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Low-cost molecular methods to characterise gastrointestinal nematode co-infections of goats in Africa

Parasites & Vectors volume 16, Article number: 216 (2023) Cite this article

Abstract

Background

Veterinary diagnostics aid intervention strategies, track zoonoses, and direct selective breeding programs in livestock. In ruminants, gastrointestinal nematode (GIN) parasites are a major cause of production losses, but morphologically similar species limit our understanding of how specific GIN co-infections impact health in resource-limited settings. To estimate the presence and relative abundance of GINs and other helminths at the species level, we sought to develop a low-cost and low-resource molecular toolkit applied to goats from rural Malawi smallholdings.

Methods

Goats were subjected to health scoring and faecal sampling on smallholdings in Lilongwe district, Malawi. Infection intensities were estimated by faecal nematode egg counts with a faecal subsample desiccated for DNA analysis. Two DNA extraction methods were tested (low-resource magbead kit vs high-resource spin-column kit), with resulting DNA screened by endpoint polymerase chain reaction (PCR), semi-quantitative PCR, quantitative PCR (qPCR), high-resolution melt curve analysis (HRMC), and ‘nemabiome’ internal transcribed spacer 2 (ITS-2) amplicon sequencing.

Results

Both DNA isolation methods yielded comparable results despite poorer DNA purity and faecal contaminant carryover from the low-resource magbead method. GINs were detected in 100% of samples regardless of infection intensity. Co-infections with GINs and coccidia (Eimeria spp.) were present in most goats, with GIN populations dominated by Haemonchus contortus, Trichostrongylus colubriformis, Trichostrongylus axei, and Oesophagostomum columbianum. Both multiplex PCR and qPCR were highly predictive of GIN species proportions obtained using nemabiome amplicon sequencing; however, HRMC was less reliable than PCR in predicting the presence of particular species.

Conclusions

These data represent the first ‘nemabiome’ sequencing of GINs from naturally infected smallholder goats in Africa and show the variable nature of GIN co-infections between individual animals. A similar level of granularity was detected by semi-quantitative PCR methods, which provided an accurate summary of species composition.

Assessing GIN co-infections is therefore possible using cost-efficient low-resource DNA extraction and PCR approaches that can increase the capacity of molecular resources in areas where sequencing platforms are not available; and also open the door to affordable molecular GIN diagnostics. Given the diverse nature of infections in livestock and wildlife, these approaches have potential for disease surveillance in other areas.

Graphical Abstract

Background

Within veterinary parasitology, molecular diagnostics have largely been limited to research environments, but falling costs of DNA sequencing technologies give promise for molecular diagnostics to complement or replace traditional morphological approaches as our understanding of parasite genomics improves [1,2,3]. Molecular techniques are critical for the advancement of livestock sciences and can facilitate improved monitoring of parasitic diseases and their drug resistance traits that threaten livestock systems and food security worldwide [4,5,6]. While a number of molecular approaches have been developed in recent years to monitor parasites of veterinary significance [2, 3, 7, 8], there has been limited optimisation of approaches amenable to low-resource areas owing largely to the lack of capacity. However, the advent of COVID-19 has resulted in a push to overcome logistical barriers limiting molecular techniques in lower-resource areas and has increased the capacity for molecular diagnostics worldwide in health settings [9,10,11]. As such, the development of approaches to monitor veterinary parasites at low cost and with reduced resources has potential for application worldwide. Currently, the utility of molecular techniques to detect gastrointestinal nematode (GIN) parasites is also limited in commercial livestock settings, but may become more essential as tropical species, notably the GIN Haemonchus contortus, spread under climate change [12,13,14].

In low-income rural areas, livestock holdings are often inextricably linked to livelihoods. For instance, in rural Malawi, goat smallholdings buffer against food insecurity and provide supplemental income [15,16,17]. However, goat smallholdings also suffer from animal losses and reduced productivity driven by co-infections with a mix of gastrointestinal parasites including numerous GIN species [17,18,19,20,21]. GINs play a major role in production losses of small ruminant livestock systems worldwide [22, 23], causing gastroenteritis and stress on the goat immune system, resulting in reduced weight gain, weight loss, anaemia, or death [24,25,26]. While the impact of individual species is known, the prevalence and relative abundance of these species is poorly described in smallholder systems. Additionally, the population dynamics of GIN co-infections and their impact on individual goats have yet to be determined. This is partly due to the fact that in low-resource settings like those of rural Malawi, pragmatic and accessible tools such as the Five Point Check© (FPC) are used to measure signs of GIN infections [27,28,29], but these do not measure infection intensity, the presence of specific species, or drug resistance traits. Beyond the FPC, faecal egg counts (FECs) can be performed on fresh faecal samples [30, 31]; however, more dominant and pathogenic species of strongyles, including H. contortus, Oesophagostomum columbianum, Trichostrongylus colubriformis, and Trichostrongylus axei, are not readily distinguishable from each other or from less pathogenic species by egg morphology alone. Since the presence of different GIN species can alter infection outcomes, as well as results of drug efficacy testing, it is useful to identify both the different species present and their relative abundance in co-infections. This is possible by morphological identification of third-stage larvae (L3). However, this approach is time-consuming and labour-intensive, requires specific expertise, and critically can only be performed on fresh faecal material which cannot be easily biobanked, especially in tropical conditions.

Molecular approaches offer an alternative to determine GIN species from biobanked material with preserved DNA. Faecal DNA extractions have been shown to predict infection burden [32,33,34] and can be more sensitive than FECs for detection and quantitation of GIN infections [35]. GINs can be identified to species by a number of methods including standard polymerase chain reaction (PCR) [32, 33, 36, 37], loop-mediated isothermal amplification (LAMP) [38, 39], and high-resolution melt curve (HRMC) analyses [32, 40,41,42] among others. However, quantification of GIN populations has so far relied on advanced and costly methods such as real-time PCR [34, 35, 43, 44], digital PCR [45, 46], or internal transcribed spacer 2 (ITS-2) deep amplicon sequencing [47,48,49,50,51]. While quantitative approaches are robust, there is potential to utilise standard PCR techniques to estimate relative GIN species abundance at a significantly reduced cost. Studies investigating the quantification of GINs by standard PCR techniques typically involve artificial infections and published studies of PCR or HRMC applications in rural goats in real-world settings are lacking.

To reduce the cost, effort, and expertise required to profile the relative abundance of different GIN species present in infected individual goats, we sought to probe low-cost molecular methods using a simple heat desiccation biobanking method on goat faecal pellets and validate findings against real-time PCR [34, 35, 43, 44] and nemabiome deep-amplicon sequencing techniques [47,48,49,50,51].

Methods

Faecal sample collection and demographics

With permission from smallholder farmers in the Dedza region of central Malawi (Mkwinda and Chinkowe Group Village Head areas) a total of 47 goats were examined across 15 farms (range = 1–6 goats per farmer, median = 3) on February 4–5, 2020. To ensure goats in the study were given proper care, all goats included in the study were assessed by the Five Point Check© [52] and were provided anthelmintic intervention if in poor condition. Goat age was estimated by dental examination and weight was estimated by calibrated goat girth weigh tape (Coburn, cat. 44558). Pregnancy and lactation status data were collected from farmers. After examination, faecal pellets were collected by stimulating the anal sphincter using gloved hands with lubricant. Faecal samples from individual goats were immediately transferred to plastic bags (1 bag per goat) and placed in a cooler box with ice until processing.

Five Point Check© (FPC) scores

Baseline goat health was measured by the FPC as described previously [27]. The score includes examination of (1) nasal discharge, (2) anaemia by conjunctival examination using a Faffa Malan color chart (FAMACHA), (3) presence of pitting oedema or ‘bottle jaw’, (4) body condition score (BCS), and (5) presence of absence of scour or dag, i.e., perineal faecal staining (Fig. 1A). Scores are shown in Additional file 1: Table S1.

