Cynops orientalis) for Batrachochytrium salamandrivorans, an emergent fungal pathogen of salamanders. All animals tested negative for the fungus. However, a virus was cultured from internal organs from 7 of the 11 individual dead salamanders and from two pools of tissues from four additional dead animals. Sequencing of a portion of the glycoprotein gene from all viral isolates indicated 100% identity and that they were most closely related to spring viraemia of carp virus (SVCV). Subsequently, SVCV-specific PCR testing indicated the presence of virus in internal organs from each of the four animals previously pooled, and whole-genome sequencing of one of the viral isolates confirmed genomic arrangement characteristic of SVCV. SVCV is a rhabdovirus pathogen of cyprinid fish that is listed as notifiable to the Office International des Epizooties. This discovery reveals a novel route for potential spillover of this economically important pathogen as rhabdovirus has not previously been documented in amphibians.Global translocation of plants and animals is a well-recognized mechanism for introduction of pathogens into new regions. To mitigate this risk, various tools such as preshipment health certificates, quarantines, screening for specific disease agents and outright bans have been implemented. However, such measures only target known infectious agents and their hosts and may fail to prevent translocation of even well-recognized pathogens if they are carried by novel host species. In a recent example, we screened an imported shipment of Chinese firebelly newts ( The animals did not harbor Bsal but rather tested positive for spring viraemia of carp virus (SVCV), a pathogen not previously known to infect amphibians. Inadvertent introduction of SVCV, the cause of a reportable foreign animal disease in the United States, would present an economic threat to the commercial cyprinid aquaculture industry. Overall, this example highlights the challenges of reducing risks for translocation of pathogens when knowledge of host–pathogen relationships is incomplete.Recently published assessments of the global risk that Cynops orientalis) carcasses that were part of a larger shipment of live newts imported to the United States from Hong Kong, China in August 2015 were subjected to virus culture to screen for ranavirus, a major pathogen of amphibia.9 Pools of sterilely collected organs from dead salamanders, including liver, kidney and spleen, were homogenized in Dulbecco's minimal essential medium in a 1:10 (weight to volume) ratio and inoculated into either epithelioma papulosum cyprini (EPC10) or zebrafish (ZF411) epithelial cell monolayers. A different set of dissection instruments was used for each animal. Cultures were incubated at 10 °C–16 °C and examined for cytopathic effects (CPE) daily for 7–10 days. Cultures that did not exhibit CPE were blind passed at least once before being classified as negative. Cultures that exhibited CPE were frozen at −80 °C and thawed three times, and cellular debris was removed by centrifugation at 1000 g for 30 min at 4 °C. For electron microscopic analysis, aliquots of the supernatant were re-centrifuged at 100 000 g for 10 min in a Beckman Airfuge . The pelleted viral particles were negatively stained with 2% phosphotungstic acid and examined in a Hitachi H-7600 transmission electron microscope .Eleven Chinese firebelly newt  were examined using a 1% agarose gel, samples with the expected amplicon size (714 bp) were sequenced with the PCR primers and the resulting sequence was characterized by BLAST analysis. A real-time RT-PCR (rRT-PCR) assay targeting the G gene was used to determine individual infection status of animals whose tissues had originally been pooled for virus isolation.13For reverse transcription-PCR (RT-PCR) analysis, aliquots of the initial low-speed supernatant were extracted using the following kits according to the manufacturer's instructions: Qiagen DNeasy Blood and Tissue Kit  (for DNA) or Ambion MagMAX Viral RNA Isolation Kit  (for RNA). Primer SVCV F1 (5′-TCT TGG AGC CAA ATA GCT CAR RTC-3′) and SVCV R2 (5′-AGA TGG TAT GGA CCC CAA TAC ATH ACN CAY-3′) were used in a 25-μL one-step RT-PCR reaction based on Stone 14 of isolate salamander/USA/202238/2015 (hereafter 202238). The assembled sequence has been deposited in GenBank (accession KU230365).Total RNA was prepared from cell cultures that were positive for SVCV using the Ambion MagMAX Viral RNA Isolation Kit according to the manufacturer's instructions. Samples were sequenced at the National Veterinary Services Laboratories  on an Ion Torrent PGM instrument  using Ion Total RNA-Seq (v2) chemistries as per the manufacturer's instructions. Sequencing reads were assembled against reference sequences (GenBank KJ513477 and DQ491000) selected from results of BLAST analysis of a 2500-nt fragment generated by Sanger sequence analysis15 and the best-fit model of nucleotide substitution was determined using MEGA 7.0.16 Median joining network analysis was performed on the aligned sequences using Population Analysis with Reticulate Trees  with epsilon=0. The network was recalculated 1000 times. Where not specified, default parameters were used. The aligned sequences were also analyzed using maximum likelihood by RAxML-HPC2 8.2.4 with 1000 bootstraps and GTRCAT model used during bootstrapping.17 Sequences were additionally compared by Bayesian analysis using MrBayes 3.1.218 employing an HKY model with gamma distribution and invariant sites. The analysis was performed for 1 000 000 generations (with 25% burn-in) and sampled every 10 000 generations. Where not specified, default parameters were used.Representative nucleotide sequences from SVCV were aligned using MUSCLECynops orientalis has resulted in the discovery of cryptic species.20 To confirm the species of salamander that were part of the imported shipment, DNA was extracted from pooled organs  of a subset of animals (n=4) using the Gentra Puregene Tissue Kit (Qiagen) according to the manufacturer's instructions. A portion of the mitochondrial NADH dehydrogenase subunit two gene (and adjacent tRNAs) was amplified with primers 3787F: 5′-TCG TGC ACC CAC TAC ACT AC-3′ and 5081R: 5′-GTC GTA GGG TCA AAG CCT GC-3′ . PCR amplification was conducted in 50-μL reaction volumes using GoTaq Flexi DNA polymerase  and 1 μL of DNA template that had been diluted 100-fold. Cycling conditions were as follow: 94 °C for 2 min; 35 cycles of 94 °C for 30 s, 52 °C for 45 s, and 72 °C for 90 s; and a final extension for 5 min at 72 °C. Amplicons were sequenced using the amplification primers and internal sequencing primer 4416F: 5′-ATA GCA TAC TCA TCC ATT GCA CA-3′.21 Chromatograms of the resulting sequences were viewed and edited (as necessary) in Lasergene 5.0 . Final sequences were aligned with existing DNA sequences in GenBank, primarily following taxon sampling by Wu et al.19 The alignment was conducted using MAFFT22 through the online program GUIDANCE24 with the following settings: 100 bootstrap repeats, 1000 maximum iterations, and ‘globalpair' pairwise alignment method. Phylogenetic analyses using maximum likelihood and Bayesian methods were conducted on the final alignment with the programs RAxML-HPC2 version 8.2.417 and MrBayes version 3.2.6,18 respectively, using the CIPRES Science Gateway.25 For both analyses, the general time reversible model with gamma distribution was used. For maximum likelihood analysis, the number of bootstrap iterations was set at 1000; all other RAxML parameters were left as default. For the Bayesian analysis, the number of generations was increased to 5 000 000 and remaining settings were default.Recent re-evaluation of 26 An amplicon of the expected size (260 nt) was generated, and sequence analysis showed closest similarity with the genus Sprivivirus in the family Rhabdoviridae . All four animals previously pooled for virus isolation purposes were individually positive using a SVCV-specific rRT-PCR test  was also chosen for whole-genome sequencing. A near complete contig of 10 991 nt that encoded five genes  in an order that is characteristic of the genus Sprivivirus was obtained following assembly of the genome .27To further characterize the nine viral isolates, a 714-nt fragment of the viral glycoprotein (G) gene was amplified by PCR.28 This finding further supported an Asian origin for the salamander SVCV isolates.Bayesian and maximum likelihood phylogenetic analyses of the G gene fragments indicated that the salamander isolates clustered most closely with SVCV genogroup I . Median-et al.19 The salamanders that were positive for SVCV were part of a well-supported clade  with C. orientalis that originated from the vicinity of Hangzhou, Zhejiang province, China, suggesting that the infected salamanders may have been collected from wild populations in that area.19To confirm the species identity of the salamanders infected with SVCV, a portion of the host mitochondrial NADH dehydrogenase subunit two gene (and adjacent tRNAs) was sequenced from a subset of four animals. The portion of DNA analyzed was 100% identical between the four animals examined. A representative sequence was