{"paper_id":"b0b8c85a-0855-41b9-b02b-e3bc5c627934","body_text":"1 \n \nFull title: Development of a qPCR assay and tremabiome deep amplicon sequencing 1 \nmethod for differentiation of fluke species in livestock 2 \nShort title: Detection techniques for fluke infection 3 \nMuhammad Abbas1, Kezia Kozel1, Olukayode Daramola2, Nick Selemetas3, Qasim Ali4, Shoaib 4 \nAshraf5,6, , Isah Ibrahim7, Inaki Deza-Cruz8, Sai Fingerhood9, Mark W. Robinson10, Eric R 5 \nMorgan10, Umer Chaudhry 11 *, Martha Betson1*  6 \n1 Department of Comparative Biomedical Sciences, School of Veterinary Medicine, 7 \nUniversity of Surrey, Guildford, UK 8 \n2School of Veterinary Medicine, University of Lancashire, Preston, United Kingdom 9 \n3 Department of Microbial Sciences, School of Veterinary Biosciences, University of Surrey, 10 \nGuildford, UK 11 \n4 Department of Parasitology, Agriculture University Dera Ismail Khan, Pakistan 12 \n5Department of Pathobiology, College of Veterinary Medicine, Riphah International 13 \nUniversity, 54000 Lahore, Pakistan 14 \n6Department of Biomedical Sciences, Ross University, School of Veterinary Medicine 15 \n(RUSVM), St Kitts and Nevis, West Indies 16 \n7Department of Veterinary Parasitology and Entomology, Faculty of Veterinary Medicine, 17 \nAhmadu Bello University, Zaria, Nigeria 18 \n8The Royal (Dick) School of Veterinary Studies and The Roslin Institute, The University of 19 \nEdinburgh Easter Bush Veterinary Centre, Midlothian, EH25 9RG 20 \n9Department of Veterinary Pathology, University of Nottingham, UK 21 \n10School of Biological Sciences, Queen’s University, Belfast, UK 22 \n11Department of Veterinary Biomedical Sciences, Lewyt College of Veterinary Medicine, 23 \nLong Island University, USA 24 \nCorresponding authors: 25 \n*Martha Betson, m.betson@surrey.ac.uk  26 \n* Umer Chaudhry, Umer.Chaudhry@liu.edu 27 \n 28 \n 29 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n2 \n \nAbstract 30 \nBackground:  31 \nTrematode parasites, or flukes, are a significant economic threat to ruminant production 32 \nworldwide. Traditional diagnostic methods rely on egg sedimentation from faeces, a time-33 \nconsuming methodology lacking sensitivity and specificity. This study aimed to develop and 34 \nvalidate two diagnostic methods: firstly, qPCR for accurate identification of Fasciola spp., 35 \nand secondly, tremabiome, deep amplicon sequencing technique for identifying fluke 36 \nspecies using faecal egg DNA. 37 \nMethodology: 38 \nTo detect fluke infection primers targeting mitochondrial DNA were repurposed to develop 39 \na SYBR Green qPCR diagnostic. For the identification of fluke species, a tremabiome 40 \napproach was developed. A reference sequence library and taxonomy file were generated 41 \nfor 21 fluke species, enabling species sequence read separation and extracting amplicon 42 \nsequence variants (ASVs). To validate the qPCR and tremabiome approach, 402 faecal 43 \nsamples were collected from cattle and sheep across the UK. Fluke eggs were isolated by 44 \nsedimentation, detected by microscopy and qPCR, and tremabiome used to identify fluke 45 \neggs to species level. 46 \nResults:  47 \nqPCR demonstrated high analytical sensitivity, detecting Fasciola hepatica DNA down to 48 \n19.2fg and F. gigantica down to 6.4fg, with no cross-amplification of other flukes.  49 \nTremabiome was able to detect as few as five F. hepatica and Calicophoron daubneyi eggs 50 \nand identify mixed infections. High levels of co-infection (14.4%) of F. hepatica and C. 51 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n3 \n \ndaubneyi were observed in faecal samples, followed by single infections with C. daubneyi 52 \n(12.6%) and F. hepatica (3.2%). Notably, tremabiome detected F. hepatica in 20 samples 53 \nmissed by qPCR. Data analysis identified 55 and 32 ASVs for F. hepatica and C. daubneyi, 54 \nrespectively, with phylogenetic clustering within their respective clades.   55 \nConclusion:  56 \nThis study developed qPCR assay for Fasciola detection and validated a tremabiome deep 57 \namplicon sequencing for fluke species differentiation. These approaches have improved 58 \ncapacity to identify fluke species compared to microscopy and are valuable tools for 59 \nenhancing fasciolosis surveillance and control. 60 \nKeywords: trematode; amplicon deep sequencing; Fasciola; molecular detection; speciation 61 \nAuthor Summary 62 \nFlukes are flatworm parasites that cause disease domestic and wild animals and humans. 63 \nThe main species infecting cattle and sheep globally are the liver flukes F. hepatica and F. 64 \ngigantica, with other species including the rumen fluke Calicophoron daubneyi locally 65 \nimportant or emerging. Infections result in serious economic losses. The traditional method 66 \nof diagnosing fluke infection involves observation of eggs in faecal samples under the 67 \nmicroscope, but this can be time-consuming and error prone, since the eggs of different 68 \nspecies often look similar. In this study, we developed and validated two methods to 69 \nimprove detection: qPCR, a sensitive DNA-based test to identify Fasciola infections, and 70 \ntremabiome, a DNA sequencing technique that can accurately differentiate between 71 \ndifferent fluke species. We tested these methods using faecal samples collected from cattle 72 \nand sheep across the UK. The qPCR could detect small amounts of Fasciola DNA, while 73 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n4 \n \ntremabiome was more sensitive, identifying different fluke species from as few as five eggs. 74 \nOur study found that co-infections of F. hepatica and C. daubneyi are common in the UK. 75 \nThe approaches we have developed could be valuable tools for to improve fluke diagnosis 76 \nand enable better control of this important parasitic disease. 77 \nIntroduction 78 \nTrematode parasites, or flukes, are widespread globally and include several species that 79 \ncause serious disease in animals and humans. Fasciolosis is a neglected foodborne tropical 80 \ndisease caused by the zoonotic flukes Fasciola hepatica and Fasciola gigantica. Unlike other 81 \nneglected tropical diseases, Fasciola infections in humans and animals have a broad reach 82 \nglobally, being found in more than 75 countries, with 2.4 million people infected, and 83 \nmillions more at risk [1]. The prevalence in livestock is less well known, however, a recent 84 \nmeta-analysis suggests the global prevalence of fasciolosis in cattle and sheep across 85 \ncontinents ranges from 12-97% and 9-58% respectively across continents [2].  86 \nEnsuring food security is increasingly challenging with a growing global population. In 2020, 87 \nthe agri-food sector contributed 115 billion GBP, making up 6.0% of the UK economy [3], 88 \nwith other national economies considerably more dependent on farming. Recent global 89 \nestimates indicate that fasciolosis may cost annual losses in animal productivity exceeding 90 \nUS$3.2 billion [4,5]. In the UK, fasciolosis prevails in ruminants, costing the cattle industry 91 \n13-40 million GBP yearly, reducing dairy farms’ net profit by 12% and beef farms’ by 6% [6]. 92 \nFasciola infections can lead to delayed animal slaughter [7], and condemnation of damaged 93 \nlivers [8]. According to the Food Standards Agency in 2014, 22% of British cattle livers are 94 \ncondemned due to fluke [9]. Losses to fasciolosis are widespread; for example, Australia 95 \nfaces one of the highest disease burdens, with estimated annual losses reaching 96 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n5 \n \napproximately 129 million (range 38–193 million) AUD annually [10]. Infected animals suffer 97 \nreduced weight, anaemia, reduced milk yield and fat content [8], lower reproduction, and 98 \nhigher mortality [11]. 99 \nFlukes other than Fasciola spp. are also economically important. Calicophoron daubneyi 100 \nbelongs to the family Paramphistomidae, a group of flukes typically found in the 101 \nforestomachs of ruminants. Unlike the flattened morphology common to most trematodes, 102 \nthese flukes exhibit a distinct conical shape as adults [12]. C. daubneyi is considered an 103 \nemerging threat in Europe due to its impact on livestock productivity. The larval stage of 104 \nrumen fluke are released into the duodenum, where they attach to the intestinal lining and 105 \ncauses tissue damage. Although chronic C. daubneyi infection is not typically associated with 106 \nclinical disease, some negative effects on production have been reported [12]. Recent 107 \nstudies suggest that paramphistomosis is now more prevalent than fasciolosis in certain 108 \nregions of the UK [9–11]. However, currently, diagnostic options for rumen fluke are limited 109 \nand need further research. 