Fig. 1
figure 1

Study design. Health status and GIN infections were assessed by A the Five Point Check© score for signs of disease including (1) presence of nasal discharge, (2) anaemia measured by a FAMACHA card, (3) presence of pitting oedema or bottle jaw, (4) body condition scoring, and (5) presence of dag or scour. B Direct faecal samples from individual goats were used for faecal egg counts to detect eggs of Strongyloides, strongyle, or Trichuris nematodes, or coccidial oocysts. C The same faecal samples from part B were desiccated and subjected to DNA extraction using a low-resource proteinase K and magbead system or a high-resource bead homogenisation and filter centrifugation system. D Extracted DNA was assessed by species-specific molecular methods on all or a subset of samples to detect the presence and relative abundance of GIN species. Worm symbol species-specific nematode identities, ITS-2 pan-nematode region of the internal transcribed spacer 2

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Faecal egg counts (FEC)

Counts of egg density using the McMaster technique and morphological identification of egg types were performed per goat with 2 g of fresh faeces (< 24 h after collection) ground through a coarse sieve with a spoon into 28 ml of 1.2 g specific gravity sugar flotation solution (1290 g sucrose/1 l water). Two millilitres of the middle of the flotation solution was checked by McMaster slide to assess eggs per gram (EPG) for strongyle, Strongyloides, and Trichuris nematode eggs by morphology, in addition to qualitative coccidia counts. Coccidial oocysts per gram (OPG) were defined as follows: 0 = 0, 1 = 1–300, 2 = 300–1000, 3   ≥ 1000. Raw data are presented in Additional file 1: Table S1.

Faecal DNA preservation

At the same time as FECs, ~ 0.3 g of fresh faecal material per goat (typically one faecal pellet) was picked by single-use toothpicks and wrapped in pre-cut foil to avoid cross-contamination. A single pellet was chosen because it is convenient to sample and limits the amount needed from voided samples given the limited total volume available and other demands on it such as for FEC, and because of the likelihood that at high egg densities, the main species present are well represented in this volume. Samples were labelled and preserved by oven drying at 68 °C for 24–36 h and then stored in sealed desiccant jars with silica beads until extraction.

DNA extraction

Two methods were tested, with modifications (illustrated in Fig. 1): a ‘low-resource’ kit (MagaZorb® DNA Mini-Prep Kit, cat. MB1004, Promega, WI, USA) with an accompanying magnetic separation rack (cat. CD4002, Promega, WI, USA), and a ‘high-resource’ kit (Quick-DNA Fecal/Soil Microbe Miniprep Kit, cat. D6010, Zymo, CA, USA). Both were performed according to the manufacturers’ instructions, with minor variations. For the MagaZorb® DNA Mini-Prep Kit, 0.027–0.153 g of dried faeces from 13 samples was subjected to extraction with extended lysis (30 min instead of 10 min) along with an additional wash buffer step and a single centrifugation step (10,000g for 1 min) after elution of DNA to reduce carryover debris. The Quick-DNA Fecal/Soil Microbe Miniprep Kit was performed according to the manufacturer's instructions with 0.038–0.13 g of faecal tissue across 34 samples. Samples were ground by mortar and pestle and subsequently homogenised for 2 × 3 min cycles at 50 Hz in a TissueLyser LT (cat. 69980, Qiagen, Germany). For most samples, homogenisation efficiency was measured by 600 nm absorbance (A:600). A:600 was calculated after the addition of genomic lysis buffer (Zymo kit, with mixed genomic lysis buffer and bashing bead buffer blank) or binding buffer (for the Promega kit with mixed lysis buffer and binding buffer blank). DNA quality and yields were calculated by a NanoDrop 1000 Spectrophotometer (Thermo Scientific) with three readings taken per sample. All samples were standardised to 4.8 ng/μl (lowest sample average) in nuclease-free H2O in a 48-well plate array (including a no-template control well). DNA input weight, yield, purity, and A:600 readings are shown in Additional file 1: Table S1.

Endpoint PCR

Species-specific PCR targeting H. contortus and Haemonchus placei [36, 53], T. axei and T. colubriformis [36], Fasciola gigantica and Fasciola hepatica [54], O. columbianum [55], Bunostomum trigonocephalum [36], Chabertia ovina [36], Teladorsagia circumcincta [36, 56], Nematodirus spathiger (this study), and the genus Eimeria [57] using primers from cited work (Additional file 2: Table S2). The T. axei primer can also be used for Trichostrongylus vitrinus PCR, but no T. vitrinus was identified in this study. GoTaq® G2 Hot Start Taq Polymerase (cat. M7405, Promega, WI, USA) was used for all PCR reactions according to the manufacturer's instructions with 0.5 μl of DNA template per 12.5 μl PCR reaction. Cycling conditions included an initial denaturation (95 °C/2 min), 35 cycles (detailed per primer set in Additional file 2: Table S2), and final extension (72 °C/5 min) on a MiniAmp™ Plus Thermal Cycler (Applied Biosystems™, cat. A37835).

Semi-quantitative PCR

Detection of the strongyle species H. contortus, T. colubriformis, and T. axei was performed with pan-nematode ITS-2 primers used as an internal reference (Additional file 2: Table S2). For singleplex PCR, O. columbianum was also detected with a specific primer set (Additional file 2: Table S2). PCR was performed as described above but with 4.8 ng of DNA template in 50 μl reaction volumes. For multiplex PCR, 2 μl Tcol R1, 2 μl Tvit F1, 2 μl Hcon F3, 4 μl ITS F, and 2 μl ITS R primers were used per reaction at recommended concentrations (Additional file 2: Table S2). Band intensities were assessed at 25, 29, and 35 cycles, with 29 cycles selected for singleplex PCR and 35 cycles selected for multiplex PCR.

Quantitative PCR (qPCR) and HRMC analyses

Twenty nanograms of template DNA was added per 15 μl reaction (SYBR™ Select, Thermo Scientific, cat. 4472937) with 0.6 μl of H. contortus, T. colubriformis, or ITS-2 primers (10 μM stock) at the recommended concentration (Additional file 2: Table S2). Cycling was performed in strip tubes (Cleaver Scientific, cat. RGCS-250) on a Rotor-Gene Q (Qiagen) instrument with the following conditions: UDG activation (50 °C/2 min), initial denaturation (95 °C/2 min), amplification (40 cycles of 95 °C/15 s, 54 °C/15 s, 72 °C/30 s), and melt curve (0.5 °C from 70–95 °C). All reactions were performed in triplicate with average Ct values and melt curve peaks used for analysis. For qPCR analysis, the relative abundance of H. contortus versus T. colubriformis was calculated as a percentage difference from the average fold-change compared to the ITS-2 internal control (ΔCt method). HRMC analysis was performed on melt curve average peaks from triplicate ITS-2 products following qPCR.

Gel electrophoresis relative abundance analysis

For all standard PCR analyses, 5 μl of PCR product or 5 μl ladder (PCRBIO Ladder IV, cat. PB40.14-05, PCR Biosystems) was loaded into 1.4% agarose gels using Tris–boric acid-EDTA buffer pre-cast with GelRed® Nucleic Acid Gel Stain (Biotium, cat. 41003) run at 90–110 V for 30–60 min or until bands were visibly separated. Custom-designed laser-cut multichannel gel combs were produced to facilitate rapid and accurate loading of samples (Additional file 7: File S1). Tag image file format (tiff) images were taken under a gel imager with automated exposure and analysed in FIJI [58]. Images were inverted, saturation thresholds were automatically normalised with the brightness and contrast tool, and the background was removed with a 50-pixel rolling-ball radius. PCR product band intensities were then measured horizontally (endpoint PCR scans of entire gel row) or vertically per band (singleplex and multiplex) as demonstrated in Fig. 3A–C using the GelAnalyzer tool. Relative abundance was calculated as the percent of total peak area among specific species identified with ITS-2 bands used as an internal reference only.

Nemabiome ITS-2 deep amplicon sequencing

To assess for GINs across goat samples, 4.8 ng from each DNA extraction was pooled and mixed by vortex (n = 47). In addition, eight individuals were selected as case studies for cross-comparison with PCR findings. Amplicon sequencing was performed according to published protocols [47] available at https://nemabiome.ca with modifications. Twenty nanograms of template DNA was added per 30 μl PCR reaction (Q5® High-Fidelity DNA Polymerase, NEB, cat. M0491L) with a mix of ITS-2 primers containing 0–3 ‘N’ bases and an Illumina adapter (10 μM stock) used at recommended concentrations. Cycling included 35 cycles as described with primer details in Additional file 2: Table S2 with a 98 °C/2 min initial denaturation and 72 °C/2 min final extension. PCR products were assessed by 1.2% gel electrophoresis as described above and purified (Wizard® SV Gel and PCR Clean-Up System, Promega, cat. A9281). Library prep and sequencing were performed via NGSelect Amplicon 2nd PCR service (Eurofins Genomics) on the MiSeq Illumina platform with the 2 × 250 v2 Reagent Kit (Illumina) with resulting metrics from trimmed reads (by Cutadapt [59]) shown in (Additional file 3: Table S3). An average read depth of 242,720 high-quality reads (range 201,800–299,860) was achieved.