deposited in GenBank . Phylogenetic analyses resulted in trees with the same topology and were similar to those of Wu 10 The virus can be particularly devastating for the carp aquaculture industry and has also been reported to infect other commercially important species, including sheatfish (Silurus glanis), and possibly rainbow trout (Oncorhynchus mykiss) and tilapia (Sarotherodon niloticus).10 Based partly upon serological characterization, SVCV was formerly classified in the genus Vesiculovirus, family Rhabdoviridae, order Mononegavirales along with pike fry rhabdovirus (PFRV) and vesicular stomatitis virus, the type species of the genus. In 2012, most strains of PFRV and SVCV along with tench rhabdovirus (TenRV) and grass carp rhabdovirus (GrCRV) were proposed to be moved into a new genus, Sprivivirus, based on characterization of partial regions of individual gene sequences;27 this proposal was accepted in the current edition of the ICTV virus taxonomy.29SVCV is a finfish rhabdovirus of major economic importance that is reportable to the Office International des Epizooties.et al.30 and Xiao et al.31 based on the P and M genes, respectively. Our analyses focused on the G gene as this region is the most variable in the genome, it is the most consistently available sequence from isolates of SVCV, and its use conforms with current taxonomic criteria of the ICTV.27 Genomic organization and sequence analysis of the other genes of the salamander isolates also supported this taxonomic placement.We determined that the salamander isolates represented SVCV based upon sequence analysis of the G gene. Although this classification scheme is still in use, additional finer-scale differentiation of SVCV genogroups has been proposed by Miller -nt region of the G gene. Genogroup I contains classic SVCV isolates; genogroup II contains isolates of GrCRV; and genogroups III and IV contain PFRV and TenRV, respectively. Genogroup I is further divided into genogroups Ia, Ib, Ic and Id.12 Genogroup Ia contains SVCV isolates from Asia and strains introduced into Europe and North America; genogroup Ib contains isolates of SVCV from eastern Europe; genogroup Ic contains isolates of SVCV from Russia and additional isolates from eastern Europe; and genogroup Id contains isolates of SVCV from western Europe. The salamander SVCV isolates are members of genotype Ia according to Bayesian and maximum likelihood  contaminated materials. Nonetheless, these findings demonstrate the potential for translocated amphibians to serve as a source for the introduction of SVCV. Specifically, hundreds of thousands of C. orientalis are collected each year in China, and many of these animals enter the United States pet trade, creating the possibility that some individuals could escape or be released into the wild.35 Such releases, even in the absence of exotic salamander species becoming established, could represent a possible mechanism by which SVCV or other pathogens are spread. Furthermore, identification of C. orientalis as a host for SVCV raises the question of whether additional amphibian species imported into the United States could cryptically harbor pathogens of economic importance.Phylogenetic analyses confirmed the identity of imported salamanders infected with SVCV as  China19 . Phyloge36 If, however, C. orientalis co-evolved with SVCV and can be infected without apparent detriment, this detection may further implicate Southeast Asia as the region from which this SVCV originated. Under such a scenario, salamanders from other parts of the world could represent potentially naive hosts subject to developing more severe disease upon exposure to this virus.The susceptibility of amphibians worldwide to SVCV is not known but of particular concern is whether SVCV could act as a potential pathogen in already imperiled amphibian populations. Although the dead salamanders we examined were in good body condition, freezing and thawing of carcasses confounded attempts to observe lesions during histopathological examination. Thus we could not ascertain whether SVCV contributed to mortality or occurred without causing clinical signs or lesions. Indeed, non-lethal or asymptomatic SVCV infections have been documented in some wild and captive fish populations.37 and mitigation of unintended global translocations of exotic pathogens will require a multifaceted approach. An example of one strategy is the recently imposed ban on importation and interstate transport within the United States of salamander species that may harbor the fungal pathogen Bsal (50 CFR Part 16). Recognition of the serious threat presented by Bsal and actions taken to prevent this invasive pathogen from reaching North America represent a substantial advance in proactive response to EIDs. Although the 11 newt carcasses and 75 skin swabs collected from live newts in the same shipment were negative for Bsal,38 discovery of SVCV, a major finfish pathogen in an imported amphibian species, provides further support for the importation ban. However, such restriction can be controversial, having economic, political, conservation and cultural impacts. Furthermore, as demonstrated by this study in which viable SVCV was identified in a novel order of vertebrate hosts, banning translocation of host species known to harbor a certain pathogen does not guarantee that a different host will not harbor the same pathogen. Similarly, bans targeting pathogens of concern may fail to prevent the movement of cryptic infectious agents that are currently unrecognized because they do not cause disease in hosts within their native ranges. In such cases, agent-agnostic diagnostic tools, such as virus isolation and Next-Generation Sequencing, may be necessary to provide comprehensive pathogen surveillance. Coordinated surveillance systems together with increased understanding of potentially novel pathogens offer the ability to augment importation restrictions by facilitating early detection and eradication of pathogens should they be introduced. Overall, restrictions on movement of some host species, active surveillance, quarantine practices, screening for known pathogens, basic research aimed at identifying possible pathogens before they emerge and educational campaigns emphasizing potential risks associated with release of invasive species can together underpin an international strategy to reduce risks of pathogen introduction.Preventing translocation of infectious pathogens through anthropogenic activities, including the global pet trade, has the potential to reduce the frequency and magnitude of EIDs that can affect human health, food security and global biodiversity. However, the scale of legal trade in wildlife species alone was valued at $323 billion USD in 2009,


Epstein–Barr virus (EBV) is the etiological agent of acute infectious mononucleosis (IM). Since acute IM is a self-resolving disease with most patients regaining health in 1–3 weeks there have been few studies examining molecular signatures in early acute stages of the disease. MicroRNAs (miRNAs) have been shown, however, to influence immune cell function and consequently the generation of antibody responses in IM. In this study, we performed a comprehensive analysis of differentially expressed miRNAs in early stage uncomplicated acute IM. miRNAs were profiled from patient peripheral blood obtained at the time of IM diagnosis and at subsequent time points, and pathway analysis performed to identify important immune and cell signaling pathways. We identified 215 differentially regulated miRNAs at the most acute stage of infection when the patients initially sought medical help. The number of differentially expressed miRNAs decreased to 148 and 68 at 1 and 2 months post-primary infection, with no significantly changed miRNAs identified at 7 months post-infection. Interferon signaling, T and B cell signaling and antigen presentation were the top pathways influenced by the miRNAs associated with IM. Thus, a dynamic and regulated expression profile of miRNA accompanies the early acute immune response, and resolution of infection, in IM. Epstein–Barr virus is a gamma-herpes family virus that infects over 95% of humans globally and is the etiological agent of many acute and chronic infections as well as human malignancies . While E+ effector T cell response to mCMV  who visited Vaden Student Health Center at Stanford University and were diagnosed with clinical IM. Samples were obtained upon diagnosis (time point 0) and then at, 1, 2, and 7 months. Blood samples were processed using Ficoll-Paque PLUS  then PBMC frozen and maintained in liquid nitrogen until use. PBMCs were isolated from blood derived from healthy age-matched individuals. This study was carried out in accordance with the recommendations of Institutional Review Board at Stanford University with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki.n = 16) and healthy control (n = 3) PBMCs were suspended and lysed in TRIzol Reagent  for isolation of total RNA. Glycogen (20 μg) was used for RNA precipitation. Isolated RNA was DNase treated (Qiagen) and clean-up was done on RNeasy spin columns (Qiagen) using a modified