The study area is comprised of the Gilgit Baltistan and Khyber Pakhtunkhwa provinces of Pakistan (Fig. 1). Gilgit Baltistan has a border with China through the Khunjerab pass, which occupies an area of over 72,971 km2. One district of Gilgit Baltistan was included in the study; (i) Gilgit district in the southwest of Karakoram range. The weather conditions include average rainfall of 120 to 240 mm annually. Additional irrigation is obtained from the rivers, which are abundant with melting snow water from higher altitudes. The Khyber Pakhtunkhwa has a border with Afghanistan to the west and north and spreads over an area of over 74,521 km2. Three districts of Khyber Pakhtunkhwa were included in the study; (ii) Chitral district to the north of the Indus river, which originates close to the holy mountain of Kailash in western Tibet. The average elevation is 1,500 m and the daily mean temperature ranges from 4.1°C to 15.6°C, creating an arid environment with only patchy coniferous tree cover, and providing habitats that are hostile to many snail species; (iii) Swat district surrounded by Chitral and Dir districts. The area is predominantly rural, and most residents live in villages. The average elevation is 980 m, resulting in a considerably cool and wet climate with lush forests, verdant alpine meadows, and snow-capped mountains. The climate of the Swat district is warm and humid with short and moderate summer, temperature rarely rises above 37°C. The annual rainfall averages around 33 inches with about 17 inches during June-September; (iv) Dir district borders to Afghanistan on the north and the Swat district to the east. The climate is cold, with average rainfall is 700 mm and the temperature varies from 6°C to 38°C.
Study design and sample collection
The study was carried out from July 2018 to September 2019. Random sampling was conducted and a total of 381 animals [Gilgit (n = 126), Chitral (n = 214), Swat (n = 41)] were examined for flukes recovery, animals belonged to 56 sheep flocks and 24 goat herds. The flukes were washed with phosphate-buffered saline (PBS) to remove adherent debris followed by Dicrocoelid morphological identification. A total of 6060 blood samples [Gilgit (n = 3020), Chitral (n = 2140), Swat (n=670) and Dir (n = 230)] were collected from 112 sheep and 48 goat herds. The blood samples were taken from the jugular vein of the animal herds and stored at 4°C for 4-6 hours before sera were separated. The number of blood samples to be collected was determined using the formula: n =Z2P (1-P)/d2 (Daniel and Cross, 1999), where n was the sample size, Z was the desired confidence interval (95%), P was a conservative estimate of the proportion of infected animals in the population (0.5) and d was precision of estimation or range in which the true population proportion is estimated to be (5%).
Liver sample processing for antigen extraction
The liver samples were inspected for Dicrocoelid flukes to determine the infection rate among sheep and goats. Excretory/secretory (ES) and somatic antigens were extracted from Dicrocoelid flukes recovered from 33 positive liver samples as described by González-Lanza et al. (2000) with some modifications. Briefly, flukes were incubated in RPMI 1640 medium (Biosera, Boussens, France) supplemented with 200 mM N-acetyl-L-alanil-L-glutamine (Sigma), 0.3 g/l sodium bicarbonate 7.5% (Sigma) and 40 mg/l gentamycin at 37oC for 48 h. After removal of the flukes, the medium was collected and centrifuged at 10,000 g for 15 min at 4 oC. To obtain a somatic extract, flukes were homogenised in tissue lysis buffer, added according to the weight of tissue in a ratio of 1000 µl buffer/100 mg of tissue. The homogenate was then transferred to pre-chilled Eppendorf tubes and centrifuged at 10,000 rpm at 4°C for 10 minutes. The supernatant was filtered through 0.22 μm pore size filter units and Protease Inhibitor Cocktail (P8340; Sigma) was added. Protein concentration was determined by the Bradford method (Bradford, 1967). Samples were aliquoted and stored at -80 oC until further processing.