Bioinformatics analysis

Run quality was assessed by DADA2 [60] and phyloseq [61]. Phyloseq was used for comparison purposes only. Taxonomic sequence assessments were made in Mothur version 1.48.0 (https://mothur.org/) [62] as described previously [47] using pipeline information at nemabiome.ca. Trimmed reads were merged into contigs 200–450 base pairs in length and reads containing ambiguities removed. Contigs were aligned to a curated nematode ITS-2 ribosomal DNA (rDNA) database [63] with a 90% sequence similarity cut-off. Non-aligned sequences were classified by the k-nearest neighbor algorithm where k = 3, with remainders classified to the next taxonomic level (i.e., genus level if species matching fails). PCR bias correction factors were applied to raw reads including T. axei (0.9647), T. colubriformis (1.0239), and H. contortus (0.6970) based on recommended protocols at nemabiome.ca from prior optimisations [49].

Data presentation and statistical analyses

Data were tabulated in Microsoft Excel, with all statistical tests and graphical outputs generated with R (version 4.0.4 ‘Lost Library Book’) or GraphPad Prism (version 9.0.0 for Windows, GraphPad Software, San Diego, CA, USA). Illustrations and figure layouts were generated in Adobe® Illustrator 2022 (Adobe Inc.). All cost data are shown in British pounds (£) and are derived from prices paid for reagents at the time of the study.

Results

Baseline goat health assessments

The relationship between health, GIN burden, and GIN species, was assessed by measuring signs of disease using the FPC, FECs, and by faecal DNA mixed molecular methods (Fig. 1). In total, 47 goats were assessed, ranging in age, weight, sex, and pregnancy status across two village areas (Fig. 2A). FPC scores taken from individual goats included occasional instances of bottle jaw, nasal discharge and dag; and variable health status based on BCS (median = 2, mean = 1.89) and FAMACHA score (median = 2, mean = 2.49), indicative of GIN infections causing disease in many individuals (Fig. 2B). Overall, 10 goats were classified as unhealthy at the time of inspection, 16 in borderline health, and 21 healthy.

Fig. 2
figure 2

Individual goat health variation and co-infection burden. Forty-seven local breed goats assessed to determine A baseline goat demographics, B health as measured by the Five Point Check© with percentage of at-risk individuals, C faecal egg counts of eggs per gram (EPG) or oocysts per gram (OPG), with OPG defined as 0 = 0, 1 = 1–300, 2 = 300–1000, 3 =  > 1000, and D presence of nematode and other helminths in faecal DNA by endpoint PCR with species-specific primers. Five Point Check© scores were recorded as binary values (0 = healthy, 1 = needs intervention) or standardised scales FAMACHA (1/2 = healthy, 3 = borderline, 4 = needs intervention), BCS (2.5–2 = healthy, 1.5 = borderline, 1 = needs intervention). Goat IDs are stacked into contiguous columns for parts A–F (black arrowheads are shown for representation). Percentages from parts B–D are detection rates across specimens. For part D, presence was determined by band intensity as viewed by eye with faint bands deemed inconclusive and off-target hits determined by the presence of bands present outside of the expected size range for the target. na = not available

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FECs identified a broad range of strongyle eggs (range 100–19,600, mean = 3457, SD = 4329 EPG) and high coccidial oocyst counts on average (median = 300–1000 OPG). Overall there were very low levels of Strongyloides (median = 0 EPG) and no Trichuris found (Fig. 2C).

Testing low-resource DNA preservation and DNA extraction methods

To enable molecular determination of GIN populations in lower resource settings, we tested a low-cost desiccation approach to preserving faecal DNA by drying ~ 0.3 g of faeces > 16 h at 68 °C. Desiccation preserves DNA in faeces [64] eliminating the need for chemicals or cold-chain infrastructure, but the impact of desiccation on GIN egg DNA is unknown. Bulk faecal DNA was purified by a ‘low-resource’ magbead or a ‘high-resource’ column DNA extraction kit designated due to differences in the cost of equipment and reagents as well as the need for electricity (Additional file 4: Table S4). The low-resource kit requires little to no electricity since the only heating step can be performed with a flame and thermometer, while a magnetic rack is used for cleaning and purification steps. The high-resource column purification kit is approximately 3.63 times more expensive per reaction and requires more expensive equipment including a mechanical bead-beater and a high-speed centrifuge (Additional file 4: Table S4). There was no difference in overall DNA yield between the methods (Additional file 8: Fig. S1A), but the low-resource method resulted in lower DNA purity (Additional file 8: Fig. S1B). The low-resource method also required a short low-speed centrifugation step in some samples to remove carry-over PCR inhibitors that blocked detection of GIN DNA in samples with more faecal contamination (Additional file 8: Fig. S1C, D). However, the coloration of faeces during extraction (a measure of homogenisation and initial composition), as measured by spectrometry (A:600 nm), did not correlate to DNA yield (Additional file 8: Fig. S1E).

GIN and other helminth species identification by species-specific PCR

Both the low-resource and high-resource methods of extraction were capable of identifying key GIN species by Endpoint PCR (Fig. 2D). Four of the nine strongyle species tested were identified in the majority of samples, with near ubiquitous prevalence of H. contortus (96%) and T. colubriformis (98%). Some species resulted in spurious off-target hits suggesting other similar species may be present, but this could also be due to random off-target hits within faecal material (Fig. 2D). Among non-strongyle PCR tests, Eimeria was identified in all samples where coccidial oocysts were detected by FEC (see Fig. 2C, D), but neither F. hepatica nor F. gigantica was reliably identified. This may be due to low egg counts typical of these parasites in faeces, or these goats may not carry patent infections.

For strongyle GIN species, the infection intensity (as measured by FEC) was weakly correlated to the number of GIN species detected when using endpoint PCR as well as singleplex semi-quantitative PCR (Additional file 8: Fig. S2A, B). Critically, the detection of parasites by standard endpoint PCR was not significantly different between the low-resource (average 3.15 ± 0.8 species detected) and high-resource (average 3.55 ± 0.85 species detected) DNA extraction methods (t-test, P = 0.14). This result did not change when normalising GIN detection to infection intensity (Additional file 8: Fig. S2C). Overall, generalised correlations comparing the detection of GIN species with the strongyle FEC identified significant co-occurrence of T. axei alongside T. colubriformis and O. columbianum (Additional file 8: Fig. S2D). Only O. columbianum was significantly more likely to be identified in individuals with higher FECs (Additional file 8: Fig. S2D).

Relative quantification of GIN co-infections by low-cost PCR methods

The species composition of GIN infra-populations was assessed using several low-cost PCR techniques as compared to qPCR (Fig. 3; Table 1). Of the different PCR methods tested, multiplex semi-quantitative PCR was the most economical and the highest throughput capable of detecting the relative abundance of three GIN species for one-third of the cost of singleplex PCR and 22.5 times less than qPCR methods (Table 1).

Fig. 3
figure 3

Cross-validation of PCR methods to determine GIN infection species composition. PCR methods tested on faecal DNA extractions from Goat IDs 1–47 and a negative control = C, subjected to low-resource or high-resource extraction methods. Relative abundance analyses performed by A endpoint 35-cycle PCR with unbalanced DNA, B singleplex 29-cycle semi-quantitative PCR with 4.8 ng standardised input DNA, C Multiplex 35-cycle PCR with 4.8 ng standardised input DNA, and D–F qPCR with standardised 20 ng input DNA. Band intensities determined from normalised gel images for A-C and normalised fluorescence intensity from part D with specimen 42 shown for representation. Direction of band intensity scanning shown by white arrowheads. Goat IDs are stacked into contiguous columns for parts A–F (black arrowheads shown for representation)

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Table 1 Costs of molecular methods tested
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The relative abundance of the most commonly identified GIN species was assessed by PCR product band intensity on agarose gels, including endpoint PCR (Fig. 3A), singleplex semi-quantitative PCR (Fig. 3B), and multiplex semi-quantitative PCR (Fig. 3C) techniques. Singleplex PCR and multiplex PCR methods were compared to pan-nematode ITS-2 PCR product band intensities, which varied between samples (Fig. 3B, C). Haemonchus contortus was the most prevalent species identified across all semi-quantitative PCR methods and was also the dominant species in the majority of samples. However, samples differed in the relative abundance of different species identified, and cases of T. colubriformis dominance over H. contortus were generally concordant between the two semi-quantitative PCR methods.