110 \nTraditionally, diagnosis of both Fasciola and Calicophoron infections relies on microscopic 111 \nidentification of fluke eggs in the host faeces [13–15], with eggs usually observed 10−12 112 \nweeks post-infection and thereafter [14–16]. The effectiveness of microscopy relies on 113 \npersonnel training and expertise. Moreover, it becomes labour-intensive when handling a 114 \nlarge number of samples, particularly if the person lacks sufficient experience, leading to 115 \nlow sensitivity [17]. Distinguishing between F. hepatica and C. daubneyi solely based on egg 116 \nmorphology in faecal samples can be challenging as both parasites produce eggs with 117 \ncomparable sizes and shapes [18,19]. Although, it is reported that F. hepatica eggs can be 118 \nidentified by their operculum [20] and yellowish colour [21], these features can be difficult 119 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n6 \n \nto observe consistently using standard light microscopy. As a result, trusting solely in egg 120 \nmorphology for diagnosis may lead to misidentification. Molecular diagnostics based on 121 \nFasciola DNA detection are rapidly progressing; for instance, qPCR [22] and PCR techniques 122 \nhave been applied to adult worms and infected snails [23]. Additionally, techniques such as 123 \nnested PCR [14] and Loop-Mediated Isothermal Amplification (LAMP) are being explored 124 \n[24] and are under assessment for their speed, reliability, and accuracy compared to other 125 \nmethods [25]. However, significant challenges remain in detecting Fasciola DNA in faecal 126 \nmaterial, highlighting the need for a reliable, time-efficient and accurate diagnostic method, 127 \nwhich can handle medium to large sample sizes and is capable of differentiating F. hepatica 128 \ninfections from other fluke species.  129 \nNext-generation sequencing technologies are transforming the diagnosis of infectious 130 \ndiseases whilst also paving the way for new research areas, such as microbiome studies [26–131 \n28].  Amplicon sequencing using next-generation approaches has been applied to identify 132 \ngastrointestinal nematode species in ruminants (“nemabiome”) [29], to quantify 133 \ntrypanosome and piroplasm species in ruminant blood samples (“haemoprotobiome”) 134 \n[30,31]. Similar “tremabiome” technology has been applied to quantify single species adult 135 \nfluke infections in ruminants [32–34]. Despite the prevalence of mixed fluke infections in 136 \nruminants [32,33], this approach has not yet been used to understand fluke egg 137 \ncommunities in single and mixed species infections.  138 \nThis study aimed to develop a user-friendly real time diagnostic tool to detect F. hepatica 139 \nand F. gigantica infections, and to distinguish them from other species such as rumen fluke. 140 \nSpecifically, a SYBR Green qPCR-based assay was utilised to amplify the mitochondrial 141 \nNADH1 (mt-ND1) DNA marker without the need for fluorescent probes. This assay was 142 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n7 \n \ndesigned to screen Fasciola spp. infections from faecal sedimented material of naturally 143 \ninfected cattle and sheep. Additionally, we combined two previously published deep 144 \namplicon sequencing approaches tested on adult flukes of Fasciola and Calicophoron spp. 145 \n[32,34] to create a tremabiome approach capable of differentiating between various species 146 \nof fluke. Finally, both methods were validated using field samples of fluke eggs and adults 147 \ncollected from different regions across the UK and compared to microscopy. 148 \nMaterials and Methods 149 \nEthical statement 150 \nNon-invasive collection of faecal samples was approved by the NASPA (Non-Animal 151 \nScientific Procedures Act) sub-committee of AWERB, University of Surrey, UK, under the 152 \nreference NASPA-2122-04 for the project “Developing Novel Rapid Diagnostics for 153 \nNeglected Parasitic Diseases.” Adult F. hepatica were collected at licenced slaughterhouses 154 \nand through post-mortem examination. Completion of a University of Surrey SAGE-AR 155 \nindicated that no formal ethical approval was required for adult fluke sampling.  156 \nPositive control samples  157 \nAll adult F. hepatica worms and sedimented eggs were collected from UK, whilst adult 158 \nworms and eggs purified from adult worms of F. gigantica, C. daubneyi, Paramphistomum 159 \nepiclitum and Explanatum explanatum were collected from abattoirs in Pakistan in our 160 \nprevious studies [32–36]. Adult worm tissue was processed following a previously described 161 \nprotocol [33] and DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen, USA). 162 \nDNA was then extracted from eggs of positive controls (F. hepatica, F. gigantica, C. 163 \ndaubneyi, E. explanatum) utilising the DNeasy PowerSoil Pro Kits (Qiagen, USA), with slight 164 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n8 \n \nmodification. Briefly these modifications included the incubation of sedimented material 165 \nwith the lysis buffer (CD1) for 10 minutes at 65°C, followed by 10 minutes of bead-beating in 166 \na TissueLyser LT (Qiagen, USA). The manufacturer's protocol was then followed, with DNA 167 \nbeing eluted in 10mM Tris buffer and stored at -80°C for downstream analysis. 168 \nField sample collection  169 \nTo engage cattle and sheep farmers, the study was advertised by email to registered 170 \nveterinary practitioners using the Royal College of Veterinary Surgeons Find a Vet site 171 \nfiltering for practices specialising in cattle, sheep and/or goats, and camelids. In addition, 172 \nsocieties for sheep and cattle listed by the Department for Environment, Food and Rural 173 \nAffairs (https://www.gov.uk/government/publications/lists-of-recognised-animal-breeding-174 \norganisations) in the UK were also approached via the email contact listed. Participants 175 \nwere sent a Royal Mail prepaid SafeBox with a sampling kit, a short questionnaire, and 176 \nparticipant information sheet and consent form according to ethical requirements. No 177 \nsamples were used without the written informed consent of the farmer. A total of 402 178 \nfaecal samples were collected from 19 cattle and sheep farms across various geographical 179 \nregions of the UK through 10 registered veterinary practitioners from December 2022 to 180 \nMay 2024 (Table S1).  181 \nIn addition to faecal samples, adult F. hepatica worms were collected from abattoirs and at 182 \npost-mortem analysis from cattle (n=2) originating from West Sussex, and East Sussex, as 183 \nwell as from sheep (n=10) from West Sussex, Kent, Derbyshire, Renfrewshire Scotland and 184 \nCounty Tyrone Northern Ireland. All samples were transported to the School of Veterinary 185 \nMedicine at the University of Surrey, UK, and subsequently stored at -20°C for further 186 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n9 \n \nanalysis. DNA from the adult flukes was extracted as described in section “Positive control 187 \nsamples”.  188 \nMorphological identification of fluke eggs 189 \nFaecal samples were initially processed using standard sedimentation methodology [37] and 190 \nthen to streamline the process a time-saving method Flukefinder® (Diagnostic System, USA) 191 \nwas also used [18,38], with slight adjustments. Both methods utilised 7-10 grams of faecal 192 \nmaterial which was combined with 50 ml of water, sieved through gauze and then passed 193 \nthrough the Flukefinder® apparatus. The collected filtered material from both methods was 194 \nthen mixed with 250 ml of water in a conical beaker. After three minutes, the supernatant 195 \nwas removed; this step was repeated thrice for clarity. The sediment was transferred to a 50 196 \nml centrifuge tube filled with water, and the supernatant was aspirated after 3 minutes. This 197 \nprocess was repeated with a 15 ml centrifuge tube. Finally, the sediment was transferred to 198 \na 1.5 ml Eppendorf tube in 1 ml of PBS and was stored at 4°C for subsequent microscopy 199 \nand DNA isolation.  200 \nMorphological egg examination involved inspecting 100 to 500 μl of sedimented faeces 201 \nstained with 0.5% methylene blue (Pro-Lab, UK) in a counting chamber (Graticules Optics 202 \nLimited, UK) under a compound microscope (Nikon, Japan) at 100× magnification with a lens 203 \ncontaining a graticule. Samples positive for fluke eggs (n=128) were assessed for the 204 \npresence of F. hepatica or C. daubneyi eggs based on their morphological characteristics, 205 \nincluding size, shape, colour and operculum [18–21]. The overall workflow is summarised in 206 \nFig. 1 (a). 207 \nAny sedimented faecal samples which were identified as positive for fluke eggs by 208 \nmicroscopy were subsequently subjected to DNA isolation utilising the DNeasy PowerSoil 209 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n10 \n \nPro Kits (Qiagen, USA) according to the manufacturer’s instructions as described in the 210 \nsection “Positive control samples”.  211 \nMolecular identification of fluke eggs  212 \nPCR was performed using universal ITS2 primers [39] on selected fluke egg-positive samples 213 \nand all positive controls. Further, Fasciola specific mt-ND1 primers [33] were applied to 214 \nselected samples to confirm the diagnostic accuracy of the PCR targets. This was followed by 215 \nSanger sequencing to confirm the presence and correct amplification of DNA for different 216 \nfluke species.  