PLoS Neglected Tropical Diseases
Public Library of Science
image
Regulation of hepatic microRNAs in response to early stage Echinococcus multilocularis egg infection in C57BL/6 mice
DOI 10.1371/journal.pntd.0007640 , Volume: 14 , Issue: 5

Table of Contents

Highlights

Notes

Abstract

Various infectious diseases in humans have been associated with altered expression patterns of microRNAs (miRNAs), a class of small non-coding RNAs involved in negative regulation of gene expression. Herein, we revealed that significant alteration of miRNAs expression occurred in murine liver subsequently to experimental infection with E. multilocularis eggs when compared to non-infected controls. At the early stage of murine AE, hepatic miRNAs were mainly down-regulated. Respective target genes of the most extensively down-regulated miRNAs were involved in angiogenesis and fatty acid synthesis. Furthermore, we found higher mRNA levels of three angiogenic and two lipogenic genes in E. multilocularis infected livers compared to non-infected controls. Angiogenesis and fatty acid biosynthesis may be beneficial for development of the E. multilocularis metacestodes. In fact the formation of new blood vessels in the periparasitic area may ensure that parasites are supplied with oxygen and nutrients and get rid of waste products. Additionally, E. multilocularis is not able to undertake de novo fatty acid synthesis, thus lipids must be scavenged from its host. More research on the regulation of the hepatic miRNA transcriptome at more advanced stages of AE is needed.

Keywords
Boubaker, Strempel, Hemphill, Müller, Wang, Gottstein, Spiliotis, and Rinaldi: Regulation of hepatic microRNAs in response to early stage Echinococcus multilocularis egg infection in C57BL/6 mice

Introduction

Human alveolar echinococcosis (AE) is a parasitic disease caused by infection with the larval stage (metacestode) of the cyclophyllidean tapeworm E. multilocularis (Cestoda, Taeniidae) [1]. In Europe, E. multilocularis undergoes a sylvatic life cycle that predominantly includes the red fox (Vulpes vulpes ) as major definitive host (HD) and rodents (family Arvicolidae) acting as intermediate hosts (IHs) [2]. For humans, accidental infection with E. multilocularis eggs through the oral route can lead to the development of AE, affecting primarily the liver in 98% of cases [1]. Within the liver tissue, the asexual proliferation of metacestodes occurs by exogenous budding of new vesicles, thus the larval mass progrediently invades the surrounding hepatic tissue, with a potential of metastasis formation in distant sites such as lungs, brain and other organs [36]. Disease progression is largely supported by the ability of E. multilocularis metacestodes to modulate immunological host-defense mechanisms [7,8].

AE is listed as one of the rare and neglected tropical diseases by the World Health Organization, and if left untreated the disease results in mortality in more than 90% of cases [9]. Radical surgical excision of the parasite tissue complemented by adjuvant chemotherapy is the only curative treatment for hepatic AE, but this applies to only ~ 30% of patients [10]. For inoperable cases, the only currently licensed chemotherapeutical option is based on the benzimidazole derivatives albendazole and mebendazole. Benzimidazole-therapy has increased the survival rate of affected patients to 85–90% [4]. However, these drugs exhibit a parasitostatic rather than parasitocidal activity; hence patients must take these drugs lifelong. Currently, there is no alternative to benzimidazole-based chemotherapy for patients suffering from benzimidazole intolerance [11]. The claim for a better management and control of AE calls for new treatment concepts, thus highlighting once more the necessity to gain deeper insights into the molecular basis of AE-induced liver pathology.

MicroRNAs (miRNAs) are a class of 21–24 nucleotides (nt) small non-coding RNAs discovered in the early 1990s as key regulatory factors of developmental timing in Caenorhabditis elegans [12]. The biogenesis of miRNAs is a two-step process; it begins in the nucleus where a miRNA gene is transcribed to primary miRNA (pri-miRNA) which will be processed by nuclear RNase III Drosha-like nucleases to generate a precursor hairpin miRNA (pre-miRNA). This pre-miRNA is then exported to the cytoplasm to become a mature miRNA [13,14]. A miRNA can specifically bind to its target mRNA at the 3’ untranslated regions (3’UTRs), 5’ UTRs, exons and/or introns [15,16]. This results in repression of the target mRNA expression by diverse mechanisms, including inhibition of the translational machinery, disruption of cap–poly (A) tail interactions, and exonuclease-mediated mRNA cleavage [17]. Thus, miRNAs are major elements of negative post-transcriptional regulation of gene expression. The numbers of human miRNA-generating loci are continuously increasing and range between 2000 to 4000 [1821]. Based on computational predictions it is estimated that more than 60% of all human protein-coding genes harbor at least one conserved miRNA-binding site [22,23]. Several cellular and biological processes such as cell proliferation, metabolism, apoptosis and immune defenses are orchestrated by miRNAs [2426].

Alterations in microRNA gene expression have been reported for a wide range of human pathologies such as cancers, metabolic disorders and cardiovascular diseases [27,28]. In hepatocellular carcinoma (HCC), dysregulated miRNAs have been assessed as drug targets [29,30], disease stage / survival rate predictors [31] and as markers of responsiveness to therapy [32].

In recent years, significant efforts have been made in outlining and defining the roles of miRNAs in host-pathogen interactions, with a main focus on host miRNAs. In this context, evidence was provided that hepatitis C virus replication is completely dependent on the liver-specific miR-122 [33]. Similarly, changes of host-miRNA expression profiles have been associated to different helminthiases [3437] where miRNAs dysregulation was relevant to tissue dysfunction and to the type of immune response [38]. In the case of Schistosoma japonicum , high serum level of hepatic miR-223 was correlated with active infection, and responsiveness to praziquantel therapy was characterized by a return to normal levels [39]. E. multilocularis was also found to quantitatively modulate circulating and liver miRNAs in a mouse model of secondary (intraperitoneal) infection [40,41]. Similarly, the closely related E. granulosus also caused changes in the intestinal miRNA transcriptome of Kazakh sheep [42].

To date, miRNA-directed therapy against helminthiases is highly appealing [43]; both parasite—and host- derived miRNAs can be targeted, which allows (i) to interfere in essential biological processes of the parasite [4446] and (ii) to ensure that the host environment returns to a normal biological state [47].

Growth of E. multiocularis larvae induces changes to liver metabolism that collectively result in a net mobilization of glucose, lipid and amino acids [48,49]. Other studies demonstrated deviations in hepatic gene expression at early stage of experimental primary E. multilocularis infection in the murine model compared to non-infected liver tissue [50,51]; however, how and whether miRNAs are involved in post-transcriptional regulation of gene expression remains unknown. In immunocompetent individuals, human AE usually takes decades before symptoms arise, and once diagnosed, patients often have already reached an advanced stage of disease hampering the prospect to achieve complete parasite clearance. Accordingly, understanding of early cellular and molecular events that take place along with intra-hepatic establishment of E. multilocularis larvae is a crucial task for the development of novel means of prevention and treatment.

In this study, we applied Illumina next-generation sequencing (NGS) to comprehensively analyze the miRNAs expression profile in the mouse liver at the early stage (one month post-infection) of primary AE, and compared miRNAs expression levels in infected livers to non-infected tissue samples. Results were validated based on quantitative stem-loop RT-PCR. Hepatic miRNAs that exhibited significantly altered disease specific expression levels were further studied by Reactome and KEGG enrichment analyses. Furthermore, we used infected and non-infected livers tissue samples to comparatively measure the relative mRNA levels of five genes that have been identified as targets of dysregulated miRNAs. This is the first survey of miRNAs regulation in early AE, which will contribute to a better understanding of the role of hepatic miRNAs in promoting parasite survival and growth.

Materials and methods

Ethics statement

Mice were housed and handled under standard laboratory conditions and in agreement with the Swiss Animal Welfare Legislation (animal experimentation license BE 103/11 and BE 112/14).

Mouse model and primary AE infection

Ten 8-week-old female C57BL/6 mice were obtained from Charles River GmbH, Germany. The animals were divided into two groups, five animals each. The control group remained uninfected, and the other group was orally infected with E. multilocularis eggs. The eggs used in this study were obtained from a naturally infected fox that was shot during the official Swiss hunting season. Parasite eggs for subsequent infection of mice were prepared as described by Deplazes & Eckert (1996) [52]. Briefly, the fox intestine was removed under appropriate safety precautions and cut into 4 pieces. After longitudinal opening of the intestinal segments, the worm-containing mucus was scraped out and put into petri dishes containing sterile water. Subsequently, the mucosal suspension was serially filtered through a 500μm and then 250μm metal sieve, by concurrently disrupting the worms with an inversed 2 ml syringe top. This suspension was further filtered through a 105 μm nylon sieve. The eggs were then washed by repeated sedimentation (1xg, 30 min., room temperature) in sterile water containing 1% Penicillin/Streptomycin and stored in the same solution at 4 °C. The sodium hypochlorite resistance test was used to assess egg viability [53], and to ascertain efficiency of infectivity, a preliminary test was done in two female C57BL/6 mice. After confirmation of infectivity, five mice were each inoculated with approximately 1×103 eggs suspended in 100 μl sterile water by gavage. The control mice received sterile water only. At one month post-infection, all animals were euthanized, and their livers were removed under sterile conditions for further analyses. All animal infections were performed in a biosafety level 3 unit (governmental permit no. A990006/3). One mouse from the uninfected control group died during the experiment from unknown causes and was thus not included in this experiment.

Liver samples, total RNA extraction, small RNA library preparation and NGS

Liver tissue samples from E. multilocularis-infected mice were taken from the peri-parasitic area, precisely 3 to 4 mm adjacent to parasite lesions, which appeared as small white or yellowish spots. Liver tissue samples from uninfected animals were collected from the same hepatic areas. The obtained liver tissues were minced quickly, mixed at a ratio of 1:10 with QIAzol lysis reagent (Qiagen, Cat: 79306) and homogenized by bead beating (FastPrep-24 Instrument, MP Biomedicals, Cat: 116004500). Total RNA was extracted according to the manufacturer’s instruction of the QIAzol lysis reagent with the exception that chloroform was replaced by 1-bromo-3-chloropropane for the phase separation step (Sigma, cat: B9673). To remove genomic DNA contamination in RNA samples, an enzymatic digestion step using DNase I (Thermo Fisher Scientific, Cat: EN0521) was carried out. Finally, total RNA was re-suspended in RNase-free water. RNA quantity and RNA quality number (RQN ≥ 8) was determined by the Fragment Analyzer CE12 (Advanced Analytics).

Two to five RNA extractions were prepared simultaneously from each liver, in order to subsequently choose the preparation with the best RQN. Samples from the infected mouse group were named as follows; AE-1pm-1.1, AE-1pm-2.1, AE-1pm-3.2, AE-1pm-4.1 and AE-1pm-5.1. Total RNA preparations from the uninfected control group were labelled ctr-1pm-1.1, ctr-1pm-3.2, ctr-1pm-4.2 and ctr-1pm-5.1 (first digit is the number of the mouse within its corresponding group and the second digit indicates the number of RNA preparation).

Sequencing library construction and sequencing itself were performed by Microsynth (Balgach, Switzerland). Briefly, five libraries were generated from five mice (three mice from E. multilocularis-infected group and two mice from the uninfected control group). The total RNA was checked on an Agilent 2100 Bioanalyzer instrument for degradation. Subsequently, the CleanTag Ligation Kit (TriLink BioTechnologies) was used to prepare small RNA stranded libraries from total RNA (1μg RNA per library). Libraries were analyzed a second time on the Bioanalyzer to check for the expected miRNAs fragment peak at 141 bp and subsequently a Sage Science Pippin Prep instrument was used to select the expected fragment size range. In a last step before the deep sequencing, the quality and concentration of each final library (the eluted 141 bp band) were submitted to PicoGreen analysis (Thermo Fisher Scientific, Cat:P7589). The so refined libraries were then sequenced on an Illumina NextSeq 500 instrument using a high-output v2 Kit (75-cycles sequencing run) and targeting for an output of 50 million pass-filter reads.

Raw sequence data, processed- and metadata generated in this study have been deposited in NCBI Gene Expression Omnibus (GEO) repository (http://www.ncbi.nlm.nih.gov/geo/) under the following accessions; GSM4367926 (ctr-1pm-3.2), GSM4367927 (ctr-1pm-4.2), GSM4367928 (AE-1pm-2.1), GSM4367929 (AE-1pm-3.2) and GSM4367930 (AE-1pm-5.1).