To test whether semi-quantitative methods were accurate, qPCR was performed on a representative subset of samples to determine the relative abundance of H. contortus versus T. colubriformis as these two species predominated in almost all individuals (Fig. 3D, F). The relative abundance of parasite DNA determined by semi-quantitative PCR methods was cross-validated against qPCR findings, focusing on H. contortus and T. colubriformis (Fig. 4A). All of the standard PCR methods significantly correlated to qPCR findings, including the endpoint PCR method, indicating that even non-quantitative PCR may provide a generalised view of the relative abundance among different GIN species. Of these methods, the multiplex method was the best fit compared to qPCR (Fig. 4A) but also suffered from drop-out, failing to identify some species in some samples compared to the endpoint method.

Fig. 4
figure 4

Cross-validation of PCR techniques. Regression cross-validation of standard PCR approaches versus qPCR using H. contortus versus T. colubriformis relative abundance. *P < 0.05, **P < 0.01, ***P < 0.001

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The relative proportion of GINs across goats was assessed using the endpoint PCR method, identifying the most abundant species to be H. contortus (51.2% of individual goats), T. colubriformis (39.5%), T. axei (2.3%), and O. columbianum (2.3%) across the dataset. These results were similar to singleplex and multiplex PCR results, although proportions differed in terms of PCR product band intensity (see Fig. 3A–C).

Assessing HRMC from faecal DNA

To go beyond species-specific PCR to predict predominant GIN species in a non-target-specific manner, we utilised pan-nematode ITS-2 primers to produce a PCR product pool for all target species (see Additional file 2: Table S2). In this instance, HRMC can present a means to assess specific targets by their relative peaks, as has been demonstrated for other helminths [40, 41]. However, this technique has not been tested in the context of faecal DNA screening, or in conditions where unknown co-infections occur. A subset of 15 samples revealed distinct melt curve patterns in cases of H. contortus-dominant versus Trichostrongylus-dominant infections (Fig. 5; Additional file 8: Fig. S3). Haemonchus contortus was associated with a peak at ≈79.76 °C (see Fig. 5A) while Trichostrongylus species were associated with peaks at ≈78.59 °C and ≈81.58 °C (Fig. 5B, C; Additional file 5: Table S5). Overall peaks varied by up to 0.24 °C from the average and no clear distinction between Trichostrongylus species could be made in this analysis. Also, peaks suffered from minor variation between replicates, limiting the utility of specific temperatures marking the presence of particular species in unknown specimens.

Fig. 5
figure 5

High-resolution melt curves with pan-nematode ITS-2 primers. Example samples include A a H. contortus-dominant infection with a single peak at 79.77 °C ± SD 0.04 °C, B a Trichostrongylus-dominant infection with peaks at 78.63 °C ± SD 0.027 °C and 81.6 °C ± SD 0.052 °C, and C a mixed infection with a H. contortus-associated peak at 79.63 °C ± SD 0.113 °C and a second Trichostrongylus-associated peak at 81.48 °C ± SD 0.04 °C. Insets show relative species abundance (as percentage) from EP  endpoint PCR, SP singleplex PCR, MP multiplex PCR, and Q qPCR analyses where H con  H. contortus, T col  T. colubriformis, O col  O. columbianum

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Nemabiome deep amplicon sequencing from faecal DNA

While PCR methods can accurately determine the relative abundance of specific GIN species, the number of detectable species is limited to the number of primers or probes used. To see whether other GIN species are present in goat faecal DNA, and to measure the relative proportion of reads from each species, we turned to nemabiome deep amplicon sequencing for a subset of samples as well as a pooled sample from all 47 individuals (Fig. 6). Nemabiome reads as assessed by the Mothur pipeline revealed the four main species identified by PCR (H. contortus, T. axei, T. colubriformis, O. columbianum) to be by far the most common species detected by nemabiome representing 91.45% of reads from the pooled DNA sample (Fig. 6A). Haemonchus contortus was the most abundant GIN from the pooled sample representing 51.3% of reads. However, among Trichostrongylus species 28% of reads could not be determined to species level, accounting for 8.54% of total reads for the pooled sample. The proportion of unclassified Trichostrongylus species differed between samples but made just over 25% of reads in samples 19 and 42 (Fig. 6A). Classifying taxa using the DADA2 pipeline with IDTAXA resulted in all Trichostrongylus reads assigned to either T. axei or T. colubriformis (Additional file 8: Fig. S4A). Comparing the two pipelines revealed almost all unspeciated Trichostrongylus reads from Mothur to be classified as T. colubriformis by IDTAXA (Additional file 6: Table S6). Conversely, IDTAXA did not identify to species the majority of Oesophagostomum reads, which were all classified as O. columbianum by Mothur (Additional file 6: Table S6). IDTAXA also classified a small number of reads as Ostertagia and Spiculopteragia species (Additional file 8: Fig. S4; Additional file 6: Table S6). Overall GIN proportion differences between Mothur and IDTAXA pipelines were minimal when accounting for these differences.

Fig. 6
figure 6

Nemabiome ITS-2 amplicon sequencing of faecal DNA. A Mothur-resolved proportional GIN species and unresolved Trichostrongylus species from select goat samples as well as pooled DNA from all goat samples. B Bray–Curtis non-metric multidimensional scaling (NMDS) clusters coloured by most abundant species detected by DADA2

Full size image

Nemabiome results revealed drastic differences in species proportions between individual goats, highlighting differences in GIN infra-population composition (Fig. 6A). To assess whether co-infections follow specific signatures, we performed non-metric multidimensional scaling (NMDS) from DADA2 analyses (Fig. 6B). Despite the small number of species identified distinct clusters formed, which appeared to be determined by the most abundant species detected.

Validating low-cost PCR methods and HRMC analyses with nemabiome results

To validate PCR strategies used, species proportions were cross-correlated between quantitative PCR approaches and nemabiome results using the Mothur pipeline (Fig. 7) or the DADA2 pipeline with IDTAXA (Additional file 8: Fig. S5). All species predicted by PCR were significantly correlated to both nemabiome pipelines. The main differences in relative detection were associated with T. colubriformis which could be skewed due to differences in Trichostrongylus read classification between pipelines. Predictive proportions were most accurate for H. contortus for singleplex PCR (Fig. 7A) and T. axei for multiplex PCR (Fig. 7B; Additional file 8: Fig. S5A). qPCR was also highly correlated to both nemabiome pipelines but somewhat overestimated T. colubriformis abundance (Fig. 7C; Additional file 8: Fig. S5B).

Fig. 7
figure 7

Nemabiome validation of quantitative PCR and HRMC analyses. Cross-validation of PCR methods to nemabiome species proportions normalised to number of species tested including A singleplex semi-quantitative PCR with four species, B multiplex semi-quantitative PCR with three species, and C qPCR with two species. *P < 0.05, **P < 0.01, ***P < 0.001

Full size image

Discussion

At the current time, the utility of molecular techniques for monitoring veterinary parasites is limited in low-resource subsistence farming areas as well as in most commercial livestock settings [1,2,3]. In the present study, we demonstrate the utility of combining low-resource GIN monitoring techniques (FPC and FECs) with molecular diagnostics on heat-desiccated preserved faeces and explore molecular options to determine the presence and abundance of different parasite species. In-depth molecular assessment of GINs typically requires culture and isolation of L3 larvae, which can then be preserved in ethanol [47], but eggs and L1 larvae are also sufficient for analysis and can avoid potential biases in larval hatching [49]. However, extraction and purification of eggs or larvae takes time and requires fresh faecal samples. Rural communities and resource-poor areas may not have the capacity to process fresh samples and may also lack immediate access to clean ethanol or other preservatives which do not interfere with DNA quality. To develop a protocol which is as amenable as possible to rural settings, we turned to heat desiccation, which can be performed via a number of chemical- and electrical-free means such as by sunlight or heating over a low flame.

