Cyclopropane

Study design. This study is part of the Children’s Pesticide Exposure Study–Washington (CPES-WA) in which 23 children, 3–11 years of age and living in suburban Seattle, Washington, participated in an organic diet substitution study from 2003 to 2004; details have been reported elsewhere (Lu et al. 2008 (link), 2009 (link)). Briefly, the children were recruited from two local public elementary schools and one Montessori preschool. The children participated in consecutive day urine sampling periods in July/August 2003 (median, 15 days; range, 15–16 days); October/November 2003 (median, 12 days; range, 11–13 days); January/February 2004 (median, 7 days; range, 7–8 days); and April/May 2004 (median, 7 days; range, 5–9 days). In the summer and fall sampling periods, an organic diet substitution phase (from day 4 to day 8) was incorporated into the study design to assess the dietary pesticide exposures. For the present study we included only samples collected during the conventional diet portions of the study so that metabolites measured in the urine samples would be representative of typical exposures in the children. Participating children in each session numbered 23 in the summer, 21 in the fall, 20 in the winter, and 19 in the spring. Written informed consent was provided by older children and by the parents of all participants, and oral assent was provided by younger children. The study was approved by the University of Washington Human Subjects Division.
Urine collection and laboratory analysis. Each child provided two urine samples per day: the last void before bedtime and the following first morning void. Previous studies have demonstrated that first voids are good predictors of overall daily exposure for OPs (Kissel et al. 2005 (link)), and in combination with last voids, a large portion of the daily exposure is represented. Additional spot urine samples collected at different times of the day during the study were excluded from the present analysis to increase the comparability of the samples evaluated.
After collection, urine samples were stored on ice or refrigerated before processing in the laboratory and then stored at –20^oC. Samples were analyzed at the National Center for Environmental Health at the Centers for Disease Control and Prevention (CDC; Atlanta, GA) using high performance liquid chromatography–tandem mass spectrometry (Olsson et al. 2004 (link)). Target OP metabolites were malathion dicarboxylic acid (MDA), TCPy, 2-isopropyl-4-methyl-6-hydroxypyrimidinol (IMPy), and 2-diethylamino-6-methyl-pyrmidin-4-ol (DEAMPy). Target pyrethroid insecticide metabolites were 3-phenoxybenzoic acid (PBA), 4-fluoro-3-phenoxybenzoic acid (4F3PBA), cis-2,2-(dichloro)-2-dimethylvinylcyclopropane carboxylic acid (cis-DCCA), trans-2,2-(dichloro)-2-dimethylvinylcyclopropane carboxylic acid (trans-DCCA), and cis-2,2-(dibromo)-2-dimethylvinyl-cyclopropane carboxylic acid (DBCA).
Data analysis. Metabolite concentrations were adjusted for specific gravity to control for dilution using a reference specific gravity of 1.019 g/cm³, the 2007–2008 National Health and Nutrition Examination Survey (NHANES) mean for children 6–11 years of age (CDC 2009 ; Levine and Fahy 1945 ). Because carryover from a previous day could be expected due to the biological half-life (hours to a couple of days) of these compounds, we confirmed that the conventional diet days following the end of the organic diet portions of the original study were not significantly different from other conventional diet days before including them in the analyses (t-test p-value > 0.05).
Intraclass correlation coefficients (ICC), defined as the ratio of between-subject variance to total variance, were calculated as a measure of the reproducibility of measurements over time within individuals. ICCs can range from 0 to 1; ≥ 0.75 indicates excellent reproducibility and ≤ 0.4 indicates poor reproducibility (Rosner 2006 ). Between- and within-subject variances were calculated with a linear mixed effects model using maximum likelihood estimation (MLE) modified to account for values below the limit of detection (LOD) and repeated measurements as implemented in SAS 9.3 (SAS Institute Inc., Cary, NC) using PROC NLMIXED, assuming a compound symmetry covariance structure (Jin et al. 2011 (link)). Age (3–6, 7–11 years), sex, and season were included as covariates in the model:
Ln(Y) = β₀ + β₁(age) + β₂(sex) + β₃(season) + b₁ + ε, [1]
where Y is the metabolite concentration adjusted for specific gravity, b₁ is the between-subject random effect, and ε is the within-subject error.
For metabolites with high percentages of nondetects, the model’s stipulation of a normal distribution of the data was difficult to evaluate. Therefore, we restricted the ICC analyses to the four metabolites (MDA, TCPy, PBA, and trans-DCCA) that were > LOD in > 50% of samples. Data were natural log-transformed before analysis. We performed a sensitivity analysis on the calculation of the ICCs by substituting LOD/2 for values < LOD and using an unmodified NLMIXED procedure. A further sensitivity analysis was used to test the model’s sensitivity to the assumption of equal covariance. The ICC calculations were repeated using a subset of the data with an equal covariance pattern, where only samples with at least 2 intervening days were included in the subset per season.
To address how much exposure misclassification may develop when participants are categorized into exposure groups and how many samples may be necessary to improve the categorization, we performed surrogate category analyses for the first and last voids separately (Hauser et al. 2004 (link); Willet 1998 ) with an additional scoring step (Figure 1). We calculated the geometric mean value of a metabolite across all samples collected from each participant, resulting in 23 participant mean values. Next, we assigned each participant to an exposure quartile (a “surrogate category”) based on the metabolite concentration of a single sample selected at random from each participant’s pool of samples. Then we populated each surrogate category with the children’s geometric means and calculated the group grand means. Then we evaluated the performance of the category assignment. It would not be possible to directly determine whether individuals were correctly assigned according to their “true” and unobserved distribution in the population; however, if surrogate categories were correctly assigned, the mean value of each category should increase monotonically from the lowest to the highest exposure category. If this were the case, we assigned the run a score of 1, and 0 otherwise. Then we repeated the sampling and classification steps 1,000 times and used the mean value of the 1,000 scores (expressed as a percentage) to indicate an average “success rate.” We performed this process three additional times based on the mean value of two, three, and four randomly selected samples for each participant. For this analysis, we substituted instrument-read values (when available), or the LOD/2, as the concentration for all samples with measurements < LOD.