217 \nAll PCR reactions were prepared with DreamTaq Green PCR master mix (Thermo Scientific, 218 \nUSA) in a 25 μl reaction mix, with primer concentrations of 200 nM and 4 μl of sample DNA 219 \ntemplate. The PCR cycling conditions were initial denaturation at 95°C for 5 minutes, 220 \nfollowed by 35 cycles of denaturation at 95°C for 1 minute, annealing at 55°C (ITS2 primers), 221 \n50°C (mt-ND1 primers) for 1 minute, and extension at 72°C for 1 minute. The final extension 222 \nstep was carried out at 72°C for 5 minutes. Positive controls consisted of DNA from F. 223 \nhepatica and F. gigantica adult worms. The resulting PCR products were purified and 224 \ncleaned using a NucleoMag kit for clean-up and size selection of NGS library prep reactions 225 \n(MACHEREY-NAGEL, GmbH & Co.KG). Sanger sequencing of the PCR product was performed 226 \nby Source Biosciences, UK and Eurofins Genomics, Germany. Selected samples were 227 \nsubjected to conventional PCR and Sanger sequencing due to limited resources. All obtained 228 \nsequences were visualised using Geneious version 8.0.5 (https://www.geneious.com), and 229 \nthe FASTA sequences were submited to the BLASTn tool on NCBI to confirm fluke species 230 \nidentity. 231 \nDevelopment and validation of a SYBR green qPCR to detect Fasciola eggs at species level 232 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n11 \n \nA SYBR green qPCR assay was developed to detect Fasciola spp., ingus mt-ND1 primers. 233 \nThese mt-ND1 primers were previously designed and employed in a meta-barcoded PCR 234 \n[33]. In the SYBR green assay, 3 μl of DNA was subjected to qPCR using 10 μl of 2X 235 \nSsoAdvanced Universal SYBR Green Supermix (Bio-Rad, USA), resulting in a total reaction 236 \nmix volume of 20 μL, including 500 nM of mt-ND1 primers. 237 \nThe cycling program was initial denaturation at 98 ℃ for 3 mins, followed by 40 cycles of 238 \ndenaturation at 98 ℃ for 15 secs and annealing at 60 ℃ for 30 secs on a CFX96 Real-Time 239 \nPCR machine (Bio-Rad, USA). Subsequently, a melt curve was generated from 65 ℃ to 95 ℃ 240 \nwith an increment of 0.5 ℃ for 0.05 secs per plate read. Positive and negative controls were 241 \nemployed as described above. All samples were subjected to qPCR in triplicate, and the 242 \nresulting data were visualised using CFX Maestro Version: 5.3.022.1030 (Bio-Rad, USA). 243 \nDetection sensitivity limits of the assay were assessed using five-fold serial dilutions of F. 244 \nhepatica (ranging from 300 pg to 0.768 fg) and F. gigantica (ranging from 500 pg to 1.28 fg) 245 \nadult worm DNA. The DNA was quantified using Qubit™ dsDNA HS and BR Assay Kits 246 \n(Invitrogen™). The analytical specificity of the assays was evaluated by testing 1 ng of DNA 247 \nfrom other prevalent flukes and nematodes found in sheep and cattle. These included C. 248 \ndaubneyi, P. epiclitum, E. explanatum, and the nematode Teladorsagia circumcincta. The 249 \nsensitivity and specificity tests were conducted in triplicate and repeated twice. 250 \nThe reliability of the method was measured by comparing inter- and intra-assay variations in 251 \nCq values for F. hepatica and F. gigantica DNA.  For validation, the newly developed qPCR 252 \nassay was applied to egg DNA extracted from sedimented faecal samples. 253 \nDevelopment and validation of deep amplicon sequencing to detect fluke eggs at species 254 \nlevel 255 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n12 \n \nA universal ITS2 rDNA marker  for a range of 21 different fluke species  (Table S2; DOI: 256 \n10.17632/zyvwc6ppy8.1) targeting coding regions of 5.8S and 28S rDNA  was used, expected 257 \nto produce a fragment of 490 –743 bp [39]. The primers were meta -barcoded by adding 258 \nIllumina adaptor sequences to the both forward and reverse primers, along with up to three 259 \nrandom 'N' nucleotides positioned between the adaptor sequences and the locus -specific 260 \nprimers. Additionally, modified phosphate bonds were added between the last three 261 \nnucleotides of each primer to enhance their stability (Table S3) and used in PCR amplification 262 \nto detect fluke species.  To assess the representation of spec ies read depth , mock DNA 263 \nmixtures were prepared in triplicate by pooling 250 eggs of each species, including F. 264 \nhepatica, F. gigantica, and C. daubneyi  and subjected to amplicon sequencing in triplicate. 265 \nPCR cycle numbers were adjusted 35x, 30x and 25x in the first round to examine their effect 266 \non species representation . Further, to evaluate species representation  using egg DNA , we 267 \ncreated seven mock egg pools  in triplicate , adjusting the proportion of F. hepatica  and C. 268 \ndaubneyi in an approximate total of 250 eggs with ratios of 99:1, 90:10, 70:30, 50:50, 30:70, 269 \n10:90, and 1:99. Moreover, to test the threshold of deep amplicon sequencing, pools of equal 270 \negg proportions (50 eggs) were prepared from three out of  four species ( F. hepatica , F. 271 \ngigantica, E. explanatum, C. daubneyi). The fourth species ( F. hepatica or C. daubneyi) was 272 \nthen added in decreasing numbers of 500, 50, 20, 15, 5, and 0 eggs, creating six mock pools. 273 \nThe first round of PCR was performed using the KAPA HiFi PCR Kit (KAPA BIOSYSTEMS, South 274 \nAfrica). The modified primer sets, adaptors, barcoded PCR amplification conditions, magnetic 275 \nbead purification methods and bioinformatic analysis were based on our previously described 276 \nmethods [33]. The first-round PCR products were subjected to a second -round PCR using a 277 \nbarcoded primer set to  attach a unique barcode index fragment required for Illumina 278 \nsequencing [30] Fig. 1 (b). 279 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n13 \n \n10 μl of each second round barcoded PCR product were combined to create a pooled library 280 \nand then purified by agarose gel electrophoresis to remove non-specific products and adaptor 281 \ndimers. During post -run processing, the MiSeq system separated all sequencing data by 282 \nsample quality using the barcoded indices to generate FASTQ files  (raw sequence read files 283 \navailable at Mendeley database DOI: 10.17632/zyvwc6ppy8.1), see workflow diagram Fig. 1 284 \n(b). 285 \nThe FASTQ files obtained from the post -run Illumina MiSeq (BioProject ID PRJNA1273189 ) 286 \nwere analysed in Mothur/1.41.0-Python-2.7.15 [40] using the High-Performance Cluster (HPC) 287 \nsystem at the University of Surrey, UK . Pipelines described in our previous study  [41] were 288 \nutilised with modifications of the newly developed reference sequence library  (Script 289 \navailable at DOI: 10.17632/zyvwc6ppy8.1).  290 \nTo generate a taxonomy file, ITS2 rDNA reference sequences (n=545) were obtained from 291 \nNCBI, representing 21 fluke species (Table S2). The genetic distances between different fluke 292 \nspecies were then calculated based on the sequenced region. Overall, a variation in genetic 293 \nidentity was found between different fluke species  ranging from 40% to 99% (Table S4, DOI: 294 \n10.17632/zyvwc6ppy8.1). Finally, a  phylogenetic tree of 154 unique sequences showed a 295 \ndistinct clustering of each fluke species (Fig. 2)  as described in the section phylogenetic 296 \nanalysis. 297 \nFollowing extraction of the taxonomy file, quality filtering  was conducted  for the 298 \nidentification of  unique fluke sequences, detailed count tables  were generated  and an 299 \nalignment (ALIGN) file of sequences across all samples was produced. This workflow ensured 300 \na robust, high -quality dataset suitable for downstream taxonomic studies for flukes using a 301 \nseries of commands (Script available at DOI: 10.17632/zyvwc6ppy8.1).  302 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n14 \n \nThe count table and alignment (ALIGN) file were further used for the extraction of amplicon 303 \nsequence variants (ASVs) . The R script extracted specific ASVs corresponding to the target 304 \nspecies of flukes applying cutoff values of 250 reads  each (NCBI accession numbers; 305 \nPV752375-PV752429, PV752431 -PV752462). The R script started by cleaning sequence 306 \nnames, trimming whitespace and converting them to lowercase  to ensure consistency for 307 \nsuccessful merging. After identifying unmatched sequences, the datasets were merged based 308 \non sequence names. The merged data was filtered to retain only rows with a ‘total’ count of 309 \nat least 250 reads, ensuring that only high-quality consensus sequences remained. A custom 310 \nfunction was employed to write the sequences into a combined FASTA file, preserving both 311 \nthe sequence names and read counts. Next, the script utilised the Biostrings package to clean 312 \nthe sequences by removing ‘N’ characters and ambiguous bases, saving the high -quality 313 \nsequences to a final FASTA file  (R script available at D OI: 10.17632/zyvwc6ppy8.1).  This 314 \ncomprehensive approach ensure d accurate sequence extraction for subsequent analysis. 315 \nFinally, the sample-wise separated FASTA files were subjected to remote NCBI BLASTn loop 316 \ncommand using “ blastn: 2.16.0+ ” in the HPC cluster system ( command line available DOI: 317 \n10.17632/zyvwc6ppy8.1). Mismatched sequences with the NCBI database were considered 318 \ncontaminated sequences and discarded.  319 \nStatistical analysis of qPCR and deep amplicon sequencing data  320 \nFor qPCR analysis, the raw Cq values were extracted from CFX Maestro Version: 321 \n5.3.022.1030 (Bio-Rad, USA) and a linear standard curve was created by plotting DNA 322 \nquantities against the average Cq values for each concentration tested. For deep amplicon 323 \nsequence reads data analysis, the sequences from the bioinformatics pipeline were further 324 \nanalysed for sequence accuracy and percentage identity using remote blastn: 2.16.0+ with 325 \nthe NCBI database. To determine the percentage composition of F. hepatica, F. gigantica, C. 326 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n15 \n \ndaubneyi, and E. explanatum in mock egg mixtures (positive control) and field samples, 327 \nspecies composition percentages were calculated by dividing the classified sequence reads 328 \nfor each species by the total reads per sample. A one-way ANOVA was applied to assess the 329 \nproportional representation of mock mixes comprising different fluke species eggs across 330 \nvarying PCR amplification cycles.  