Desiccation is a proven means of preserving DNA from faeces [64] and blood [65], but has yet to be utilised to measure GINs. Faecal DNA extractions have been shown to predict infection burden [32,33,34] and detect as low as 500 fg of parasite DNA [35] or 10 EPG [33] and can be more sensitive than FEC for detection and quantitation of GIN infections [35]. In this study, GINs could be identified from all individuals, demonstrating that parasite DNA is preserved effectively by desiccation (see Fig. 2). Since these are natural infections, determining the limit of parasite detection was not possible, but a correlation was identified between FEC and the number of strongyle species detected (see Additional file 8: Fig. S2A, B).

Collection of faecal material can facilitate biobanking, but low-cost and low-resource methods are also needed to enable the study of goat health in developing countries. Molecular diagnostics differ drastically in cost, throughput, and ability to quantify outputs (see Additional file 2: Table S2; Additional file 4: Table S4). To this end, this study compared low-resource magbead and high-resource spin-column extraction methods, with the high-resource method yielding higher-quality DNA (Additional file 8: Fig. S1B). Despite differences in resources required, there were no noticeably different results regarding species identification among different DNA extraction methods used on the same goat population (Additional file 8: Fig. S2C). Since there were considerable differences in overall costs among the methods tested, the low-resource method produces satisfactory results for cases when the high-resource method is out of reach. The use of paramagnetic beads for DNA extraction from difficult samples also shows promise for ‘kit-free’ approaches [66]. However, the low-resource method did require centrifugation in some cases to remove carryover contaminants and would also require additional quality control steps in field or remote lab settings (Additional file 8: Fig. S1C, D).

Profiling co-infection species composition typically requires advanced and costly molecular methods [34, 45, 47]. While standard endpoint PCR is conventionally considered insufficient for relative quantification of PCR targets, we identify that endpoint and simple semi-quantitative approaches closely mirror qPCR findings (Fig. 4), while being more amenable to increased throughput at a lower cost. Both the singleplex and multiplex semi-quantitative approaches can close the gap for molecular detection of parasites and other diseases in cost-limited farming systems. Overall, semi-quantitative multiplex PCR was the most promising method, as multiplexing increased throughput, reduced reagent costs, and was the method most comparable to quantitative and sequencing results. However, in this study we were limited by the number of different target species detectable per multiplex reaction. Issues of throughput regarding number of possible target species could be overcome through improved separation of PCR products such as by capillary electrophoresis, which is amenable to low-cost and field-applicable labs [67]. In rural or field settings, PCR is achievable from solar or battery packs [68, 69] and can avoid cold-chain limitations when using freeze-dried reagents [70]. Detection of PCR products can also forgo electrophoresis entirely in settings with limited electricity through DNA dipsticks, which are nucleic acid lateral flow assays that can be used to detect specific species through modified probes [71,72,73]. There are other, more direct alternatives available including LAMP assays which have been optimised for GINs and have great promise for field settings [38, 39]. Whether dipstick or LAMP methods will be cost-effective compared to standard PCR to detect residual abundance of helminth infections requires further optimisation.

Validating PCR results by nemabiome deep amplicon sequencing provided the first attempt to use this platform to assess GIN infections in African small ruminants and revealed infections to be dominated by H. contortus in the majority of individuals, but some individuals were potentially more heavily infected with T. axei or T. colubriformis. Interestingly, performing nemabiome on desiccated faecal samples resulted in high-quality reads and as such, DNA degradation was minimal following desiccation and preservation. This method may prove useful to survey wildlife faecal samples when chemical preservatives are out of reach. Overall, nemabiome results mirrored PCR results and did not reveal any other highly present GIN species. As such, semi-quantitative PCR may be a sufficient route to determine parasite populations in low-resource settings. It should be noted that nemabiome style amplicon sequencing can offer distinct advantages over species-specific PCR, principally the capacity to detect novel and unaccounted-for species that may be present in a sample. However, the cost of performing amplicon sequencing will always be far more expensive than PCR alone, since amplicon sequencing relies on initial production of amplicons through multiple rounds of PCR, in addition to PCR product clean-up, quality control steps, library preparation, sequencing, and downstream bioinformatics analyses.

In addition to PCR and qPCR techniques, HRMC analyses were performed using pan-nematode ITS-2 primers which should amplify strongyle GIN species equally and avoid biases such as primer binding and PCR product size impact that can occur between primer sets and probes on mixed DNA samples. HRMC can also provide a semi-quantitative picture of parasitic nematode identification when PCR products from different species have distinct melting temperatures due to sequence variation [41, 74]. HRMC has recently been utilised to distinguish Trichostrongylus species [40], as well as H. contortus alongside other GINs from sheep [41]. In this study, we identify distinct melt curves for high H. contortus and T. colubriformis samples (see Additional file 5: Table S5), but the addition of other species or mixes of species resulted in melt temperature shifts which could not be rationally deciphered in this dataset as compared to more controlled studies. While HRMC is more expensive than traditional PCR techniques, it can save costs overall by reducing tube numbers required for the detection of different species compared to qPCR (see Table 1).

The use of field-applicable techniques to monitor goat health and parasite burden can have lasting impacts on sustainable livestock farming systems. Localised goat breeds are an invaluable asset to global food security, but determining co-infection burden along with GIN species composition is essential to understand the potential limiting impacts of parasites on local breed production and guide intervention decisions. This is valuable considering GIN species have differential impacts on goat heath. For instance, the hematophagous H. contortus is highly fecund and can cause anaemia and death in heavily infected or susceptible small ruminants [31, 75], while O. columbianum is a far less abundant species, but can cause scour and weight loss resulting from intestinal nodules [22]. Other GINs such as Trichostrongylus species can damage intestinal walls resulting in enteritis and loss of protein through haemorrhage of the mucosal lining, mirroring signs of malnutrition [76, 77]. Knowledge of the main GIN species present and variation in species composition between individuals and across seasons can underpin optimised monitoring systems, for example by simplifying the FPC to focus on dominant GIN-driven pathologies, and/or support adaptive breed improvement strategies. In some regions, breeding programs have focused on improved resistance or resilience to GIN species [26, 78, 79]. However, in Malawi, goats can and often do succumb to GIN infections (despite being resilient in comparison to Boer and other exotic breed goats) with up to 80% of younger adult goats dying due to diseases including helminthiasis [20, 21]. Development of GIN-resilient and/or GIN-resistant breeds requires careful monitoring of GIN presence and co-infection, but the technology to characterise GIN co-infection burden is limited in rural areas. Monitoring of GIN populations in Malawi and other low-income areas will be bolstered significantly by the introduction of molecular methods to determine the spread and abundance of different GIN species. This is especially important since FPC and FEC offer a generalised view of the GIN infection intensity or burden of infection [27, 37] but are unable to determine the relative abundance of different species present by egg morphology alone. As such, monitoring of specific species can aid monitoring and breeding programs alike, and also improve the accuracy of tests for anthelmintic drug resistance based on FEC, which are confounded by mixed species infections [80]. The use of a single pellet sample limits the amount of faeces required while fairly reflecting overall egg output [81], whereas preservation by desiccation and use of conventional PCR technology greatly improves the accessibility of the method, especially in remote or rudimentary settings. Overall, chemical-free biobanking of samples with later DNA extraction and low-cost PCR diagnostics will enable monitoring not only of goat GINs but potentially of other parasitic helminths in remote settings.

Conclusions

We find that heat-desiccated faeces not only retains high-quality DNA but also enables quantitative detection of various parasitic species by PCR, qPCR, HRMC, and deep amplicon sequencing (nemabiome) analyses. Lower-cost semi-quantitative methods also highly correlate to more expensive methods and may prove valuable for use in species-specific diagnostics from rural or low-resource biobanked samples. Low-cost techniques can also link GIN co-infection compositions to health checks and goat performance data to identify underlying causes of livestock losses. Given the diverse nature of infections in livestock and wildlife, further study is needed to tailor these methods for the surveillance and monitoring of parasites and possibly host genetic traits in rural areas.

Availability of data and materials

Raw diagnostic, DNA extraction, and PCR data are shown in Additional file 1: Table S1. All other original results including tabulated data, PCR results, and other materials are available on request where not included in the manuscript.

