Abstract
Chronic infection with O. viverrini has been linked to the development of cholangiocarcinoma (CCA), which is a major public health burden in the Lower Mekong River Basin countries, including Thailand, Lao PDR, Vietnam and Cambodia. Despite its importance, the exact mechanisms by which O. viverrini promotes CCA are largely unknown. In this study, we characterized different extracellular vesicle populations released by O. viverrini (OvEVs) using proteomic and transcriptomic analyses and investigated their potential role in host-parasite interactions. While 120k OvEVs promoted cell proliferation in H69 cells at different concentrations, 15k OvEVs did not produce any effect compared to controls. The proteomic analysis of both populations showed differences in their composition that could contribute to this differential effect. Furthermore, the miRNAs present in 120k EVs were analysed and their potential interactions with human host genes was explored by computational target prediction. Different pathways involved in inflammation, immune response and apoptosis were identified as potentially targeted by the miRNAs present in this population of EVs. This is the first study showing specific roles for different EV populations in the pathogenesis of a parasitic helminth, and more importantly, an important advance towards deciphering the mechanisms used in establishment of opisthorchiasis and liver fluke infection-associated malignancy.
Keywords: Opisthorchis viverrini, extracellular vesicles, miRNAs, RNA-seq, cholangiocarcinoma
1. Introduction
Opisthorchiasis remains a major public health problem in East Asia and Eastern Europe. The main species affecting South East Asia, Opisthorchis viverrini, affects near 10 million people, particularly in Thailand and Laos (1). In addition to infection-associated morbidity (including cholangitis, choledocholithiasis and periductal fibrosis), liver fluke infection with O. viverrini has been strongly linked to cholangiocarcinoma (CCA), a form of bile duct cancer which has the highest global prevalence in the Northeast region of Thailand (2). Multiple factors are involved in the progression of CCA, including mechanical damage from physical attachment of the liver fluke to the biliary epithelium, chronic immunopathological processes that induce pro-inflammatory cytokines, and the release of parasite-derived excretory/secretory (ES) products (including soluble proteins and extracellular vesicles (EVs)) into the bile ducts that promote cell proliferation (3–6).
These ES products constitute the main players in the crosstalk between the parasite and its host, and blocking this interaction has been shown to eliminate or impair establishment of the worms, reducing cell proliferation and the development of cholangiocarcinoma (3, 6–11). For instance, over 300 proteins have been identified in the ES products from O. viverrini (4), one of which is a granulin-like growth factor termed Ov-GRN-1, which drives proliferation of biliary epithelial cells. Ov-GRN-1-induced cell proliferation can be inhibited with antibodies raised to the recombinant protein (12), and moreover, infection of hamsters with Ov-grn-1 knock-out flukes results in reduced biliary fibrosis and cholangiocarcinoma compared to hamsters infected with control flukes (8, 13). In addition, we reported that O. viverrini secretes small extracellular vesicles (EVs) of 40–100 nm in size that can be internalized by host cells (3), and that blocking internalization of these EVs using antibodies against a member of the tetraspanin protein family significantly reduced the secretion of pro-inflammatory cytokines including IL-6 (3). Furthermore, vaccination of hamsters with small EVs resulted in decreased worm burdens and worm growth retardation as well as reduced egg secretion (9). However, the exact mechanisms by which EVs promote biliary cell proliferation and aids the establishment of Opisthorchis infection remain unknown.
EVs can be categorized into different subpopulations based on the origin and size of the vesicles, although, for most cell types and organisms studied, there are no reliable and specific markers for each population (14, 15). Small EVs (usually named exosomes) have an endocytic origin and have a size of 30–150 nm, whereas larger EVs such as microvesicles form by direct budding of the plasma membrane and typically range from 100 to 1000 nm (1 μm) (16). Whereas small vesicles have been described so far from O. viverrini, other flukes have been shown to secrete both populations of EVs, including Fasciola hepatica, and Schistosoma mansoni (17, 18), although their specific roles in parasite-host communication remain unclear.
While the proteomic content of EVs from 17 different helminth species has been well characterized, and several species- or class-specific markers have been proposed, the miRNA cargo of EVs has not been characterized for all helminths studied (reviewed in (19)). In schistosomes, parasite-specific miRNAs have been proposed as new diagnostic candidates in infected human subjects (20), and it has been hypothesized that EV miRNAs from the nematode Nippostrongylus brasiliensis have anti-inflammatory and immunomodulatory roles (21). Furthermore, Schistosoma japonicum EV miR-125b and bantam miRNAs promote macrophage proliferation and TNF-α production (22), and S. mansoni miR-10 is implicated in the polarization towards a Th1 response (23) while Sja-miR-71a present in S. japonicum egg EVs can suppress liver fibrosis (24). However, no association has been found so far between O. viverrini miRNAs and malignancy.
In the present study we show, for the first time, the secretion of both types of EVs by O. viverrini and highlight several proteins that are unique for each vesicle type, and that could be used as specific markers for the isolation and characterization of these subpopulations of EVs. Furthermore, we delve into the mechanisms used by the parasite to promote cell proliferation in cholangiocytes via small EVs but not MVs, which highlights the role of EVs in inter-phylum cross-talk and opens new avenues for the treatment of O. viverrini-induced cholangiocarcinoma.
2. Materials and methods
2.1. Animal ethics
Six- to eight-week-old male Syrian golden hamsters were infected with O. viverrini metacercariae and maintained in the animal house facility at the Faculty of Medicine, Khon Kaen University, Thailand for 8 weeks before being euthanized. Animal experiments were approved by the Animal Ethics Committee of Khon Kaen University (IACUC-KKU 93/2565).
2.2. Isolation and purification of extracellular vesicles
Hamsters were necropsied at 8 weeks post-infection and adult worms collected, washed in PBS and cultured in RPMI 1640 containing 1% glucose, 100 units/ml Penicillin, 100 units/ml Streptomycin (Life Technologies, Grand Island, NY) and 1 nM E64 (Thermo scientific, USA) at 37°C, 5% CO2 for 7 days. For the isolation and purification of O. viverrini EVs (OvEVs), a previously published method was followed (18). Briefly, O. viverrini ES products (OvES) were collected every day, centrifuged at 500 g for 10 min to remove eggs and large debris, and subsequently centrifuged at 2,000 g for 30 min, 4,000 g for 30 min and 15,000 g for 45 min to remove smaller debris and MVs. MVs were washed twice with PBS, centrifuged at 12,000 g and stored at −80°C until use. Following removal of MVs, supernatant was concentrated using a 10 kDa cut-off Amicon filter (Merck Millipore, USA) and ultracentrifuged at 120,000 g for 3 hours to pellet smaller (120k) vesicles. The pellet was resuspended in 70 μl of PBS, laid on a discontinuous gradient (40%, 20%, 10%, 5%) built with OptiPrepTM Density Gradient (Millipore Sigma, USA) as described previously (25) and centrifuged for 18 h at 4°C. EVs isolated from grapes (termed “grape Evs”) were isolated from Vitis vinifera Thompson seedless grapes as described elsewhere (21). The size and concentrations of all Evs were analysed using a qNano instrument (Izon Science, New Zealand) and protein content was determined using a BCA kit (Bio-Rad).