Bioinformatics analysis of small RNA sequencing data

In a preprocessing step, reads were subjected to de-multiplexing and trimming of the TriLink adapter residuals using Illumina bcl2fastq v2 analysis software (bcl2fastq2 Conversion Software v2.19.1.). Quality of reads was checked with the software FastQC (v. 0.11.5) (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/); thus, reads shorter than 10 bases or longer than 25 bases, or reads containing any “N” base, were discarded to refine the input for the statistical analysis. In a next step, clean reads of samples derived from the same experimental group (AE-1pm or ctr-1pm) were pooled together and then dereplicated using the software usearch (v. 8.1.1681) [54]. This resulted in a list of unique sequences that were annotated and recorded based on their frequency of occurrence. Within each dataset (AE-1pm and ctr-1pm), sequences having a read count of at least 10 were blasted against the miRBase [55,56] mature miRNAs mouse sequences (Mus musculus , mouse mm10 reference genome). Mapping of cleaned reads to the mouse genome (v. mm10) was performed using the RNA mapping software STAR (v. 2.5.1b) [57]. Major parameters used were a minimum of 16 matching bases between the read and the reference, whereas a maximum of 1 mismatch was allowed and splice aware mapping was switched off. Raw counting of uniquely mapped reads to annotated mature miRNAs was done with the software htseq-count from the HTSeq software suite (v. 0.6.1p1) [58]. The miRNAs annotations stem from miRBase matching the mm10 reference genome.

Lastly and in an exploratory step to check for what else might be present in our data in addition to what the miRBase provided, we performed a blast against the Rfam collection of RNA families (v. 12.3) [59]. This blast concerned only sequences that did not show a hit with an e-value of at least 1e-6 or smaller during the blast against the miRBase. Therefore for the blast against Rfam, we choose an Expect value (E) threshold of less than 1; the E- value criteria describe the number of expected hits occurring by chance. According to this, we considered hits with an E-value ranged between 0 and 1 as significant matches.

Normalization of the raw miRNAs read counts and differential miRNAs expression analysis were conducted using the R package DESeq2 (v. 1.12.4); the DESeq2 method combines shrinkage estimation for dispersions and fold changes (log2FC) which allow a more sophisticated quantitative analysis [60,61].

We applied principal component analysis in two dimensions (2D-PCA) [62] to check whether control mice (ctr-1pm) could be distinguished based on their miRNAs expression profiles from those experimentally infected with E. multilocularis eggs. The PCA statistical approach is applied to complex data sets and aims to reduce the dimensionality of the data matrix while simultaneously retaining the maximum amount of variance. The term 2D refers to the number of used principal components: the first component, PC 1, represents the direction of the highest variance of the data and PC 2 points into the directions of the highest of the remaining variance orthogonal to the first component.

In parallel to 2D-PCA, we also applied the hierarchical clustering method [63] for analyzing hepatic miRNA transcriptome data from infected and non-infected mice. Hierarchical clustering consists in building clusters of libraries with similar patterns of expression. Results are displayed as a tree diagram (dendrogram).

Stem-loop reverse transcription (RT) and real-time quantitative (Stem-loop RT-qPCR) of dysregulated mature miRNAs

To validate the expression profile of the most dysregulated miRNAs obtained from the analysis of NGS data, we assessed the relative expression levels of twelve miRNAs by stem-loop RT-qPCR, as previously described [64]. The small nucleolar RNA 234 gene (sno234 ) was included as reference for microRNA normalization [65]. In total, 9 liver tissue samples derived from the AE-infected (5 mice) and uninfected mice (4 mice) were individually assessed.

Briefly, 1 μg of total RNA template was reverse transcribed to cDNA by M-MLV reverse transcriptase (Promega) using specific stem-loop RT primer and under the following conditions: 5 min at 25 °C, 60 min at 42°C, 15 minutes at 70°C, reactions were then cooled down to 4°C. All q-PCRs were performed in Rotor-Gene 6000 (Corbett Life Science) and using FastStart Essential DNA Green Master Kit (Roche, Switzerland). The total volume for qPCR was 10 μl, consisting of 5 μl of Faststart Essential DNA Green Master Mix (2xconc), 1 μl of forward and reverse primers, and 2.5 μl of cDNA template (diluted 1:5). All experiments were run in triplicates. Quantitative PCRs were performed according to the following program: an initial hold at 95°C for 15 min, 40 cycles (94°C for 15 s, annealing at 63°C for 20 s, extension at 72°C for 20 s) and a final denaturation step from 50 °C to 95 °C. Specificity of each qPCR and presence of primer dimers were checked based on analysis of the generated melting curve. All primers characteristics are listed in S1 Table.

For relative miRNA expression, data were expressed as median ± standard deviation (SD) and examined for statistical significance with the nonparametric Mann–Whitney U test. P-values of less than 0.05 were considered to be statistically significant.

MicroRNA target prediction and pathway enrichment analysis

Significantly differentially expressed miRNAs with FC ≥ 1.5 or FC ≤ 0.66 were chosen for target prediction. Since miRNAs inhibit their target mRNAs, reduction of miRNA expression lead to up-regulation of the target genes and vice versa. For miRNA-mRNA target predictions, we used miRNet [66], a database for network-based visual analysis of miRNAs, targets and functions. MiRNet integrates high-quality miRNA-target interaction databases (miRTarBase v6.0, TarBase v6.0 and miRecords); these databases provide direct experimental evidence regarding the miRNA–target interaction. For functional and pathway enrichment analysis, we used two pathway databases, including Reactome [67] and KEGG [68]. Statistical significance (P < 0.01) was measured by applying hypergeometric test [66].

Furthemore, miRNet allowed us to visualize miRNA-target interactions in a network context.

Thus, pathway enrichment analysis and visualization of interaction networks can be reproduced at any time using miRNet, a web-based tool freely available at http://www.mirnet.ca. For that, the list of down- or up- regulated miRNAs (miRBase Accession) is used as input; selected parameters are M. musculus (mouse) for organism, miRBase Accession for the ID type and genes for target type. When choosing KEGG or Reactome database, a table containing the list of pathways and the number of involved genes will be shown. By clicking on a given pathway, the names of involved genes will be displayed.

Expression analysis of miRNA target genes using RT-qPCR

We used RT-qPCR to comparatively assess the relative expression of five genes involved in angiogenesis and fatty acid biosynthesis and activation. The three pro-angiogenic genes were vascular endothelial growth factor A (VEGFA), the mechanistic target of rapamycin (MTOR); and the hypoxia inducible factor 1, α subunit (HIF1α). The two examined lipogenic genes were fatty acid synthase (FASN) and acyl-CoA synthetase long-chain family member 1 (ACSL1). These genes were chosen using the following criteria: (1) predicted as a target of at least one of the down-regulated miRNA; (2) experimentally validated target of at least one of the down-regulated miRNA; (3) significantly relevant gene in the considered pathway; (4) a combination of 1, 2 and 3 (S2 Table).

All genes were assessed individually in five liver tissue samples from AE-infected and four samples from uninfected mice (same experiment). Thus, nine cDNA preparations were synthesized on the same RNA templates which were used for validation of most dysregulated miRNAs by Stem-loop RT-qPCR (see section above). The qPCRs were performed as described above, with the exception that the annealing temperature was set at 62° C for all five genes. We used the glyceraldehyde-3-phosphate dehydrogenase (gapdh ) as endogenous reference. Detailed information on qPCR primers are provided in S2 Table.

Results

Next generation sequencing (NGS) data

To characterize the miRNA transcriptome in murine liver during early stage of primary AE, small RNA libraries from three E. multilocularis-infected and two uninfected control mice were constructed and subjected to high-throughput sequencing.

The number of uniquely mapped reads with an average mapped length of 21 nucleotides ranged from 4.831.114 to 1.749.597, representing thus 66 to 60% of the total cleaned reads. More than 95% of those uniquely mapped reads were assigned to miRBase annotated miRNAs (Table 1).

Table 1
Summary of counts for clean reads, unique mapped reads and reads on feature of miRNAs.
LibraryaCleaned ReadsbUniquely Mapped ReadscReads On FeatureReads No FeatureReads Ambiguous
AE-1pm-2.16802673384008736593661807183
95.3%4.7%0%
AE-1pm-3.27315799483111446988251322872
97.3%2.7%0%
AE-1pm-5.17098967459101444597991312105
97.1%2.9%0%
ctr-1pm-3.2290167517495971690171594260
96.6%3.4%0%
ctr-1pm-4.26683305386102637046681563580
96%4%0%
a: reads considered for mapping
b: the reads that mapped to exactly one location within the reference genome
c: features of miRNAs

For all libraries, length distribution analyses revealed that length of most abundant sequences ranged from 18 to 24 nt with a peak at 21 nt (S1A Fig). In both groups, most miRNAs were detected with read counts below 100 (S1B Fig).

Overview of miRNA expression profile in infected and uninfected mouse liver

A total of 699 known miRNAs were identified from both infected (AE-1pm) and uninfected (ctr-1pm) groups. Among these molecules, 530 were common to both groups with 124 and 45 miRNAs specifically expressed in the AE-1pm and ctr-1pm libraries, respectively. Specific miRNAs to AE-1pm (124 miRNAs) or to ctr-1pm (45 miRNAs) libraries were all present with read counts below 100. From the co-expressed miRNAs cluster, a set of 87 miRNAs with a read count > 1000 in at least one of the two experimental groups was identified (Fig 1A). In liver-tissue samples derived from AE-infected mice, mmu-miR-122-5p, mmu-miR-21a-5p and mmu-miR-192-5p accounted for up than 70% of the total normalized miRNAs counts, and they still comprised more than 50% total miRNAs abundance in the control group (Fig 1B).

miRNA expression profile in livers of AE-infected and uninfected mice.
Fig 1
(A) Venn diagram showing the overlap of expressed miRNA in liver-tissue samples from AE- infected and uninfected animals. (B) Top 10 most abundant miRNA and their frequency (%) in both groups. The two miRNAs in gray rectangles were present only among the top 10 expressed hepatic miRNAs in AE- infected mice, whereas miRNAs in black rectangles were only among the top 10 expressed hepatic miRNAs in uninfected control mice.miRNA expression profile in livers of AE-infected and uninfected mice.

Alteration of the hepatic microRNA expression profile at early stage of hepatic AE

The 2D-PCA (Fig 2A) as well as the heatmap of sample-to-sample distances (Fig 2B) showed a clear separation of miRNA expression patterns between E. multilocularis egg infection versus healthy control. Globally, the observed clustering of samples from the same group indicated that a change in miRNAs expression pattern had occurred following infection.

Principal component analysis (A) and (B) hierarchical clustering both revealed a clear separation between hepatic miRNA profiles from infected and uninfected mice.
Fig 2
All the 699 miRNAs, identified in this study, with the normalized read-count in each animal group, are listed in S3 Table.Principal component analysis (A) and (B) hierarchical clustering both revealed a clear separation between hepatic miRNA profiles from infected and uninfected mice.

A global comparative analysis of all miRNA read counts between both experimental mouse groups was carried-out and revealed the presence of a set of miRNAs whose fold change was significant. From this latter cluster, we considered as significantly dysregulated miRNAs only those with (i) a normalized read count ≥ 1000, and (ii) FC ≥ 1.5 (Log2FC ≥ 0.58) or FC ≤ 0.66 (Log2FC ≤ -0.58). Thus, a total of 28 miRNAs were found to be differentially expressed in diseased livers compared to healthy controls (Fig 3).

Twenty-eight dysregulated hepatic miRNAs in primary AE-infected mice compared to control healthy individuals: Nine were up-regulated (Log2FC ≥ 0.58) and nineteen were down-regulated (Log2FC ≤ -0.58).
Fig 3
Twenty-eight dysregulated hepatic miRNAs in primary AE-infected mice compared to control healthy individuals: Nine were up-regulated (Log2FC ≥ 0.58) and nineteen were down-regulated (Log2FC ≤ -0.58).

More information on detailed counts is shown in Table 2. The highest up-regulated miRNA in E. multilocularis infected livers was mmu-miR-21a-5p with a FC = 2.3. Conversely, the expression of mmu-miR-148a-3p was ~8-fold lower as compared to control liver samples.