protocol which replaced RW1 buffer with RWT buffer for washing. RNA was suspended in RNase free water and stored at -80°C. RNA quantification was done using Nanodrop 2000 Spectrophotometer . Approximately 200 ng RNA was used for microarray analysis using Affymetrix GeneChip miRNA 4.0 arrays  through the PAN (Protein and Nucleic acid) facility at Stanford University. Briefly, 130 ng total RNA was assessed for quality using Bioanalyzer 2100 (Agilent), followed by Poly-A tailing and labeling with biotin using a Affymetrix FlashTag Biotin HSR RNA Labeling kit  according to the manufacturer’s protocol. The labeled targets are hybridized to the GeneChip® miRNA 4.0 arrays per standard miRNA protocol, which includes 16 h (overnight) hybridization at 48°C at 60 RPM in the Affymetrix GeneChip Hybridization oven 645. The arrays were then washed and stained in the Affymetrix GeneChip Fluidics Station 450. The arrays were scanned using the Affymetrix GeneChip Scanner 3000 7G. The GeneChip® miRNA 4.0 arrays contain 30,424 total mature miRNA probe sets including 2578 mature human miRNAs, 2025 pre-miRNA human probes, 1996 Human snoRNA and scaRNA probe sets and miRNAs of 202 other organisms.Patient (P ≤ 0.05). After running ANOVA, the software performs multi-testing correction using Benjamini–Hochberg Step-Up FDR-controlling procedure for all the expressed miRNAs (default FDR Alpha level 0.05). Venn diagram depicting intersection of differentially expressed miRNAs and Unsupervised Hierarchical Clustering analysis was also performed. Raw Fold change and adjusted p-value data was exported to dot excel file and data selection and graphic visualization was done using Power BI software (Microsoft Inc.). Differentially expressed EBV miRNAs were also identified using the same miRNA microarray dataset using the miRNA filter as “EBV,” mRNA expression data from a publicly available GEO dataset (GSE45924) was used to perform miRNA–mRNA integrated analysis. Briefly, SOFT formatted family file (GSE45924-GPL6883_series_matrix.piv.txt.fmt) were downloaded from the GEO Database and imported into Partek Genomics Suite for mRNA “Gene Expression” workflow. Illumina HumanRef-8 v3.0 expression beadchip annotation files were uploaded to the Partek Software to obtain Fold Change Values for mRNA differential expression between comparison groups of Acute IM subjects (PBMC) and control subjects (PBMC). Pre-normalized data was log2 transformed, followed by one-way ANOVA statistical testing to identify significantly differentially expressed mRNAs and multiple-testing correction using Benjamini–Hochberg Step-Up FDR-controlling procedure (default FDR Alpha level 0.05). Fold increases of >2, and fold decreases of >2 were considered significant. The differentially expressed mRNA data was exported into a text file and converted into a suitable format for upload to IPA for further analysis.Raw microarray data were statistically analyzed using Partek Genomics Suite software (Partek Inc.). Normalization was done suing Robust Multi-array Average (RMA) and log2 transformation of data was performed, followed by one-way ANOVA statistical testing to identify significantly differentially expressed miRNA between different groups . A list of all twofold differentially expressed miRNAs obtained from Partek Genomics Suite software analysis was uploaded into IPA for analysis. miRNA target enrichment was performed using the “miRNA Target Filter” function, generating a list of 181 miRNAs with available targeting information. An mRNA gene expression dataset (GSE45924) obtained from GEO database was added to the “miRNA Target Filter” setting in IPA and “Expression pairing” was performed. An additional filter applied to this list was “Applied filters: confidence = Experimentally Observed OR High (predicted).” Further “Core Analysis” was performed on the target mRNAs based on different experimental time points to determine the affected networks and canonical pathways. Throughout the analysis, a Figure 1). The remaining samples from 1, 2, and 7 months are loosely segregated into groups and fall between these two groups on PC1 with the samples obtained at 7 months grouping closely with those from healthy controls. These findings indicate that the differentially expressed miRNAs are maximal at the time of IM diagnosis and return to the healthy control pattern in subsequent months.As a means of quality control to determine the biological separation of the groups based on probe intensities, principal component analysis (PCA) was performed using Partek Genomics software. Two separate groups of samples from IM patients at 0 month (at the time of diagnosis) and healthy controls are clearly observed . We identified 215 differentially regulated miRNAs at the most acute stage of infection . The number of differentially expressed miRNAs decreased to 148 and 68 during the course of 1 and 2 months post-primary infection, with no significantly changed miRNAs identified at 7 months post-infection (data not shown). The 27 top differentially expressed miRNAs at diagnosis, and 1 and 2 months after diagnosis, show that most of the differentially expressed miRNAs are increased  .The differential expression of miRNAs were compared between patients at the time of IM diagnosis (0 month), and at 1 and 2 months after diagnosis, compared to age-matched healthy controls , 1 month  of 0.05 was considered significant in Partek Genomics Suite Software, with black depicting decreased expression levels and blue depicting upregulation. Furthermore, the differentially expressed miRNAs demonstrate a decreasing absolute fold change over time, with the highest fold change generally observed at the time of diagnosis . These 41 miRNAs exhibit a dynamic differential expression pattern that trends toward healthy control levels at 2 months post IM diagnosis. Two miRNAs that are highly upregulated, miR-4417 and miR-4485 (data not shown), exhibit a similar trend of decreasing differential expression from diagnosis to 2 months. A compilation of all statistically significant differentially expressed miRNAs at diagnosis and one, and 2 months versus healthy controls is included in Supplementary Table Figure 5). The intersection shows that 56 miRNAs are differentially expressed at the three time points, thus providing important information regarding possible disease specific signatures. Interestingly, miRNAs uniquely expressed at diagnosis, 1 and 2 months after diagnosis also decrease in number during the course of the infection toward recovery .Unsupervised hierarchical clustering depicted as a heatmap shows the relative intensity for the 215, 148, and 68 differentially expressed miRNAs at diagnosis . Comparative analysis of mRNA targets highlighted the most canonical pathways as well as disease and biological functions . Furthermore, functional pathway analysis in IPA using the list of differentially expressed miRNAs at 0 month suggests the most impacted networks include antigen presentation, infectious disease, humoral immune response, protein synthesis, cell cycle, and cell death mechanisms. Networks have been shown in addition to their scores and number of mRNA targets (including particular molecules that pass our set filter criterion) (Table 1). Further analysis in IPA for top canonical pathways targeted by our list of differentially expressed miRNAs at IM diagnosis and based on the Illumina HumanRef-8 v3.0 gene superset shows candidates including interferon signaling, altered T and B cell signaling, primary immunodeficiency signaling, antigen presentation and communication between innate and adaptive immune cells as the major affected canonical pathways  (Table 2). It is notable that no differential expression of any EBV miRNAs is observed at 7 months post-infection, which is expected due to resolution of the symptoms in the acute phase and establishment of EBV latency. Thus, of the more than 40 known EBV miRNAs only two, miR-BART5-3p and miR-BART-16 are expressed during acute IM  on unfractionated PBMC, whereas the Gao group specifically examined a small subset of 84 miRNAs in sorted B and T cells via PCR. Further, the ages of the subjects were different and there could be other differences including environmental factors and variant strains of EBV. EBV expresses more miRNAs than other characterized human viruses, with at least 44 EBV miRNAs identified to date , type II IRGs, and cell cycle/metabolism genes.) during acute infection. This pathway analysis agrees with the pathways predicted to be altered by our miRNA profile during acute IM.In addition to EBV-induced miRNA regulation in infected B cells, others have focused on understanding the EBV pathogenesis and host responses by transcriptomic  analysis on PBMCs of human subjects in a prospective study of naturally acquired primary EBV infection . The autWe have defined the host immune cell and EBV miRNA profile elicited by primary EBV infection during acute IM. This profile is indicative of a response that is accompanied by complete resolution of disease. Thus, it may be useful in predicting which individuals that present with IM will have an uncomplicated course and ultimately may shed insight into the host immune response to acute EBV infection.VK designed and performed experiments, analyzed the data, and wrote the manuscript. KW obtained the samples and contributed to development of study design. SB contributed to development of study design and critically revised the manuscript. DB contributed to development of study design. CE contributed to development of study design. OM contributed to study design, provided critical review of the experimental design, wrote, and provided critical review of the manuscript. SK designed the study, analyzed the data, wrote, and critically reviewed the manuscript. All authors reviewed and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