2.3. Tunable Resistive Pulse Sensing analysis of extracellular vesicles
Tunable resistive pulse sensing (TRPS) was employed using a qNano system (Izon Science) to measure the particle concentration and size distribution of Evs. Briefly, a nanopore NP150 or NP400 (for 12k and 15k Evs, respectively) was used, and pressure and voltage values were set to optimize the signal to ensure high sensitivity. All samples were diluted (1:10 for OvEVs and 1:20 for grapesomes) before applying to the nanopore, and CP100 carboxylated polystyrene calibration particles (Izon Science) were used for calibration. Size distribution and concentration of particles were analyzed using the software provided by Izon (version 3.2).
2.4. Proteomic analysis of 15k OvEVs
EV-surface exposed peptides were released by trypsin hydrolysis following the methodology described by Chaiyadet and co-workers (26). Briefly, EVs were treated with trypsin (1 μg/μl) for 15 min at 37°C to cleave surface-exposed proteins, centrifuged at 15,000 g for 1 hour at 4°C, and supernatant containing the surface peptides collected. Pellet containing “shaved” EVs was resuspended in water, sonicated and released EV cargo content was recovered from the supernatant after centrifugation at 15,000 g for 1 hour at 4°C. Finally, the pellet was solubilized in 1% Triton X-100/2% sodium dodecyl sulphate (SDS) at 37°C for 15 min to recover membrane-associated proteins. For the proteomic analysis, cargo and membrane-associated proteins were electrophoresed on a 10% SDS-PAGE gel and in-gel digestion was performed as described previously with minor modifications (27).
All samples (trypsin-liberated, cargo and membrane peptides) were injected into an Eksigent nanoLC 415 system using an Eksigent Trap-column (C18-CL, 3 μm, 120 Å, 350 μm × 0.5 mm) for the pre-concentration step followed by separation in a 15 cm long Eksigent column (C18-CL particle size 3 μm, 120 Å, 75 μm ID) using a linear gradient of 3–40% solvent B (100 acetonitrile/0.1% formic acid [aq]) in solvent A (0.1% formic acid [aq]) for 45 min followed by 40–80 % solvent B in 5 min. A flow rate of 300 nl/min was used for all experiments. Eluates from the RP-HPLC column were directly introduced into the PicoView ESI ionisation source of a TripleTOF 6600 MS/MS System (AB Sciex) operated in positive ion electrospray mode. All analyses were performed using Information Dependent Acquisition. Analyst Software 1.7.1 (Applied Biosystems) was used for data analysis. Briefly, the acquisition protocol consisted of the use of an Enhanced Mass Spectrum scan with 15 sec exclusion time and 100 ppm mass tolerance. A cycle time of 1800 ms was used to acquire full scan TOF-MS data over the mass range 400–1250 m/z and product ion scans over the mass range of 100–1500 m/z for up to 30 of the most abundant ions with a relative intensity above 150 and a charge state of +2 – +5. Full product ion spectra for each of the selected precursors were then used for subsequent database searches.
Peak lists obtained from MS/MS spectra were identified using X!Tandem version X! Tandem Vengeance (2015.12.15.2) (28), MS-GF+ version Release (v2018.04.09) (29) and Tide (30). The search was conducted using SearchGUI version 3.3.15 (31). Protein identification was conducted against a concatenated target/decoy version of the O. viverrini genome and the common repository of adventitious proteins (cRAP database) (10,876 (target) sequences). The decoy sequences were created by reversing the target sequences in SearchGUI. The identification settings were as follows: Trypsin (specific), with a maximum of 2 missed cleavages, 20.0 ppm as MS1 and 0.2 Da as MS2 tolerances; fixed modifications: Carbamidomethylation of C (+57.021464 Da), variable modifications: Deamidation of N (+0.984016 Da), Deamidation of Q (+0.984016 Da), Oxidation of M (+15.994915 Da), fixed modifications during refinement procedure: Carbamidomethylation of C (+57.021464 Da), variable modifications during refinement procedure: Acetylation of protein N-term (+42.010565 Da), Pyrolidone from E (--18.010565 Da), Pyrolidone from Q (--17.026549 Da), Pyrolidone from carbamidomethylated C (--17.026549 Da).
Peptides and proteins were inferred from the spectrum identification results using PeptideShaker version 1.16.40 (32). Peptide Spectrum Matches (PSMs), peptides and proteins were validated at a 1.0% False Discovery Rate (FDR) estimated using the decoy hit distribution. The mass spectrometry data along with the identification results have been deposited in ProteomeXchange Consortium (33) via the PRIDE partner repository with the dataset identifier PXD020356 and doi:10.6019/PXD020356. During the review process, the data can be accessed with the following credentials upon login to the PRIDE website (http://www.ebi.ac.uk/pride/archive/login): Username: [reviewer03276@ebi.ac.uk], Password: [fL0hevyQ].
Blast2GO software (34) was employed for the Gene Ontology (GO) analysis. Only children GO terms were used for subsequent analysis to avoid redundancy in GO terms. Protein family (Pfam) analysis was performed using the gathering bit score (--cut_ga) threshold using HMMER v3.
2.4. mRNA and miRNA isolation
mRNA and miRNA of OvEVs were obtained from different batches of worms as described previously (21) with sequencing performed on four mRNA and two miRNA biological replicates of the 120k EVs. Briefly, total RNA and miRNA were extracted using the mirVanaTM miRNA Isolation Kit (ThermoFisher) and stored at −80°C until analysed.
2.5. RNA sequencing and transcript annotation
The RNA quality, yield and size of total and small RNAs were analysed using capillary electrophoresis (Agilent 2100 Bioanalyzer, Agilent Technologies, Santa Clara, CA, USA). The TruSeq Small RNA-seq preparation kit (Illumina) was used for miRNA sequencing according to the manufacturer’s instructions. For mRNA-seq, ribosomal RNA was removed from samples, and mRNA was prepared for sequencing using an Illumina TruSeq stranded mRNA-seq library preparation kit according to the manufacturer’s instructions. RNAseq was performed on a NextSeq 500 (Illumina, 75-bp PE mid-output run, approximately 30M reads per sample). Quality control, library preparation and sequencing were performed at the Ramaciotti Centre for Genomics at the University of New South Wales, Sydney.
High-throughput RNA-seq data were aligned to the O. viverrini reference genome model (WormBase WS255; http://parasite.wormbase.org; (35)) using the STAR transcriptome aligner (36). Differentially expressed genes were identified using consensusDE (37). Prior to downstream analysis, rRNA-like sequences were removed from the metatranscriptomic dataset using riboPicker-0.4.3 (http://ribopicker.sourceforge.net; (38)). BLASTn algorithm (39) was used to compare the non-redundant mRNA dataset for O. viverrini EVs to the nucleotide sequence collection (40) from NCBI (www.ncbi.nlm.nih.gov) to identify putative homologues in a range of other organisms (cutoff: <1E-03). Corresponding hits homologous to the murine host, with a transcriptional alignment coverage <95% (based on the effective transcript length divided by length of the gene), and with an expression level <10 fragments per kilobase of exon model per million mapped reads (FPKM) normalized by the length of the gene, were removed from the list. The final list of mRNA transcripts from O. viverrini EVs was assigned to protein families (Pfam) and GO categories (Blast2GO).