Table 2
Dysregulated miRNAs in primary AE.
miR_IDMean AE-1pmAE-1pm-2.1AE-1pm-3.2AE-1pm-5.1Mean ctr-1pm-1pmctr-1pm-3.2ctr-1pm-4.2log2FCp-ValueAdjusted p-ValueFC% exp.
Down-regulated
mmu-miR-148a-3p21984181782003527738185456171132199781-3.085.47E-412.84E-380.112
mmu-miR-101a-3p81506877733810235522415325651226-2.681.82E-333.15E-310.216
mmu-miR-148b-3p1383126413171568829571229468-2.584.87E-341.27E-310.217
mmu-miR-148a-5p209173228227120010831317-2.522.28E-282.96E-260.217
mmu-miR-92a-3p608633454737265424092899-2.133.21E-182.62E-160.223
mmu-miR-143-3p4046359536784866163401568616994-2.013.55E-223.69E-200.225
mmu-miR-101b-3p30534259492475840895103675103319104031-1.769.17E-134.34E-110.329
mmu-miR-340-5p2483201925662863604160875994-1.289.80E-103.18E-080.441
mmu-miR-451a2403236229681879565553755935-1.238.31E-082.15E-060.442
mmu-let-7i-5p1048510057982711571227752227923271-1.126.41E-102.38E-080.546
mmu-miR-378a-3p1177913061783814438253642374726980-1.112.51E-052.90E-040.546
mmu-miR-30a-3p6101561048447850129071168514130-1.087.86E-061.24E-040.547
mmu-miR-130a-3p6004615660145843123101237912241-1.041.12E-093.42E-080.549
mmu-miR-27b-3p19530184791743822672384973769639299-0.985.34E-071.16E-050.551
mmu-miR-22-3p44461482903459250501868108782585795-0.978.37E-061.28E-040.551
mmu-miR-126a-5p41918383044758939862738987469873098-0.821.30E-051.77E-040.657
mmu-miR-152-3p5466495457225722952499669081-0.801.21E-051.74E-040.657
mmu-miR-15a-5p2876288827363004498851044871-0.796.29E-061.02E-040.658
mmu-miR-30a-5p147634129644125683187576253258228418278098-0.788.39E-046.61E-030.658
Up-regulated
mmu-miR-21a-5p78363255697011449876489383513953485083542821.165.63E-056.10E-042.3223
mmu-miR-301a-3p15141336189413126947306571.131.29E-062.49E-052.2218
mmu-let-7d-3p12141326108612305615855381.113.33E-089.62E-072.2216
mmu-miR-23a-3p27282617330222641330128213791.042.56E-064.44E-052.1205
mmu-miR-28a-5p42925070393238752152222320811.001.11E-062.21E-052199
mmu-miR-106b-5p16201735169914258959678220.862.43E-052.87E-041.8181
mmu-miR-23b-3p51835412515149862873289728500.851.04E-062.16E-051.8180
mmu-miR-122-5p1081837107155711776059963496033466055246011690.842.27E-064.08E-051.8179
mmu-miR-1839-5p46774335488348132693281225750.801.09E-051.62E-041.7174
FC: fold change, % exp. = (Mean AE/Meanctr)*100

Stem-loop RT-qPCR was applied to validate the miRNAs NGS data of 12 out of the 28 differentially expressed miRNAs. In order to optimize the statistical significance of this study, two additional samples were included into each group, thus the AE-1pm group comprised five animals and the ctr-1pm included four samples. Overall, the Stem-loop RT-qPCR results largely confirmed the results obtained through NGS as shown in Fig 4 (Fig 4). Seven miRNAs; mmu-miR-148a-3p, mmu-miR-143-3p, mmu-miR-101b-3p, mmu-miR-340-5p, mmu-miR-22-3p, mmu-miR-152-3p, mmu-miR-30a-5p were significantly less expressed in AE-1pm samples compared to ctr-1pm samples. In contrast, infected mice exhibited significantly higher expression levels of mmu-miR-21a-5p, mmu-miR-28a-5p, mmu-miR-122-5p and mmu-miR-1839-5p compared to the control mice. No significant difference in expression levels in the two groups were noted for mmu-miR-301a-3p.

miRNAs expression validation by stem-loop RT-qPCR.
Fig 4
Relative miRNA expression levels in livers of AE-infected and uninfected control mice. Normalization was done using sno234 as the endogenous control. Bars represent median ± standard deviation. All stem-loop RT-qPCRs for each miR used biological replicates (n = 5 infected group and n = 4 uninfected group), with three technical replicates per experiment.miRNAs expression validation by stem-loop RT-qPCR.

miRNA precursors: Preference selection of 5p- and 3p arm in E. multilocularis-infected–and uninfected liver tissues

In order to interrogate arm selection preferences of miRNA pairs (miRNA-5p/3p) in normal and infected liver tissues, we investigated the expression levels of miR-5p and miR-3p strands of the same miRNA precursor (pre-miRNA). We identified a total of 127 miRNAs pairs that were co-expressed in all tissue samples and that exhibited a normalized read count ≥ 100 for 5p or 3p strand under infected and non-infected conditions. For each miRNA duplex, we calculated the selection rate (S) of 5p and 3p arm from the total read count (5p and 3p). For 25 (20%) and 21 (17%) out of these 127 miRNA pairs, mature miRNA was strictly derived from 3p (S%_miR-5p = 0; S%_miR-3p = 100) and 5p (S%_miR-5p = 100; S%_miR-3p = 0), respectively, and this was independent from the primary AE infection. Regarding the remaining 81 pre-miRNAs out of the total 127 identified pairs, mature miRNAs were derived from both arms. For 16 pre-miRNAs (out of the 81), 5p and 3p selection rates remained unchanged between infected and non-infected liver tissue, whereas selection preference for either the 5p- or the 3p-strand differed for the lasting 65 miRNA pairs (out of the 81). Linear regression analysis of 5p-arm selection rate in the 127 miRNA pairs showed a strong correlation (R2 = 0.9764) between the two groups of mice (Fig 5A). Particularly for nine pre-miRNA (miR-106b, miR-144, miR-16-1, miR-1981, miR-214, miR-28a, miR-335, miR-345 and miR-532) the difference in 5p- and 3p-arm expression between the two groups ranged between 10% and 33% (P < 0.01) as shown in Fig 5B.

Arm selection preference (5p and 3p) in hepatic pre-miRNAs: AE-infection versus no-infection (control).
Fig 5
(A) Linear regression analysis of the correlation in 5p-arm selection rate between E. multilocularis-infected (AE-1pm) and uninfected mice (ctr-1pm). The 5p-arm selection rate was determined for 127 miRNA pairs. (B) Nine pre-miRNAs exhibited a significant difference in 5p- and 3p-arm expression between E. multilocularis-infected (AE-1pm) and uninfected animals (ctr-1pm). For each of the nine miRNAs, the 100% stacked bars compare the 5p- and 3p-arm expression rate between AE-1pm and ctr-1pm mouse group. Black error bars represent standard deviation in percent of the 3p arm, and white bars represent standard deviation in percent of the 5p arm.Arm selection preference (5p and 3p) in hepatic pre-miRNAs: AE-infection versus no-infection (control).

Prediction of the target genes of dysregulated host miRNAs

Potential targets of the 28 differentially expressed host miRNAs were predicted using the miRNet tool which was based on miRTarBase v6.0, TarBase v6.0 and miRecords algorithms. In total 1645 target genes were identified for 25 miRNAs, while for the remaining three miRNAs (mmu-miR-148a-5p, mmu-miR-30a-3p and mmu-let-7d-3p), no target genes were found. Overall, from the 1645 targets, two clusters of unequal sizes were defined: a major group (1484/1645; 90%) includes target genes that are unique to one miRNA and a smaller set of 161 genes that are commonly controlled by two or more of the dysregulated miRNAs.

For the 17 down-regulated miRNAs, a set of 1426 target genes were found; almost 80% (1140/1426) of these miRNA-target interactions (MITs) were supported by experimental evidence. Among the 1426 target genes, 134 (10%) were shared by at least two miRNA molecules; out of these 134 targets, 49% (66/134) were common between mmu-miR-15a-5p and mmu-miR-340-5p.

On the other hand, for the eight up-regulated miRNAs, 219 target genes were identified, and a smaller number of MITs (117/219; 50%) were experimentally validated. Among the 219 identified target genes, 89 were regulated only by mmu-miR-122-5p, with 71 MITs being experimentally validated. In total, twenty six genes were common between two microRNAs; mmu-miR-23a-3p and mmu-miR-23b-3p share 21 targets, representing 81% of genes targeted by two different up-regulated miRNAs.

The 1645 putative target genes covered a wide range of biological functions, notably those related to immunity, metabolism and epigenetic modifications such as DNA methylation and histone modification. Genes relevant to immunity included IL-1β targeted by mmu-miR-122-5p, SMAD3/4 transcription factors targeted by mmu-miR-27b-3p and mmu-miR-122-5p, calcium/calmodulin-dependent protein kinase II alpha (Camk2a) targeted by mmu-miR-340-5p, mmu-miR-148a-3p, mmu-miR-148b-3p and mmu-miR-152-3p, interferon regulatory factor (IRF) 7/8 targeted respectively by mmu-miR-122-5p and mmu-miR-22-3p, IL-17 receptor A (IL-17RA) targeted by mmu-miR-23a/b-3p, inducible T-cell costimulator (ICOS) targeted by mmu-miR-101a-3p and vascular cell adhesion molecule (V-CAM)- 1 target by mmu-miR-340-5p.

Two genes were involved in fatty acids (FAs) biosynthesis and activation; acyl-CoA synthetase long-chain family member 1 (ACSL1) and Fatty Acid Synthase (Fasn) targeted by mmu-miR-340-5p and mmu-miR-15a-5p.

Relevant genes involved in epigenetic modifications included histone deacetylase (HDAC)-2/4/9 targeted by mmu-miR-340-5p, mmu-miR-22-3p and mmu-miR-340-5p, respectively, and DNA methyltransferases (Dnmt)-1, targeted by mmu-miR-148a/b-3p and mmu-miR-152-3p, respectively.

To elucidate regulatory relationships between up- or down- regulated miRNAs and the 1645 identified target genes, two interaction networks of miRNAs-mRNA targets were constructed and are shown in S2 and S3 Figs.

Pathway enrichment analysis for dysregulated miRNAs

To get an overview on cellular pathways in which dysregulated miRNAs could be involved, the putative target genes were subjected to Reactome and KEGG for functional enrichment and pathway analysis. Analyses for down- and up-regulated miRNAs were made separately.

For down-regulated miRNAs: 56-Reactome- and 68 KEGG pathways (P -values < 0.01) were associated with up-regulated target genes. According to Fig 6A (Fig 6A), Reactome pathway enrichment analysis revealed that the main enriched biological processes targeted by down-regulated miRNAs were signaling pathways activated by growth factor receptors, namely vascular endothelial growth receptor 2 (VEGFR2), fibroblast growth factor receptor (FGFR), epidermal growth factor receptor (EGFR) and platelet-derived growth factor (PDGF). Twelve genes were shared between these four signaling pathways (S4 Table). A set of 25 genes involved in “VEGFA-VEGFR2” are highlighted in the network of down-regulated miRNAs- mRNA targets (S2 Fig).

Functional analysis of predicted targets of down- regulated miRNAs.
Fig 6
A and B: Top 30 overrepresented canonical pathways for up-regulated gene targets of down-regulated miRNAs according to Reactome and KEGG database, respectively. Pathways are ranked by score (-log10 (P-value). A higher score indicates that the pathway is more significantly associated with genes of interest. Numbers inside the end of each bar indicate the number of genes involved in each pathway.Functional analysis of predicted targets of down- regulated miRNAs.

The KEGG enrichment analysis revealed that target genes of down-regulated miRNAs were abundantly present in pathways that are involved in various cancers (Fig 6B).

Top 30 Reactome- and KEGG- -enriched pathways among up-regulated genes are shown in Fig 6 (Fig 6A and 6B). Genes associated to each Reactome and KEGG enriched pathway are listed in S4 Table.

As shown in Fig 7 (Fig 7), 6-Reactome and 23 KEGG pathways were significantly enriched in target genes of up-regulated miRNAs. Pathway analysis using Reactome database showed that heme biosynthesis was the most affected pathway with seven involved genes (Fig 7A) which are highlighted in the network of up-regulated miRNAs- mRNA targets (S3 Fig). Identified KEGG processes included mostly pathways associated with cancer (8 out the 23 KEGG pathways) (Fig 7B).

Functional analysis of predicted targets of down- and up-regulated miRNAs.
Fig 7
A and B: Significant over-represented canonical pathways for down-regulated gene targets of up-regulated miRNAs according to Reactome and KEGG database, respectively. Pathways are ranked by score (-log10 (P-value)). A higher score indicates that the pathway is more significantly associated with genes of interest. Numbers inside the end of each bar indicate the number of genes involved in each pathway.Functional analysis of predicted targets of down- and up-regulated miRNAs.

Expression analysis of key pro-angiogenic and fatty acid synthesis –associated genes as targets of down-regulated miRNAs

Relative expression levels of five genes that were identified as targets of down-regulated miRNAs and are involved in angiogenesis (vegfa, mtor and hif1α) and lipid metabolism (fasn and acsl1 ), were further assessed by RT-qPCR in livers from AE-infected and non-infected mice (S2 Table). As shown in Fig 8, all five genes exhibited significantly increased mRNA levels in livers from E. multilocularis -infected mice relative to the non-infected control group (Fig 8).

Relative mRNA level of angiogenesis- and lipid metabolism-related genes.
Fig 8
Normalization was done using gapdh as endogenous reference. Bars represent median ± standard deviation. All qPCRs were carried-out using biological replicates (n = 5 mice from the infected group and n = 4 mice from uninfected group), with three technical replicates per sample.Relative mRNA level of angiogenesis- and lipid metabolism-related genes.