The association between qPCR and tremabiome results 331 \nwas assessed using Chi-squared analysis. All visualisations of data were performed in R 332 \nversion 4.3.3 (R scripts are available at DOI: 10.17632/zyvwc6ppy8.1) 333 \nPhylogenetic analysis  334 \nPhylogenetic trees were generated from unique reference sequences of ITS2 rDNA from 21 335 \ndifferent fluke species downloaded from NCBI GenBank (Table S2). The sequences were 336 \naligned using the MUSCLE alignment tool in Geneious v8.0.5 (Biomatters Ltd, New Zealand) 337 \nand genetic distances were calculated (Table S4). Further, a phylogenetic tree of the unique 338 \nITS2 rDNA sequences for all 21 fluke species and ASVs of the flukes was constructed using 339 \nthe Neighbor-Joining method [42]. The evolutionary distances were computed using the 340 \nMaximum Composite Likelihood method [43] in MEGA11 [44] with a bootstrap value of 2000 341 \n[45]. 342 \nResults 343 \nFluke identification by microscopy 344 \nA total of 402 faecal samples were examined, out of which 128 were positive for fluke eggs. 345 \nThe sampled animals included cattle (n=154), sheep (n=233), water buffalo (n=1), alpaca 346 \n(n=4), and goats (n=2), with animal species unspecified for 8 samples. Of these samples, 191 347 \nhad a history of Fasciola infection, 119 had no history, and 92 had an unknown history 348 \n(Table S1). The cattle and sheep sampled represented diverse age groups, ranging from 349 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n16 \n \ncalves and lambs to adults, providing a comprehensive representation of the animal host 350 \npopulation. Notably, only one sheep farm from Wales was included, and no faecal samples 351 \nwere collected from Northern Ireland.  Based on egg morphology and staining, 30 samples 352 \nwere identified as C. daubneyi, 71 as F. hepatica, five as mixed infection, and 22 remained 353 \nundecided (Table S5).  354 \nPCR and Sanger sequencing for fluke identification and comparison with microscopy 355 \nOut of 128 samples positive for fluke eggs, DNA was successfully extracted from 125. PCR 356 \nwas performed on 85 randomly selected samples using universal ITS2 primers, with bands 357 \nobserved in 73 samples.  From the 71 samples believed to be F. hepatica by microscopy, 37 358 \nwere screened by PCR, of which 31 samples were analysed by Sanger sequencing. Of these 359 \n31, three were confirmed as F. hepatica, 19 as C. daubneyi and nine samples demonstrated 360 \npoor sequence quality. Of the 30 C. daubneyi positive samples identified by microscopy, 30 361 \nwere screened by PCR, of which 27 samples were then analysed by Sanger Sequencing. Of 362 \nthese 27 samples, 16 were confirmed as C. daubneyi, one was confirmed to be F. hepatica, 363 \none was identified as Paramphistomum epiclitum, and nine demonstrated poor sequence 364 \nquality. Of the five samples which were identified as mixed infections by microscopy (F. 365 \nhepatica and C. daubneyi), three were screened by PCR and analysed by Sanger sequencing. 366 \nOf these three mixed infection samples, Sanger sequencing identified one sample as F. 367 \nhepatica, and two as C. daubneyi. In the 22 samples where microscopy could not determine 368 \nthe fluke species present, 15 samples were screened by PCR, of which 14 were subsequently 369 \nanalysed by Sanger sequencing. From these, two samples were confirmed as F. hepatica, six 370 \nas C. daubneyi, and six demonstrated poor sequence quality (Table 1, Table S5). 371 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n17 \n \nIn addition to the ITS2 PCR and Sanger sequencing, a third PCR based diagnostic test, 372 \ntargeting mitochondrial target mt-ND1 and specific for identifying Fasciola spp. was utilised. 373 \nFor this, 15 samples were randomly selected within the diagnostic categories already 374 \ncreated (F. hepatica, C. daubneyi, mixed and unknown). From the two morphologically 375 \nidentified C. daubneyi samples selected, one was confirmed as F. hepatica, while the other 376 \nhad poor sequence quality. For instance, from the nine morphologically identified F. 377 \nhepatica samples, two were initially identified as F. hepatica but were confirmed as C. 378 \ndaubneyi by ITS2, whereas mt-ND1 sequencing confirmed them as F. hepatica. This suggests 379 \nthat microscopy failed to identify mixed infections in those two samples. Additionally, one 380 \nsample had poor ITS2 sequence quality but was confirmed as F. hepatica by mt-ND1 and 381 \nthree samples were not sequenced for ITS2 but were confirmed as F. hepatica by mt-ND1. 382 \nAdditionally, one morphologically identified mixed infection by microscopy was supported 383 \nby ITS2 and ND1 PCR assays and subsequent Sanger sequencing, confirming the presence of 384 \nC. daubneyi and F. hepatica, respectively. Among the three morphologically unidentified 385 \nsamples, one was confirmed as C. daubneyi by ITS2, with mt-ND1 sequencing demonstrating 386 \npoor sequence quality, while another was not sequenced for ITS2 but confirmed as F. 387 \nhepatica by mt-ND1.  (Table 2, Table S5). These findings highlight discrepancies between 388 \nmorphological identification and molecular confirmation using a nuclear and mitochondrial 389 \nDNA target, emphasising the need for more accurate molecular techniques.  390 \nDetection of Fasciola species using a newly developed qPCR assay 391 \nTo provide a simple, low-cost, sensitive, universal, and accurate molecular method for 392 \ndiagnosing Fasciola infections in faeces, a SYBR green qPCR assay was developed using a 393 \nrepurposed primer set targeting mt-ND1 specific to F. hepatica and F. gigantica. The assay's 394 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n18 \n \nanalytical sensitivity was first assessed using quantified F. hepatica and F. gigantica adult 395 \nworm DNA (positive controls). The assay’s limit of detection based on a 1:5 DNA dilution 396 \nseries was found to be 19.2 fg for F. hepatica and 6.4 fg for F. gigantica DNA. A linear 397 \nstandard curve was generated, showing an efficiency of 97% (R² = 0.9759) for F. hepatica 398 \nand 99% (R² = 0.9995) for F. gigantica, demonstrating efficient primer binding and target 399 \namplification. Additionally, qPCR melt curve analysis identified distinct peaks at 81.50°C for 400 \nF. hepatica and F. gigantica (Fig. S1, A and B), confirming the specificity of primer binding to 401 \nthe same DNA target, and absence of nonspecific primer interactions. The specificity of the 402 \nqPCR assay was evaluated against DNA from other prevalent fluke and nematode species. 403 \nThe melt curve analysis confirmed that only F. hepatica and F. gigantica produced 404 \namplification peaks at 81.50°C, with no cross-amplification (Fig. S2). The assay exhibited 405 \nstrong reproducibility, with coefficients of variation (CV) below 6.0% for intra-assay and 406 \ninter-assay. The mean Cq values ranged from 21.96 to 38.26 for F. hepatica with DNA 407 \ndilutions ranging from 300 pg to 19.2 fg and 18.62 to 38.24 for F. gigantica with DNA 408 \ndilutions ranging from 500 pg to 6.4 fg), maintaining consistency across replicates (Table S6). 409 \nTo gain further clarification on which Fasciola species were present in the 128 egg-positive 410 \nfield samples obtained, the newly developed specific SYBR green qPCR was utilised. Of the 411 \n128 samples screened, 57 were positive for F. hepatica, 66 were negative, and five were 412 \neither not determined or failed DNA extraction. 413 \nComparison of microscopy and qPCR for fluke species diagnosis  414 \nAmong the 71 samples identified as F. hepatica positive by microscopy, qPCR confirmed F. 415 \nhepatica in 34 samples, while 33 samples were negative, and four were not determined or 416 \nfailed DNA extraction. From the 30 samples identified as C. daubneyi by microscopy, qPCR 417 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n19 \n \ndetected F. hepatica in seven samples, whereas 23 samples tested negative. From the five 418 \nsamples identified as mixed infections by microscopy, qPCR confirmed four as positive for F. 419 \nhepatica infections, while one sample tested negative. Finally, of the 22 samples which were 420 \nundecided using microscopy, qPCR identified 12 as F. hepatica, nine as negative, and one 421 \nsample was not determined or with failed DNA extraction (Table 3, Table S5). 422 \nDetection of fluke species using a newly developed tremabiome deep amplicon 423 \nsequencing method 424 \nTo accurately identify mixed-species as well as single-species fluke infections, a tremabiome 425 \ndeep amplicon sequencing assay was developed using a universal primer set targeting rDNA 426 \nITS2, specific to  fluke species. Initially, the sequence representation of three different fluke 427 \nspecies F. hepatica, F. gigantica, and C. daubneyi in the deep amplicon sequencing assay was 428 \ndetermined (Fig. 3a). This allowed the evaluation of proportional DNA sequence output reads 429 \nrelative to known species ratios. Each species showed significant representation in the 430 \nsequence counts in each mix (Fig. 3a). Furthermore, the number of cycles (25X, 30X and 35X) 431 \nused during the adaptor PCR were validated to ensure sufficient DNA was generated for 432 \nsequencing whilst maintaining a balance between amplification efficiency and accuracy. 