2.6. miRNA analysis and target prediction
The miRDeep2 package (41) was used to identify known and putative novel miRNAs present in both miRNA samples. As there are no O. viverrini miRNAs available in miRBase release 21 [36], the miRNAs from the flatworms Schmidtea mediterranea, Echinococcus granulosus and Echinococcus multilocularis, S. japonicum and S. mansoni were utilized as a training set for the algorithm. Only miRNA sequences commonly identified in all replicates were included for further analyses.
The interaction between human host genes and the identified miRNAs were bioinformatically predicted using three different software programs: MR-microT (42), mirDB (43) and miRANDA (44). miRDB and MRMicroT are web-based algorithms and default settings were used, except that only targets with scores ≥0.7 and ≥60, respectively, were selected. In the case of miRANDA, input 3ʹUTR from the Homo sapiens GRCm38.p4 assembly was retrieved from the Ensembl database release 100. The software was run with strict 5ʹ seed pairing, energy threshold of −20 kcal/mol and default settings for gap open and gap extend penalties. Interacting hits were filtered by conservative cut-off values for pairing score (>155) and matches (>80%). For a more robust target prediction, only targets commonly predicted by all three software for a single miRNA were further analysed by the Panther classification system (http://pantherdb.org/) using pathway classification (45).
2.7. Mammalian cell culture
The nonmalignant cholangiocyte cell line H69 is a SV40-transformed human bile duct epithelial cell line derived from human liver, kindly provided by Dr. Gregory J. Gores, Mayo Clinic, Rochester, Minnesota. H69 cells (46) were maintained in T75 cm2 vented flasks (Corning) as monolayers as described (47) with minor modifications. Cells were maintained with regular splitting using 1x TrypLE express trypsin (Gibco) every 2–5 days in complete media (Gibco): growth factor-supplemented DMEM/F12 with high glucose media with 10% FCS, 2× antibiotic/antimycotic, 25 μg/mL adenine, 5 μg/mL insulin, 1 μg/mL epinephrine, 8.3 μg/mL holo-transferrin, 0.62 μg/mL hydrocortisone, 13.6 ng/mL T3, and 10 ng/mL EGF. Low nutrient media for cell proliferation assays was 5% complete media, i.e., 0.5% FCS and 1/20 of the growth factor concentrations listed above for complete media. The identity (human-derived) of the cell line was confirmed with single tandem repeat (STR) analysis in January 2018 (15/15 positive loci across two alleles) and mycoplasma free status was determined at the DNA Diagnostics Centre (DDC)–Medical (U.S.A.), accredited/certified by CAP, ISO/IEC 17025:2005, through ACLASS.
2.8. Cell Proliferation Monitoring in Real Time Using xCELLigence
Cells were seeded at 3,000 cells/well in 150 μL of complete media (above) in E-plates (Agilent) and grown overnight while being monitored with an xCELLigence SP system (Agilent), which monitors cellular events in real time by measuring electrical impedance across interdigitated gold microelectrodes integrated into the base of tissue culture plates. As previously described (48), cells were washed three times with PBS prior to addition of 150 μL of low nutrient media (above) and incubated for a minimum of 6 h before further treatment. Treatments were prepared at 8.5× concentration and added to each well in 20 μL, for a final in-well 1× concentration. The xCELLigence system recorded cell indexes at intervals of 1 h for 5–6 days following treatment. Readings for the cell index were normalized prior to treatment, and cell proliferation ratios represent the relative numbers of cells to controls at each timepoint from 6 replicates. Dose response curves for each treatment at day 3 were from two experiments.
2.8. Statistical analysis
Comparisons of induction of cell proliferation in response to treatments were accomplished using Two-Way ANOVA test with Holm-Sidak multiple comparison correction, using GraphPad Prism 6.03. Data were expressed as the mean ± standard error of three independent experiments using Graphpad Prism Software Version 6.03 (www.graphpad.com).
3. Results
3.1. Purification of EVs
Two different populations of OvEVs were isolated and purified from the OvES products. The 15k OvEVs were isolated and purified by ultracentrifugation, while 120k OvEVs and grape EVs were further purified using Optiprep® gradient and highly pure fractions combined as described previously (21). Particle diameter and concentration was measured using a qNano instrument, protein concentration using the BCA assay and purity was obtained following Webber and Clayton approach (49). While 15k OvEVs had a mean particle diameter of 320±138 nm (mode 246 nm) and a total concentration of 1.06E+09 particles/ml, the 120k OvEVs had a mean particle diameter of 135±32.7 nm (mode 117 nm) and a total concentration of 4.27E+10 particles/ml (Figure 1). Grape EVs had a mean particle size of 143±30.4 nm and a concentration of 2.41E+10 (Figure 1). Purity of vesicles ranged from 4.61E+07 for the 15k OvEVs to 1.82E+09 for 120k OvEVs (Supplementary Table 1).
Figure 1. Size and concentration of Opisthorchis viverrini-derived 120 k and 15k extracellular vesicles (EVs).
Tunable Resistive Pulse Sensing was used to analyse the diameter and concentration of 120k O. viverrini EVs as well as grape EVs (A) and 15k O. viverrini EVs.
3.2. 120k OvEVs but not 15k OvEVs promote cholangiocyte proliferation in vitro
To further investigate the role of the different populations of OvEVs in driving proliferation of cholangiocytes, H69 immortalised cholangiocytes were incubated with different concentrations of 120k and 15k O. viverrini EVs; grape EVs and media alone were used as controls. 120k EVs significantly promoted cell proliferation from 9.3E+07 up to 7.4E+08 particles/ml (corresponding to 50 ng/ml and 400 ng/ml, respectively) in all concentrations tested (Figure 2A, P<0.0001). O. viverrini 120k EVs promoted cell proliferation 24 h after incubation with cholangiocytes at different concentrations when compared to controls (Figure 2B, P<0.05–0.0001).
Figure 2. Opisthorchis viverrini 120k but not 15k extracellular vesicles (EVs) drive proliferation of human cholangiocytes.
(A) O. viverrini 120k EVs (open circles) but not 15k EVs (red squares) promoted proliferation of human cholangiocytes at different concentrations when compared to media alone and to grape EVs (blue circles). (40) O. viverrini 120k EVs at 3.7E+08 particles/ml (200 ng/ml) (open circles) and 7.4E+08 particles/ml (400 ng/ml) (orange diamond) promoted proliferation of human cholangiocytes significantly after 24 h of incubation.
3.3. Proteomic profile of 15k EVs secreted by O. viverrini
The proteomic content of the 15k OvEVs was analysed by mass spectrometry following established methods designed to localise the proteins within the different fractions of the EVs (17, 26). A total of 718 unique proteins were identified with 2 or more unique peptides, including 334 as trypsin-liberated (or “shaved”) proteins, 352 as membrane proteins and 648 as cargo proteins (Supplementary Tables 2–4). A comparison of the results obtained in this analysis was performed against the proteomic data obtained by Chaiyadet et al. (26), since both analyses were performed following the same methodology and using the same mass spectrometers. Interestingly, the cargo from the 15k and 120k OvEVs have the highest number of unique proteins (155 and 49, respectively), while 76 proteins were common to both populations of OvEVs (Figure 2). Furthermore, 46 and 21 proteins are uniquely present in the shaved fractions analysed from the 15k and the 120k population, respectively. (Figure 3, Tables 1, 2).
Figure 3. Proteomic identifications of the 120k and 15k populations of O. viverrini-derived extracellular vesicles (OvEVs).