Discussion

In the present study we undertook NGS-based miRNAs profiling in liver tissues of mice isolated at an early stage of primary AE. The cellular composition is heterogeneous, and includes hepatocytes, stellate cells, Kupffer cells and liver endothelial cells. In addition, hepatic AE is characterized by a periparasitic infiltration of immune cells [8]. Thus our differential expression analysis of hepatic miRNAs concerns all these cell types which might be present in the isolated liver tissue samples from infected and non-infected mice.

In total, we identified 28 miRNAs that were differentially expressed between infected and non-infected mice at 1 month post-infection. In general, alterations in the microRNA transcriptome have been reported for a wide range of infectious- and non-infectious liver diseases [69].

Thirteen out of the 19 down-regulated miRNAs identified in our study were also down-regulated in experimental secondary AE in DBA/2 mice at three months post-infection [40,41]. However, none of the 9 differentially up-regulated miRNAs identified herein has been shown to exhibit altered expression levels in experimental secondary AE [40,41]. This unconformity is most likely due to differences between experiments such as the route of infection, mouse strain, and infection stage.

On the other hand, 19 out of the identified 28 dysregulated miRNAs were reported to be also affected in hepatocellular carcinoma (HCC) which is the most common type of primary liver cancer in adults [7078]. Nonetheless, it remains to be shown whether changes in expression levels of the 19 miRNAs are common to various liver diseases, or whether they result from common induced cellular events/mechanisms that occur in liver cancerous cells as well in periparasitic liver tissue.

In our study, both miR-148a-5p, miR-148a-3p and miR-15a-5p were down-regulated and mRNA levels of the two lipogenic enzymes FASN and ACSL1 were higher in livers of E. multilocularis -infected mice when compared to controls. MiR-148a (5p and 3p) is directly involved in controlling cholesterol and triglyceride homeostasis [7981]; down-regulation of miR miR-148a leads to an increased expression of genes related to lipogenesis and fatty acid uptake [82]. In the liver, overexpression of ACSL1 [83] results in increased proportion of oleic acid in diacylglycerol (DAG) and phospholipids (PLs), and promotes synthesis of triglyceride (TG) from free fatty acids and its accumulation in hepatoma cells [84].

Fatty acid synthase (FASN) [85] is a primary target of miR-15a-5p [8688], and its mRNA expression was found to be significantly up-regulated in mouse liver at early stage of AE infection [51]. However, one has to consider that a single mRNA can be targeted by many different miRNAs [89] and that regulation of gene expression can also mediated by post-translational modifications. Therefore the relative increase of fasn and acsl1 expression levels can only be partially explained by the decrease of miR-148a and miR-15a-5p.

Another miRNA known to play important role in regulating cholesterol and fatty acid (FAs) metabolism is miR 122, a liver-specific miRNA [9092]. In mice, inhibition of miR-122 by in vivo antisense targeting resulted in a decrease in hepatic FAs and cholesterol synthesis rates [93]. In our study, we observed a high expression of miR-122-5p in AE- infected mice. Since E. multilocularis is unable to synthesize FAs, cholesterol and other sterols de novo , [94] it is not surprising that AE might promote lipid synthesis in the liver. It was suggested that experimental AE in jirds could stimulate fatty acid biosynthesis [95], nevertheless further investigations are required to determine quantitative and qualitative effects of AE on host lipid metabolism and to define the exact role of miRNAs in this mechanism.

Since one miRNA may regulate many genes as its targets, it is also important to note that our results cannot be used to rule out the occurrence of other metabolic pathways which might be controlled by dysregulation of miR-148a, miR-15a-5p and miR-122-5p.

In macrophages, miR-148a-3p together with miR-30a-5p modulate inflammation by repressing NF-κB signaling and its respective pro-inflammatory consequences [96,97]. In this direction, downregulation of miR-148a-3p together with miR-30a-5p found in our study may suggest a role for these molecules in the gradual shift of Th1 to Th2 immunodominance associated with hepatic AE [7,8].

Interestingly, our pathway enrichment analysis of down-regulated miRNAs revealed a clear enrichment of growth factor-associated signaling pathways with VEGFA/VEGFR2 being ranked the first by statistical significance. In endothelial cells, binding of endothelial growth factor A (VEGFA) to VEGF receptor 2 (VEGFR2) results in the formation of new blood vessels from existing vessels [98,99]. Under hypoxic conditions, transcription of vegfa is promoted by hypoxia-inducible factor 1-alpha (HIF1-α) [100]. In our analysis, the HIF-1 signaling pathway was among significantly enriched KEGG pathways in the targeted genes of down-regulated microRNAs. In relation to miRNAs, both vegfa and hif1-α are experimentally validated targets of mmu-miR-15a-5p, mmu-miR-126a-5p and mmu-miR-101a/b-3p [101105] (down-regulated miRNAs in this study). Herein, relative mRNA levels of vegfa and hif1-α were significantly higher in E. multilocularis infected liver tissues. In a previous study in Wistar rats, a higher protein level of HIF-1α was found in the actively multiplying infiltrative region of the AE liver lesions in comparison to the hepatic parenchyma [106].

The two miRNAs; mmu-miR-101a/b-3p mmu-miR-15a-5p have been reported to negatively regulate expression of the mechanistic target of rapamycin (mTOR ) [107109], which has recently emerged as a regulator linking inflammation to angiogenesis trough activation of the TNFα/IKKβ signaling pathway, which in turn lead to the production of extracellular matrix-degrading and remodeling enzymes [110112]. In our experiment, we observed an increase in mRNA expression of mtor in infected mice, however further examination on the protein level is needed. Overall, although the presence of factors favoring formation of new blood vessels, precisely the tissue hypoxia caused by a continuous growth of metacestodes and the early host inflammatory reaction [113,114], the occurrence of angiogenesis during AE needs to be profoundly explored; it was recently demontrated that an angiographic vascularity takes place around liver lesions caused by E. multilocularis [115].

Target genes of miR-122 are mostly enriched in heme biosynthesis and porphyrin metabolism, this may be linked to the recently reported critical role of miR-122 for regulation of systemic iron metabolism [116]. To date there is growing evidence that miRNAs activate gene expression under certain conditions; nuclear miRNAs can activate gene transcription by targeting enhancers [117] and cytoplasmic miRNAs can function to post-transcriptionally stimulate gene expression [118]. Consequently, prediction analysis of cellular pathways that could be targeted for negative regulation by the up-regulated miRNAs must be approached cautiously and in a relative manner.

In infectious diseases, molecular mechanisms underlying the differential miRNA expression patterns remain unidentified. In addition, regulation of miRNAs expression is a complex and multilevel-process [119]. Expression of four genes involved in miRNAs biogenesis was found to be altered subsequently to liver infection with E. multilocularis [41], however this cannot explain the reported selectivity in regulating miRNAs expression. Further investigations are needed to clarify the contribution of parasites in changing expression and abundance of host miRNAs, particularly regarding the potential role of epigenetic mechanisms. In this respect, it has been reported that epigenetics, including DNA methylation and post-translational modifications of histones, regulates the expression of a notable number of miRNA genes [120].

In this study, we also examined arm selection preferences (5p or 3p) in miRNA pairs of normal and E. multilocularis infected liver tissues. In fact, it was widely reported that the double stranded pre-miRNAs produced only one mature functional miRNA at one arm, either 5p or 3p. This selection relied largely on the thermodynamic stability of the strands as a chief factor in determining which arm of the duplex will be incorporated in the RISC complex as functional miRNA. Recent evidence revealed that many pre-miRNAs may yield two mature products deriving from both arms (5p and 3p) with different selection rates [121124]. Furthermore, it was shown that in response to a pathological condition e.g. cancer [125,126] and infection [127], the 5p- and 3p selection preference of some pre-miRNAs was altered. So this so-called “arm switching” phenomenon is now recognized as a miRNA post-transcriptional regulatory mechanism [128]. In our study, we found nine miRNAs showing significant differences in 5p- and 3p-arm selection preference between normal and infected liver tissue. Arm switching may be explained by the fact that the miR-5p and miR-3p resulting from the same pre-miRNA may act on different mRNA targets, thus they might be subjected to inverse regulation [129].

In conclusion, we demonstrated that primary murine AE significantly alters the hepatic miRNA transcriptome during the early stage (1 month post-infection). 28 miRNAs exhibited altered expression levels, with 19 miRNAs being down-regulated and 8 being up-regulated. Our analysis indicates that target genes of dysregulated miRNAs might be involved in angiogenesis and metabolic pathways, in particular lipid and heme biosynthesis. Future studies on transcriptome characterization of both mRNA and miRNA during more advanced AE-stages are urgently required with the prospective goal to first identify miRNAs signatures associated with disease progression and second to get a deep and direct insight into the cellular pathways which could be potentially targeted by dysregulated miRNAs.

Acknowledgements

The authors gratefully acknowledge Dr. Marcela Cucher for critical reading of the manuscript (Instituto de Investigaciones en Microbiología y Parasitología Médica, Buenos Aires, Argentina).

References

1 

    J Eckert, P Deplazes. . Biological, Epidemiological, and Clinical Aspects of Echinococcosis, a Zoonosis of Increasing Concern. Clin Microbiol Rev. 2004;17: , pp.107–135. , doi: 10.1128/CMR.17.1.107-135.2004

2 

    B Otero-Abad, SR Rüegg, D Hegglin, P Deplazes, PR Torgerson. . Mathematical modelling of Echinococcus multilocularis abundance in foxes in Zurich, Switzerland. Parasit Vectors. 2017;10: , pp.21, doi: 10.1186/s13071-016-1951-1

3 

    K Bardonnet, DA Vuitton, F Grenouillet, GA Mantion, E Delabrousse, O Blagosklonov, et al. 30-yr course and favorable outcome of alveolar echinococcosis despite multiple metastatic organ involvement in a non-immune suppressed patient. Ann Clin Microbiol Antimicrob. 2013;12: , pp.1, doi: 10.1186/1476-0711-12-1

4 

    E Brunetti, P Kern, DA Vuitton, Writing Panel for the WHO-IWGE. . Expert consensus for the diagnosis and treatment of cystic and alveolar echinococcosis in humans. Acta Trop. 2010;114: , pp.1–16. , doi: 10.1016/j.actatropica.2009.11.001

5 

    L Caire Nail, E Rodríguez Reimundes, C Weibel Galluzzo, D Lebowitz, YL Ibrahim, JA Lobrinus, et al. Disseminated alveolar echinococcosis resembling metastatic malignancy: a case report. J Med Case Rep. 2017;11: , pp.113, doi: 10.1186/s13256-017-1279-2

6 

    D Tappe, D Weise, U Ziegler, A Müller, W Müllges, A Stich. . Brain and lung metastasis of alveolar echinococcosis in a refugee from a hyperendemic area. J Med Microbiol. 2008;57: , pp.1420–1423. , doi: 10.1099/jmm.0.2008/002816-0

7 

    N Mejri, A Hemphill, B Gottstein. . Triggering and modulation of the host-parasite interplay by Echinococcus multilocularis: a review. Parasitology. 2010;137: , pp.557–568. , doi: 10.1017/S0031182009991533

8 

    DA Vuitton, B Gottstein. . Echinococcus multilocularis and Its Intermediate Host: A Model of Parasite-Host Interplay. In: BioMed Research International [Internet]. 2010 [cited 20 Dec 2017]. , doi: 10.1155/2010/923193

9 

    PR Torgerson, A Schweiger, P Deplazes, M Pohar, J Reichen, RW Ammann, et al. Alveolar echinococcosis: from a deadly disease to a well-controlled infection. Relative survival and economic analysis in Switzerland over the last 35 years. J Hepatol. 2008;49: , pp.72–77. , doi: 10.1016/j.jhep.2008.03.023

10 

    Guidelines for treatment of cystic and alveolar echinococcosis in humans. . WHO Informal Working Group on Echinococcosis. Bull World Health Organ. 1996;74: , pp.231–242.

11 

    A Hemphill, B Stadelmann, R Rufener, M Spiliotis, G Boubaker, J Müller, et al. Treatment of echinococcosis: albendazole and mebendazole—what else?Parasite. 2014;21: , pp.70, doi: 10.1051/parasite/2014073

12 

    RC Lee, RL Feinbaum, V Ambros. . The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 1993;75: , pp.843–854. , doi: 10.1016/0092-8674(93)90529-y

13 

    M Ha, VN Kim. . Regulation of microRNA biogenesis. Nat Rev Mol Cell Biol. 2014;15: , pp.509–524. , doi: 10.1038/nrm3838

14 

    VN Kim. . Small RNAs: classification, biogenesis, and function. Mol Cells. 2005;19: , pp.1–15.