433 \nWhilst it is known that an appropriate number of cycles helps minimise deviations and PCR 434 \nbias, and over -amplification causing sequence dominanc e, we found that the number of 435 \ncycles did not affect the sequence representation of any species. In each mock pool, C. 436 \ndaubneyi generated the highest number of sequence reads, followed by F. gigantica and F. 437 \nhepatica. Despite these trends, no statistically significant differences were observed in the 438 \nproportional representation of any species across the different PCR amplification cycles ( F. 439 \ngigantica, P = 0.730; F. hepatica, P = 0.774; and C. daubneyi, P = 0.258) (Fig. 3a) .  440 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n20 \n \nWe further assessed the assay’s accuracy in identifying relative species proportions in mixed 441 \ninfections by testing pairwise mixtures of F. hepatica and C. daubneyi eggs (Fig. 3b).  This 442 \nrange of mixes enabled thorough validation of the sequencing assay, demonstrating reliable 443 \ndetection of the two species across egg ratios, which is essential for accurately identifying 444 \nspecies representation in mixed infections. These species were selected due to their high 445 \nprevalence, frequent co -occurrence in UK cattle and sheep herds, and availability in our 446 \nlaboratory (Fig. 3b, File S1). This approach addresse d the sensitivity of deep amplicon 447 \nsequencing assays in detecting trace -level amplicons. We observed minimal variation in 448 \nspecies representation across different mixes, which did not affect the overall interpretation 449 \nof relative species abundance. For example, the 99% F. hepatica: 1% C. daubneyi mix and the 450 \n90% F. hepatica: 10% C. daubneyi mix displayed similar species representations further . We 451 \ntested the thresholds of the deep amplicon assay for fluke egg DNA with decreasing egg levels 452 \nin mixed populations, for example, F. hepatica and C. daubneyi  (Fig. 4a and 4b, File S2, and 453 \nFile S3). A notable observation was the production of a lower number of sequenced reads, 454 \nparticularly for F. hepatica; however, this does not affect the identification. Importantly, the 455 \nassay detected both F. hepatica and C. daubneyi DNA at levels down to 5 eggs per pool. 456 \nAfter validation, the assay was applied to 125 of the 128 fluke egg-positive samples to analyse 457 \nfluke species distributions in natural infections in cattle and sheep. In cattle, 67 samples (eggs: 458 \nn=65, worms: n=2) produced sequence reads. The data revealed that F. hepatica was present 459 \nin 4 samples (eggs: n=2, worms: n=2), C. daubneyi in 28, and mixed infections in 35 faecal 460 \nsamples. Similarly, in sheep, 67 samples (eggs: n=57, worms: n=10) generated sequence 461 \nreads. The data showed F. hepatica in 21 samples (eggs: n=11, worms: n=10), C. daubneyi in 462 \n23, and mixed infections in 23 faecal samples (Fig. 5, File S4). Notably, out of the 125 faecal 463 \nsamples sequenced, 122 produced reads, as 3 samples failed to yield sequencing reads. This 464 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n21 \n \nanalysis highlights a higher prevalence of mixed infections followed by C. daubneyi and F. 465 \nhepatica singular infections in cattle and sheep. The sequence reads of all samples were 466 \naligned with F. hepatica and C. daubneyi with BLASTn (DOI: 10.17632/zyvwc6ppy8.1). 467 \nFinally, ASVs were generated for C. daubneyi and F. hepatica from all sequence reads of the 468 \nsamples collected from different counties in the UK. ASVs were up to 461 bp and 527 bp for 469 \nC. daubneyi  and F. hepatica , respectively . In total, 87 ASVs were identified, including F. 470 \nhepatica (n=55) and C. daubneyi (n=32) (DOI: 10.17632/zyvwc6ppy8.1). A phylogenetic tree 471 \nof all ASVs with reference sequences of 21 fluke species (outlined in the methodology section) 472 \nshowed that F. hepatica and C. daubneyi species separated into distinct clades (Fig. 6). 473 \nComparison of microscopy and tremabiome deep amplicon sequening  for fluke species 474 \ndiagnosis 475 \nOf the 71 samples identified as F. hepatica by microscopy, tremabiome detected only 11 as  476 \nsingle F. hepatica  infections. However, tremabiome classified 26 of the 71 samples as C. 477 \ndaubneyi and 30 as mixed infections, suggesting that some samples identified as F. hepatica 478 \nby qPCR  (n=34) were mixed infections.  Among the 30 samples marked as C. daubneyi  by 479 \nmicroscopy, tremabiome detected two as F. hepatica, 21 as C. daubneyi, and seven as mixed 480 \ninfections. For the microscopically recognised five mixed infections, tremabiome confirmed 481 \nfour as mixed infections, while one was classified as C. daubneyi. Lastly, among the 22 samples 482 \nthat were undecided by microscopy, tremabiome identified three as C. daubneyi, 17 as mixed 483 \ninfections, and two as not determined or with failed DNA extraction (Table. 3, Table S5). 484 \nComparison of qPCR and tremabiome deep amplicon sequencing for fluke species diagnosis 485 \nA significant correlation (p<0.001) was noted between the identification of F. hepatica  486 \ninfections using qPCR and the  tremabiome approach. Nevertheless, there were 20 samples 487 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n22 \n \nwhich tested negative by qPCR, but  F. hepatica sequences were detected by deep amplicon 488 \n(three single and 17 mixed infections). Conversely, deep amplicon sequencing did not produce 489 \nF. hepatica reads for 8 qPCR -positive samples (Table 4, Table S5). For these samples , FECs 490 \nranged from 1-221 eggs per gram of faecal material, but due to mixed infections of F. hepatica 491 \nand C. daubneyi, the egg counts for individual fluke species are not clear . Interestingly, C. 492 \ndaubneyi reads were generated for the 8 samples, which were positive by Fasciola qPCR, but 493 \ndid not have F. hepatica reads in tremabiome, despite the qPCR demonstrating no specificity 494 \nissues towards C. daubneyi during assay validation. 495 \nComparison of Sanger sequencing and tremabiome deep amplicon sequencing for species 496 \nidentification 497 \nSanger sequencing identified 7 samples as F. hepatica of which tremabiome confirmed two 498 \nas F. hepatica, and five as mixed infections, and none as C. daubneyi. Among the 43 samples 499 \nidentified as C. daubneyi by Sanger sequencing, tremabiome confirmed 29 as C. daubneyi and 500 \n14 as mixed infections.  Notably, the sample identified as P. epiclitum by Sanger sequencing 501 \nwas detected as F. hepatica in tremabiome sequencng (Table. 5, Table S5). 502 \nDiscussion 503 \nIn this study we present new approaches for detecting and identifying fluke species in faecal 504 \nsamples in the form of qPCR with high analytic al sensitivity and specificity for the detection 505 \nof Fasciola spp. and a deep amplicon sequencing assay which can accurately identify and 506 \ndifferentiate between closely related fluke species, such as F. hepatica, F. gigantica, and C. 507 \ndaubneyi. These methods overcome important limitations of microscopic egg examination. 508 \nWe selected the ITS2 and mt-ND1 genetic markers based on their previous application in fluke 509 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n23 \n \nspecies identification and their potential to differentiate between closely related flukes 510 \n[32,39].  511 \nMultiple direct and indirect diagnostic approaches are available for diagnosing fluke 512 \ninfections, each with its own set of limitations. However, farmers still require highly sensitive, 513 \nspecific, and cost -effective early diagnostic procedures. Historically, the most widely used 514 \ntraditional direct identification method is detecting fluke eggs in the hosts faeces. This can be 515 \naccomplished through various techniques such as FLOTAC [46], sedimentation, Flukefinder, 516 \nor the Kato -Katz thick smear method [47]. Whilst the  use of fluke egg count s is simple, 517 \nhowever, this diagnostic route can be unreliable due to low intermittent egg deposition in the 518 \nfaeces [48], and it is time-consuming and requires highly trained laboratory staff [37].  519 \nProgress in the development of molecular-level diagnostic methods based on DNA detection 520 \nis underway, including PCR techniques [23], qPCR [22] and the LAMP approach [24]. High 521 \nthroughput deep amplicon sequencing approach of metabarcoded DNA from parasite 522 \npopulations using the Illumina MiSeq platform  can offer a low -cost and potentially more 523 \naccurate alternative to traditional microscopic methods. For instance, adult Fasciola spp. and 524 \nC. daubneyi  flukes ha ve previously been detected using tremabiome deep amplicon 525 \nsequencing [32–34] but this method has not  been applied to the detection of eggs in faecal 526 \nsamples.    527 \nIn the present  study, s pecies identification through m icroscopic examination was first 528 \nchecked by PCR followed by Sanger sequencing. Our findings demonstrated that microscopy 529 \nhas limitations in accurately identifying fluke species compared to Sanger sequencing and 530 \nqPCR and is a time-consuming process, as previously reported by Calvani et al.,  (2017) [37]. 