Predictors of Enteric Pathogens in the Domestic Environment from Human and Animal Sources in Rural Bangladesh

Fecal indicator organisms are measured to indicate the presence of fecal pollution, yet the association between indicators and pathogens varies by context. The goal of this study was to empirically evaluate the relationships between indicator Escherichia coli, microbial source tracking markers, select enteric pathogen genes, and potential sources of enteric pathogens in 600 rural Bangladeshi households. We measured indicators and pathogen genes in stored drinking water, soil, and on mother and child hands. Additionally, survey and observational data on sanitation and domestic hygiene practices were collected. Log10 concentrations of indicator E. coli were positively associated with the prevalence of pathogenic E. coli genes in all sample types. Given the current need to rely on indicators to assess fecal contamination in the field, it is significant that in this study context indicator E. coli concentrations, measured by IDEXX Colilert-18, provided quantitative information on the presence of pathogenic E. coli in different sample types. There were no significant associations between the human fecal marker (HumM2) and human-specific pathogens in any environmental sample type. There was an increase in the prevalence of Giardia lamblia genes, any E. coli virulence gene, and the specific E. coli virulence genes stx1/2 with every log10 increase in the concentration of the animal fecal marker (BacCow) on mothers’ hands. Thus, domestic animals were important contributors to enteric pathogens in these households.


Recovery of Cryptosporidium DNA
Four test filters and four test soil samples were spiked with 10 3 -10 4 Cryptosporidium parvum oocysts (Waterborne Inc. New Orleans, LA) to test filter and soil extraction protocols for recovery of protozoan DNA. The median (IQR) of recovery of C. parvum DNA in filter samples was 49.9 (46.2-58.8) % and 42.4 (27.8-60.7) % in soil samples. Spike concentrations were determined from the reported concentration of oocysts from the vendor, measured using the Neubauer RBC hemocytometer method. Gene copies were measured in the eluent using qPCR.

Quantitative PCR
Reaction Setup qPCR assays, described in Table S3 were run on a StepOnePlus (Applied Biosystems, Foster City, CA). All cycling conditions were run in accordance with the manufacturer's instructions for the corresponding mastermix. Samples were run in triplicate and each plate contained standards ranging from 10 to 10 5 gene copies/2 µL and 3 no template controls. 2µL of template was added to each reaction.

Inhibition Testing
To test for inhibition in all qPCR assays (BacCow, HumM2, G. lamblia, Cryptosporidium spp., pph6, MS2 and norovirus GII), we used the spike and dilute method. 10 Undiluted nucleic acid extract was spiked with 3 × 10 3 -1 × 10 5 gene copies of standard. Inhibition was tested on a subset of samples from a total of 10 filters (collected for the purpose of preliminary testing) and 13 soil samples (9 collected for preliminary testing and 4 actual samples). Samples were subsequently diluted and assessed for inhibition based on a comparison of Ct values between diluted and undiluted samples. Inhibition was present in the undiluted sample if the Ct difference between the diluted sample and the undiluted sample was at least 1 cycle less than the expected difference for a specific dilution, accounting for the standard curve efficiency. The expected difference was calculated using the equation from Cao et al. (2012) 10 : ∆C t = log amp dil (eq. 1) Where ∆C t = expected cycle threshold difference for a specific dilution, amp = standard curve amplification factor, dil = dilution factor The amplification factor was calculated using the standard curve efficiency: Filter samples were uninhibited for all DNA and RNA assays (Table S4). In soil samples, Cryptosporidium spp. and MS2 assays were inhibited in some or all samples (Table S5). HumM2, BacCow, G. lamblia, pph6, and Norovirus GII were uninhibited for all samples. A 1:10 dilution for extraction efficiency of RNA in soil was used for MS2. We decided not to dilute Cryptosporidium due to the resulting increase in the detection limit.

Standard Curves
The standard curves generated from each plate run were used to create a master standard curve. We used a linear model with batch effects to account for the plate to plate variation due to the high number of plates processed for many assays. 11 Standard curves determined by mixed models are shown in Table S6 along with the standard curves that result from pooling the data for comparison.

Limits of Detection and Quantification
Most samples did not amplify within the quantifiable range for norovirus GII, G. lamblia and HumM2. Samples were considered positive if there was amplification in at least 1 of 3 replicates. Limits of detection (LOD) for norovirus GII and Giardia on hands were estimated to be 62.5 and 1020 target copies per 2 hands, respectively. For HumM2, the LOD was 173 target copies per 2 hands, 21-103 target copies per 100 mL of stored water, and 254-478 target copies per gram dry soil. The LOD was determined based on the lowest gene copy that amplified in at least 1 of 3 replicates in each sample type. The range of LODs for stored water and soil correspond to variation in volumes filtered (100mL -500mL) and soil moisture content (0 -88%). Many samples did amplify within the quantifiable range for BacCow. For BacCow, samples that amplified below the limit of quantification (LOQ) were assigned the midpoint between the LOQ and the LOD. Samples that did not amplify were treated as below the LOD and assigned a value of half the LOD. We conducted a sensitivity analysis to investigate the influence of the assigned values on the outcomes (prevalence ratios) in this study. Samples below the LOD for BacCow were (i) omitted and (ii) set to a small number (close to 0). The same approach was used in a sensitivity analysis for the indicator E. coli results. For indicator E. coli, an additional scenario was investigated in which samples above the limit of quantification were omitted. The study conclusions did not change in these analyses.