UpSetR analysis visualising intersections between the proteins identified in the different compartments analysed from the 120k and 15k populations of O. viverrini-derived EVs.
Table 1.
Unique proteins from 120k OvEVs shaved sample.
| Protein name | Description | Pfam family/domain | N° unique peptides |
|---|---|---|---|
|
| |||
| OON13804.1 | Cell division cycle and apoptosis regulator protein 1 | SAP Family | 6 |
| OON21748.1 | Transcription elongation regulator 1 | FF Family / WW Domain | 5 |
| OON22257.1 | TRPM8 channel-associated factor 2 | Peptidase_M60 Domain | 4 |
| OON21215.1 | Hsp90 protein | HSP90 Family | 4 |
| OON20288.1 | Immunoglobulin domain protein | Ig 3 Domain | 3 |
| OON19373.1 | Oxidoreductase, 2OG-Fe(II) oxygenase family protein | - | 3 |
| OON16932.1 | Putative chaperone protein DnaK | HSP70 Family | 3 |
| OON23969.1 | Ribosomal protein L19e | Ribosomal L19e Family | 2 |
| OON21776.1 | Large subunit ribosomal protein L12.e | Ribosomal L11 N Domain | 2 |
| OON20086.1 | Hypothetical protein X801_04039, partial | Vinculin Family | 2 |
| OON19823.1 | High mobility group protein DSP1 | HMG box 2 Domain | 2 |
| OON18290.1 | Elongation factor 1-beta | EF-1_beta_acid Domain | 2 |
| OON17975.1 | Protein hu-li tai shao | Aldolase II Domain | 2 |
| OON17798.1 | Putative maleylacetoacetate isomerase Neuroblast differentiation-associated | GST_N_3 Domain | 2 |
| OON16907.1 | protein AHNAK | - | 2 |
| OON16165.1 | Linker histone H1 and H5 family protein | Linker histone Domain | 2 |
| OON16018.1 | Histone H3 | Histone Domain | 2 |
| OON15665.1 | Carbohydrate phosphorylase | Phosphorylase Family | 2 |
| OON15027.1 | D-aspartate oxidase | DAO Domain | 2 |
| OON13880.1 | Putative cystathionine beta-synthase | PALP Family | 2 |
| OON13684.1 | Cell division protein | Septin Domain | 2 |
Table 2.
Unique proteins from 15k OvEVs shaving sample.
| Protein name |
Description | Pfam family/domain | Number of unique peptides |
|---|---|---|---|
|
| |||
| OON23431.1 | Hypothetical protein X801_00663 | ADK Domain | 10 |
| OON13409.1 | Putative glycerol kinase | FGGY N Domain | 7 |
| OON24055.1 | Fumarate hydratase class I, partial | Fumerase Family | 6 |
| OON20815.1 | Acetyl-CoA C-acetyltransferase, partial | Thiolase N Domain | 5 |
| NADH dehydrogenase, G subunit | NADH-G 4Fe-4S 3 Domain | ||
| OON19817.1 | / Molybdopterin Family | 5 | |
| HMGL-like Domain / | |||
| OON19105.1 | Putative pyruvate carboxylase, partial | HMGL-like Domain / Biotin lipoyl Domain | 5 |
| OON19149.1 | Hypothetical protein X801_04987, partial | Mt ATP-synt B Family | 4 |
| OON17488.1 | ADP,ATP carrier protein 1 | Mito carr Family | 4 |
| OON23432.1 | Hypothetical protein X801_00664 | ADK Domain | 3 |
| OON22871.1 | EF hand | EF-hand 7 Domain | 3 |
| OON16330.1 | Tubulin/FtsZ family, GTPase domain protein | Tubulin Domain | 3 |
| OON15185.1 | FAD dependent oxidoreductase | DAO Domain | 3 |
| OON14585.1 | 3-hydroxyacyl-CoA dehydrogenase protein | 3HCDH Domain | 3 |
| OON14250.1 | Tubulin/FtsZ family, GTPase domain protein | Tubulin Domain | 3 |
| OON14231.1 | Zn-finger in ubiquitin-hydrolase and other protein | UCH Family / zf-UBP Domain | 3 |
| OON14218.1 | Hypothetical protein X801_09994, partial | - | 3 |
| OON13911.1 | Putative acetyl-CoA C-acyltransferase, partial | Thiolase N Domain | 3 |
| OON13809.1 | Ubiquinol-cytochrome c reductase, iron-sulfur subunit | UCR TM Family / Rieske Domain | 3 |
| OON13698.1 | Putative succinate-semialdehyde dehydrogenase | Aldedh Family | 3 |
| OON23750.1 | Glyceraldehyde-3-phosphate dehydrogenase, type I | Fumerase Family | 2 |
|
OON23460.1 OON23448.1 |
Hypothetical protein X801_00629 Class II Aldolase and Adducin domain protein |
Gp_dh_N Domain Aldolase II Domain | 2 2 |
| OON23332.1 | Actin | Actin Family | 2 |
| OON22490.1 | Peptidase M16 inactive domain protein | Peptidase_M16 Family | 2 |
| Pyridine nucleotide-disulfide | |||
| OON22407.1 | oxidoreductase, dimerization domain protein | Pyr redox dim Domain | 2 |
| OON21233.1 | Hypothetical protein X801_02875 | - | 2 |
| OON20884.1 | Saccharopine dehydrogenase | Sacchrp_dh_NADP Family | 2 |
| OON20768.1 | Hypothetical protein X801_03347 | STI1 Domain | 2 |
| OON20759.1 | Hypothetical protein X801_03354 | SHIPPO-rpt Repeat | 2 |
| OON19861.1 | Hypothetical protein X801_04266, partial | - | 2 |
| OON19439.1 | Hypothetical protein X801_04695 | - | 2 |
| OON19131.1 | 3-oxoacid CoA-transferase, B subunit | CoA trans Domain | 2 |
| OON19104.1 | Hypothetical protein X801_05032, partial | - | 2 |
| OON18343.1 | Enolase, TIM barrel domain protein, partial | Enolase C Domain | 2 |
| OON17540.1 | NAD dependent epimerase/dehydratase family protein | Epimerase Family | 2 |
| OON17517.1 | V-type ATPase, G subunit | V-ATPase G Family | 2 |
| OON17270.1 | Hypothetical protein X801_06891 | Fer2 3 Domain | 2 |
| OON16462.1 | Hypothetical protein X801_07725, partial | BAR Domain | 2 |
| OON16247.1 | Hypothetical protein X801_07941 | - | 2 |
| OON16079.1 | Citrate (Si)-synthase, eukaryotic, partial | Citrate_synt Domain | 2 |
| OON15491.1 | ATP synthase, subunit E | vATP-synt E Family | 2 |
| OON15476.1 | Putative dihydrolipoyllysine-residue succinyltransferase | 2-oxoacid dh Domain | 2 |
| OON15404.1 | Hypothetical protein X801_08795, partial | ARPC4 Family | 2 |
| OON15034.1 | EF hand | - | 2 |
| OON14958.1 | Tau and MAP protein, tubulin-binding repeat protein | Tubulin-binding Family | 2 |
| OON14808.1 | core histone H2A/H2B/H3/H4 | Histone Domain | 2 |
3.4. Characterisation of the genomic content and in-silico target prediction of miRNAs present in 120k OvEVs
By sequencing and screening biological duplicates for mRNAs and miRNAs in the cargo of 120k OvEVs, a total of 2,478 full-length mRNA transcripts mapping to O. viverrini gene models were common to both replicates (Supplementary File 1). The identified hits were subjected to a Pfam and GO analysis. The most represented biological processes based on the nodescore provided by Blast2GO were signal transduction (GO:0007165) and transport (GO:0006810), with 195 and 227 sequences matching these processes respectively (Supplementary Figure 1). Similarly, metal ion binding (GO:0046872) and ATP binding (GO:0005524) were the molecular functions with the highest nodescore. Interestingly, proteins encoded by these mRNAs contained at least 1,640 domains based on a Pfam analysis, with Reverse transcriptase (PF00078) and Protein kinase (PF00069) being the most represented (Supplementary Table 5). Similarly, 64 miRNAs were common to both biological replicates, 26 of which have close homologues in other trematodes (Supplementary Table 6).