Enzyme-linked immunosorbent assay
ELISA has performed on 96 wells microtiter plates as previously determined all incubation time by checkerboard titration and followed the method described by (Anuracpreeda et al., 2016). Briefly, each eluted antigen was mixed with coating buffer NaHCO3/Na2CO3 (Merck) in equal proportion (1:1) and 100 µl was added to each well of the microtiter plate and incubated overnight at 4°C. The plates were washed three times with PBS containing 0.05% Tween 20 (Merck) and blocked with 0.05% BSA for 2 hours at room temperature. 100 µl of the diluted sera from infected and control animals was added to each well and incubated for 2 hr at 37°C and washed three times with PBS containing 0.05% Tween 20. After washing, 100 µl/well goat anti-bovine IgG secondary antibodies (1: 10,000), conjugated with alkaline phosphatase (InvitrogenTM Cat. nos. WP20006, WP20007) were added and incubated for 1 hr at room temperature. After washing the plates, 100 µl of the substrate para-nitrophenyle phosphate (PNPP) (Thermo ScientificTM Cat. No. 37621) was added and incubated at room temperature for 20 min. Finally, the reaction was stopped by the addition of 50 µl of 3N NaOH solution, and the optical density (OD) value was recorded at 405 nm using an automated microplate reader. The sensitivity of the test was measured at 88%, and specificity was 95%, respectively (Supplementary Table S1). The sensitivity of the assay was determined using the formula: Sensitivity = [a / (a+c)]×100; where ‘a’ is the number of animals positive by ELISA and liver analysis (true positive), while ‘c’ is the number of animals positive by liver analysis but negative by ELISA (false negative). Similarly, Specificity = [d / (b+d)] ×100; where ‘d’ is the number of animals negative by ELISA and liver analysis (true negative), while ‘b’ is the number of animals negative by liver analysis but positive by ELISA (false positive). The cut-off was calculated by the mean optical density (OD) of the negative reference serum, plus three times standard deviations (0.14+3*0.08=0.38). The cut-off value was set at 0.38, and sera with OD value higher or equal to 0.38 were considered as positive.
Species distribution models (SDMs)
Nineteen bioclimatic variables were obtained from the WorldClim (https://www.worldclim.org) global climate database (Fick and Hijmans, 2017) with the finest available resolution of approximately 1 km2. These layers were readable in ASCII format using ArcGIS 10.2 (ESRI, Redlands, CA, USA). The spatial patterns of Dicrocoelium infection were measured with MaxEnt based modelling with MaxEnt version 3.4.4 (Phillips et al., 2004; 2006) Maxent is freely downloadable at http://www.cs.princeton.edu/~schapire/maxent/. Field visits were conducted to obtain the geographic coordinates of Dicrocoelium infected animals, and Global Positioning System (GPS) location was used to obtain the precise coordinates of infected animal flocks and herds. If a flock or herd had multiple infected animals, only one point was recorded to avoid the spatial clusters of localities.
The occurrence data of Dicrocoelium based on liver and blood samples were filtered to reduce bias and to improve the performance of the ecological niches modelling. The SDM toolbox in ArGIS 10.2 software (ESRI, Redlands, CA, USA) was used to reduce occurrence locations of each infected animal to a single point within 5 km. By eliminating duplicate occurrence points within the same pixel, Dicrocoelium presence points were reduced to 63 points from 160 presence points; 80% were used for the training and 20% for testing the model. 10045 points were used to determine the MaxEnt distribution (background points and presence points). The model was run with the logistic output format where predicted values range from 0 (impossible) to 1 (optimal).
The performance of predicting the ecological niches of Dicrocoelium infection was evaluated using threshold-independent receiver operating characteristic (ROC) assessment, where the area under the ROC (AUC) was obtained for plotting the model’s sensitivity and speciﬁcity in MaxEnt. The geographical distribution of Dicrocoelium infection was mapped using a geographic information system (GIS). The presence points were marked on a world geodetic system (WGS84) reference coordinate system using high-resolution Google Earth and GIS coordinates. The parasite data were saved in an excel sheet and comma-separated values (CSV) files were used for the analysis. Compilation of geographic data and mapping was done by converting the excel data to the GIS format through Arc-Map (ESRI, Redlands, CA, USA).
To remove the autocorrelation among the 19 bioclimatic variables, Pearson’s correlation was used at (r2 ≥|0.8|) through the SDM Tools function in ArcGIS 10.2 (Universal tool; Explore climate data; Remove highly correlated variable). Five bioclimatic variables [Bio2 = Mean Diurnal Range (Mean of monthly (max temp - min temp), Bio4 = Temperature Seasonality (standard deviation ×100), Bio6 = Min Temperature of Coldest Month, Bio12 = Annual Precipitation and Bio15 = Precipitation Seasonality (Coefficient of Variation)] were used for the analysis. Additional variables with the same resolution as the bioclimatic variables were included in the evaluation; these were normalised difference vegetation index (NDVI) extracted from moderate resolution imaging spectroradiometer (MODIS) images, calculated from the visible and near-infrared light reflected by vegetation (NDVI data are available in Raster data images, each of which has several blocks which have specific values for different vegetation; and can be processed in a MaxEnt readable format using specific conversion tools), forest cover, elevation, derived from the digital elevation model (DEM) in ArcGIS 10.2, and distance to buildings or settlements. The environmental variables used in the MaxEnt model are summarised in Supplementary Table S2. The environmental variables associated with dicrocoeliosis were generated using a jacknife test in MaxEnt version 3.4.4 (Phillips et al., 2004; 2006).
The relatedness of Dicrocoelium prevalence, based on blood and liver samples examination, with associated environmental and climatic risk factors, was calculated by using the chi-square test of independence in a statistical package for the social sciences (SPSS) version 20 (Armonk, NY: IBM Corp).