531 \nOnly about half of the microscopically positive samples (n=51) were confirmed by ITS2 Sanger 532 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n24 \n \nsequencing to contain F. hepatica or C. daubneyi DNA. A major contributing factor to this 533 \nfinding could be the presence of mixed infections since fluke eggs are morphologically similar, 534 \nand this was confirmed by mt-ND1 Sanger sequencing [18,19]. PCR bands were observed on 535 \nthe agarose gel  for most of the samples , which indicated successful DNA amplification . 536 \nHowever, Sanger sequencing produced poor-quality sequence reads for many samples. Poor 537 \nsequencing quality can be due to  non-specific amplifications, artefacts, and samples 538 \ncontaining mixed DNA templates from double infections [49]. Since DNA was extracted from 539 \nfaecal material, additional factors such as low DNA concentration may also have contributed 540 \nto low-quality Sanger sequencing results. These findings indicated the need for more sensitive 541 \nand specific molecular diagnostic tools , such as qPCR and tremabiome deep amplicon 542 \nsequencing, to improve detection accuracy, particularly in complex natural infections. 543 \nWe repurposed mt-ND1 markers to develop a SYBR Green qPCR assay to detect Fasciola spp. 544 \nThe choice to use SYBR Green over fluorescence probe -based systems was due to its cost -545 \neffectiveness and simplicity.  In contrast , previous studies used TaqMan probes to identify 546 \nFasciola species [22,37,50]. Our assay's analytical sensitivity (19.2 fg for F. hepatica and 6.4 fg 547 \nfor F. gigantica DNA) is lower than that reported in previous studies. For instance, Shi et al. , 548 \n(2020) achieved a detection limit of 1.67 pg of DNA using a SYBR Green qPCR assay targeting 549 \nthe ITS2 region  [51]. Similarly, previous studies have demonstrated the ability to detect F. 550 \nhepatica at levels below 10 eggs per gram directly from 150 mg of faecal material using a 551 \nTaqMan qPCR assay [37] and sensitivities as low as 1 pg/μL [22] and 1.6 pg/μL when targeting 552 \nthe ITS1 region [52].  F. hepatica eDNA (14-50 fg) was detected in water samples with similar 553 \nsensitivity to our assay [53]. In 2024, a qPCR assay was reported which could detect 10 fg of 554 \nFasciola DNA in water and 1 pg in human stool samples  [54]. A limitation of our study is that 555 \nqPCR was only performed on fluke egg-positive samples due to limited resources. Thus, it was 556 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n25 \n \nnot possible to formally determine diagnostic performance. This will be determined in future 557 \nwork. The diagnostic sensitivity of other qPCR methods was reported to be 66% in human 558 \nstool samples [52] and 91 –100% in sheep and cattle compared to  microscopy-based 559 \ntechniques [37]. These estimates assume, however, that microscopy is the gold standard, 560 \nwhereas it could miss positive cases that are detectable using molecular methods.   561 \nA limitation of our qPCR assay is that we are only able to detect Fasciola in DNA extracted 562 \nfrom sedimented material, not DNA extracted directly from faecal samples. Raw faecal 563 \nsamples were tested in this study using the same DNA extraction methodology as 564 \nsedimented eggs, but it was not possible to detect Fasciola DNA using the qPCR 565 \nmethodology described in the study (data not shown). However, sensitivity improved when 566 \nFasciola eggs were first concentrated using faecal egg sedimentation before applying the 567 \nbead-beating approach for DNA isolation [37]. Similarly, other studies also employed 568 \nmolecular procedures following the sedimentation process [18,50,55], and a few studies 569 \nhave applied molecular techniques to detect natural Fasciola infections directly from faecal 570 \nmaterial and reported limitations [37,55]. One possible approach is LAMP [56], as this has 571 \ndemonstrated low detection limits for Fasciola spp. DNA and results can be observed with 572 \nthe naked eye [24,25]. A recent study successfully detected F. hepatica in DNA extracted 573 \ndirectly from faeces with a commercial kit, using both LAMP and PCR methodology targeting 574 \nITS2 region [57]. Further work is needed to simplify extraction protocols.   575 \nIn ruminants, fluke species often occur in complex and overlapping infections; for instance, F. 576 \nhepatica and C. daubneyi  co-infections have been observed in cattle and sheep  in the UK 577 \n[58,59] and the same has been reported elsewhere in Europe [18,60], including in Ireland [61] 578 \nand Germany [62]. We found that the microscopic egg identification for closely related flukes, 579 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n26 \n \nsuch as F. hepatica  and C. daubneyi , is challenging due to their similar size and shape. 580 \nTherefore, the tremabiome technology was developed utilising universal  ITS2 rDNA markers 581 \nto differentiate between multiple fluke species . The techniques were validated using fluke 582 \negg DNA, isolated from faecal samples.  To our knowledge, this is first time the tremabiome 583 \ndeep amplicon sequencing approach has been applied to detect mixed fluke infections in 584 \nfaeces. Previously the approach was applied to detect adult fluke samples [33,34]. 585 \nThe tremabiome technique generated sequence reads  for F. hepatica, F. gigantica, and C. 586 \ndaubneyi. However, the proportion of sequence reads deviated from expected percentages . 587 \nFor instance, we evaluated the assay's ability to accurately determine the relative species 588 \nproportions in pairwise combinations of F. hepatica and C. daubneyi. The results consistently 589 \nshowed a higher proportion of reads for C. daubneyi  compared to F. hepatica  across all 590 \nmixtures. Such variation may arise from factors including the primers used for the target loci, 591 \nconserved priming sites, variations in DNA template concentrations during sample handling, 592 \nthe number of PCR cycles, and the copy number of the target DNA locus  [63]. Previously, for 593 \nnematodes, species-specific representation biases were addressed by calculating correction 594 \nfactors using L3 larva l population DNA from different nematode species [29]. In the present 595 \nstudy, while working with fluke egg DNA  obtained via faecal sedimentation , calculation of 596 \ncorrection factors did not remove sequence biases (File S5). This might be due to differences 597 \nin the eggshell chemistry or stability hardness of eggshells that has been described between 598 \nF. hepatica and C. daubneyi [64], leading to variations in DNA extraction efficiency. However, 599 \nwe employed mechanical disruption before DNA isolation to mitigate this issue [37]. 600 \nAdditionally, we used universal ITS2 primers to detect multiple fluke species in a single deep 601 \namplicon sequencing run. Using species -specific primers in deep amplicon sequencing could 602 \nbe a potential solution to reduce sequence biases [65]. Further, bioinformatics analysis of 603 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n27 \n \nsequencing data might introduce biases, leading to inaccuracies in species proportion 604 \nestimations. One major challenge can be the limited availability of reference sequence reads 605 \nin the NCBI database, for example, for F. hepatica 50 unique sequences and for C. daubneyi 606 \n19 unique sequences were found , which can impact species specific reads identification. 607 \nAdditionally, taxa represented by low numbers of sequencing reads may pose a problem, as 608 \nthese low -frequency reads were removed during data filtering while eliminating artefacts, 609 \nresulting in the underrepresentation of actual sequence reads. Therefore, there is a need for 610 \nmore reference sequence data, which can enhance our capability of accurately distinguishing 611 \ntrue sequences.  When the  tremabiome technology  was applied to field samples , many F. 612 \nhepatica and C. daubneyi co-infections were identified. Since F. hepatica is more pathogenic 613 \nand economically detrimental [6,66] than C. daubneyi [67,68], and treatment choices differ, 614 \nour method provides a valuable tool for differentiating co -infections of these two significant 615 \nparasites using faecal egg samples. 616 \nWhen applying the tremabiome approach to field samples, just over half of the 617 \nmicroscopically positive F. hepatica samples were confirmed by tremabiome deep amplicon 618 \nsequencing. Moreover, there was a significant correlation between the identification of F. 619 \nhepatica infections using qPCR and the  tremabiome approach. Notably, the tremabiome 620 \napproach generated F. hepatica sequence reads in samples which tested negative by qPCR. 621 \nConversely, tremabiome deep amplicon sequencing did not produce F. hepatica reads for a 622 \nfew qPCR-positive samples. It has been reported that false negative results for molecular tests 623 \nmay be due to low egg count in faecal samples [18]. Furthermore, one sample identified as P. 624 \nepiclitum by Sanger sequencing was not confirmed by tremabiome, which instead identified 625 \nit as F. hepatica . Previously P. leydeni  has been reported in sheep [69], and deer [70] in 626 \nIreland, but was not identified in the UK in our study. Therefore, each method has limitations, 627 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n28 \n \nbut tremabiome remained the preferred tool for species -level fluke identification as it can 628 \nidentify mixed infections.  