Potential interactions of O. viverrini 120k EV miRNAs with human host genes was explored by computational target prediction. To obtain more robust target predictions, three different target prediction software programs were used and only common targets predicted by all three software for a particular miRNA were taken into consideration for further analysis. From the total 64 miRNAs identified, 50 had targets commonly predicted by all three software (Supplementary Table 7), and, from these, 30 had targets that could be mapped to biological pathways by PantherDB. A total of 85 different pathways could be mapped by the predicted miRNA targets, with the Gonadotropin-releasing hormone receptor pathway being the pathway mapped to the greatest number of genes (22 predicted targets from 18 different miRNAs). Interestingly, genes belonging to different pathways associated with the immune system such as the Inflammation mediated by chemokine and cytokine signaling pathway (P00031), T cell activation (P00053), B cell activation (P00010), Interleukin signaling pathway (P00036) and TGF-beta signaling pathway (P00052) were targeted by O. viverrini 120k EV miRNAs (Figure 4, Supplementary Table 7). Genes from different signaling pathways (i.e. Wnt signalling pathway (P00057), the Integrin signalling pathway (P00034) or the PDGF signaling pathway (P00047)) were also predicted to be targeted by O. viverrini 120k EV miRNAs. Other pathways targeted by these miRNAs were the Angiogenesis (P00005) pathway, the Apoptosis signaling pathway (P00006) and the VEGF signaling pathway (P00056) (Figure 4, Supplementary Table 7).
Figure 4. Prediction of O. viverrini 120k extracellular vesicle (EV) miRNA target interactions to human host genes.
Individual targeted host genes were categorized by PantherDB pathway analysis (heatmap corresponds to individual targeted genes commonly identified by three different target prediction software in the human host). Top axis shows the 30 identified miRNAs in O. viverrini EVs containing at least one gene involved in a biological pathway and their abundances (Log2 average mean read counts from two biological replicates).
Discussion
Discrete types of EVs can be produced by most types of cells, including exosome-like EVs (30–150 nm in diameter) that derive from the endosomal pathway and are formed by inward budding of the multivesicular body membrane, or MVs, which are larger in diameter (100–1,000 nm) and bud directly from the plasma membrane (50). Species of parasitic helminths have been shown to produce both types of EVs, including the human blood fluke, S. mansoni and the livestock liver fluke, Fasciola hepatica (17, 18). We now report that O. viverrini produces at least 20 times more 120k OvEVs than 15k OvEVs. This contrasts with what was observed for S. mansoni, which produced more 15k SmEVs than 120k SmEVs (18). However, those studies were performed using only one biological replicate of EVs, and additional replicates should be analysed to confirm these data, as well as to validate it using additional parameters for normalization as recommended in the recently published guidelines for the study of extracellular vesicles from parasitic helminths (15). Our study found that 15k OvEVs are significantly larger in diameter than 120k OvEVs.
Infection with O. viverrini remains a major public health concern in liver fluke endemic regions given the risk of CCA or bile duct cancer (PMID: 34504109) . Despite notable efforts to determine the molecules involved in the promotion of cell proliferation and tumorigenesis, the exact mechanisms remain only partially defined (6). For instance, single molecules such as Ov-GRN-1 have been strongly linked to cell proliferation, although, it has also recently been demonstrated that more complex structures such as EVs can also induce the release of cytokines involved in fibrosis and tumour progression (3). Indeed, blocking internalization of EVs by host target cells using specific antibodies can significantly reduce cholangiocyte proliferation and release of proinflammatory IL-6 (3). Notably, antibodies targeting the tetraspanin protein, Ov-TSP-1 (OON13593.1 in this study), which is found on the surface of both 120k and 15k OvEVs, was able to block EV uptake by host cells. Tetraspanins have been shown to be key molecules for both the release of EVs by donor cells and the internalization of EVs by target cells (51); however, despite their utility as markers to differentiate EV subpopulations in other organisms (52), we have not yet detected members of this family of proteins that are uniquely present in either population. This is likely due to the physiological nature of the trematode tegument, which is enriched in tetraspanins. Chaiyadet et al. also studied the importance of other tetraspanins in the secretion of EVs (26). Using RNAi they showed that silencing the expression of two different genes encoding tetraspanins (Ov-TSP2 and Ov-TSP3; OON16870.1 and OON14450.1, respectively in our study), resulted in significant reductions in 120k OvEV production (26). Studies analysing the impact of RNAi on the release of 15k OvEVs should be performed in the future.
Although we did not detect tetraspanins that were unique to either type of OvEV and therefore of value as specific vesicle markers, trypsinization of the EV membranes revealed 21 and 46 proteins specifically present on the 120k or 15k OvEVs, respectively. Since these molecules have extra-vesicular domains and peptides that can be potentially targeted by antibodies, they could be used to discriminate between both types of EVs, even in in vivo experiments requiring labelling of native structures. Among these molecules we identified members of the heat shock protein 90 and 70 families, peptidases and immunoglobulin-like proteins in the case of 120k OvEVs, and kinases, hydratases, acetyltransferase and EF-hand domain containing proteins in the case of 15k OvEVs. Interestingly, a recent report analysing all proteins present in EVs from trematodes, nematodes and cestodes found that proteins belonging to the heat-shock protein 70 and EF-hand protein families were present in EVs from all trematodes studied (19), although this report did not differ between EV types. Furthermore, this study found that proteins belonging to the Tubulin and Tubulin_C families were more represented in datasets of EVs secreted by liver flukes compared to other helminths (19). Clearly, both populations of OvEVs contain different types of proteins, which might exert diverse functions in recipient cells. Indeed, it is tempting to speculate that the different proteins present on the surface of 120k and 15k OvEVs might target different populations of host cells, contributing to cell-specific effects in the host.