15 

    M Hafner, M Landthaler, L Burger, M Khorshid, J Hausser, P Berninger, et al. Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell. 2010;141: , pp.129–141. , doi: 10.1016/j.cell.2010.03.009

16 

    JR Lytle, TA Yario, JA Steitz. . Target mRNAs are repressed as efficiently by microRNA-binding sites in the 5’ UTR as in the 3’ UTR. Proc Natl Acad Sci USA. 2007;104: , pp.9667–9672. , doi: 10.1073/pnas.0703820104

17 

    E Huntzinger, E Izaurralde. . Gene silencing by microRNAs: contributions of translational repression and mRNA decay. Nat Rev Genet. 2011;12: , pp.99–110. , doi: 10.1038/nrg2936

18 

    I Bentwich, A Avniel, Y Karov, R Aharonov, S Gilad, O Barad, et al. Identification of hundreds of conserved and nonconserved human microRNAs. Nat Genet. 2005;37: , pp.766–770. , doi: 10.1038/ng1590

19 

    P Flicek, MR Amode, D Barrell, K Beal, K Billis, S Brent, et al. Ensembl 2014. Nucleic Acids Res. 2014;42: , pp.D749–755. , doi: 10.1093/nar/gkt1196

20 

    E Londin, P Loher, AG Telonis, K Quann, P Clark, Y Jing, et al. Analysis of 13 cell types reveals evidence for the expression of numerous novel primate- and tissue-specific microRNAs. Proc Natl Acad Sci USA. 2015;112: , pp.E1106–1115. , doi: 10.1073/pnas.1420955112

21 

    J Alles, T Fehlmann, U Fischer, C Backes, V Galata, M Minet, et al. An estimate of the total number of true human miRNAs. Nucleic Acids Res. 2019;47: , pp.3353–3364. , doi: 10.1093/nar/gkz097

22 

    RC Friedman, KK-H Farh, CB Burge, DP Bartel. . Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 2009;19: , pp.92–105. , doi: 10.1101/gr.082701.108

23 

    BP Lewis, CB Burge, DP Bartel. . Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005;120: , pp.15–20. , doi: 10.1016/j.cell.2004.12.035

24 

    O Dumortier, C Hinault, E Van Obberghen. . MicroRNAs and metabolism crosstalk in energy homeostasis. Cell Metab. 2013;18: , pp.312–324. , doi: 10.1016/j.cmet.2013.06.004

25 

    A Mehta, D Baltimore. . MicroRNAs as regulatory elements in immune system logic. Nat Rev Immunol. 2016;16: , pp.279–294. , doi: 10.1038/nri.2016.40

26 

    G Song, AD Sharma, GR Roll, R Ng, AY Lee, RH Blelloch, et al. MicroRNAs control hepatocyte proliferation during liver regeneration. Hepatology. 2010;51: , pp.1735–1743. , doi: 10.1002/hep.23547

27 

    J Das, S Podder, TC Ghosh. . Insights into the miRNA regulations in human disease genes. BMC Genomics. 2014;15: , pp.1010, doi: 10.1186/1471-2164-15-1010

28 

    S Lin, RI Gregory. . MicroRNA biogenesis pathways in cancer. Nat Rev Cancer. 2015;15: , pp.321–333. , doi: 10.1038/nrc3932

29 

    J Kota, RR Chivukula, KA O’Donnell, EA Wentzel, CL Montgomery, H-W Hwang, et al. Therapeutic microRNA delivery suppresses tumorigenesis in a murine liver cancer model. Cell. 2009;137: , pp.1005–1017. , doi: 10.1016/j.cell.2009.04.021

30 

    H Ling, M Fabbri, GA Calin. . MicroRNAs and other non-coding RNAs as targets for anticancer drug development. Nat Rev Drug Discov. 2013;12: , pp.847–865. , doi: 10.1038/nrd4140

31 

    J Zhang, CCN Chong, GG Chen, PBS Lai. . A Seven-microRNA Expression Signature Predicts Survival in Hepatocellular Carcinoma. PLoS ONE. 2015;10: , pp.e0128628, doi: 10.1371/journal.pone.0128628

32 

    J Ji, J Shi, A Budhu, Z Yu, M Forgues, S Roessler, et al. MicroRNA expression, survival, and response to interferon in liver cancer. N Engl J Med. 2009;361: , pp.1437–1447. , doi: 10.1056/NEJMoa0901282

33 

    CL Jopling, M Yi, AM Lancaster, SM Lemon, P Sarnow. . Modulation of hepatitis C virus RNA abundance by a liver-specific MicroRNA. Science. 2005;309: , pp.1577–1581. , doi: 10.1126/science.1113329

34 

    C Britton, AD Winter, ND Marks, H Gu, TN McNeilly, V Gillan, et al. Application of small RNA technology for improved control of parasitic helminths. Vet Parasitol. 2015;212: , pp.47–53. , doi: 10.1016/j.vetpar.2015.06.003

35 

    S Cabantous, X Hou, L Louis, H He, O Mariani, X Sastre, et al. Evidence for an important role of host microRNAs in regulating hepatic fibrosis in humans infected with Schistosoma japonicum. Int J Parasitol. 2017;47: , pp.823–830. , doi: 10.1016/j.ijpara.2017.05.007

36 

    P Cai, GN Gobert, DP McManus. . MicroRNAs in Parasitic Helminthiases: Current Status and Future Perspectives. Trends Parasitol. 2016;32: , pp.71–86. , doi: 10.1016/j.pt.2015.09.003

37 

    S Han, Q Tang, X Lu, R Chen, Y Li, J Shu, et al. Dysregulation of hepatic microRNA expression profiles with Clonorchis sinensis infection. BMC Infect Dis. 2016;16: , pp.724, doi: 10.1186/s12879-016-2058-1

38 

    N Arora, S Tripathi, AK Singh, P Mondal, A Mishra, A Prasad. . Micromanagement of Immune System: Role of miRNAs in Helminthic Infections. Front Microbiol. 2017;8: , pp.586, doi: 10.3389/fmicb.2017.00586

39 

    X He, X Sai, C Chen, Y Zhang, X Xu, D Zhang, et al. Host serum miR-223 is a potential new biomarker for Schistosoma japonicum infection and the response to chemotherapy. Parasit Vectors. 2013;6: , pp.272, doi: 10.1186/1756-3305-6-272

40 

    X Guo, Y Zheng. . Expression profiling of circulating miRNAs in mouse serum in response to Echinococcus multilocularis infection. Parasitology. 2017;144: , pp.1079–1087. , doi: 10.1017/S0031182017000300

41 

    X Jin, X Guo, D Zhu, M Ayaz, Y Zheng. . miRNA profiling in the mice in response to Echinococcus multilocularis infection. Acta Trop. 2017;166: , pp.39–44. , doi: 10.1016/j.actatropica.2016.10.024

42 

    S Jiang, X Li, X Wang, Q Ban, W Hui, B Jia. . MicroRNA profiling of the intestinal tissue of Kazakh sheep after experimental Echinococcus granulosus infection, using a high-throughput approach. Parasite. 2016;23: , pp.23, doi: 10.1051/parasite/2016023

43 

    AF Christopher, RP Kaur, G Kaur, A Kaur, V Gupta, P Bansal. . MicroRNA therapeutics: Discovering novel targets and developing specific therapy. Perspect Clin Res. 2016;7: , pp.68–74. , doi: 10.4103/2229-3485.179431

44 

    ME Ancarola, A Marcilla, M Herz, N Macchiaroli, M Pérez, S Asurmendi, et al. Cestode parasites release extracellular vesicles with microRNAs and immunodiagnostic protein cargo. Int J Parasitol. 2017;47: , pp.675–686. , doi: 10.1016/j.ijpara.2017.05.003

45 

    N Macchiaroli, LL Maldonado, M Zarowiecki, M Cucher, MI Gismondi, L Kamenetzky, et al. Genome-wide identification of microRNA targets in the neglected disease pathogens of the genus Echinococcus. Mol Biochem Parasitol. 2017;214: , pp.91–100. , doi: 10.1016/j.molbiopara.2017.04.001

46 

    M Cucher, N Macchiaroli, L Kamenetzky, L Maldonado, K Brehm, MC Rosenzvit. . High-throughput characterization of Echinococcus spp. metacestode miRNomes. Int J Parasitol. 2015;45: , pp.253–267. , doi: 10.1016/j.ijpara.2014.12.003

47 

    C Britton, AD Winter, V Gillan, E Devaney. . microRNAs of parasitic helminths—Identification, characterization and potential as drug targets. Int J Parasitol Drugs Drug Resist. 2014;4: , pp.85–94. , doi: 10.1016/j.ijpddr.2014.03.001

48 

    C Kepron, M Novak, BJ Blackburn. . Effect of Echinococcus multilocularis on the origin of acetyl-coA entering the tricarboxylic acid cycle in host liver. J Helminthol. 2002;76: , pp.31–36. , doi: 10.1079/joh200188

49 

    M Novak, A Modha, BJ Blackburn. . Metabolic alterations in organs of Meriones unguiculatus infected with Echinococcus multilocularis. Comp Biochem Physiol, B. 1993;105: , pp.517–521. , doi: 10.1016/0305-0491(93)90082-g

50 

    B Gottstein, M Wittwer, M Schild, M Merli, SL Leib, N Müller, et al. Hepatic Gene Expression Profile in Mice Perorally Infected with Echinococcus multilocularis Eggs. PLoS ONE. 2010;5: , pp.e9779, doi: 10.1371/journal.pone.0009779

51 

    R Lin, G Lü, J Wang, C Zhang, W Xie, X Lu, et al. Time course of gene expression profiling in the liver of experimental mice infected with Echinococcus multilocularis. PLoS ONE. 2011;6: , pp.e14557, doi: 10.1371/journal.pone.0014557

52 

    P Deplazes, J Eckert. . Diagnosis of the Echinococcus multilocularis infection in final hosts. Appl Parasitol. 1996;37: , pp.245–252.

53 

    P Deplazes, F Grimm, T Sydler, I Tanner, CMO Kapel. . Experimental alveolar echinococcosis in pigs, lesion development and serological follow up. Vet Parasitol. 2005;130: , pp.213–222. , doi: 10.1016/j.vetpar.2005.03.034

54 

    RC Edgar. . Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26: , pp.2460–2461. , doi: 10.1093/bioinformatics/btq461

55 

    A Kozomara, S Griffiths-Jones. . miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014;42: , pp.D68–D73. , doi: 10.1093/nar/gkt1181

56 

    S Griffiths-Jones, HK Saini, S van Dongen, AJ Enright. . miRBase: tools for microRNA genomics. Nucleic Acids Res. 2008;36: , pp.D154–D158. , doi: 10.1093/nar/gkm952

57 

    A Dobin, CA Davis, F Schlesinger, J Drenkow, C Zaleski, S Jha, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29: , pp.15–21. , doi: 10.1093/bioinformatics/bts635

58 

    S Anders, PT Pyl, W Huber. . HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31: , pp.166–169. , doi: 10.1093/bioinformatics/btu638

59 

    EP Nawrocki, SW Burge, A Bateman, J Daub, RY Eberhardt, SR Eddy, et al. Rfam 12.0: updates to the RNA families database. Nucleic Acids Res. 2015;43: , pp.D130–137. , doi: 10.1093/nar/gku1063

60 

    MI Love, W Huber, S Anders. . Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15: , pp.550, doi: 10.1186/s13059-014-0550-8

61 

    MD Robinson, DJ McCarthy, GK Smyth. . edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26: , pp.139–140. , doi: 10.1093/bioinformatics/btp616

62 

    M Ringnér. . What is principal component analysis?Nat Biotechnol. 2008;26: , pp.303–304. , doi: 10.1038/nbt0308-303

63 

    MB Eisen, PT Spellman, PO Brown, D Botstein. . Cluster analysis and display of genome-wide expression patterns. PNAS. 1998;95: , pp.14863–14868. , doi: 10.1073/pnas.95.25.14863

64 

    C Chen, DA Ridzon, AJ Broomer, Z Zhou, DH Lee, JT Nguyen, et al. Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res. 2005;33: , pp.e179, doi: 10.1093/nar/gni178

65 

    P Matoušková, H Bártíková, I Boušová, V Hanušová, B Szotáková, L Skálová. . Reference genes for real-time PCR quantification of messenger RNAs and microRNAs in mouse model of obesity. PLoS ONE. 2014;9: , pp.e86033, doi: 10.1371/journal.pone.0086033

66 

    Y Fan, K Siklenka, SK Arora, P Ribeiro, S Kimmins, J Xia. . miRNet—dissecting miRNA-target interactions and functional associations through network-based visual analysis. Nucleic Acids Res. 2016;44: , pp.W135–141. , doi: 10.1093/nar/gkw288

67 

    P D’Eustachio. . Reactome knowledgebase of human biological pathways and processes. Methods Mol Biol. 2011;694: , pp.49–61. , doi: 10.1007/978-1-60761-977-2_4

68 

    M Kanehisa, M Araki, S Goto, M Hattori, M Hirakawa, M Itoh, et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2008;36: , pp.D480–484. , doi: 10.1093/nar/gkm882

69 

    G Szabo, S Bala. . MicroRNAs in liver disease. Nat Rev Gastroenterol Hepatol. 2013;10: , pp.542–552. , doi: 10.1038/nrgastro.2013.87

70 

    Y Li, X Deng, X Zeng, X Peng. . The Role of Mir-148a in Cancer. Journal of Cancer. 2016;7: , pp.1233–1241. , doi: 10.7150/jca.14616

71 

    B Liu, T Sun, G Wu, H Shang-Guan, Z-J Jiang, J-R Zhang, et al. MiR-15a suppresses hepatocarcinoma cell migration and invasion by directly targeting cMyb. Am J Transl Res. 2017;9: , pp.520–532.