629 \nRegarding the implementation of these methods, microscopy can be a suitable option for 630 \nanalysing a small number of samples due to its low cost and accessibility, although, there were 631 \npotential issues of misidentifications [18,37]. In particular, a high proportion of samples 632 \nidentified as positive for F. hepatica  based on egg morphology were confirmed by 633 \ntremabiome to contain C. daubneyi, despite careful identification. For medium to high sample 634 \nvolumes, qPCR is suitable to identify Fasciola spp. infections only and tremabiome deep 635 \namplicon sequencing is potentially more effective choices for species level differentiation of 636 \ndifferent flukes with an advantage of high throughput, as a single Illumina MiSeq run can 637 \nprocess up to 384 samples simultaneously. This capability makes the method suitable for both 638 \nresearch and diagnostic applications . Presently, t his study offers evidence of the high 639 \nprevalence of F. hepatica and C. daubneyi in UK ruminants. Expanding this method to larger 640 \nsample sizes across the UK and in other countries would provide a more comprehensive 641 \nepidemiological understanding of these infections. 642 \nThe sequencing data generated from the set of natural field samples enhanced our 643 \nunderstanding of the genetic variation within fluke populations by revealing their ASVs . 644 \nNotably with ITS2 markers , we observed more ASVs for F. hepatica  than C. daubneyi , 645 \nindicating possible greater genetic diversity within F. hepatica  populations. Previously, 646 \nFasciola species in Pakistan were differentiated using ITS2 markers in adult  worms [32,39], 647 \nwhile high genetic diversity was reported using mt -ND1 markers [32,33]. Similarly, a study 648 \nfrom Spain and Peru identified Fasciola flukes using nuclear DNA markers but reported high 649 \ngenetic diversity based on mitochondrial markers  [71]. Further, high genetic diversity and 650 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n29 \n \ngene flow among Fasciola populations have been reported i n the UK,  using microsatellite 651 \nmarkers [72,73]. Therefore, t he ITS2 rDNA provides a useful taxonomic resolution  [32,39], 652 \nhowever, mitochondrial markers such as (ND1 and COX1) and genetic markers  are typically 653 \npreferred for detailed population genetic studies  [33,71,72,74–76]. Therefore, further 654 \ninvestigations using mitochondrial ND1 and nuclear genetic markers are required to 655 \nunderstand the genetic diversity of these fluke populations and is work in progress. 656 \nAlthough validated on cattle and sheep samples in this study, both the qPCR and tremabiome 657 \nmethods have a strong potential for the Fasciola spp. d etection in humans, particularly in 658 \nendemic regions where there is a possibility of zoonotic transmission of F. hepatica and F. 659 \ngigantica. Application of  these methods on human faecal samples could improve case 660 \ndetection, fluke species identification, and epidemiological understanding. 661 \nConclusion  662 \nIn conclusion, this study presents the first use of tremabiome deep amplicon sequencing for 663 \ndetecting mixed infections of F. hepatica and C. daubneyi, and provides a direct comparison 664 \nbetween microscopy, PCR, Sanger sequencing, qPCR and tremabiome methods for 665 \ndifferentiating between fluke species. The tremabiome approach was highly effective for 666 \ndetecting mixed fluke infections, compared to other techniques utilised in this study, 667 \ndemonstrating high frequency of C. daubneyi and F. hepatica co-infections in farmed 668 \nruminants in the UK. The methods were primarily validated using samples from natural 669 \ninfections, with DNA extracted from faecal sedimented eggs, which allowed an easy and 670 \nnon-invasive sampling approach at the farm level. Tremabiome and qPCR are promising 671 \ntools to complement microscopy in fluke disease surveillance and control in livestock and 672 \nhumans. 673 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n30 \n \nSupporting information 674 \n 675 \nFile S1  Relative proportions of F. hepatica and C. daubneyi in egg DNA mixtures (XLSX)  676 \nFile S2  Mock egg mixtures with gradually decreasing counts of F. hepatica eggs (XLSX) 677 \nFile S3  Mock egg mixtures with gradually decreasing counts of C. daubneyi eggs (XLSX) 678 \nFile S4  Deep amplicon sequence reads generated from field samples from cattle and 679 \nsheep (XLSX) 680 \nFile S5 Correction factors calculations (PDF) 681 \nTable S1 Sample information (XLSX) 682 \nTable S2 Reference sequences downloaded from NCBI and unique sequence count (XLSX)  683 \nTable S3 mt-ND1 and ITS2 primer sequences (PDF) 684 \nTable S4 Genetic distances for different fluke species based on ITS2 marker (XLSX) 685 \nTable S5 Sample (n=128) details used for comparison of techniques for microscopy, PCR, 686 \nSanger sequencing, qPCR, and tremabiome (XLSX) 687 \nTable S6 Coefficients of variation for qPCR (PDF) 688 \nFig. S1 and Fig. S2 Analytical sensitivity and specificity of qPCR (PDF) 689 \nAcknowledgements 690 \nPart of this work was carried out using the computational HPC facilities and support provided 691 \nby the Research Computing Services team within IT Services at University of Surrey, 692 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n31 \n \nspecifically the Eureka2 HPC cluster 693 \n(https://docs.pages.surrey.ac.uk/research_computing/hpc/clusters/eureka2.html). 694 \nThis research was funded in whole, or in part, by the UK Research and Innovation (UKRI), 695 \nBiotechnology and Biological Sciences Research Council (BBSRC) through the FoodBioSystems 696 \nDoctoral Training Programme (BB/T008776/1) and the Sir Halley Stewart Trust (3153). For the 697 \npurpose of Open Access, the author s have applied a Creative Commons Attribution (CC BY) 698 \npublic copyright licence to any Author Accepted Manuscript version arising from this 699 \nsubmission. 700 \nCredit authorship contribution statement 701 \nMuhammad Abbas : conceptualisation, investigation, methodology, bioinformatics, 702 \nvalidation, visualisation, data curations and analysis, writing original draft, review and editing; 703 \nKezia Kozel : investigation, methodology, formal analysis,  validation, writing original draft , 704 \nreview and editing; Olukayode Daramola: writing original draft, review, formal analysis, data 705 \ncuration, supervision; Nick Selemetas: review and editing, supervision; Qasim Ali: resources; 706 \nShoaib Ashraf : resources;  Isah Ibrahim : resources;  Inaki Deza -Cruz: resources;  review and 707 \nediting; Sai Fingerhood: resources; review and editing; Mark W Robinson: resources; review 708 \nand editing ; Eric R Morgan: funding acquisition; supervision, review and editing ; Umer 709 \nChaudhry: conceptualisation, formal analysis, validation, data curation, writing, review and 710 \nediting, supervision; Martha Betson: conceptualisation, formal analysis, writing review and 711 \nediting, supervision, funding acquisition, project administration. 712 \nData availability 713 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n32 \n \nAll sequencing data produced in the paper are available at NCBI BioProject ID PRJNA1273189, 714 \nand accession numbers PV752160 -PV752182, PV752186 -PV752205, PV752431 -PV752462, 715 \nPV752238-PV752240, PV752248-PV752250, PV752375-PV752429, PV752270. 716 \nIn addition, sequence data, scripts and codes are available at Mendeley data base DOI: 717 \n10.17632/zyvwc6ppy8.1. 718 \nAll other data are reported in the paper and associated supplementary material. 719 \nFunding 720 \nMuhammad Abbas received funding from the UK Research and Innovation (UKRI), 721 \nBiotechnology and Biological Sciences Research Council (BBSRC) through the FoodBioSystems 722 \nDoctoral Training Programme for project ID FBS2022 titled “New tools for sustainable control 723 \nof liver fluke in ruminants”  Grant Ref: BB/T008776/1 . Further, t his research was funded by 724 \nthe Sir Halley Stewart Trust under the project “Rapid Diagnostics for Neglected Parasites.” 725 \nCompeting Interest 726 \nThe authors declare that no financial interests or personal relationships could have influenced 727 \nthe work reported in this paper. 728 \nReferences   729 \n1.  WHO. Neglected tropical diseases: Fascioliasis. [cited 14 Apr 2025]. Available: 730 \nhttps://www.who.int/news-room/questions-and-answers/item/q-a-on-fascioliasis 731 \n2.  Lan Z, Zhang X-H, Xing J-L, Zhang A-H, Wang H-R, Zhang X-C, et al. Global prevalence of 732 \nliver disease in human and domestic animals caused by  Fasciola: A systematic review 733 \nand meta-analysis. J Glob Health. 2024;14: 04223. doi:10.7189/jogh.14.04223 734 \n3.  DEFRA. Chapter 14: The food chain. 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Cabrera G, Cabezas C, Estay-Olea D, Stoore C, Baquedano MS, Paredes R, et al. 960 \nMolecular characterization of Fasciola hepatica obtained from cattle and horse in Central 961 \nChile. Vet Parasitol Reg Stud Reports. 2024;56: 101130. doi:10.1016/j.vprsr.2024.101130 962 \n75.  