However, few studies have investigated the role of Wnt signaling in the specification of cardiac progenitor cells (CPCs) toward downstream fates. Using transgenic mice and hPSCs, we tracked endothelial cells (ECs) that originated from CPCs expressing NKX2.5. Analysis of EC-fated CPCs at discrete phenotypic milestones during hPSC differentiation identified reduced Wnt activity as a hallmark of EC specification, and the enforced activation or inhibition of Wnt reduced or increased, respectively, the degree of vascular commitment within the CPC population during both hPSC differentiation and mouse embryogenesis. Wnt5a, which has been shown to exert an inhibitory influence on Wnt signaling during cardiac development, was dynamically expressed during vascular commitment of hPSC-derived CPCs, and ectopic Wnt5a promoted vascular specification of hPSC-derived and mouse embryonic CPCs.Several studies have demonstrated a multiphasic role for Wnt signaling during embryonic cardiogenesis and developed protocols that enrich for cardiac derivatives during  Summary: Non-canonical inhibitory Wnt signaling via Wnt5a fosters endothelial differentiation of Nkx2.5+ cardiac progenitor cells during mouse embryogenesis and human pluripotent stem cell differentiation. One out of every three deaths in the USA results from cardiovascular disease . DespiteRedirection of cardiac wound healing processes in adult hearts might be a favorable approach for improving cardiac function following ischemic injury. Adult zebrafish and neonatal mice are capable of undergoing constructive heart regeneration , and rec+ populations in mouse embryos and pluripotent stem cell cultures (+ precursors (+ CD31 (PECAM1)+ endothelial progenitors from hPSCs  and VE-cadherin (CDH5)+  derivatives during differentiation. Utilizing this line and a cross of vascular  and β-cateninflEx3/flEx3 [provided by Makoto Taketo ]. For quantification of NkxEC versus nEC number, 10 µm sections were collected for the entire span of the heart and colocalization masking distinguished EC subtypes.All procedures were approved by the Institutional Animal Care and Use Ethics Committee of Weill Cornell Medical College. Breeding was performed using animals between 12 weeks and 1 year of age. The mouse strains that were intercrossed to generate the animals in this study were: t-test was performed.Experiments were repeated at least three times, with the technician performing quantitative analysis blinded to the experimental conditions. We included all tested animals for quantification to analyze the statistical difference. A representative image from experimental groups is presented in corresponding figures. No statistical method was used to predetermine sample size. Differences between groups containing more than one condition were assessed by one-way ANOVA, and to quantify statistical difference of each condition to control an unpaired two-tailed


Integrator Complex Subunit 1 (INTS1) and Subunit 8 (INTS8) gene mutations are associated with rare recessive human neurodevelopmental syndromes. Three unrelated individuals of Dutch ancestry showed the same homozygous truncating INTS1 mutation. Three siblings harboured compound heterozygous INTS8 mutations. Shared features by these six individuals are severe neurodevelopmental delay and a distinctive appearance. The INTS8 family in addition presented with neuronal migration defects (periventricular nodular heterotopia). We show that the first INTS8 mutation, a nine base-pair deletion, leads to a protein that disrupts INT complex stability, while the second missense mutation introduces an alternative splice site leading to an unstable messenger. Cells from patients with INTS8 mutations show increased levels of unprocessed UsnRNA, compatible with the INT function in the 3’-end maturation of UsnRNA, and display significant disruptions in gene expression and RNA processing. Finally, the introduction of the INTS8 deletion mutation in P19 cells using genome editing alters gene expression throughout the course of retinoic acid-induced neural differentiation. Altogether, our results confirm the essential role of Integrator to transcriptome integrity and point to the requirement of the Integrator complex in human brain development.Integrator is an RNA polymerase II (RNAPII)-associated complex that was recently identified to have a broad role in both RNA processing and transcription regulation. Importantly, its role in human development and disease is so far largely unexplored. Here, we provide evidence that biallelic  Integrator Complex Subunit 1 gene (INTS1) and Subunit 8 gene (INTS8) cause a severe neurodevelopmental syndrome, characterized by profound intellectual disability, epilepsy, spasticity, facial and limb dysmorphism and subtle structural brain abnormalities. While the role of the Integrator complex in neuronal migration has recently been established, we provide evidence that INTS8 mutations lead in vitro to instability of the complex and impaired function. In patients cultured fibroblasts we found evidence for abnormalities in mRNA transcription and processing. In addition, introduction of INTS8 mutations in an in vitro model of retinoic acid-induced neuronal differentiation results also in transcription alterations. Altogether our results suggest an evolutionary conserved requirement of INTS1 and INTS8 in brain development.Neurodevelopmental disorders often have a genetic cause, however the genes and the underlying mechanisms that are involved are increasingly diverse, pointing to the complexity of brain development. For normal cell function and in general for normal development, mechanisms that regulate gene transcription into mRNA are of outermost importance as proper spatial and temporal expression of key developmentally regulated transcripts is essential. The Integrator complex was recently identified to have a broad role in both RNA processing and transcription regulation. This complex is assembled from at least 14 different subunits and several animal studies have pointed to an important role in development. Nevertheless, studies directly demonstrating the relevance of this complex in human health and development have been lacking until now. We show here that mutations in the  WDR62 and TUBA1A  and andIntegINTS8 expression we cultured cell lines from primary fibroblasts from the three affected siblings and their unaffected brother, who is heterozygous only for the INTSΔEVL mutation. qRT-PCR analysis indicates that INTS8 expression is reduced in patient cells (~50% reduction) suggesting that one of the mutant INTS8 alleles is not expressed  data from exon arrays performed on fibroblasts showed a large number of significantly dysregulated genes, including INTS8, in the patient vs. control cells  and mutually exclusive exons  and hindbrain   across different human brain areas and multiple individuals, obtained from BrainSpan, shows that INTS8 expression peaks during early embryonic development, and decreases to retain a stable level during postnatal life  data in mouse embryonic brain show a high expression of indbrain 44]. In . In INTSek (pcw) . In the tal life . The spaINTS8ΔEVL mutation could have an impact on neuron differentiation we used CRISPR/Cas9-mediated genome editing to introduce a homozygous EVL deletion in mouse P19 pluripotent embryonic carcinoma cells (INTS8ΔEVL mutations (ΔEVLA and ΔEVLB). Similarly to what we observed in fibroblast cells from patients, western blot analysis of Integrator complex subunits shows that expression levels of several subunits are reduced in the mutant cell lines compared to the parental line -induced neuron differentiation. After RA treatment, we followed by qRT-PCR the expression of different markers for pluripotency, retinoic acid response, neuronal precursor and neuronal differentiation. While the timing of the appearance or the morphology of differentiated neurons did not significantly differ between the two ΔEVL mutants and the parental P19 control, we observed marked differences in gene expression over the course of the process  is markedly reduced in the mutant lines even though rapid downregulation of pluripotency genes like Oct4 is not altered. Later on, although not altered in its intensity, the induction of the neuronal stem cell marker nestin (nes) is temporally distinct between the control and the mutants, where its expression is delayed at first and later on more rapidly induced than in the control