Abbreviations

BCS:

Body condition score

EPG:

Eggs per gram

FAMACHA:

Faffa Malan chart

FEC:

Faecal egg count

FPC:

Five Point Check©

GIN:

Gastrointestinal nematode

HRMC:

High-resolution melt curve

ITS-2:

Internal transcribed spacer 2

LAMP:

Loop-mediated isothermal amplification

NMDS:

Non-metric multidimensional scaling

OPG:

Oocysts per gram

PCR:

Polymerase chain reaction

qPCR:

Quantitative polymerase chain reaction

References

  1. Gilleard JS, Kotze AC, Leathwick D, Nisbet AJ, McNeilly TN, Besier B. A journey through 50 years of research relevant to the control of gastrointestinal nematodes in ruminant livestock and thoughts on future directions. Int J Parasitol. 2021;51:1133–51.

    PubMed  Google Scholar 

  2. Kotze AC, Gilleard JS, Doyle SR, Prichard RK. Challenges and opportunities for the adoption of molecular diagnostics for anthelmintic resistance. Int J Parasitol Drugs Drug Resist. 2020;14:264–73.

    PubMed  PubMed Central  Google Scholar 

  3. Ranjan K, Minakshi P, Prasad G. Application of molecular and serological diagnostics in veterinary parasitology. J Adv Parasitol. 2015;2:80–99.

    Google Scholar 

  4. Whittaker JH, Carlson SA, Jones DE, Brewer MT. Molecular mechanisms for anthelmintic resistance in strongyle nematode parasites of veterinary importance. J Vet Pharmacol Ther. 2017;40:105–15.

    CAS  PubMed  Google Scholar 

  5. Roeber F, Jex AR, Gasser RB. Impact of gastrointestinal parasitic nematodes of sheep, and the role of advanced molecular tools for exploring epidemiology and drug resistance—an Australian perspective. Parasit Vectors. 2013;6:153.

    PubMed  PubMed Central  Google Scholar 

  6. de Camargo GMF. The role of molecular genetics in livestock production. Anim Product Sci. 2018;59:201–6.

    Google Scholar 

  7. Kumar S, Gupta S, Mohmad A, Fular A, Parthasarathi BC, Chaubey AK. Molecular tools-advances, opportunities and prospects for the control of parasites of veterinary importance. Int J Trop Insect Sci. 2021;41:33–42.

    PubMed  Google Scholar 

  8. Hassan NMF, Ghazy AA. Advances in diagnosis and control of anthelmintic resistant gastrointestinal helminths infecting ruminants. J Parasit Dis. 2022;46:901–15.

    PubMed  Google Scholar 

  9. Okeke IN, Ihekweazu C. The importance of molecular diagnostics for infectious diseases in low-resource settings. Nat Rev Microbiol. 2021;19:547–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Saha S, Pai M. Can COVID-19 innovations and systems help low- and middle-income countries to re-imagine healthcare delivery? Med (N Y). 2021;2:369–73.

    CAS  Google Scholar 

  11. Ntinginya NE, Kuchaka D, Orina F, Mwebaza I, Liyoyo A, Miheso B, et al. Unlocking the health system barriers to maximise the uptake and utilisation of molecular diagnostics in low-income and middle-income country setting. BMJ Glob Health. 2021;6:e005357.

    PubMed  PubMed Central  Google Scholar 

  12. Bartley DJ, Andrews L, Melville LA, McBean D, Skuce P, Morrison AA. Integrating applied parasitological and molecular epidemiological methodologies to investigate the capacity of Haemonchus contortus to over-winter on pasture in Scotland. Vet Rec. 2021;189:e137.

    PubMed  Google Scholar 

  13. Emery DL, Hunt PW, Le Jambre LF. Haemonchus contortus: the then and now, and where to from here? Int J Parasitol Elsevier. 2016;46:755–69.

    Google Scholar 

  14. Rose H, Caminade C, Bolajoko MB, Phelan P, van Dijk J, Baylis M, et al. Climate-driven changes to the spatio-temporal distribution of the parasitic nematode, Haemonchus contortus, in sheep in Europe. Glob Chang Biol Wiley. 2016;22:1271–85.

    Google Scholar 

  15. Freeman HA. Livestock, Livelihoods, and Vulnerability in Lesotho, Malawi, and Zambia: Designing Livestock Interventions for Emergency Situations. ILRI (aka ILCA and ILRAD); 2008.

  16. Kaumbata W, Banda L, Mészáros G, Gondwe T, Woodward-Greene MJ, Rosen BD, et al. Tangible and intangible benefits of local goats rearing in smallholder farms in Malawi. Small Rumin Res. 2020;187:106095.

    Google Scholar 

  17. Gwaze FR, Rumosa Gwaze F, Chimonyo M, Dzama K. Communal goat production in Southern Africa: a review. Trop Anim Health Prod. 2009;41:1157–68.

    Google Scholar 

  18. Besier RB, Kahn LP, Sargison ND, Van Wyk JA. The pathophysiology, ecology and epidemiology of Haemonchus contortus infection in small ruminants. Adv Parasitol. 2016;93:95–143.

    CAS  PubMed  Google Scholar 

  19. Githigia SM, Thamsborg SM, Munyua WK, Maingi N. Impact of gastrointestinal helminths on production in goats in Kenya. Small Rumin Res. 2001;42:21–9.

    Google Scholar 

  20. Nandolo W, Wurzinger M, Mészáros G, Van Tassell C, Gondwe T, Mulindwa H, et al. Identification of breeding objectives in community-based goat breeding programmes in Malawi. Acta Agric Slov. 2016;5:104.

    Google Scholar 

  21. Banda JW, Ayoade JA, Karua SK, Kamwanja LA. The local Malawi goat. World animal review. 1993;74–75:49–57.

    Google Scholar 

  22. Fabiyi JP. Production losses and control of helminths in ruminants of tropical regions. Int J Parasitol. 1987;17:435–42.

    CAS  PubMed  Google Scholar 

  23. Nansen P. Production losses and control of helminths in ruminants of temperate regions. Int J Parasitol. 1987;17:425–33.

    CAS  PubMed  Google Scholar 

  24. Rinaldi L, Veneziano V, Cringoli G. Dairy goat production and the importance of gastrointestinal strongyle parasitism. Trans R Soc Trop Med Hyg; 2007;101:745–6.

  25. Hoste H, Sotiraki S, Landau SY, Jackson F, Beveridge I. Goat-Nematode interactions: think differently. Trends Parasitol. 2010;26:376–81.

    PubMed  Google Scholar 

  26. Bambou JC, Larcher T, Ceï W, Dumoulin PJ, Mandonnet N. Effect of experimental infection with Haemonchus contortus on parasitological and local cellular responses in resistant and susceptible young Creole goats. Biomed Res Int. 2013;2013:902759.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Bath GF, van Wyk JA. The Five Point Check© for targeted selective treatment of internal parasites in small ruminants. Small Rumin Res. 2009;86:6–13.

    Google Scholar 

  28. van Wyk JA, Bath GF. The FAMACHA system for managing haemonchosis in sheep and goats by clinically identifying individual animals for treatment. Vet Res; 2002;33:509–29.

  29. Mahieu M, Arquet R, Kandassamy T, Mandonnet N, Hoste H. Evaluation of targeted drenching using Famacha© method in Creole goat: reduction of anthelmintic use, and effects on kid production and pasture contamination. Vet Parasitol. 2007;146:135–47.

    PubMed  Google Scholar 

  30. Cabaret J, Gasnier N, Jacquiet P. Faecal egg counts are representative of digestive-tract strongyle worm burdens in sheep and goats. Parasite. 1998;5:137–42.

    CAS  PubMed  Google Scholar 

  31. Taylor MA, Coop RL, Wall RL. Veterinary parasitology. John Wiley & Sons; 2013.

  32. Demeler J, Ramünke S, Wolken S, Ianiello D, Rinaldi L, Gahutu JB, et al. Discrimination of gastrointestinal nematode eggs from crude fecal egg preparations by inhibitor-resistant conventional and real-time PCR. PLoS ONE. 2013;8:e61285.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. McNally J, Callan D, Andronicos N, Bott N, Hunt PW. DNA-based methodology for the quantification of gastrointestinal nematode eggs in sheep faeces. Vet Parasitol. 2013;198:325–35.

    CAS  PubMed  Google Scholar 

  34. Roeber F, Hassan EB, Skuce P, Morrison A, Claerebout E, Casaert S, et al. An automated, multiplex-tandem PCR platform for the diagnosis of gastrointestinal nematode infections in cattle: an Australian-European validation study. Vet Parasitol. 2017;239:62–75.

