|A)=0.37/(0.02+0.37) (Table 3). 115 Therefore, P(B)=P(B|A) x P(A) / P(A/B) = 0.10~0.38. 116 117 Second, consider the worst case scenario (P(B) = 0.38), 40% Enterococcus target assay 118 inhibition was assumed for ScorpionN and adjusted for other qPCR methods based on their 119 relative susceptibility to inhibition (Table 2). Then, the extent of false negative due to 120 Enterococcus qPCR inhibition not being captured by IC and the percentage of unusable data due 8 121 to "detected" inhibition by ICs were estimated based on performance of ICs for each IC-qPCR 122 method combination (Table 3). 123 Then the impact of inhibition on Enterococcus qPCR enumeration can be assessed (Table S3). 124 125 Table S3. Simulation to assess impact of inhibition on rapid recreational water monitoring using Enterococcus qPCR methods in southern California beach sites. % Target inhibition in ambient water* 20% % Target inhibition resolved by 1:5 dilution† 95% TaqFast 26% 95% TaqFastfast 60% 79% TaqEnviron** ScorpionN 4% 40% 100% 78% qPCR method TaqRegular 126 127 128 129 130 131 132 133 134 135 136 137 IC Sketa UCP Sketa UCP Sketa UCP n/a Sketa IAC SSC * % Target assay inhibition captured by IC‡ % False negative 26% 56% 27% 80% 28% 92% n/a 74% 72% 39% 0.7% 0.4% 1.0% 0.3% 9.1% 1.0% 0 2.3% 2.5% 5.4% § % False alarm by IC‡ % Unusabl e data§ 0% 15% 0% 40% 5% 38% n/a 4% 24% 6% 0% 15% 0% 41% 8% 45% 0 10% 28% 8% Enterococcus target assay inhibition in ambient water was estimated by Bayes' Theorem based on the hypothetic % inhibition detected by ScorpionN-Sketa. † Values obtained from Table 2. A 1:5 dilution of unpurified DNA extract was assumed prior to qPCR. ‡ Values obtained from Table 3. A threshold of 3.0 cycles was used for all ICs for detecting inhibition. § A false negative occurs when an inhibited sample is not flagged by the IC, leading to report of an underestimated Enterococcus concentration. Data from a sample is deemed unusable if this sample is declared inhibited by the IC used. ** Further simulation was not applicable as target assay inhibition in TaqEnviron qPCR was fully resolved after 1:5 dilution. 9 138 139 Appendix S2. Standard Operating Procedure for a spiking-followed-by-dilution approach to detect inhibition for qPCR assays 140 141 Background: 142 Because different assays are inhibited differently by different inhibitors, only assessment based 143 on the target assay itself can truly reflect inhibition on the target assay. This standard operating 144 procedure (SOP) is based upon the dilution approach: If the sample is inhibited, then the 145 difference between Ct values (ΔCtdil) of the undiluted and diluted DNA extracts will be smaller 146 than expected for samples not inhibited. A 5-fold dilution is used in this SOP. However, to 147 ensure the diluted samples (when not inhibited) still have enough targets to produce precise Ct 148 values, a spiking-followed-by-dilution approach is used for detection of inhibition. Reference 149 material used as standards for quantification in the corresponding qPCR will be used to spike 150 samples. Although the standard reference material could be different from the actual DNA 151 targets in real samples, it is considered appropriate for detection of inhibition if it is used as the 152 basis for quantification in the corresponding qPCR assay. An EXCEL spreadsheet that 153 automates all calculations in this SOP is available upon request. 154 155 156 157 158 Procedure: 1. Perform standard curve measurements as described in the corresponding qPCR SOP to determine 1) Amplification efficiency (E): E will be calculated from slope estimated using the standard curves using the formula E=10-1/slope. 159 160 2) Spiking concentration: a spiking concentration (S28) equivalent to approximately a 161 Ct of 28 is selected (based on standard curves). 162 163 2. Prepare spiked-undiluted and spiked-diluted template (i.e., DNA extract from a sample) 164 for all samples to be tested (see step 3). Also prepare no template reference: S28 and 165 diluted S28 without any DNA extracts from samples. 166 167 168 3. Perform qPCR in duplicate (this SOP) or triplicate as described in corresponding qPCR SOP. 10 169 For each batch of samples tested, run S28 and 1:5 diluted S28. This will serve as a 170 reference to see if expected Ct values are obtained, for both quality control and 171 determination of inhibition. 172 For each sample, there will be 4 reactions with 2 reactions for each of the following. 173 174 175 a) Spiked-undiluted: template spiked with standard DNA (final conc. of standard DNA = S28) b) Spiked-diluted: 1:5 dilution of the spiked template (i.e., 3(a)) 176 177 178 179 180 181 182 4. Assess inhibition based on Ct3(a) and ΔCtb-a= Ct3(b) -Ct3(a), i.e., the average Ct difference between 3(b) and 3(a) 1) Calculate expected Ct difference between 3(a) and 3(b) when there is no inhibition: ΔCtdil,exp = logE5 2) Define minimum Ct3(a) and acceptable ΔCtdil for uninhibited sample Ct3(a) min = CtS28 - 1, i.e., in absence of inhibition, the spiked-undiluted 183 sample should at a minimum produce a Ct as small as that produced by 184 S28. 185 ΔCtdil =ΔCtdil,exp - 1 (ΔCtdil,exp = logE5). 186 This above assumes 0.5 cycle natural variability for Ct values from 187 replicate qPCR reactions. ΔCtdil outside this natural variability is 188 deemed to have been caused by inhibition. Empirically determined 189 standard deviation (SD) for a given qPCR assay can also be used in place 190 of the 0.5 assumed natural variability, in which case ΔCtdil =ΔCtdil,exp- 2 x 191 SD. Similarly, Ct3(a)min = CtS28 - 2 x SD. 192 193 3) Determine if the DNA extract, when undiluted, is inhibitory for the target qPCR assay being tested. 194 If Ct3(a) < Ct3(a) min, the undiluted extract from the sample is inhibitory. 195 If ΔCtb-a < ΔCtdil, the undiluted extract from the sample is inhibitory. 196 If ΔCtb-a ≥ ΔCtdil, the undiluted extract from the sample is not inhibitory (a 197 ΔCtb-a much higher than logE5 may indicate a data error) 198 11