line. Finally, the induction of several neuronal markers such as tubulin beta-3 (tubb3) or reelin is reduced while others remain similar to the control . These experiments confirm that the INTS8ΔEVL mutant protein can have a profound effect on gene expression during a neuronal differentiation process.We then used these cell lines as an  process . In respINTS8 in three siblings and in Integrator complex subunit INTS1 in three unrelated individuals with a rare and severe developmental brain disorder and similar phenotypic abnormalities. The INTS1 mutation leads to strong reduction of its mRNA expression in skin fibroblasts. In the case of INTS8, we show that one INTS8 mutant allele leads to rapid mRNA decay while the other translates into a protein lacking three residues in the C-terminus that impacts the overall stability of the Integrator complex. Our results indicate that Integrator-deficient patient cells contain global transcriptome perturbations manifesting as both altered splicing patterns and differential gene expression. Finally, the introduction of the INTS8ΔEVL mutation in P19 embryonic carcinoma cells also alters the pattern of differentiation marker expression during RA-induced neuronal differentiation.The present study provides a rare insight into the effect of an Integrator complex deficiency in humans. We identified biallelic mutations in the Integrator complex subunit INTS8 and INTS1 mutations is similar, combining profound intellectual disability, epilepsy, lack of speech, facial and limb dysmorphism, altogether forming a recognizable syndrome. In patients with INTS8 mutations, it is also associated with a rare combination of structural brain malformations including PNH and cerebellar hypoplasia. This association of PNH, a neuron migration disorder, with an Integrator subunit mutation is particularly interesting in view of the recent observation of a neuronal migration defect in mouse embryonic brain after Ints1 and ints11 knock-down and could implicate the Integrator complex function in the etiology of the disorder [INTS8 mutations in other individuals with similar combination of PNH and cerebellar hypoplasia, which suggests that either this association of brain malformations is not constant in this disorder or this is an ultra-rare disorder  [INTS1 mutations supports the former, while the high CADD score of the mutations and the very few INTS8 deleterious variants reported in control populations support the latter  . The face latter . This all stages –28. Yet,SPTAN1 is of particular interest  [RNU12, result in U12-type exon retention and are associated with cerebellar ataxia[INTS8 mutations is possible and could result in the differential splicing patterns that we observed in patient versus control cells.Using in-depth analysis of genome-wide alternative exon usage we show that in patient cells the splicing pattern of up to 215 individual genes is affected. This finding could be compatible with a functional defect of the spliceosome and correlates with the increased level of misprocessed UsnRNAs that we observe in patient cells. Several of these alternatively spliced genes can be individually linked to brain-related phenotypes and could therefore individually or collectively contribute to the phenotype even though no gene known to be directly involved in cortical malformation  show abnormal splicing in patient fibroblasts . Howeverinterest . Indeed,lination . Hence, elopment –54. Moreelopment . This ispoplasia . In addineration . Similar (MOPD1) , 57. Mutar ataxia. TherefoNOG  and TUBA1B that both have a primary role in brain and development  shows its highest expression in brain and is one of the several tubulin genes essential for normal cortical development in human. Tubulin-beta forms a dimer with tubulin-alpha, encoded by TUBA1A, a gene mutated in syndromic cortical malformation with microcephaly, cerebral and cerebellar dysgyria [INTS8 mutation on gene expression could be linked to the newly identified function of Integrator complex in the regulation of RNAPII-dependent transcriptional initiation, pause-release and elongation of protein-coding genes [INTS8 mutations also results from widespread transcription deregulation, apart from abnormal splicing.Our results show a great number of differentially expressed genes in the patient cells. Significantly and reproducibly the top dysregulated genes in exon arrays are elopment . Noggin dysgyria . On a meng genes , 16, 17.ng genes . Moreoveng genes –41. It iINTS8 role in neuronal differentiation and in brain development. Our results in P19 differentiation by retinoic acid indicates that INTS8ΔEVL mutation can indeed cause misprocessing of snRNA and can affect expression of many differentiation-regulated genes and could therefore have consequences on neuronal and brain development. The recent progress in genetic engineering ushered by the development of CRISPR/Cas9-based genome editing tools will enable the development of animal models tailored in the future to address these questions. Likewise, progress in cell reprogramming and induced pluripotent cell production should allow for the direct use of patient cells to study the impact of INTS8 mutations on neuron differentiation in their original genetic background.A definitive answer will require a detailed analysis of INTS8 is essential for the structural and functional integrity of Integrator complex, and mutated INTS8 causes increased UsnRNA misprocessing, increased AS events and altered gene expression, confirming a central role for Integrator complex in transcriptional regulation and, together with INTS1 mutations, an unexpected role in human brain development.Our study provides the first evidence for a crucial role of the Integrator complex during human brain development. All study participants or their legal caretakers gave written informed consent to participate in this study, and for publication of images, according to Erasmus MC institutional review board requirements (protocol METC-2012387).Details of the Whole-genome sequencing (WGS), whole-exome sequencing (WES) and Sanger sequencing are provided in Supplemental methods . Data arQuantitative qRT-PCR of RNA extracted from cultured fibroblasts of the affected siblings, their unaffected brother, and two control cell lines was carried out using a KAPA SYBR FAST qPCR Kit (Kapa Biosystems) in the CFX96 Real-Time system (BioRad). Details are provided in qRT-PCR was performed using RNA from cultured fibroblasts on a Stratagene Mx3000P real-time PCR system (Agilent) using the KAPA SYBR FAST qPCR Kit (Kapa Biosystems) according to manufacturer’s instructions. Details are provided in INTS8 gene were amplified from HEK293T genomic DNA by PCR , INTS1 , INTS3 , INTS4 , INTS5 , INTS9 , INTS11  and INTS12 . Oligonucleotide sequences are listed in The human  tag See . The EVLCultured fibroblasts were used for western blots of Integrator complex components. Approximately 32 μg of the clarified whole cell extract were separated on an 8% acrylamide SDS-PAGE gel. After transfer to a PVDF membrane, the presence of the different Integrator complex subunits was assessed by Western blot using the antibodies listed above.http://www.r-project.org). Top down- and upregulated genes with p values <0.02 were analyzed for enriched gene ontology terms  using DAVID (medium stringency) to identify clusters with significant enrichment (enriched score ≥ 1.3). CEL file microarray data are available under GEO accession number GSE48849 (http://www.ncbi.nlm.nih.gov/geo/).Six Affymetrix GeneChip Human Exon 1.0 arrays of RNA extracted from cultured fibroblasts of all three affected siblings and three age and sex-matched controls were robust multi-array (rma) normalized on transcript level using the R package (Poly(A) mRNA fractions isolated from cultured fibroblasts from patient III-2, III-4, and two controls were subjected to RNA-Seq analysis. In order to detect even slight changes in splicing, we utilized high depth RNA sequencing . The res7 exponentially growing cells were seeded on agarose-coated plate in a culture medium without serum containing 1μM all-trans Retinoic Acid (sigma) and N-2 supplement (Thermo Fischer). After 48h, embryonic bodies were collected and seeded on tissue culture plates in medium with serum. After 48h, medium was replaced with culture medium without serum containing N-2 supplement. Half of the culture medium was replaced every 48H with fresh medium. RNA was extracted at day0, day2, day4 and day8 using Trizol (Thermo Fisher). The mRNA expression levels of neuronal differentiation markers were monitored by qRT-PCR (see Differentiation of P19 cells was conducted according to . BrieflyGenome editing was conducted as in  with minS1 Text(DOCX)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S6 Fig(PDF)Click here for additional data file.S7 Fig(PDF)Click here for additional data file.S8 Fig(PDF)Click here for additional data file.S9 Fig(PDF)Click here for additional data file.S10 Fig(PDF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(XLS)Click here for additional data file.S7 Table(XLS)Click here for additional data file.S8 Table(XLS)Click here for additional data file.