    CAS  PubMed  Google Scholar 

  35. Reslova N, Skorpikova L, Kyrianova IA, Vadlejch J, Höglund J, Skuce P, et al. The identification and semi-quantitative assessment of gastrointestinal nematodes in faecal samples using multiplex real-time PCR assays. Parasit Vectors. 2021;14:391.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Bisset SA, Knight JS, Bouchet CLG. A multiplex PCR-based method to identify strongylid parasite larvae recovered from ovine faecal cultures and/or pasture samples. Vet Parasitol. 2014;200:117–27.

    CAS  PubMed  Google Scholar 

  37. Sweeny JPA, Robertson ID, Ryan UM, Jacobson C, Woodgate RG. Comparison of molecular and McMaster microscopy techniques to confirm the presence of naturally acquired strongylid nematode infections in sheep. Mol Biochem Parasitol. 2011;180:62–7.

    CAS  PubMed  Google Scholar 

  38. Melville L, Kenyon F, Javed S, McElarney I, Demeler J, Skuce P. Development of a loop-mediated isothermal amplification (LAMP) assay for the sensitive detection of Haemonchus contortus eggs in ovine faecal samples. Vet Parasitol. 2014;206:308–12.

    CAS  PubMed  Google Scholar 

  39. Sahoo PR, Sethy K, Mohapatra S, Panda D. Loop mediated isothermal amplification: an innovative gene amplification technique for animal diseases. Vet World. 2016;9:465–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Arbabi M, Hooshyar H, Lotfinia M, Bakhshi MA. Molecular detection of Trichostrongylus species through PCR followed by high resolution melt analysis of ITS-2 rDNA sequences. Mol Biochem Parasitol. 2020;236:111260.

    CAS  PubMed  Google Scholar 

  41. Skorpikova L, Reslova N, Magdalek J, Vadlejch J, Kasny M. The use of high resolution melting analysis of ITS-1 for rapid differentiation of parasitic nematodes Haemonchus contortus and Ashworthius sidemi. Sci Rep. 2020;10:15984.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Rashid MH, Gebrekidan H, Jabbar A. Multiplexed-tandem PCR (MT-PCR) assay to detect and differentiate gastrointestinal nematodes of alpacas. Parasit Vectors. 2018;11:370.

    PubMed  PubMed Central  Google Scholar 

  43. Bott NJ, Campbell BE, Beveridge I, Chilton NB, Rees D, Hunt PW, et al. A combined microscopic-molecular method for the diagnosis of strongylid infections in sheep. Int J Parasitol. 2009;39:1277–87.

    CAS  PubMed  Google Scholar 

  44. Roeber F, Jex AR, Campbell AJD, Campbell BE, Anderson GA, Gasser RB. Evaluation and application of a molecular method to assess the composition of strongylid nematode populations in sheep with naturally acquired infections. Infect Genet Evol. 2011;11:849–54.

    PubMed  Google Scholar 

  45. Elmahalawy ST, Halvarsson P, Skarin M, Höglund J. Droplet digital polymerase chain reaction (ddPCR) as a novel method for absolute quantification of major gastrointestinal nematodes in sheep. Vet Parasitol. 2018;261:1–8.

    CAS  PubMed  Google Scholar 

  46. Höglund J, Elmahalawy ST, Halvarsson P, Gustafsson K. Detection of Haemonchus contortus on sheep farms increases using an enhanced sampling protocol combined with PCR based diagnostics. Vet Parasitol. 2019;276:100018.

    Google Scholar 

  47. Avramenko RW, Redman EM, Lewis R, Yazwinski TA, Wasmuth JD, Gilleard JS. Exploring the gastrointestinal “Nemabiome”: Deep amplicon sequencing to quantify the species composition of parasitic nematode communities. PLoS One. 2015;10:e0143559.

  48. Santos LL, Salgado JA, Drummond MG, Bastianetto E, Santos CP, Brasil BSAF, et al. Molecular method for the semiquantitative identification of gastrointestinal nematodes in domestic ruminants. Parasitol Res. 2020;119:529–43.

    PubMed  Google Scholar 

  49. Redman E, Queiroz C, Bartley DJ, Levy M, Avramenko RW, Gilleard JS. Validation of ITS-2 rDNA nemabiome sequencing for ovine gastrointestinal nematodes and its application to a large scale survey of UK sheep farms. Vet Parasitol. 2019;275:108933.

    CAS  PubMed  Google Scholar 

  50. Borkowski EA, Redman EM, Chant R, Avula J, Menzies PI, Karrow NA, et al. Comparison of ITS-2 rDNA nemabiome sequencing with morphological identification to quantify gastrointestinal nematode community species composition in small ruminant feces. Vet Parasitol. 2020;282:109104.

    CAS  PubMed  Google Scholar 

  51. Davey ML, Utaaker KS, Fossøy F. Characterizing parasitic nematode faunas in faeces and soil using DNA metabarcoding. Parasit Vectors. 2021;14:422.

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Bath GF, Wy JA, Malan FS. Targeted selective treatment of sheep using the Five Point Check©. J Common Vet Assoc. 2010;26:29–32.

  53. Amarante MRV, Santos MC, Bassetto CC, Amarante AFT. PCR primers for straightforward differentiation of Haemonchus contortus, Haemonchus placei and their hybrids. J Helminthol. 2017;91:757–61.

    CAS  PubMed  Google Scholar 

  54. Le TH, Nguyen KT, Nguyen NTB, Doan HTT, Le XTK, Hoang CTM, et al. Development and evaluation of a single-step duplex PCR for simultaneous detection of Fasciola hepatica and Fasciola gigantica (family Fasciolidae, class Trematoda, phylum Platyhelminthes). J Clin Microbiol. 2012;50:2720–6.

    PubMed  PubMed Central  Google Scholar 

  55. Bandyopadhyay S, Naskar S, Devi P, Bera AK, De S, Pan D, et al. Multiplex PCR for identification of parasitic eggs of Oesophagostomum spp. Isolated from sheep and goat. Proc Zool Soc Lond. 2009;62:125–9.

  56. Burgess CGS, Bartley Y, Redman E, Skuce PJ, Nath M, Whitelaw F, et al. A survey of the trichostrongylid nematode species present on UK sheep farms and associated anthelmintic control practices. Vet Parasitol Elsevier. 2012;189:299–307.

    CAS  Google Scholar 

  57. Kawahara F, Zhang G, Mingala CN, Tamura Y, Koiwa M, Onuma M, et al. Genetic analysis and development of species-specific PCR assays based on ITS-1 region of rRNA in bovine Eimeria parasites. Vet Parasitol. 2010;174:49–57.

    CAS  PubMed  Google Scholar 

  58. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, et al. Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9:676–82.

    CAS  PubMed  Google Scholar 

  59. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal. 2011;17:10–2.

  60. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Callahan BJ, Sankaran K, Fukuyama JA, McMurdie PJ, Holmes SP. Bioconductor Workflow for Microbiome Data Analysis: from raw reads to community analyses. F1000Res. 2016;5:1492.

  62. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–41.

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Workentine ML, Chen R, Zhu S, Gavriliuc S, Shaw N, de Rijke J, et al. A database for ITS2 sequences from nematodes. BMC Genet. 2020;21:74.

    PubMed  PubMed Central  Google Scholar 

  64. Frantzen MA, Silk JB, Ferguson JW, Wayne RK, Kohn MH. Empirical evaluation of preservation methods for faecal DNA. Mol Ecol. 1998;7:1423–8.

    CAS  PubMed  Google Scholar 

  65. Michaud V, Gil P, Kwiatek O, Prome S, Dixon L, Romero L, et al. Long-term storage at tropical temperature of dried-blood filter papers for detection and genotyping of RNA and DNA viruses by direct PCR. J Virol Methods. 2007;146:257–65.

    CAS  PubMed  Google Scholar 

  66. Pearlman SI, Leelawong M, Richardson KA, Adams NM, Russ PK, Pask ME, et al. Low-resource nucleic acid extraction method enabled by high-gradient magnetic separation. ACS Appl Mater Interfaces. 2020;12:12457–67.

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Pan J-Z, Fang P, Fang X-X, Hu T-T, Fang J, Fang Q. A low-cost palmtop high-speed capillary electrophoresis bioanalyzer with laser induced fluorescence detection. Sci Rep. 2018;8:1791.