In addition to proteins, genetic material can also be transported by EVs as a means of cell-to-cell communication, modifying the translational profile of the recipient cell and exerting different functions (19, 53, 54). In helminths, the effects of miRNAs on host gene expression has primarily been investigated in relation to immune modulation and parasite survival (53, 55, 56). Despite their important roles, the miRNA content of only four trematode species has been investigated so far, including Dicrocoelium dendriticum, F. hepatica, S. mansoni and S. japonicum (57–63). miRNAs mir-71, mir-10, mir-190, let-7 and mir-2 were present in all four trematode species with relative abundance (19). In our study, miRNAs belonging to the mir-71 and mir-10 families (Ov_miRNA_EV_32 and Ov_miRNA_EV_6, respectively) were among the most abundant in 120k OvEVs. mir-71 has also been shown to be abundantly expressed by Clonorchis sinensis, and has been suggested to have a role in parasite survival and metabolism inside the host (64). Furthermore, other miRNAs from the let-7 and mir-2 families were also identified in this sample. Let-7 has been suggested as a potential biomarker for cestode infections (65, 66), while miR-2 has been suggested to regulate growth, development and parasite-host interaction during the migration within the definitive host (67). We used three different target prediction software programs to bioinformatically predict potential host genes that could be the target of the identified miRNAs. Furthermore, to obtain more robust target predictions, only common targets predicted by all three software for a particular miRNA were taken into consideration for further analysis. We also grouped the identified targets into the pathways in which they participate to obtain a broader picture of the effects of miRNAs in the host. Of note, the Gonadotropin-releasing hormone receptor pathway was the pathway to which the greatest number of genes mapped (22 predicted targets from 18 different miRNAs). Interestingly, this pathway has been strongly linked to CCA and other cancers such as pancreatic cancer (68, 69). Although the knockdown of Gonadotropin-releasing hormone decreased cholangiocyte proliferation and fibrosis, this hormone has also been shown to inhibit cell proliferation in other carcinomas including breast, pancreas and liver (70). Furthermore, genes involved in other pathways such as the Angiogenesis (P00005) pathway, the Apoptosis signaling pathway (P00006) and the VEGF signaling pathway (P00056), all implicated in the development of CCA, can also be targeted by miRNAs present in the 120k OvEVs (71, 72). Interestingly, other pathways involved in immunomodulation can also be targeted by miRNAs present in the 120k OvEVs, which might contribute to the formation of an inflammatory microenvironment that favours the development of fibrosis and CCA. Ideally, the roles of the miRNAs involved in the different pathways found in this study should be individually validated in future studies.
In this work we have delved deeper into the responses induced by different populations of fluke EVs in human cholangiocytes with a particular focus on cell proliferation. Furthermore, we have performed a proteomic comparative analysis and the first miRNA analysis of 120k OvEVs to obtain a more accurate picture of the mechanisms used by O. viverrini in host-parasite interactions. Finally, the proteomic and miRNA transcriptomic analyses performed will also allow us to identify specific proteins that could be used to discriminate both types of vesicles as well as potential miRNAs implicated in pathogenesis.
Supplementary Material
Figure S1. Gene ontology (GO) analysis of molecular function terms associated to proteins present in adult O. viverrini-derived 15k (A) and 120k vesicles (40). Similarity-based scatterplots showing the most abundantly represented GO terms ranked by nodescore (Blast2GO). Increasing heatmap score signifies increasing nodescore from Blast2GO, while circle size denotes the frequency of the GO term from the underlying database.
Figure S2. Gene ontology (GO) analysis of biological process terms associated with proteins present in adult O. viverrini-derived 15k (A) and 120k vesicles (40). Similarity-based scatterplots showing the most abundantly represented GO terms ranked by nodescore (Blast2GO). Increasing heatmap score signifies increasing nodescore from Blast2GO, while circle size denotes the frequency of the GO term from the underlying database.
Acknowledgments
This research was supported from a project grant from the National Health and Medical Research Council of Australia (NHMRC), grant identification number APP1085309, the National Cancer Institute, National Institutes of Health, award number R01CA164719, and the Fundamental Fund, Khon Kaen University. AL is supported by a Level Three NHMRC Investigator Grant APP2008450. JS is supported by a Ramon y Cajal fellowship (RYC2021-032443-I) from the Ministerio de Ciencia e Innovacion from Spain.
References
- 1.Sripa B, Bethony JM, Sithithaworn P, Kaewkes S, Mairiang E, Loukas A, et al. Opisthorchiasis and Opisthorchis-associated cholangiocarcinoma in Thailand and Laos. Acta Trop. 2011;120 Suppl 1:S158–68. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Sripa B, Kaewkes S, Sithithaworn P, Mairiang E, Laha T, Smout M, et al. Liver fluke induces cholangiocarcinoma. PLoS Med. 2007;4(7):e201. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Chaiyadet S, Sotillo J, Smout M, Cantacessi C, Jones MK, Johnson MS, et al. Carcinogenic Liver Fluke Secretes Extracellular Vesicles That Promote Cholangiocytes to Adopt a Tumorigenic Phenotype. J Infect Dis. 2015;212(10):1636–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Mulvenna J, Sripa B, Brindley PJ, Gorman J, Jones MK, Colgrave ML, et al. The secreted and surface proteomes of the adult stage of the carcinogenic human liver fluke Opisthorchis viverrini. Proteomics. 2010;10(5):1063–78. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Sripa B, Tangkawattana S, Brindley PJ. Update on Pathogenesis of Opisthorchiasis and Cholangiocarcinoma. Adv Parasitol. 2018;102:97–113. [DOI] [PubMed] [Google Scholar]
- 6.Suttiprapa S, Sotillo J, Smout M, Suyapoh W, Chaiyadet S, Tripathi T, et al. Opisthorchis viverrini Proteome and Host-Parasite Interactions. Adv Parasitol. 2018;102:45–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Arunsan P, Chaidee A, Cochran CJ, Mann VH, Tanno T, Kumkhaek C, et al. Liver fluke granulin promotes extracellular vesicle-mediated crosstalk and cellular microenvironment conducive to cholangiocarcinoma. Neoplasia. 2020;22(5):203–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Arunsan P, Ittiprasert W, Smout MJ, Cochran CJ, Mann VH, Chaiyadet S, et al. Programmed knockout mutation of liver fluke granulin attenuates virulence of infection-induced hepatobiliary morbidity. Elife. 2019;8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Chaiyadet S, Sotillo J, Krueajampa W, Thongsen S, Brindley PJ, Sripa B, et al. Vaccination of hamsters with Opisthorchis viverrini extracellular vesicles and vesicle-derived recombinant tetraspanins induces antibodies that block vesicle uptake by cholangiocytes and reduce parasite burden after challenge infection. PLoS Negl Trop Dis. 2019;13(5):e0007450. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Papatpremsiri A, Smout MJ, Loukas A, Brindley PJ, Sripa B, Laha T. Suppression of Ov-grn-1 encoding granulin of Opisthorchis viverrini inhibits proliferation of biliary epithelial cells. Exp Parasitol. 