Cellular responses to stimuli are rapid and continuous and yet the vast majority of investigations of transcriptional responses during developmental transitions typically use long interval time courses; limiting the available interpretive power. Moreover, such experiments typically focus on protein-coding transcripts, ignoring the important impact of long noncoding RNAs. We therefore evaluated coding and noncoding expression dynamics at unprecedented temporal resolution (6-hourly) in differentiating mouse embryonic stem cells and report new insight into molecular processes and genome organization. We present a highly resolved differentiation cascade that exhibits coding and noncoding transcriptional alterations, transcription factor network interactions and alternative splicing events, little of which can be resolved by long-interval developmental time-courses. We describe novel short lived and cycling patterns of gene expression and dissect temporally ordered gene expression changes in response to transcription factors. We elucidate patterns in gene co-expression across the genome, describe asynchronous transcription at bidirectional promoters and functionally annotate known and novel regulatory lncRNAs. These findings highlight the complex and dynamic molecular events underlying mammalian differentiation that can only be observed though a temporally resolved time course. Over the past decade, transcriptomic investigations into the of nature embryonic stem cell (ESC) differentiation have elucidated key biochemical features of stemness and differentiation. Increasingly, it has become apparent that understanding the dynamics and coordination of gene expression signatures over time during the key phases of differentiation is critical to adequate characterization of fundamental biological processes.1. Microarray and RNA sequencing have provided a means to characterize the molecular transitions in gene expression underlying ESC biology and more recently single cell transcriptomic studies have provided the first glimpses into the molecular history of these cells2. However, it is clear that much more of the transcriptional landscape of ESC remains to be elucidated3.Mouse ESC differentiation is a highly complex cascade of gene expression changes that allow single pluripotent cells in culture to progress to an organoid composed of cells reflecting three germ lineages within only five days. The spontaneous differentiation of these cells in culture has provided key insights into the developmental processes underlying the generation of the primary germ cell layers4. MPS of the transcriptome validated this observation and revealed that the majority of the mammalian genome is pervasively transcribed as interlaced and overlapping RNAs5, many of which lack protein-coding potential6. The large number of long-noncoding transcripts (lncRNA) has become the focus of significant interest due to their exquisite cell type specific expression7, potent biological function9, and rapid transactivation of cellular processes10. However, in general, lncRNAs are lowly expressed and short lived11, possibly because, unlike mRNAs that require translation, are able to exert their function directly. These qualities obfuscate their identification and characterization with traditional approaches that are tuned to the properties of mRNAs12. Owing to the relative infancy of the field, the vast majority of noncoding transcripts are of unknown function13. Additionally, the expression patterns of these genes imply that their function is dependent on cellular context and likely regulatory8, thus the identification of these molecules and the context in which they act remains a research priority14.Access to new technologies, such as massively parallel sequencing (MPS), has led to a dramatic increase in our knowledge of the mammalian transcriptome. Early genomic tiling array analysis indicated that most of the genome was transcribed into RNA18, have been used to explore the molecular changes occurring during ES cell development, typically at 24-hourly or more. This potentially has lead to incomplete gene expression relationships through the phenomenon of temporal aggregation bias whereby each time point is assumed to represent all the signaling changes occurring in that time window19. In contrast to single cell based approaches- which provide insight into the state of individual cells - examinations of whole cell populations provides system-wide behavior and a practical means to explore gene expression dynamics across time. The combination of these techniques has recently shed light on the molecular framework of cellular differentiation20. Higher temporal resolution has also shown rapid induction (within two hours of retinoic acid stimulation) of lncRNAs associated with the HOX locus21. Furthermore, high temporal resolution has provided valuable insights into transcriptional annotation and regulation in drosophila23, Xenopus24 and C.elegans25.Various expression profiling studies, using both microarrays and RNA-seqHere we show that additional temporal resolution of the global transcriptome in spontaneously differentiating mESC cells following LIF withdrawal enables the capture of the rapid and complex dynamic regulatory and noncoding changes occurring during ES development. We analyzed the transcriptome of differentiating mouse ESCs at six-hourly intervals over a five-day period, over which time the three primordial germ layers are specified. Using this fine-resolution temporal sampling approach, we identify significant transitions in the transcriptome and large-scale shifts in observable transcription factor activities that could not be observed at 24 hourly sampling periods. Moreover, we identify entirely novel coding and noncoding transcripts that are expressed only within specific sub-24-hour window. By leveraging the high sampling frequency of the data, we are able to both accurately recapitulate known regulatory cascades in ES development and predict and refine others. Finally, using correlative approaches, we can infer functions for uncharacterized lncRNAs and predict the regulatory centers across the genome that coordinate early development.26, then normalized for library size and transformed for data visualization and differential gene expression analysis. Evaluation of 24 hourly time points indicated that our data was comparable to previously published data in a similar model27 .A median 42-million, paired-end 100-bp reads  was performed on the 2,000 most variable genes Fig. . This anTo evaluate characteristics of sub-24 hour gene expression patterns in in the transcriptome of developing ESCs, we observed that, compared to 24 hour time points, 417 more genes had counts data considered sufficient for differential gene expression analysis (>1 CPM in at least two samples); this was associated with a relative increase in detected noncoding genes (13% (588 vs. 520); defined as antisense, lincRNA and processed transcript biotypes) over protein coding, (2% (13336 vs. 13036) despite being underrepresented in the total pool  target gene expression patterns. Divergence of gene targets responsive to groups of TF at each time point, either 24-hourly or 6-hourly  target genes using a cross-correlation approach similar to ref. Pou5F1 expression . Periodogram analysis was utilized to ascertain the dominant cycling period for each gene. We found 137 genes, which we termed cycling RNAs (cycRNAs), sharing the same dominant cycling period of less than 36 hours in both biological replicate experiments . This revealed 59 regions with a mean size of 821 kb -each containing 5–14 genes (mean of 6) genes. To examine the higher order chromatin architecture of these regions, we compared these regions to published data on topological associated domains (TADs) for mouse ESCs39. We found that the majority of the regions were each contained within a single TAD  to guide the partitioning of the genome into regulatory domains.Having shown that rapid changes in lncRNAs are a key feature of ES differentiation, and that co-expression analysis is a powerful tool for understanding gene regulation with augmented temporal resolution, we sought to unravel the roles that lncRNAs might play in ESC differentiation.42 and the poly-A selection for cDNA would have missed enhancer RNAs, miRNA precursors and Nuclease P processed lncRNAs43. Clustering the observed lncRNA expression patterns with time-dependent protein coding gene expression showed that lncRNAs were enriched at lower expression levels and shared related expression profiles to protein coding genes 44, Neat1 & Rian (association with gene repression)45 and Meg3 (tumour suppressor)46. Interestingly, these data suggest that the pro-tumorigenic function of Malat1 may be mediated through facilitating the increase of MAPK signaling molecules. Importantly, these data also provide testable evidence for seven previously uncharacterized lncRNAs role in ES development and describes a map of regulatory interactions potentially driven by lncRNAs  and cycling (cycRNA), that were unobservable outside this framework. That many slRNAs exhibited changes in expression over the first 24 hours of differentiation is consistent with rapid initial cellular response to stimulitrans) of hundreds of genes separated by large genome differences (in cis). Typical studies of this nature involve correlative analysis requiring large samples sizes and resources50. We have instead leveraged the time axis to achieve these as well as discriminate driver from passenger molecular events. This has allowed the estimation of the time delay for changes in expression of regulatory molecules to manifest in changes in their target gene transcription and we have been able to unravel a potentially complex network of gene profiles responding to lncRNA transcription. Finally, we have been able to use an integrated biological system to draw strong associations in trans relationships with bidirectional promoters. Typically these associations are observed by using thousands of gene expression profiles, yet here we have been able to do so with only 42 transcriptomes (duplicate time