    PubMed  PubMed Central  Google Scholar 

  68. Marx V. PCR heads into the field. Nat Methods. 2015;12:393–7.

    CAS  PubMed  Google Scholar 

  69. Jiang L, Mancuso M, Lu Z, Akar G, Cesarman E, Erickson D. Solar thermal polymerase chain reaction for smartphone-assisted molecular diagnostics. Sci Rep. 2014;4:4137.

    PubMed  PubMed Central  Google Scholar 

  70. Klatser PR, Kuijper S, van Ingen CW, Kolk AHJ. Stabilized, freeze-dried PCR mix for detection of mycobacteria. J Clin Microbiol. 1998;36:1798–800.

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Jauset-Rubio M, Svobodová M, Mairal T, McNeil C, Keegan N, Saeed A, et al. Ultrasensitive, rapid and inexpensive detection of DNA using paper based lateral flow assay. Sci Rep. 2016;6:37732.

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Anfossi L, Di Nardo F, Cavalera S, Giovannoli C, Baggiani C. Multiplex lateral flow immunoassay: an overview of strategies towards high-throughput point-of-need testing. Biosensors. 2018;9:2.

    PubMed  PubMed Central  Google Scholar 

  73. Xu Y, Liu Y, Wu Y, Xia X, Liao Y, Li Q. Fluorescent probe-based lateral flow assay for multiplex nucleic acid detection. Anal Chem. 2014;86:5611–4.

    CAS  PubMed  Google Scholar 

  74. Holterman MHM, Oggenfuss M, Frey JE, Kiewnick S. Evaluation of high-resolution melting curve analysis as a new tool for root-knot nematode diagnostics. J Phytopathol Wiley. 2012;160:59–66.

    CAS  Google Scholar 

  75. Lejambre LF, Ractliffe LH, Uhazy LS, Whitlock JH. Fecal egg output of lambs in relationship to Haemonchus contortus burden. Int J Parasitol. 1971;1:157–60.

    CAS  PubMed  Google Scholar 

  76. Barker IK, Titchen DA. Gastric dysfunction in sheep infected with Trichostrongylus colubriformis, a nematode inhabiting the small intestine. Int J Parasitol. 1982;12:345–56.

    CAS  PubMed  Google Scholar 

  77. Holmes PH. Pathogenesis of trichostrongylosis. Vet Parasitol. 1985;18:89–101.

    CAS  PubMed  Google Scholar 

  78. Baker RL, Mwamachi DM, Audho JO, Aduda EO, Thorpe W. Resistance of Galla and Small East African goats in the sub-humid tropics to gastrointestinal nematode infections and the peri-parturient rise in faecal egg counts. Vet Parasitol. 1998;79:53–64.

    CAS  PubMed  Google Scholar 

  79. Behnke JM, Chiejina SN, Musongong GA, Nnadi PA, Ngongeh LA, Abonyi FO, et al. Resistance and resilience of traditionally managed West African Dwarf goats from the savanna zone of northern Nigeria to naturally acquired trypanosome and gastrointestinal nematode infections. J Helminthol. 2011;85:80–91.

    CAS  PubMed  Google Scholar 

  80. Morgan ER, Lanusse C, Rinaldi L, Charlier J, Vercruysse J. Confounding factors affecting faecal egg count reduction as a measure of anthelmintic efficacy. Parasite. 2022;29:20.

    PubMed  PubMed Central  Google Scholar 

  81. Morgan ER, Cavill L, Curry GE, Wood RM, Mitchell ESE. Effects of aggregation and sample size on composite faecal egg counts in sheep. Vet Parasitol. 2005;131:79–87.

    CAS  PubMed  Google Scholar 

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Acknowledgements

The authors would like to acknowledge participant smallholding farmers for allowing assessment and sample collection from goats and the Lilongwe University of Agriculture and Natural Resources (LUANAR) for their support in this research project, specifically LUANAR members Christopher Mindozo, Victor Zilahowa, and Ian Limbe. Additionally, the authors give thanks to veterinary officers for their help in organising and liaising with participants.

Funding

This work was supported by the Biotechnology and Biological Sciences Research Council [Grant Number BB/S014748/1, 2018]. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any author-accepted manuscript version arising.

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Authors and Affiliations

  1. School of Biological Sciences, Queen’s University Belfast, Belfast, Antrim, UK

    Paul M. Airs, Javier Ventura-Cordero & Eric R. Morgan

  2. Animal Science Department, Lilongwe University of Agriculture and Natural Resources (LUANAR), Lilongwe, Malawi

    Winchester Mvula, Patson Nalivata & Andrews Safalaoh

  3. Net Zero and Resilient Farming Directorate, Rothamsted Research, Okehampton, Devon, UK

    Taro Takahashi

  4. Bristol Veterinary School, University of Bristol, Langford, Somerset, UK

    Taro Takahashi

  5. Department of Veterinary Tropical Diseases, University of Pretoria, Pretoria, South Africa

    Jan Van Wyk

Authors
  1. Paul M. Airs
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  2. Javier Ventura-Cordero
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  3. Winchester Mvula
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  4. Taro Takahashi
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  6. Patson Nalivata
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  7. Andrews Safalaoh
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  8. Eric R. Morgan
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Contributions

PM Airs—Data curation, Formal Analysis, Investigation, Methodology, Software, Visualisation, Writing—original draft, Writing—review & editing, J Ventura-Cordero—Data curation, Formal Analysis, Investigation, Methodology, Software, Visualisation, Writing—original draft, Writing—review & editing, ACL Safalaoh—Conceptualisation, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing—review & editing, W Mvula—Data curation, Investigation, T Takahashi—Conceptualisation, Funding acquisition, Project administration, J Van-Wyk—Conceptualisation, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing—review & editing, PC Nalivata—Writing—review & editing, Funding acquisition, ER Morgan—Conceptualisation, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing—original draft, Writing—review & editing.

Corresponding author

Correspondence to Eric R. Morgan.

Ethics declarations

Ethics approval and consent to participate

The manuscript does not contain clinical studies, patient data, or individual data and is an observational study of goat health. The study was conducted in accordance with research ethics procedures approved by the Animal Science Department Ethics Committee at Lilongwe University of Agriculture and Natural Resources (LUANAR), Malawi. Informed consent of goat smallholders for study participation was performed prior to health screening and sample collection from goats, with the objectives of the study explained. All participants were informed that they were free to withdraw from the study at any time, and consent was obtained through signatures for literate participants or verbally declared in the presence of LUANAR team members for illiterate participants. All goats were provided with veterinary care as needed during examination by veterinarians including J Ventura-Cordero, J Van Wyk, and E R Morgan in the presence of local veterinary officers. Veterinary officers were also provided with training and supplies for follow on veterinary care in the area.

Consent for publication

All authors consented to publication.

Competing interests

The authors declare no competing interests.

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Supplementary Information

Additional file 1. Table S1.

Raw baseline data on DNA extraction, quality, quantity and yield

Additional file 2. Table S2.

Primers and cycling conditions used for each PCR technique

Additional file 3. Table S3.

Nemabiome raw read results from Eurofins 2nd PCR amplicon sequencing

Additional file 4. Table S4.

DNA extraction costs

Additional file 5. Table S5.

HRMC peak temperatures and Singleplex PCR relative species abundance

Additional file 6. Table S6.

Proportional read differences between Mothur and DADA2 nemabiome pipelines

Additional file 7. File S1.

72-well gel electrophoresis comb design

Additional file 8. Figures S1–S5. Figure S1:

Assessing quality of DNA extraction methods. Figure S2: Assessing GIN species detection likelihoods by DNA extraction method and infection intensity. Figure S3: High-resolution melt curves with pan-nematode ITS-2 primers. Figure S4: Nemabiome ITS-2 amplicon with the DADA2 IDTAXA pipeline. Figure S5: Cross-validation of PCR methods with nemabiome using the DADA2 IDTAXA pipeline.

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Airs, P.M., Ventura-Cordero, J., Mvula, W. et al. Low-cost molecular methods to characterise gastrointestinal nematode co-infections of goats in Africa. Parasites Vectors 16, 216 (2023). https://doi.org/10.1186/s13071-023-05816-y

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Keywords

Parasites & Vectors

ISSN: 1756-3305

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