2015;148:17–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Smout MJ, Sotillo J, Laha T, Papatpremsiri A, Rinaldi G, Pimenta RN, et al. Carcinogenic Parasite Secretes Growth Factor That Accelerates Wound Healing and Potentially Promotes Neoplasia. PLoS Pathog. 2015;11(10):e1005209. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Smout MJ, Laha T, Mulvenna J, Sripa B, Suttiprapa S, Jones A, et al. A granulin-like growth factor secreted by the carcinogenic liver fluke, Opisthorchis viverrini, promotes proliferation of host cells. PLoS Pathog. 2009;5(10):e1000611. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Chaiyadet S, Tangkawattana S, Smout MJ, Ittiprasert W, Mann VH, Deenonpoe R, et al. Knockout of liver fluke granulin, Ov-grn-1, impedes malignant transformation during chronic infection with Opisthorchis viverrini. PLoS Pathog. 2022;18(9):e1010839. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Thery C, Witwer KW, Aikawa E, Alcaraz MJ, Anderson JD, Andriantsitohaina R, et al. Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. J Extracell Vesicles. 2018;7(1):1535750. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.White R, Sotillo J, Ancarola ME, Borup A, Boysen AT, Brindley PJ, et al. Special considerations for studies of extracellular vesicles from parasitic helminths: A community-led roadmap to increase rigour and reproducibility. J Extracell Vesicles. 2023;12(1):e12298. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Doyle LM, Wang MZ. Overview of Extracellular Vesicles, Their Origin, Composition, Purpose, and Methods for Exosome Isolation and Analysis. Cells. 2019;8(7). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Cwiklinski K, de la Torre-Escudero E, Trelis M, Bernal D, Dufresne PJ, Brennan GP, et al. The Extracellular Vesicles of the Helminth Pathogen, Fasciola hepatica: Biogenesis Pathways and Cargo Molecules Involved in Parasite Pathogenesis. Mol Cell Proteomics. 2015;14(12):3258–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Kifle DW, Pearson MS, Becker L, Pickering D, Loukas A, Sotillo J. Proteomic analysis of two populations of Schistosoma mansoni-derived extracellular vesicles: 15k pellet and 120k pellet vesicles. Mol Biochem Parasitol. 2020;236:111264. [DOI] [PubMed] [Google Scholar]
- 19.Sotillo J, Robinson MW, Kimber MJ, Cucher M, Ancarola ME, Nejsum P, et al. The protein and microRNA cargo of extracellular vesicles from parasitic helminths - current status and research priorities. Int J Parasitol. 2020;50(9):635–45. [DOI] [PubMed] [Google Scholar]
- 20.Meningher T, Lerman G, Regev-Rudzki N, Gold D, Ben-Dov IZ, Sidi Y, et al. Schistosomal MicroRNAs Isolated From Extracellular Vesicles in Sera of Infected Patients: A New Tool for Diagnosis and Follow-up of Human Schistosomiasis. J Infect Dis. 2017;215(3):378–86. [DOI] [PubMed] [Google Scholar]
- 21.Eichenberger RM, Ryan S, Jones L, Buitrago G, Polster R, Montes de Oca M, et al. Hookworm Secreted Extracellular Vesicles Interact With Host Cells and Prevent Inducible Colitis in Mice. Front Immunol. 2018;9:850. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Liu J, Zhu L, Wang J, Qiu L, Chen Y, Davis RE, et al. Schistosoma japonicum extracellular vesicle miRNA cargo regulates host macrophage functions facilitating parasitism. PLoS Pathog. 2019;15(6):e1007817. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Meningher T, Barsheshet Y, Ofir-Birin Y, Gold D, Brant B, Dekel E, et al. Schistosomal extracellular vesicle-enclosed miRNAs modulate host T helper cell differentiation. EMBO Rep. 2020;21(1):e47882. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Wang L, Liao Y, Yang R, Yu Z, Zhang L, Zhu Z, et al. Sja-miR-71a in Schistosome egg-derived extracellular vesicles suppresses liver fibrosis caused by schistosomiasis via targeting semaphorin 4D. J Extracell Vesicles. 2020;9(1):1785738. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Sotillo J, Pearson M, Potriquet J, Becker L, Pickering D, Mulvenna J, et al. Extracellular vesicles secreted by Schistosoma mansoni contain protein vaccine candidates. Int J Parasitol. 2016;46(1):1–5. [DOI] [PubMed] [Google Scholar]
- 26.Chaiyadet S, Sotillo J, Krueajampa W, Thongsen S, Smout M, Brindley PJ, et al. Silencing of Opisthorchis viverrini Tetraspanin Gene Expression Results in Reduced Secretion of Extracellular Vesicles. Front Cell Infect Microbiol. 2022;12:827521. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Sotillo J, Sanchez-Flores A, Cantacessi C, Harcus Y, Pickering D, Bouchery T, et al. Secreted proteomes of different developmental stages of the gastrointestinal nematode Nippostrongylus brasiliensis. Mol Cell Proteomics. 2014;13(10):2736–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Craig R, Beavis RC. TANDEM: matching proteins with tandem mass spectra. Bioinformatics. 2004;20(9):1466–7. [DOI] [PubMed] [Google Scholar]
- 29.Kim S, Pevzner PA. MS-GF+ makes progress towards a universal database search tool for proteomics. Nat Commun. 2014;5:5277. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Diament BJ, Noble WS. Faster SEQUEST searching for peptide identification from tandem mass spectra. J Proteome Res. 2011;10(9):3871–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Vaudel M, Barsnes H, Berven FS, Sickmann A, Martens L. SearchGUI: An open-source graphical user interface for simultaneous OMSSA and X!Tandem searches. Proteomics. 2011;11(5):996–9. [DOI] [PubMed] [Google Scholar]
- 32.Vaudel M, Burkhart JM, Zahedi RP, Oveland E, Berven FS, Sickmann A, et al. PeptideShaker enables reanalysis of MS-derived proteomics data sets. Nat Biotechnol. 2015;33(1):22–4. [DOI] [PubMed] [Google Scholar]
- 33.Perez-Riverol Y, Csordas A, Bai J, Bernal-Llinares M, Hewapathirana S, Kundu DJ, et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 2019;47(D1):D442–D50. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Gotz S, Garcia-Gomez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ, et al. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 2008;36(10):3420–35. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Howe KL, Bolt BJ, Cain S, Chan J, Chen WJ, Davis P, et al. WormBase 2016: expanding to enable helminth genomic research. Nucleic Acids Res. 2016;44(D1):D774–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Waardenberg AJ, Field MA. consensusDE: an R package for assessing consensus of multiple RNA-seq algorithms with RUV correction. PeerJ. 2019;7:e8206. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Schmieder R, Lim YW, Edwards R. Identification and removal of ribosomal RNA sequences from metatranscriptomes. Bioinformatics. 2012;28(3):433–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25(17):3389–402. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Young ND, Nagarajan N, Lin SJ, Korhonen PK, Jex AR, Hall RS, et al. The Opisthorchis viverrini genome provides insights into life in the bile duct. Nat Commun. 2014;5:4378. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Friedlander MR, Mackowiak SD, Li N, Chen W, Rajewsky N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 2012;40(1):37–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Kanellos I, Vergoulis T, Sacharidis D, Dalamagas T, Hatzigeorgiou A, Sartzetakis S, et al. MR-microT: a MapReduce-based MicroRNA target prediction method. Proceedings of the 26th International Conference on Scientific and Statistical Database Management; Aalborg, Denmark: Association for Computing Machinery; 2014. p. Article 47. [Google Scholar]