courses of 21 points each).Similarly, by using time to separate the order of gene transcription, we have been able to predict local gene regulation across the genome. We have been able to observe concerted gene expression  and DNase treatment was performed by RQ1 DNase (Promega) according to the manufacturer’s instructions. RNA integrity was measured on a Bioanalyzer RNA Nano chip (Agilent). RNA-Seq library preparation and sequencing of Poly-A-NGS libraries generated from 500 ng total RNA using SureSelect Strand Specific RNA Library Preparation Kit (Agilent) were performed according to the manufacturer’s instructions at the same time to minimize batch effect. Paired-end libraries were sequenced to the first 100 bp on a HiSeq 2500 (Illumina) on High Output Mode.Biological duplicate, low passage number (P18) W9.5 ESCs were cultured and differentiated as described previouslywww.bioinformatics.babraham.ac.uk/). Tophat252 was used to align reads to the December 2011 release of the mouse reference genome (mm10) as outlined by Anders et al.26. Read counts data corresponding to GENCODE vM2 transcript annotations were generated using HTSeq53. de novo transcript assembly was performed on each merged BAM file using Cufflinks’ reference annotation based transcript (RABT) assembly54, using the Gencode vM2 transcriptome55 as a guide (options: -u -I 500000 -j 1.0 -F 0.005-trim-3-dropoff-frac 0.05 -g gencode.vM2.annotation.gtf–library-type fr-firststrand). Transcript assemblies were then merged using Cuffmerge56 using default parameters, and compared to the Gencode vM2 reference transcriptome using Cuffcompare56. Novel transcripts with a Cuffcompare class code of j, i, o, u or x were filtered using three steps to find novel lncRNAs. First, a Browser Extensible Data (BED) format file was generated using a python script (https://gist.github.com/davidliwei/1155568) and any single exon transcripts were removed. Second, the FASTA-formatted sequence for each transcript was obtained using BEDTools57, the nucleotide (nt) length and open reading frame (ORF) size found using Perl scripts, and those with a length less than 200 nt or a ORF size greater than 300 nt were removed. Lastly, transcript sequences were submitted to Coding Potential Calculator (CPC)58, and those with a coding potential of >0 were removed.Library sequencing quality was determined using FastQC (Babraham Bioinformatics) and FastQ Screen (Babraham Bioinformatics). Illumina adaptor sequence and low quality read trimming (read pair removed if <20 base pairs) was performed using Trim Galore!  activity was inferred from gene expression data using DREM28 with a branching P-value of 0.001 based on curated TF-target gene lists associated with mouse ESC differentiation from ChEA63. TF-target gene was calculated by maximal Pearson’s correlation coefficient of >0.8 using a custom autocorrelation analysis and verified with the “ccf” function in R. Gene differential exon (DEX) usage was analyzed by DEXSeq64 on vM2 gene annotations using default settings and an adjusted p value cutoff of 0.001 for DEX between biological duplicates at each consecutive time-point. Genome position analyses were performed using genomic ranges65 based on vM2 annotations imported with ‘rtracklayer’66 and Pearson’s correlation coefficient of gene expression Bidirectional genes were defined as two genes with expression data on opposing strands with <2000 bp between the transcriptional start sites (TSS). Co-expressed gene clusters were defined as >5 contiguous genes with expression data displaying a Pearson’s Correlation Coefficient of >0.5 with neighbouring genes. Cluster co-expression data was visualized with corrplot67 and Cytoscape (v3.1.0)68, location of related clusters was visualized by Circos69. Gene expression periodicity was measured on 120 interpolated expression values70 for each replicate time series using GeneCycle71, candidate periodically expressed genes were identified as having the same calculated dominant cycling frequency between biological replicates. Time-dependent expression signatures were established using maSigPro72 with a replicate correlation coefficient cutoff of 0.8. Target genes of potential regulatory (top 50 most highly and/or variably expressed) lncRNAs were identified using the GeneReg package73 on 100 point-interpolated expression data based on fitted expression values between duplicates and setting a maximum time delay of 18 hours and a global correlation coefficient of 0.9 and visualized using Cytoscape. Gene lists were functionally annotated with KEGG and Reactome pathways  using the clusterProfiler and ReactomePA packages74.All analyses were performed in the R Statistical EnvironmentData has been deposited into GEO under accession number GSE75028.Supplementary Figures and Tables


Lepeophtheirus salmonis, frustrating efforts to track louse populations and improve targeted control measures. The aim of this study was to test the power of reduced representation library sequencing (IIb-RAD sequencing) coupled with random forest machine learning algorithms to define markers for fine-scale discrimination of louse populations. We identified 1286 robustly supported SNPs among four L. salmonis populations from Ireland, Scotland and Northern Norway. Only weak global structure was observed based on the full SNP dataset. The application of a random forest machine-learning algorithm identified 98 discriminatory SNPs that dramatically improved population assignment, increased global genetic structure and resulted in significant genetic population differentiation. A large proportion of SNPs found to be under directional selection were also identified to be highly discriminatory. Our data suggest that it is possible to discriminate between nearby L. salmonis populations given suitable marker selection approaches, and that such differences might have an adaptive basis. We discuss these data in light of sea lice adaption to anthropogenic and environmental pressures as well as novel approaches to track and predict sea louse dispersal.Caligid sea lice represent a significant threat to salmonid aquaculture worldwide. Population genetic analyses have consistently shown minimal population genetic structure in North Atlantic  Lepeophtheirus salmonis) is the species most commonly infecting farmed and wild salmonids1, at considerable cost to animal health, biodiversity security, and economic growth. Conservative estimates of costs and losses attributed to sea louse infections, , significant but weak (0.0022) FST was detected across the Atlantic, with no evidence for population genetic structuring within geographic regions. More recently a genome-wide SNP array was developed and deployed using 5091 variable markers, and showed similar results in terms of population structure, alongside extensive evidence of selective sweeps and linkage disequilibrium attributable, at least in part, by the use of chemotherapeutics in aquaculture13. Besnier et al., 2014 also included a linkage map for these markers onto which the current assemble of the louse genome has been superimposed . Thus, although significant progress has been made in determining population genetic signatures of selection in L. salmonis, the goal of distinguishing louse populations occurring in different regions - a valuable component of detecting dispersal of lice between farms – remains expensive and difficult.Understanding and predicting salmon louse dispersal is a crucial element for predicting infestation, connectivity and the spread of salmon lice and associated drug resistance alleles. There have been several attempts to characterize population genetic structure in 15.). In extreme cases like Anguilla rostrata, where the organism’s reproductive ecology predicts and the genetic data support panmixia among different populations, the challenge of determining genetic differences between different populations is even greater17. Screening thousands of variable SNP markers against population genetic summary statistics may be able to detect outliers, however the identification of which markers might best assign individuals to their appropriate populations, groups, or ecomorphs necessitates further computational approaches. To this end, population genetics can usefully borrow from machine learning algorithms developed in the context of genome-wide association studies18. Such approaches have been successfully used in A. rostrata, to identify SNPs that predict rearing habitat as the result of intra-generational selection19, for example. More recently similar approaches have been employed to successfully discriminate Salmo salar populations20.Determining genetic structure in pelagic marine species has always been challenging. High rates of adult and larval dispersal impede the accumulation of neutral variation among populations and regions. Nonetheless, several studies have achieved genetic stock delineation by focusing on non-neutral or putative adaptive markers in conjunction with high numbers of SNP markers  and generated genomic SNP data using a IIB restriction-enzyme associated library preparation approach21. We then tested the power of Random Forest machine learning to reveal population structure and find the method reveals previously un-recognized population differences and fine-scale population differentiation.In this study, we identify population structure and loci under selection in Lepeophtheirus salmonis were collected in four sites around the North-East Atlantic from 18–24 month old Atlantic Salmon from commercial pens in 2015. Sites included Finnkirka (NF), Lebesby, Norway; Loch Duart (LD), Scotland, UK; Kenmare Bay (SWI), Kenmare, Ireland and Kilkieran Bay (KB), Galway, Ireland  two enzymes were selected based on potential coverage: AlfI (restrictions site ^5′(10/12)GCA(N)6 TGC(12/10)3′^) and CspCI (restriction site ^5′(11/12)CAANNNNNGTGG(12/13)3′^). Digested DNA of each sample was ligated to a pair of partially double-stranded adaptors with compatible and fully degenerated overhangs (5′NNN3′). Finally, the obtained IIb-RAD tags were amplified to introduce a sample-specific 7 bp barcode and the Illumina NGS annealing sites using