Teofanova D, Kantzoura V, Walker S, Radoslavov G, Hristov P, Theodoropoulos G, et al. 963 \nGenetic diversity of liver flukes (Fasciola hepatica) from Eastern Europe. Infect Genet Evol. 964 \n2011;11: 109–115. doi:10.1016/j.meegid.2010.10.002 965 \n76.  Mogha L, Kainga H, Kamanga N, Kapalamula TF, Wood C, Thomas LF, et al. Genetic 966 \ndiversity and population structure of Fasciola gigantica isolated from cattle in Malawi. Vet 967 \nRes Commun. 2025;49: 157. doi:10.1007/s11259-025-10717-9 968 \n 969 \n 970 \n 971 \n 972 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n39 \n \n 973 \n 974 \n 975 \n 976 \n 977 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n40 \n \n978 \n 979 \nFig. 1: Developmemt of qPCR and tremabiome 980 \n(a) Workflow adopted for developing qPCR and screening of faecal samples for the presence 981 \nof F. hepatica infection. (b)  Overview of development of the tremabiome method and its 982 \napplication on natural fluke infections in ruminants in the UK. 983 \n 984 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n41 \n \n 985 \nFig. 2: Neighbor-Joining tree generated for fluke species including reference sequences of 21 986 \ndifferent fluke species. The reference sequences were downloaded from NCBI data, and 154 987 \nunique sequences were selected and aligned (Table S2, DOI: 10.17632/zyvwc6ppy8.1). 988 \nDifferent fluke species are indicated with symbols of different colours and shapes. F. 989 \nhepatica red triangle, F. gigantica black triangle, C. daubneyi black circle, Paramphistomum 990 \nleydeni red diamond, P. cervi grey diamond, C. microbothrium light blue circle, P. epiclitum 991 \nblack diamond, Gastrothylax crumenifer triangle with red boundary no fill, Fischoederius 992 \nelongatus triangle with black boundary no fill, Dicrocoelium dendriticum red square, C. 993 \ncalicophorum yellow circle, Fischoederius cobboldi circle with black boundary no fill, C. 994 \nmicrobothrioides red circle, Homalogaster paloniae circle with light blue boundary no fill, E. 995 \nexplanatum square with black boundary no fill, C. raja grey circle, Watsonius watsoni 996 \ntriangle with dark blue border no fill, Gastrodiscoides hominis triangle with light blue 997 \nboundary no fill, D. orientalis black square, D. hospes grey square, and Dicrocoelium 998 \nchinensis dark blue square. 999 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n42 \n \n 1000 \n 1001 \n 1002 \nFig. 3: Sequence representation of the mock mixture of fluke species in deep amplicon 1003 \nsequencing.  1004 \n(a) F. hepatica, F. gigantica, and C. daubneyi. DNA was extracted in triplicate from pooled 1005 \nsamples containing 250 eggs of each species. The DNA mixture was amplified using PCR at 1006 \nthree cycle levels (25X, 30X, and 35X), with triplicate testing for each pool. The x-axis 1007 \nindicates PCR cycle numbers, while the y-axis represents each species' percentage of ITS2 1008 \nrDNA sequence reads. Triplicates were averaged and grouped based on the amplification 1009 \ncycles in the last three columns.  (b) Relative proportions of F. hepatica and C. daubneyi in 1010 \negg DNA mixtures were assessed using deep amplicon sequencing. DNA was extracted from 1011 \nmock pools containing varying ratios of these two fluke species, enabling evaluation of the 1012 \nassay’s accuracy across a range of species proportions. The x-axis represents egg mixtures 1013 \nwith varying F. hepatica: C. daubneyi ratios: M1 (negative control), M2 (99:1), M3 (90:10), 1014 \nM4 (70:30), M5 (50:50), M6 (30:70), M7 (10:90), M8 (1:99), M9 (100% C. daubneyi), and 1015 \nM10 (100% F. hepatica). The y-axis shows the percentage of ITS2 rDNA sequence reads for 1016 \neach species. 1017 \n 1018 \n 1019 \n 1020 \n 1021 \n 1022 \n 1023 \n 1024 \n 1025 \n 1026 \n 1027 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n43 \n \n 1028 \n 1029 \n 1030 \n 1031 \nFig. 4. Threshold of deep amplicon sequencing  1032 \nApplication of deep amplicon sequencing to mock egg mixtures with gradually lower counts 1033 \nof F. hepatica and C. daubneyi eggs. Two sets of mixtures were designed using eggs from 1034 \nfour fluke species (F. hepatica, F. gigantica, E. explanatum, and C. daubneyi). In panel (a), 1035 \nwhich focuses on F. hepatica, MM1 contained 500 eggs of F. hepatica along with 50 eggs of 1036 \neach of the other three species, creating a high relative abundance of F. hepatica. In 1037 \nmixtures MM2 through MM6, the number of F. hepatica eggs was reduced to 50, 20, 15 5, 1038 \nand 0 eggs, respectively, while the counts for the other three fluke species remained 1039 \nconstant at 50 eggs. Panel (b) follows a similar design but targets C. daubneyi: MM1 1040 \ncontained 500 eggs of C. daubneyi plus 50 eggs each of F. hepatica, F. gigantica, and E. 1041 \nexplanatum, and in mixtures MM2 to MM6 the number of C. daubneyi eggs was reduced to 1042 \n50, 20, 15, 5, and 0 eggs, with the other species maintained at 50 eggs each. Additionally, 1043 \nsingle-species control pools were included as MM7 (F. hepatica), MM8 (F. gigantica), MM9 1044 \n(E. explanatum), and MM10 (C. daubneyi), each containing 50 eggs. The assay results show 1045 \nthe ability to detect and accurately quantify trace levels of target DNA in mixed fluke egg 1046 \npopulations. 1047 \n 1048 \n 1049 \n 1050 \n 1051 \n 1052 \n 1053 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n44 \n \n 1054 \nFig. 5: Tremabiome deep amplicon sequencing application on the field samples 1055 \nThis figure illustrates the application of the  tremabiome deep amplicon sequencing assay 1056 \non DNA extracted from sedimented faecal eggs and adult worm populations collected from 1057 \ncattle and sheep across various regions in the UK. The charts display species proportions 1058 \nbased on the percentage of sequence reads generated after 35 amplification cycles. 1059 \nPercentages of F. hepatica are represented in blue and C. daubneyi in green on the Y-axis. 1060 \n(a) Samples from cattle. (b) Samples from sheep. 1061 \n 1062 \n 1063 \n 1064 \n 1065 \n 1066 \n 1067 \n 1068 \n 1069 \n 1070 \n 1071 \n 1072 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n45 \n \n 1073 \n 1074 \nFig. 6: A Neighbor-Joining tree of rDNA ITS2 sequences constructed using 154 reference 1075 \nsequences of different fluke species downloaded from the NCBI database, along with 87 1076 \nASVs identified in this study 55 from F. hepatica and 32 from C. daubneyi. ASVs 1077 \ncorresponding to F. hepatica are marked with blue triangles, while those of C. daubneyi are 1078 \nrepresented by blue circles. The ASVs clustered closely with their respective reference taxa, 1079 \nconfirming accurate taxonomic assignment.    1080 \n 1081 \n 1082 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n46 \n \n 1083 \nTable. 1: Comparison of microscopy, ITS2 PCR and Sanger sequencing on fluke egg-positive samples 1084 \nMicroscopy \nPCR Sanger sequencing using ITS2 \nNegative Positive Not \nperformed \nND2 Total F. \nhepatica \nC. \ndaubneyi \nParamphistomum \nepiclitum \nPSQ Not \nperformed \nND2 Total \nF. hepatica 7 30 32 2 71 3 19 0 9 38 2 71 \nC. daubneyi 4 26 0 0 30 1 16 1 9 3 0 30 \nMixed 0 3 2 0 5 1 2 0 0 2 0 5 \nFluke1 1 14 6 1 22 2 6 0 6 7 1 22 \nTotal 12 73 40 3 128 7 43 1 24 50 3 128 \n1Unidentified flukes; 2 ND = DNA extraction failed; PSQ = poor sequence quality 1085 \n 1086 \n 1087 \nTable. 2: Comparison of microscopy and Sanger sequencing using mt-ND-1 markers on fluke egg-positive samples 1088 \nMicroscopy Sanger sequencing using ND1 \nF. hepatica C. daubneyi PSQ Not performed ND2 Total \nF. hepatica 6 0 3 60 2 71 \nC. daubneyi 1 0 1 28 0 30 \nMixed 1 0 0 4 0 5 \nFluke1 1 0 2 18 1 22 \nTotal 9 0 6 110 3 128 \n1Unidentified flukes; 2 ND = DNA extraction failed; PSQ = poor sequence quality 1089 \n 1090 \n 1091 \n 1092 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n47 \n \nTable. 3: Comparison of microscopy, qPCR (for F. hepatica) and tremabiome on 128 fluke egg-positive samples 1093 \nMicroscopy \n qPCR  Tremabiome \nNegative Positive ND NP Total F. hepatica C. daubneyi Mixed ND2 No \nreads \nTotal \nF. hepatica 33 34 2 2 71 11 26 30 2 2 71 \nC. daubneyi 23 7 0 0 30 2 21 7 0 0 30 \nMixed 1 4 0 0 5 0 1 4 0 0 5 \nFluke1 9 12 1 0 22 0 3 17 1 1 22 \nTotal 66 57 3 2 128 13 51 58 3 3 128 \n1Unidentified flukes; 2 ND = DNA extraction failed; NP=Not performed  1094 \n 1095 \n 1096 \nTable. 4: Comparison of qPCR and  tremabiome on 128 fluke egg-positive samples 1097 \nqPCR Tremabiome   \nC. daubneyi  F. hepatica Mixed ND No reads Total \nNegative 43 3 17 0 3 66 \nF. hepatica 8 10 39 0 0 57 \nND 0 0 0 3 0 3 \nNP 0 0 2 0 0 2 \nTotal 51 13 58 3 3 128 \nND = DNA extraction failed, NP =Not performed  1098 \n 1099 \n 1100 \n 1101 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n48 \n \n 1102 \nTable. 5: Comparison of Sanger sequencing and tremabiome on selected samples 1103 \nTremabiome \nSanger sequencing using ITS2 \nF. \nhepatica \nC. \ndaubneyi \nParamphistomum \nepiclitum \nPSQ Not \nperformed \nND1 Total \nF. hepatica 2 0 1 2 8 0 13 \nC. daubneyi 0 29 0 9 13 0 51 \nMixed 5 14 0 10 29 0 58 \nNo reads 0 0 0 3 0 0 3 \nND 0 0 0 0 0 3 3 \nTotal 7 43 1 24 50 3 128 \n1ND = DNA extraction failed; PSQ = poor sequence quality 1104 \n 1105 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted August 2, 2025. ; https://doi.org/10.1101/2025.07.31.667929doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}