72 

    E Raitoharju, I Seppälä, L-P Lyytikäinen, J Viikari, M Ala-Korpela, P Soininen, et al. Blood hsa-miR-122-5p and hsa-miR-885-5p levels associate with fatty liver and related lipoprotein metabolism-The Young Finns Study. Sci Rep. 2016;6: , pp.38262, doi: 10.1038/srep38262

73 

    X Su, H Wang, W Ge, M Yang, J Hou, T Chen, et al. An In Vivo Method to Identify microRNA Targets Not Predicted by Computation Algorithms: p21 Targeting by miR-92a in Cancer. Cancer Res. 2015;75: , pp.2875–2885. , doi: 10.1158/0008-5472.CAN-14-2218

74 

    J Sun, H Lu, X Wang, H Jin. . MicroRNAs in hepatocellular carcinoma: regulation, function, and clinical implications. ScientificWorldJournal. 2013;2013: , pp.924206, doi: 10.1155/2013/924206

75 

    T Thurnherr, W-C Mah, Z Lei, Y Jin, SG Rozen, CG Lee. . Differentially Expressed miRNAs in Hepatocellular Carcinoma Target Genes in the Genetic Information Processing and Metabolism Pathways. Sci Rep. 2016;6: , pp.20065, doi: 10.1038/srep20065

76 

    L Xu, S Beckebaum, S Iacob, G Wu, GM Kaiser, A Radtke, et al. MicroRNA-101 inhibits human hepatocellular carcinoma progression through EZH2 downregulation and increased cytostatic drug sensitivity. J Hepatol. 2014;60: , pp.590–598. , doi: 10.1016/j.jhep.2013.10.028

77 

    J Zhang, Y Yang, T Yang, Y Liu, A Li, S Fu, et al. microRNA-22, downregulated in hepatocellular carcinoma and correlated with prognosis, suppresses cell proliferation and tumourigenicity. Br J Cancer. 2010;103: , pp.1215–1220. , doi: 10.1038/sj.bjc.6605895

78 

    L Zhuang, X Wang, Z Wang, X Ma, B Han, H Zou, et al. MicroRNA-23b functions as an oncogene and activates AKT/GSK3β/β-catenin signaling by targeting ST7L in hepatocellular carcinoma. Cell Death Dis. 2017;8: , pp.e2804, doi: 10.1038/cddis.2017.216

79 

    A Wagschal, SH Najafi-Shoushtari, L Wang, L Goedeke, S Sinha, AS deLemos, et al. Genome-wide identification of microRNAs regulating cholesterol and triglyceride homeostasis. Nat Med. 2015;21: , pp.1290–1297. , doi: 10.1038/nm.3980

80 

    L Goedeke, N Rotllan, A Canfrán-Duque, JF Aranda, CM Ramírez, E Araldi, et al. MicroRNA-148a regulates LDL receptor and ABCA1 expression to control circulating lipoprotein levels. Nat Med. 2015;21: , pp.1280–1289. , doi: 10.1038/nm.3949

81 

    L Goedeke, A Wagschal, C Fernández-Hernando, AM Näär. . miRNA regulation of LDL-cholesterol metabolism. Biochim Biophys Acta. 2016;1861: , pp.2047–2052. , doi: 10.1016/j.bbalip.2016.03.007

82 

    L Cheng, Y Zhu, H Han, Q Zhang, K Cui, H Shen, et al. MicroRNA-148a deficiency promotes hepatic lipid metabolism and hepatocarcinogenesis in mice. Cell Death Dis. 2017;8: , pp.e2916, doi: 10.1038/cddis.2017.309

83 

    LO Li, JM Ellis, HA Paich, S Wang, N Gong, G Altshuller, et al. Liver-specific loss of long chain acyl-CoA synthetase-1 decreases triacylglycerol synthesis and beta-oxidation and alters phospholipid fatty acid composition. J Biol Chem. 2009;284: , pp.27816–27826. , doi: 10.1074/jbc.M109.022467

84 

    S Yan, X-F Yang, H-L Liu, N Fu, Y Ouyang, K Qing. . Long-chain acyl-CoA synthetase in fatty acid metabolism involved in liver and other diseases: An update. World J Gastroenterol. 2015;21: , pp.3492–3498. , doi: 10.3748/wjg.v21.i12.3492

85 

    C Dorn, M-O Riener, G Kirovski, M Saugspier, K Steib, TS Weiss, et al. Expression of fatty acid synthase in nonalcoholic fatty liver disease. Int J Clin Exp Pathol. 2010;3: , pp.505–514.

86 

    Z Chen, H Qiu, L Ma, J Luo, S Sun, K Kang, et al. miR-30e-5p and miR-15a Synergistically Regulate Fatty Acid Metabolism in Goat Mammary Epithelial Cells via LRP6 and YAP1. Int J Mol Sci. 2016;17, doi: 10.3390/ijms17111909

87 

    J Wang, X Zhang, J Shi, P Cao, M Wan, Q Zhang, et al. Fatty acid synthase is a primary target of MiR-15a and MiR-16-1 in breast cancer. Oncotarget. 2016;7: , pp.78566–78576. , doi: 10.18632/oncotarget.12479

88 

    M Chu, Y Zhao, S Yu, Y Hao, P Zhang, Y Feng, et al. miR-15b negatively correlates with lipid metabolism in mammary epithelial cells. Am J Physiol, Cell Physiol. 2018;314: , pp.C43–C52. , doi: 10.1152/ajpcell.00115.2017

89 

    DP Bartel. . MicroRNAs: target recognition and regulatory functions. Cell. 2009;136: , pp.215–233. , doi: 10.1016/j.cell.2009.01.002

90 

    C Jopling. . Liver-specific microRNA-122. RNA Biol. 2012;9: , pp.137–142. , doi: 10.4161/rna.18827

91 

    P Gupta, MJ Cairns, NK Saksena. . Regulation of gene expression by microRNA in HCV infection and HCV–mediated hepatocellular carcinoma. Virol J. 2014;11: , pp.64, doi: 10.1186/1743-422X-11-64

92 

    Z Yang, T Cappello, L Wang. . Emerging role of microRNAs in lipid metabolism. Acta Pharm Sin B. 2015;5: , pp.145–150. , doi: 10.1016/j.apsb.2015.01.002

93 

    C Esau, S Davis, SF Murray, XX Yu, SK Pandey, M Pear, et al. miR-122 regulation of lipid metabolism revealed by in vivo antisense targeting. Cell Metab. 2006;3: , pp.87–98. , doi: 10.1016/j.cmet.2006.01.005

94 

    G Alvite, A Esteves. . Lipid binding proteins from parasitic platyhelminthes. Front Physiol. 2012;3, doi: 10.3389/fphys.2012.00363

95 

    C Kepron, J Schoen, M Novak, JB Blackburn. . NMR study of lipid changes in organs of jirds infected with Echinococcus multilocularis. Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology. 1999;124: , pp.347–353. , doi: 10.1016/S0305-0491(99)00126-1

96 

    X Jiang, C Xu, F Lei, M Liao, W Wang, N Xu, et al. MiR-30a targets IL-1α and regulates islet functions as an inflammation buffer and response factor. Sci Rep. 2017;7: , pp.5270, doi: 10.1038/s41598-017-05560-1

97 

    V Patel, K Carrion, A Hollands, A Hinton, T Gallegos, J Dyo, et al. The stretch responsive microRNA miR-148a-3p is a novel repressor of IKBKB, NF-κB signaling, and inflammatory gene expression in human aortic valve cells. FASEB J. 2015;29: , pp.1859–1868. , doi: 10.1096/fj.14-257808

98 

    CS Abhinand, R Raju, SJ Soumya, PS Arya, PR Sudhakaran. . VEGF-A/VEGFR2 signaling network in endothelial cells relevant to angiogenesis. J Cell Commun Signal. 2016;10: , pp.347–354. , doi: 10.1007/s12079-016-0352-8

99 

    VL Bautch. . VEGF-Directed Blood Vessel Patterning: From Cells to Organism. Cold Spring Harb Perspect Med. 2012;, pp.2, doi: 10.1101/cshperspect.a006452

100 

    C Chen, T Lou. . Hypoxia inducible factors in hepatocellular carcinoma. Oncotarget. 2017;8: , pp.46691–46703. , doi: 10.18632/oncotarget.17358

101 

    C-Y Sun, X-M She, Y Qin, Z-B Chu, L Chen, L-S Ai, et al. miR-15a and miR-16 affect the angiogenesis of multiple myeloma by targeting VEGF. Carcinogenesis. 2013;34: , pp.426–435. , doi: 10.1093/carcin/bgs333

102 

    R Kong, Y Ma, J Feng, S Li, W Zhang, J Jiang, et al. The crucial role of miR-126 on suppressing progression of esophageal cancer by targeting VEGF-A. Cell Mol Biol Lett. 2016;21, doi: 10.1186/s11658-016-0004-2

103 

    J-H Kim, K-S Lee, D-K Lee, J Kim, S-N Kwak, K-S Ha, et al. Hypoxia-Responsive MicroRNA-101 Promotes Angiogenesis via Heme Oxygenase-1/Vascular Endothelial Growth Factor Axis by Targeting Cullin 3. Antioxid Redox Signal. 2014;21: , pp.2469–2482. , doi: 10.1089/ars.2014.5856

104 

    H Chen, Y Tian. . MiR-15a-5p regulates viability and matrix degradation of human osteoarthritis chondrocytes via targeting VEGFA. Biosci Trends. 2017;10: , pp.482–488. , doi: 10.5582/bst.2016.01187

105 

    B Liu, X-C Peng, X-L Zheng, J Wang, Y-W Qin. . MiR-126 restoration down-regulate VEGF and inhibit the growth of lung cancer cell lines in vitro and in vivo. Lung Cancer. 2009;66: , pp.169–175. , doi: 10.1016/j.lungcan.2009.01.010

106 

    T Song, H Li, L Yang, Y Lei, L Yao, H Wen. . Expression of Hypoxia-Inducible Factor-1α in the Infiltrative Belt Surrounding Hepatic Alveolar Echinococcosis in Rats. J Parasitol. 2015;101: , pp.369–373. , doi: 10.1645/14-685.1

107 

    Wang C-Z, Deng F, Li H, Wang D-D, Zhang W, Ding L, et al. MiR-101: a potential therapeutic target of cancers.: 12.