- 43.Chen Y, Wang X. miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res. 2020;48(D1):D127–D31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Enright AJ, John B, Gaul U, Tuschl T, Sander C, Marks DS. MicroRNA targets in Drosophila. Genome Biol. 2003;5(1):R1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Mi H, Muruganujan A, Huang X, Ebert D, Mills C, Guo X, et al. Protocol Update for large-scale genome and gene function analysis with the PANTHER classification system (v.14.0). Nat Protoc. 2019;14(3):703–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Grubman SA, Perrone RD, Lee DW, Murray SL, Rogers LC, Wolkoff LI, et al. Regulation of intracellular pH by immortalized human intrahepatic biliary epithelial cell lines. Am J Physiol. 1994;266(6 Pt 1):G1060–70. [DOI] [PubMed] [Google Scholar]
- 47.Bansal PS, Smout MJ, Wilson D, Cobos Caceres C, Dastpeyman M, Sotillo J, et al. Development of a Potent Wound Healing Agent Based on the Liver Fluke Granulin Structural Fold. J Med Chem. 2017;60(10):4258–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Dastpeyman M, Bansal PS, Wilson D, Sotillo J, Brindley PJ, Loukas A, et al. Structural Variants of a Liver Fluke Derived Granulin Peptide Potently Stimulate Wound Healing. J Med Chem. 2018;61(19):8746–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Webber J, Clayton A. How pure are your vesicles? J Extracell Vesicles. 2013;2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Kalra H, Drummen GP, Mathivanan S. Focus on Extracellular Vesicles: Introducing the Next Small Big Thing. Int J Mol Sci. 2016;17(2):170. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Andreu Z, Yanez-Mo M. Tetraspanins in extracellular vesicle formation and function. Front Immunol. 2014;5:442. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Karimi N, Dalirfardouei R, Dias T, Lotvall J, Lasser C. Tetraspanins distinguish separate extracellular vesicle subpopulations in human serum and plasma - Contributions of platelet extracellular vesicles in plasma samples. J Extracell Vesicles. 2022;11(5):e12213. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Cai Q, He B, Weiberg A, Buck AH, Jin H. Small RNAs and extracellular vesicles: New mechanisms of cross-species communication and innovative tools for disease control. PLoS Pathog. 2019;15(12):e1008090. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Munir J, Yoon JK, Ryu S. Therapeutic miRNA-Enriched Extracellular Vesicles: Current Approaches and Future Prospects. Cells. 2020;9(10). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Eichenberger RM, Sotillo J, Loukas A. Immunobiology of parasitic worm extracellular vesicles. Immunol Cell Biol. 2018. [DOI] [PubMed] [Google Scholar]
- 56.Tritten L, Geary TG. Helminth extracellular vesicles in host-parasite interactions. Curr Opin Microbiol. 2018;46:73–9. [DOI] [PubMed] [Google Scholar]
- 57.Bernal D, Trelis M, Montaner S, Cantalapiedra F, Galiano A, Hackenberg M, et al. Surface analysis of Dicrocoelium dendriticum. The molecular characterization of exosomes reveals the presence of miRNAs. J Proteomics. 2014;105:232–41. [DOI] [PubMed] [Google Scholar]
- 58.Fontenla S, Langleib M, de la Torre-Escudero E, Dominguez MF, Robinson MW, Tort J. Role of Fasciola hepatica Small RNAs in the Interaction With the Mammalian Host. Front Cell Infect Microbiol. 2021;11:812141. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Fromm B, Trelis M, Hackenberg M, Cantalapiedra F, Bernal D, Marcilla A. The revised microRNA complement of Fasciola hepatica reveals a plethora of overlooked microRNAs and evidence for enrichment of immuno-regulatory microRNAs in extracellular vesicles. Int J Parasitol. 2015;45(11):697–702. [DOI] [PubMed] [Google Scholar]
- 60.Nowacki FC, Swain MT, Klychnikov OI, Niazi U, Ivens A, Quintana JF, et al. Protein and small non-coding RNA-enriched extracellular vesicles are released by the pathogenic blood fluke Schistosoma mansoni. J Extracell Vesicles. 2015;4:28665. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Samoil V, Dagenais M, Ganapathy V, Aldridge J, Glebov A, Jardim A, et al. Vesicle-based secretion in schistosomes: Analysis of protein and microRNA (miRNA) content of exosome-like vesicles derived from Schistosoma mansoni. Sci Rep. 2018;8(1):3286. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Zhu L, Liu J, Dao J, Lu K, Li H, Gu H, et al. Molecular characterization of S. japonicum exosome-like vesicles reveals their regulatory roles in parasite-host interactions. Sci Rep. 2016;6:25885. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Zhu S, Wang S, Lin Y, Jiang P, Cui X, Wang X, et al. Release of extracellular vesicles containing small RNAs from the eggs of Schistosoma japonicum. Parasit Vectors. 2016;9(1):574. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 64.Pak JH, Kim IK, Kim SM, Maeng S, Song KJ, Na BK, et al. Induction of cancer-related microRNA expression profiling using excretory-secretory products of Clonorchis sinensis. Parasitol Res. 2014;113(12):4447–55. [DOI] [PubMed] [Google Scholar]
- 65.Cucher M, Macchiaroli N, Kamenetzky L, Maldonado L, Brehm K, Rosenzvit MC. High-throughput characterization of Echinococcus spp. metacestode miRNomes. Int J Parasitol. 2015;45(4):253–67. [DOI] [PubMed] [Google Scholar]
- 66.Macchiaroli N, Cucher M, Zarowiecki M, Maldonado L, Kamenetzky L, Rosenzvit MC. microRNA profiling in the zoonotic parasite Echinococcus canadensis using a high-throughput approach. Parasit Vectors. 2015;8:83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 67.Ricafrente A, Cwiklinski K, Nguyen H, Dalton JP, Tran N, Donnelly S. Stage-specific miRNAs regulate gene expression associated with growth, development and parasite-host interaction during the intra-mammalian migration of the zoonotic helminth parasite Fasciola hepatica. BMC Genomics. 2022;23(1):419. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 68.Ray D, Han Y, Franchitto A, DeMorrow S, Meng F, Venter J, et al. Gonadotropin-releasing hormone stimulates biliary proliferation by paracrine/autocrine mechanisms. Am J Pathol. 2015;185(4):1061–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 69.Suo L, Chang X, Xu N, Ji H. The Anti-proliferative Activity of GnRH Through Downregulation of the Akt/ERK Pathways in Pancreatic Cancer. Front Endocrinol (Lausanne). 2019;10:370. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 70.Park MK, Kanaho Y, Enomoto M. Regulation of the cell proliferation and migration as extra-pituitary functions of GnRH. Gen Comp Endocrinol. 2013;181:259–64. [DOI] [PubMed] [Google Scholar]
- 71.Cai C, Wang X, Fu Q, Chen A. The VEGF expression associated with prognosis in patients with intrahepatic cholangiocarcinoma: a systematic review and meta-analysis. World J Surg Oncol. 2022;20(1):40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 72.Simone V, Brunetti O, Lupo L, Testini M, Maiorano E, Simone M, et al. Targeting Angiogenesis in Biliary Tract Cancers: An Open Option. Int J Mol Sci. 2017;18(2). [DOI] [PMC free article] [PubMed] [Google Scholar]
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Supplementary Materials
Figure S1. Gene ontology (GO) analysis of molecular function terms associated to proteins present in adult O. viverrini-derived 15k (A) and 120k vesicles (40). Similarity-based scatterplots showing the most abundantly represented GO terms ranked by nodescore (Blast2GO). Increasing heatmap score signifies increasing nodescore from Blast2GO, while circle size denotes the frequency of the GO term from the underlying database.
Figure S2. Gene ontology (GO) analysis of biological process terms associated with proteins present in adult O. viverrini-derived 15k (A) and 120k vesicles (40). Similarity-based scatterplots showing the most abundantly represented GO terms ranked by nodescore (Blast2GO). Increasing heatmap score signifies increasing nodescore from Blast2GO, while circle size denotes the frequency of the GO term from the underlying database.