Parasite fatty acids were characterised and quantified by derivatisation to their fatty acid methyl esters (FAME) followed by gas chromatography-mass spectrometry analysis. Briefly, mid-log phase Leishmania were collected by centrifugation, washed in PBS and freeze-dried in glass tubes. Triplicate aliquots (equivalent to 2×10⁸ cells) were transferred to 2 ml glass vessels, spiked with an internal standard fatty acid C17:0 (20 µl of 1 mM) and dried under nitrogen. Base hydrolysis to release fatty acids was performed using 500 µl of concentrated ammonia and 50% propan-1-ol (1∶1), followed by incubation for 5 hr at 50°C. After cooling, samples were evaporated to dryness with nitrogen and dried twice more from 200 µl of methanol: water (1∶1) to remove all traces of ammonia. The protonated fatty acids were extracted by partitioning between 500 µl of 20 mM HCl and 500 µl of ether. The aqueous phase was re-extracted with fresh ether (500 µl) and the combined ether phases were dried under nitrogen in a glass tube. The fatty acids were converted to FAME, by adding diazomethane (3×20 µl aliquots) to the dried residue, while on ice. After 30 min, samples were allowed to warm to room temp and left to evaporate to dryness in a fume hood. The FAME products were dissolved in 10–20 µl dichloromethane and 1–2 µl analysed by GC-MS on Agilent Technologies (GC-6890N, MS detector-5973) with a ZB-5 column (30 M × 25 mm × 25 mm, Phenomenex), with a temp program of 70°C for 10 min followed by a gradient to 220°C at 5°C/min and held at 220°C for a further 15 min. Mass spectra were acquired from 50–500 amu. The identity of FAMEs was carried out by comparison of the retention time and fragmentation pattern with a bacterial FAME standard that contained both C17Δ and C19Δ (Supelco).

Three genes that had not previously been ascribed virulence roles were chosen to test the tranposon-library based predictions. The genes were chosen to fulfil two roles: to confirm the tranposon predictions and to positively identify new virulence factors. We picked pyruvate carboxylase with a well-established function; a cyclopropane-fatty-acyl-phospholipid synthase, with a poorly annotated function and a hypothetical gene with no annotated function. These were ranked 131st, 23rd and 89th most attenuated in vivo in BCG Danish (by fold change) out of 274 attenuating genes. To produce the knockout strains we constructed zeomycin and kanamycin resistant derivatives of pYUB854 (Hyg) (Bardarov et al, 2002), pANE001 (Zeo) and pANE002 (Kan). An inverse PCR using primers pYUB_inv_F and pYUB_inv_R was used to amplify the plasmid backbone of pYUB854 less the hygromycin cassette, and to add Nde1 and Mfe1 restriction ends. Zeomycin and kanamycin cassettes were amplified from plasmids pNCMTB and pMV306 [78 (link)] respectively, using primers, Zeo_casset_F/R and Kan_casset_F/R containing Nde1 and Mfe1 restriction sites at their 3′ ends. The antibiotic cassettes were then cloned into the pYUB854 backbone to give pANE001 and pANE002, and the constructs confirmed by Sanger sequencing. Genomic sequence upstream (LF) and downstream (RF) of the genes to be knocked out were PCR amplified using primers BCG2988_RF_R, BCG2988RF_L, etc. and cloned either side of the antibiotic cassettes of the cosmids pYUB854, pANE001, pANE002 and the apramycin resistant cosmid p0004S (a gift from W. R. Jacobs Jr). Transducing phage were constructed and transduced into BCG using the pHAE159 mycobacteriophage-based method of specialized transduction (Bardarov et al, 2002). The knockouts were confirmed by PCR using primers outside of the upstream and downstream flanking regions both alone and in combination with antibiotic cassette specific primers, such that PCR products would only be obtained if the antibiotic cassettes were in the predicted configuration. Primer sequences are listed in Additional file 1: Table S1.

To a solution of 1-((4-fluorophenyl)carbamoyl)cyclopropane-1-carboxylic acid (15, 2.02 g, 9.0 mmol) and 4-aminophenol (1.18 g, 10.8 mmol) in DMF (5 mL), EDC·HCl (2.07 g, 10.80 mmol) was added. The reaction solution was stirred at room temperature for 6 h and monitored by TLC. Ice water (125 mL) was added, and the precipitate was filtered off, washed, and dried in a vacuum to yield 16 as a white solid (2.28 g, 80.6%), which can be used for the next step without any purification.

To a solution N-(4-((2-chloropyrimidin-4-yl)oxy)phenyl)-N-(4-fluorophenyl)cyclopropane-1,1-dicarboxamide (16, 2.01 g, 6.37 mmol) and 2,4-dichloropyrimidine (1.04 g, 7.01 mmol) in DMF (15 mL), K₂CO₃ (0.97 g, 7.01 mmol) was added under N₂ atmosphere. The reaction solution was stirred at 80 °C for 6 h and monitored by TLC. The reaction mixture was poured into ice water (100 mL), and the precipitate was filtered off, washed, and dried in a vacuum to obtain the crude product, which was purified by silica gel chromatography using a mixture of DCM/MeOH (100:1~40:1) to afford the intermediate 17 as a white solid (2.34 g, 75.7%).