108 

    Y Zhang, H Bo, H-Y Wang, A Chang, XFS Zheng. . Emerging Role of MicroRNAs in mTOR Signaling. Cell Mol Life Sci. 2017;74: , pp.2613–2625. , doi: 10.1007/s00018-017-2485-1

109 

    J Yang, R Liu, Y Deng, J Qian, Z Lu, Y Wang, et al. MiR-15a/16 deficiency enhances anti-tumor immunity of glioma-infiltrating CD8+ T cells through targeting mTOR: MiR-15a/16 regulates glioma-infiltrating CD8+ T cells activation. International Journal of Cancer. 2017;141: , pp.2082–2092. , doi: 10.1002/ijc.30912

110 

    RCA Sainson, DA Johnston, HC Chu, MT Holderfield, MN Nakatsu, SP Crampton, et al. TNF primes endothelial cells for angiogenic sprouting by inducing a tip cell phenotype. Blood. 2008;111: , pp.4997–5007. , doi: 10.1182/blood-2007-08-108597

111 

    R Catar, J Witowski, N Zhu, C Lücht, A Derrac Soria, J Uceda Fernandez, et al. IL-6 Trans-Signaling Links Inflammation with Angiogenesis in the Peritoneal Membrane. J Am Soc Nephrol. 2017;28: , pp.1188–1199. , doi: 10.1681/ASN.2015101169

112 

    F Conciatori, C Bazzichetto, I Falcone, S Pilotto, E Bria, F Cognetti, et al. Role of mTOR Signaling in Tumor Microenvironment: An Overview. Int J Mol Sci. 2018;19, doi: 10.3390/ijms19082453

113 

    J Wang, R Lin, W Zhang, L Li, B Gottstein, O Blagosklonov, et al. Transcriptional Profiles of Cytokine/Chemokine Factors of Immune Cell-Homing to the Parasitic Lesions: A Comprehensive One-Year Course Study in the Liver of E. multilocularis-Infected Mice. PLOS ONE. 2014;9: , pp.e91638, doi: 10.1371/journal.pone.0091638

114 

    M Marco, A Baz, C Fernandez, G Gonzalez, U Hellman, G Salinas, et al. A relevant enzyme in granulomatous reaction, active matrix metalloproteinase-9, found in bovine Echinococcus granulosus hydatid cyst wall and fluid. Parasitol Res. 2006;100: , pp.131–139. , doi: 10.1007/s00436-006-0237-5

115 

    Y Jiang, J Li, J Wang, H Xiao, T Li, H Liu, et al. Assessment of Vascularity in Hepatic Alveolar Echinococcosis: Comparison of Quantified Dual-Energy CT with Histopathologic Parameters. PLoS ONE. 2016;11: , pp.e0149440, doi: 10.1371/journal.pone.0149440

116 

    M Castoldi, M Vujic Spasic, S Altamura, J Elmén, M Lindow, J Kiss, et al. The liver-specific microRNA miR-122 controls systemic iron homeostasis in mice. J Clin Invest. 2011;121: , pp.1386–1396. , doi: 10.1172/JCI44883

117 

    M Xiao, J Li, W Li, Y Wang, F Wu, Y Xi, et al. MicroRNAs activate gene transcription epigenetically as an enhancer trigger. RNA Biol. 2017;14: , pp.1326–1334. , doi: 10.1080/15476286.2015.1112487

118 

    S Vasudevan. . Posttranscriptional upregulation by microRNAs. Wiley Interdiscip Rev RNA. 2012;3: , pp.311–330. , doi: 10.1002/wrna.121

119 

    LF Gulyaeva, NE Kushlinskiy. . Regulatory mechanisms of microRNA expression. J Transl Med. 2016;14: , pp.143, doi: 10.1186/s12967-016-0893-x

120 

    S Morales, M Monzo, A Navarro. . Epigenetic regulation mechanisms of microRNA expression. Biomol Concepts. 2017;8: , pp.203–212. , doi: 10.1515/bmc-2017-0024

121 

    WP Kloosterman, FA Steiner, E Berezikov, E de Bruijn, J van de Belt, M Verheul, et al. Cloning and expression of new microRNAs from zebrafish. Nucleic Acids Res. 2006;34: , pp.2558–2569. , doi: 10.1093/nar/gkl278

122 

    JG Ruby, A Stark, WK Johnston, M Kellis, DP Bartel, EC Lai. . Evolution, biogenesis, expression, and target predictions of a substantially expanded set of Drosophila microRNAs. Genome Res. 2007;17: , pp.1850–1864. , doi: 10.1101/gr.6597907

123 

    K Okamura, MD Phillips, DM Tyler, H Duan, Y Chou, EC Lai. . The regulatory activity of microRNA* species has substantial influence on microRNA and 3’ UTR evolution. Nat Struct Mol Biol. 2008;15: , pp.354–363. , doi: 10.1038/nsmb.1409

124 

    K-W Tsai, C-M Leung, Y-H Lo, T-W Chen, W-C Chan, S-Y Yu, et al. Arm Selection Preference of MicroRNA-193a Varies in Breast Cancer. Sci Rep. 2016;6: , pp.28176, doi: 10.1038/srep28176

125 

    S-C Li, Y-L Liao, M-R Ho, K-W Tsai, C-H Lai, W Lin. . miRNA arm selection and isomiR distribution in gastric cancer. BMC Genomics. 2012;13Suppl 1: , pp.S13, doi: 10.1186/1471-2164-13-S1-S13

126 

    S-C Li, K-W Tsai, H-W Pan, Y-M Jeng, M-R Ho, W-H Li. . MicroRNA 3’ end nucleotide modification patterns and arm selection preference in liver tissues. BMC Syst Biol. 2012;6Suppl 2: , pp.S14, doi: 10.1186/1752-0509-6-S2-S14

127 

    KJ Siddle, L Tailleux, M Deschamps, Y-HE Loh, C Deluen, B Gicquel, et al. bacterial infection drives the expression dynamics of microRNAs and their isomiRs. PLoS Genet. 2015;11: , pp.e1005064, doi: 10.1371/journal.pgen.1005064

128 

    L Guo, J Yu, H Yu, Y Zhao, S Chen, C Xu, et al. Evolutionary and expression analysis of miR-#-5p and miR-#-3p at the miRNAs/isomiRs levels. Biomed Res Int. 2015;2015: , pp.168358, doi: 10.1155/2015/168358

129 

    KB Choo, YL Soon, PNN Nguyen, MSY Hiew, C-J Huang. . MicroRNA-5p and -3p co-expression and cross-targeting in colon cancer cells. J Biomed Sci. 2014;21: , pp.95, doi: 10.1186/s12929-014-0095-x

2 Oct 2019

Dear Prof. Dr. Gottstein:

Thank you very much for submitting your manuscript "Regulation of Hepatic MicroRNAs in Response to Early Stage Echinococcus multilocularis Egg Infection in C57BL/6 mice" (#PNTD-D-19-01183) for review by PLOS Neglected Tropical Diseases. Your manuscript was fully evaluated at the editorial level and by independent peer reviewers. The reviewers appreciated the attention to an important problem, but raised some substantial concerns about the manuscript as it currently stands. These issues must be addressed before we would be willing to consider a revised version of your study. We cannot, of course, promise publication at that time.

We therefore ask you to modify the manuscript according to the review recommendations before we can consider your manuscript for acceptance. Your revisions should address the specific points made by each reviewer.

When you are ready to resubmit, please be prepared to upload the following:

(1) A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript.

(2) Two versions of the manuscript: one with either highlights or tracked changes denoting where the text has been changed (uploaded as a "Revised Article with Changes Highlighted" file); the other a clean version (uploaded as the article file).

(3) If available, a striking still image (a new image if one is available or an existing one from within your manuscript). If your manuscript is accepted for publication, this image may be featured on our website. Images should ideally be high resolution, eye-catching, single panel images; where one is available, please use 'add file' at the time of resubmission and select 'striking image' as the file type.

Please provide a short caption, including credits, uploaded as a separate "Other" file. If your image is from someone other than yourself, please ensure that the artist has read and agreed to the terms and conditions of the Creative Commons Attribution License at http://journals.plos.org/plosntds/s/content-license (NOTE: we cannot publish copyrighted images).

(4) If applicable, we encourage you to add a list of accession numbers/ID numbers for genes and proteins mentioned in the text (these should be listed as a paragraph at the end of the manuscript). You can supply accession numbers for any database, so long as the database is publicly accessible and stable. Examples include LocusLink and SwissProt.

(5) To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/plosntds/s/submission-guidelines#loc-methods

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

We hope to receive your revised manuscript by Dec 01 2019 11:59PM. If you anticipate any delay in its return, we ask that you let us know the expected resubmission date by replying to this email.

To submit a revision, go to https://www.editorialmanager.com/pntd/ and log in as an Author. You will see a menu item call Submission Needing Revision. You will find your submission record there.

Sincerely,

Aaron R. Jex

Deputy Editor

PLOS Neglected Tropical Diseases

Aaron Jex

Deputy Editor

PLOS Neglected Tropical Diseases

***********************

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: The methods are appropriate.

There is one detail that I found in the methods that I do not understand:

Line 276: "We used cDNA samples prepared for the Stem-loop RT-qPCR for validating the set of 28 dysregulated mature miRNAs (see section above)." How could you use these cDNA samples for qPCR of target mRNAs? They were primed using specific stem-loop primers for each miRNA...

Reviewer #2: Some of the methods were not described in sufficient detail (see detailed comments below). Further evidence of how they determined levels of significance for miRNA differential transcription is required.

--------------------

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: Yes. It would be great to have as supplementary information a complete table of miRNA expression in each sample, including also miRNAs that did not show any significant change (i.e. a full version of table 4). I would also recommend sharing the raw RNAseq data (as far as I could see, these have not been made available in a public database).

Reviewer #2: Results clearly match the aims of the study. The results are presented in sufficient detail. The link between their results in fatty acid biosynthesis were not very clear. Perhaps more information is required (pathway genes listed? Synonyms made clear for a broad audience?)There are a lot of tables and figures presented in the main body of the manuscript. Some consolidation of figures and moving quality control results to the supplementary material is recommended. Additional comments about the results are listed below.

--------------------

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: - The main issue that I find lacking in the discussion is that there is no hypothesis regarding how AE changes miRNA expression in the surrounding tissues. In particular, an aspect that is not considered is that part of the differences could be related not only to differential gene expression of constituent hepatic cells, but also from the different cell composition that results from the inflammatory immune response (accumulation of macrophages, lymphocytes, etc, in the surrounding tissues).

- I am not convinced about some of the comparisons between AE and HCC - specifically, in lines 444 to 448, you compare miRNA changes between both conditions. It is true that the parasite grows like a tumour, and may affect the surrounding tissues in some similar ways. However, here you are comparing miRNA expression of tumour cells for HCC vs. miRNa expression in the surrounding tissues for AE.

- There are several enriched pathways in figure 7 that are related to each other and probably share constituent genes. It seems likely to me that many of the genes that result in VEGFA-VEGFR2 pathway enrichment are also resulting in the enrichment of PDGF, EGFR, FGFR pathways (many of the genes indicated in Supplementary Figure 1 are largely shared between these pathways, and as far as I can see, only VEGFA is specific). In my opinion, this should be part of the description and discussion of these results, as it tones down the specific importance of VEGF. It would be good if the genes involved in pathway enrichment for each category were provided as supplementary information.

- Even though I find this study superior in execution and design (as it involves samples from primary echinococcosis and greater statistical power), it would be worthwhile to include a more detailed comparison to the miRNAs found to be dysregulated by AE by Jin et al 2017 (cited by the authors) as some coincide between both studies.

- Although typically mRNA target levels decrease in the presence of the targeting miRNA, the precisse relationship between miRNA and target mRNA levels can be quite variable, as most regulation occurs at a translational level. Therefore, you could mention that it is likely that the true effect on gene expression is subestimated in the qPCR experiments.

Reviewer #2: The discussion is quite long and the authors have made a number of conclusions from this data set. I would recommend they be more cautious considering the small changes in transcription that were observed (particularly the mRNA validation).

--------------------

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: Line 24 and Line 66: Explaining the abbreviation "E. multilocularis" is unnecesary (it is standard to abbreviate the genus to its inital letter after the first time that the species is named).

Line 66: cyclophyllid -> cyclophyllidean

Line 100: the number of true miRNA loci is still very controversial - see for example doi: 10.1093/nar/gkz097. Perhaps mention a range of possible miRNA loci instead based on the literature.

Line 194: this should probably be "libraries from five mice"

Line 199: "intended" -> "expected". Is the 141 bp peak the result of the addition of the 21 bp miRNA cDNA plus the adapters?

Line 238: did you use the same primers for sno234 as in ref. 62?

Line 243: change "5 min_25ºC" to "5 min at 25 ºC", etc.

Line 250: add the missing parenthesis

Line 270: "Precisely" -> "Specifically"

Line 302: "compromised" -> "comprised"

Line 308: you generally used miRNA as singular and plural throughout the text, but here you used miRNAs as the plural

Line 310: "whose log2FC" -> "whose fold change"

Line 312: "FC ≤ 0.6" should probably be "FC ≤ 0.67"

Line 334: "they" -> "that"

Line 344 to 348: it would be more clear if you reversed the order of these sentences (and would result in mentioning figure 6a before figure 6b).

Line 352: "identified for the 25 miRNAs" -> "identified for 25 miRNAs"

Line 366: "alone mmu-miR-23a-3p and mmu-miR-23b-3p share 21 targets, 81% of common genes." -> I would recommend changing this to mmu-miR-23a-3p and mmu-miR-23b-3p share 21 targets, representing 81% of genes targeted by two different up-regulated miRNAs."

Line 369: "Immune relevant" -> perhaps change to "Genes relevant to immunity included..."

Line 369-378: in some cases, the same mRNA is predicted to be targeted by different miRNAs that are up and down-regulated, so the expected effect is not clear. This could be mentioned here.

Line 377: ACSL1 is not directly involved in FA biosynthesis, but in FA activation for complex lipid synthesis and catabolism.

Line 383 is not very well connected to the rest of the text.

Line 397: It could be worthwhile to mention that all mRNA related to heme biosynthesis are targeted by miR-122, an hepatic miRNA with known roles in iron homeostasis (doi: 10.1172/JCI44883)

Line 401: what do you mean here by "most significant"? They were not specifically among the enriched categories shown in Fig 7.

Line 443: "metastatis" -> "metastasis"

Line 469: actually, FASN has several domains and catalyzes all steps of FA synthesis from acetylCoA and malonylCoA.

Line 518: I believe this line should be toned down; the results suggest that this phenomenon is partially mediated via downregulation of specific miRNAs

Line 563: "evaluate" -> "to evaluate"

Line 571: "Marcella" -> "Marcela"

- In my opinion, Figure 1 could be transferred to the Supplementary Information.

Reviewer #2: Grammatical and unclear text

Lines 54 to 56. Sentence unclear, please check grammar

Lines 84. Sentence is unclear. Please check for grammatical errors

Lines 100 to 101. Sentence is unclear. Perhaps it needs to be clear what a gene is first (coding and non-coding)?

Lines 141 to 142. Unclear if you are referring to the miRNA or the mRNA that they target.

Line 184. Can you include the company and product information for the DNase I?

Lines 201 to 202. Methods is unclear here.

Lines 202 to 204. This statement is unclear. How long were the reads sequenced? is 50 Mio 50 million. What is past filter?

Lines 218 to 219. Not clear what they mean by valid here? Please revise and make it clearer.

More detail is required for the headers/description of Tables. Table 3 is difficult to understand with the information provided in the headers

Lines 403 to 407. This reads like methods to me

Lines 56. You mention pathways but have not introduced the pathways you are referring to.

Line 407. Levels... typo

Lines 423 to 424. Sentence is unclear

Body of the manuscript

Beginning of the abstract reads more like a summary of the materials and methods. I don't think the methods needs to be included in the abstract with this much detail. Summary introduces hypotheses generated from the results that were not mentioned in the abstract. Abstract needs to be revised to include more results and less materials and methods.

Introduction is very long. Some information is not required. The role of miRNA is very well established now. Perhaps the role of miRNA in parasite infections is all that needs to be introduced (i.e. human miRNA and their role is already well established and needs no introduction here).

Lines 157 to 165. I assume this method is well established and has been published previously? Perhaps there is no need to detail the methods if it has already been published?

Figures 1A could be presented as one graph. Each line would be a different library. Further consolidation of the figures in this paper would allow them to be printed larger and improve the readability. This figure could also be removed to Supplementary files.

Grey labelling of the different miRNA in Fig 2B is not described in the Figure legend. What is the significant of the different colours? Common and unique to the different groups? Some additional description in the Figure legend

would be required.

Fig 6B description is before 6A. Panels need to be swapped on Fig 6.

Discussion section is quite long. Conclusion paragraphs could be significantly reduced. For example, there is quite a bit of talk about biomarkers and this is not required here, simply mentioned at the end of the last sentence perhaps?

Tables 1 and 2 could be moved to supplementary tables. Table 3 requires more detail to improve understanding of each column

--------------------

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: This is an interesting, well executed and clearly written article in which the authors compare the miRNA transcriptome of hepatic tissues in control conditions and during early AE infection. This is a primary, hepatic infection, which is a good model for the natural infection in rodents. The authors strenghten their RNAseq results by confirming a selected number of significantly regulated miRNAs by qPCR, and they also analyze the levels of some predicted miRNA targets by qPCR.

I only have some observations and recommendations regarding the discussion of the results, and a question regarding the methods, which I already included in the previous sections. I also provided some editorial comments.

Reviewer #2: It is well established that flatworm parasites (including E. multilocularis) have the capacity to regulate their host immune response. The molecular mechanisms they use to do this are still poorly understood. Understanding changes in host mRNA and miRNA transcription in response to the intial infection will provide new insights into this exciting research area. The authors describe changes in miRNA transcription in mice livers one month after being infected with E. multilocularis (early in the infection). Results were validated for select miRNA and mRNA using qPCR. Significantly differently regulated miRNA pointed to changes in the regulation of target mRNA and pathways they are associated with. Specifically, angiogenesis, axon guidance and ??fatty acid biosynthesis?? The authors use their results to support the role of known pathways associated with parasite infection as well as proposing novel changes in molecular signalling in the liver in response to parasite infection. I observed a few limitations with this study (sample size for DESEQ2 and fold changes observed) and care should be taken from drawing too many conclusions from their results. My main concern was the lack of detail in the materials and methods section that made it difficult to assess why they chose less stringent fold-change cutoffs (1.5 fold change is quite low; was multiple testing correction used?) for changes in transcription as well as only looking at the miRNA with >1000 read counts (only a small fraction of their data). Importantly, there was no evidence that they have submitted their raw small RNA sequence reads to a public archive (e.g. NCBI SRA). This is required. I outline my concerns in detail below.

Major comments

When annotating the miRNA using BLAST: Would you not be looking for a perfect match to these nucleotide sequences? Please justify this two step process of annotating putative miRNA. Conducting BLAST searches against miRBASE and Rfam may not be the most reliable method of annotating your miRNA. For example, Rfam would recommend using Infernal (HMM-based searching) to annotate small RNA using the Rfam database.

Lines 219 to 221. The link from small RNA clustering, annotation to mapping is unclear. When mapping to the genome, how were the locations of your miRNA determined. Usually the premature miRNA would be used for positioning the miRNA within the genome. In this case, were you mapping to the mature, the star or the mature, star and hinge all at the same time? More information is required here.

Line 225. If this is the case, then how did you annotate miRNA that were not a match to miRNA encoded in the reference genome? (see Lines 217 to 218)

Lines 226 to 227. Were the distributions of miRNA transcription normally distributed? Can often be tricky when performing pairwise comparisons tests using skewed data (often observed with miRNA data). Also, what settings were used?; fold change, and multiple testing correction q value? I assume multiple test correction was performed as part of the DESeq2 pipeline. Overall, more information should be included about the steps taken to determine the differentially transcribed miRNA. Currently it is too difficult to work out what has been done.

Could the authors also comment on the use of the DESEQ2 pipeline when your number of replicates is 2 in the control group?

Lines 229 to 230. Not sure what you mean about the 2D here. Do you mean that you chose to only display 2 dimensions? Presumably the 2 dimensions that accounted for the most variation?

Line 265. What was used as a cutoff for measuring significance? Pvalue < 0.05/0.01?

Line 277. Unclear which 28 mature miRNA are being referred to here. There are less than 28 primer sets listed in Table 1.

Lines 293 to 294. So most miRNA had mapped reads aligned at a depth of 10 to 100? Is this quite low or normal for mouse studies? Unclear why you then chose to use a depth cutoff of 1000 (see Line 312). You are removing most of your data. Particularly as it was in combination with such a low fold change coverage (1.5 fold change)? Further justification for your methods is required.

In Figure 2A, you refer to 124 AE-specific miRNAs with read counts < 100. Do you mean > 100?

Methods for Figure 3B are not included in the materials and methods section. Unclear what values are used in the legend on Fig 3B. More information required.

Lines 312 to 317. How can let 7 miRNA be up and down regulated? star and mature miRNA? More detail on let 7 genes may need to be described. Is this an example of arm swapping?

Fig 6B. Unclear what the error bars represent in this panel. It is a stacked bar plot which equals 100%

Lines 383 Production of this result is not described in the materials and methods section. I suggest they be deleted. There is also no description of this result.

Lines 401 to 402. Here you mention that fatty acid synthesis was a significantly afffected pathway but earlier you state that it is angiogenesis and axon guidance. I couldn't see the link between axon guidance and fatty acid synthesis. More information is required here.

Lines 424 to 426. Without comparing miRNA profiles between human AE and other liver diseases it is difficult to see how you this statement is supported by your research.

Lines 516 to 518. Discussion concludes that there is strong evidence. Levels of transcriptional change are quite low for many of the genes you tested. I would be cautious in drawing too many conclusions from the data. At least, change the wording to make it clear that you are developing hypotheses that require further testing.

--------------------

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No


8 Jan 2020

Submitted filename: Response_letter.docx

18 Feb 2020

Dear Prof. Dr. Gottstein,

Thank you very much for submitting your manuscript "Response letter: PNTD-D-19-01183 [EMID:2740a10eb62af13a] “Regulation of Hepatic MicroRNAs in Response to Early Stage Echinococcus multilocularis Egg Infection in C57BL/6 mice”." for consideration at PLOS Neglected Tropical Diseases. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

The authors have satisfactorily addressed the reviewers' concerns, only minor editorial editions need to be addressed following reviewers' suggestions.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.  

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. 

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Gabriel Rinaldi

Associate Editor

PLOS Neglected Tropical Diseases

Aaron Jex

Deputy Editor

PLOS Neglected Tropical Diseases

***********************

The authors have satisfactorily addressed the reviewers' concerns, only minor editorial editions need to be addressed following reviewers' suggestions.

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: (No Response)

Reviewer #2: Satisfied with most changes made in R1. Response to my comments on the experimental design and use of statistics was not complete, but the design is robust enough for the revised conclusions in R1

--------------------

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: (No Response)

Reviewer #2: Satisfied with the changes made in R1

--------------------

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: (No Response)

Reviewer #2: Satisfied with the changes made in R1

--------------------

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: A few additional editorial modifications:

Line 247 - some corrections were missing

Line 308-310 - you mention first 45 ctr-1pm specific miRNAs, but then 24 ctr-1pm specific miRNAs

Line 375 and line 541 - you mention 8 up-regulated miRNAs, but they are actually 9, as mentioned elsewhere

Line 542 -"cautiously indictaes" - I would remove "cautiously";change indictaes for indicates

S Fig 1 - the x axis says "lenght" instead of "length"

Reviewer #2: Satisfied with the changes made in R1

--------------------

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: The authors have satisfactorily modified the manuscript and replied to the reviewers.

I have only included a few additional editorial comments above.

The only additional thing that I have not found in the manuscript is where the original sequencing data was deposited.

Reviewer #2: Thank you for addressing the majority of my comments

--------------------

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, PLOS recommends that you deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/plosntds/s/submission-guidelines#loc-materials-and-methods


4 Mar 2020

Submitted filename: response_letterR2.docx

5 Mar 2020

Dear Prof. Dr. Gottstein,

We are pleased to inform you that your manuscript 'Response letter: PNTD-D-19-01183 [EMID:2740a10eb62af13a] “Regulation of Hepatic MicroRNAs in Response to Early Stage Echinococcus multilocularis Egg Infection in C57BL/6 mice”.' has been provisionally accepted for publication in PLOS Neglected Tropical Diseases.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Gabriel Rinaldi

Associate Editor

PLOS Neglected Tropical Diseases

Aaron Jex

Deputy Editor

PLOS Neglected Tropical Diseases

***********************************************************


6 May 2020

Dear Prof. Dr. Gottstein,

We are delighted to inform you that your manuscript, "Regulation of Hepatic MicroRNAs in Response to Early Stage Echinococcus multilocularis Egg Infection in C57BL/6 mice," has been formally accepted for publication in PLOS Neglected Tropical Diseases.

We have now passed your article onto the PLOS Production Department who will complete the rest of the publication process. All authors will receive a confirmation email upon publication.

The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any scientific or type-setting errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript. Note: Proofs for Front Matter articles (Editorial, Viewpoint, Symposium, Review, etc...) are generated on a different schedule and may not be made available as quickly.

Soon after your final files are uploaded, the early version of your manuscript will be published online unless you opted out of this process. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.

Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Serap Aksoy

Editor-in-Chief

PLOS Neglected Tropical Diseases

Shaden Kamhawi

Editor-in-Chief

PLOS Neglected Tropical Diseases

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

https://www.researchpad.co/tools/openurl?pubtype=article&doi=10.1371/journal.pntd.0007640&title=Regulation of hepatic microRNAs in response to early stage <i>Echinococcus multilocularis</i> egg infection in C57BL/6 mice&author=&keyword=&subject=Research Article,Biology and life sciences,Biochemistry,Nucleic acids,RNA,Non-coding RNA,Natural antisense transcripts,MicroRNAs,Biology and life sciences,Genetics,Gene expression,Gene regulation,MicroRNAs,Biology and Life Sciences,Genetics,Gene Expression,Biology and Life Sciences,Genetics,Gene Expression,Gene Regulation,Biology and Life Sciences,Biochemistry,Lipids,Fatty Acids,Medicine and Health Sciences,Parasitic Diseases,Helminth Infections,Echinococcosis,Medicine and Health Sciences,Tropical Diseases,Neglected Tropical Diseases,Echinococcosis,Medicine and Health Sciences,Parasitic Diseases,Biology and Life Sciences,Biochemistry,Biosynthesis,Research and Analysis Methods,Animal Studies,Experimental Organism Systems,Model Organisms,Mouse Models,Research and Analysis Methods,Model Organisms,Mouse Models,Research and Analysis Methods,Animal Studies,Experimental Organism Systems,Animal Models,Mouse Models,