two different pairs of sequencing primers. A 1.8% agarose gel electrophoresis of the PCR products was performed to verify the presence of the expected 150 bp target band . In order to ensure an approximately equimolar contribution of each sample to the library, the concentration of each PCR product was measured from the intensity of the target band in a digital image of the 1.8% agarose gel. We prepared two libraries in total, one for each IIB-REase. The purification of the libraries from high-molecular weight fragments and primer-dimers was achieved first by removing the target band on agarose gel library and eluting them in water overnight; followed by DNA capture with magnetic beads (SPRIselect® Beckman Coulter). The DNA concentration in the purified libraries was quantified with a Qubit® Fluorometer (Invitrogen) and the libraries were combined in one single pool. Two library pools were sequenced, first on a NextSeq 500, then on a MiSeq  with a single 1 × 50 bp setup using ‘Version2’ chemistry at Glasgow Polyomics (www.polyomics.gla.ac.uk), which also implemented the read demultiplexing and quality-filtering.Library preparation was undertaken as described in Wang 22 and SNPs were called using the STACKS v1.42 package with a minimum read depth of 323. The rxstacks module was used to further screen SNPs and the population module filter and export genotypes with a minimum depth of coverage of 6, minimum minor allele frequency of 0.05, maximum observed heterozygosity of 0.5 and present in at least 60% of individuals. To avoid sequence artefacts generated by low complexity in restriction enzyme recognition site, SNPs at positions 12–26 were excluded from the analysis. For those RAD tags that retained diversity after screening for artefacts, only a single SNP per locus was selected for subsequent analysis.Short reads were aligned to the reference genome in bowtie 2adegenet24. Analyses of molecular variance (AMOVA), Weir and Cockerham estimators of FST, and summary statistics  were calculated in Genodive25. P-values for FST were FDR adjusted for multiple comparisons using a Benjamini-Hochberg correction in the R-package p-adjust. Isolation-by-distance was assessed using a Mantel test implemented in the vegan R-package. Loci putatively under positive selection were identified in Lositan26 using a FDR < 0.1 significance threshold and localised on the L. salmonis linkage map  to assess genomic correspondence with a previous population genomic study13. Lositan results were plotted using in R. Further, we performed a second outlier analysis using BayeScan, as it has a lower type I error rate compared to Lositan27). We ran BayeScan with prior odds of 100 due to the small number of SNPs and detected significant outliers with a FDR threshold of 0.05 and putative outliers with a FDR threshold of 0.1. Finally, we post hoc identified overlapping outlier SNPs between BayeScan and Lositan.Principal components analysis (PCA), discriminant analysis of principal components (DAPC), and population assignment probabilities were calculated in L. salmonis reference genome using the blastn function in the EnsemblMetazoa database. We identified all genes within those 10 kb regions and when possible determined their function using the UniprotKB database.In order to identify genes potentially under positive selection we identified all genes within a 10 kb region around each outlier SNP by blasting the sequence against the randomForest package in R. Populations were numerically coded and missing data imputed using the na.roughfix command. Three independent random forest runs with 100,000 trees each were conducted and checked for convergence between runs by performing Pearson correlation between SNP importance values. The random forest algorithm randomly subdivides the full dataset into a training dataset (66.6%), which is used to train the algorithm, and a test dataset  that is used to test the classification success of a tree. For each tree, the dataset gets subdivided into new subsets and the number of trees for which the out-of-bag (OOB) error rate stabilises was chosen, reducing the risk of overfitting. The resultant ranked dataset (R2 > 0.95) was used to select a final dataset for the backwards purging approach. As in Laporte et al., all loci with an importance < 0 were removed as non-discriminatory19. Backwards purging was performed on the remaining 317 SNPs. As such each random forest run was re-implemented (three independent iterations) and after each run the SNP with the lowest importance was removed until only two SNPs were left. We determined the subset of SNPs with the highest discriminatory power based on the lowest OOB error rate, meaning the highest rank reliability of important markers, and we used this subset for further downstream analysis19.In order to detect SNPs characteristic of each population we employed a tree-based ensemble machine learning approach using the adegenet in R to visualise population structuring and assess the population assignment accuracy. Second, we performed an AMOVA and estimated pairwise Weir and Cockerham’s Fst in Genodive. We also identified the overlap between highly discriminatory SNPs and SNPs potentially under positive selection to assess the impact of selection on discriminating L. salmonis populations.In order to assess the population discriminatory power of the random forest selected SNPs we used the same methods as for the full SNP dataset. First, we performed a PCA and DAPC in et al. (2014)13. Besnier et al. (2014)13 genotyped 547 salmon lice from 12 sampling sites from 6 geographic regions for 5091 SNPs using a custom SNP array. Missing data were imputed using the na.roughfix command. We assessed the population assignment success using the DAPC approach for the full dataset before using the random forest algorithm to identify the subset of SNPs with the highest discriminatory power between sampling regions . We ran three independent random forests with 10,000 trees each and ranked SNPs based on their average importance, measured by the ‘Mean Decrease Accuracy’ (MDA). Due to the larger number of SNPs, we selected all SNPs above the lower end of the elbow in the importance value distribution . No significant isolation-by-distance was detected .In a first approach, we assessed population genetic structure using the full dataset of 1286 SNPs by several different approaches. A PCA did not reveal any population structuring across the entire range, however using pre-defined populations in the DAPC approach revealed a weak population structuring . All pairwise comparisons showed highly significant Fst-values (range = 0.081–0.096), confirming the significant discriminatory power of the random forest detected SNP subset.In order to detect population structuring among populations we utilised a random forest machine learning approach. We detected a subset of 93–101 SNPs that minimised the out-of-bag error rate to 0.1 (compared to 0.76 for the full dataset) and maximised the discriminatory power among populations Fig. . From thst of 0.233 ± 0.083 between populations  or could not be assigned to a linkage group. We further detected 46 SNPs under balancing selection .One factor that might explain the strong discrimination of sea louse populations using the subset of random forest-selected SNPs would be divergent selection pressures, such as adaptation to different drug treatments, or local adaptation to natural environmental factors. Therefore, we performed two different tests for selecting SNPs under significant positive selection. An FDist approach implemented in Lositan detected 19 SNPs under strong positive selection (FDR < 0.1) with an average Fst = 0.507). The other two putative SNPs in BayeScan were also highly significant in Lositan (p < 0.001) and were located on linkage group 1 and 4.An analysis in BayeScan detected only one SNP (FDR < 0.01) under significant positive selection and two more putatively under selection (FDR < 0.05). All three of these SNPs were also detected to be under selection by Lositan and the significant one was also the top outlier in Lositan and located on linkage group 1  detected to be under positive selection using Lositan were also identified being highly discriminatory between populations using random forest. Locus 3621, which was also identified using BayeScan, had the highest importance in the random forest analysis, suggesting that strong local adaptation and selection distinguishes sea louse populations.L. salmonis genome. Five of the 19 SNPs were in regions containing annotated genes within 10 kb, but only one of the annotated genes has been characterized. Two of the contigs with annotated genes were on linkage group 01, two on linkage group 14 and one on linkage group 6. Contig LSalAtl2s80 (linkage group 01) contained the characterized gene PSA2, which codes for the proteasome subunit alpha type protein.In order to identify specific genes potentially involved in local adaptation and under positive selection in sea lice, we explored these regions in the annotated 13. The assignment success to the geographic region of origin increased on average by 1.9% from 94.4% to 96.6% based on only this small subset of SNPs . 43 out of those 357 SNPs (12%) were labelled as ‘diagnostic’ SNPs that were included in the SNP array  to potentially distinguish populations across the Atlantic. 43 out et al. (2014), the random forest approach improved the assignment success for each population, supporting the applicability of this approach to different datasets.Even though the variation in population assignment success was greater for the larger dataset from Besnier L. salmonis populations in the North-East Atlantic and to detect signatures of selection. We were able to achieve this using a relatively small (n = 50) number of individuals genotyped across only a limited portion of the genome (c.2.3Mbp = 0.34%). An important set of discriminatory loci was identified against a background of high genetic connectivity via a random forest machine-learning algorithm and these can be exploited to distinguish between nearby sea louse populations. A high degree of overlap between loci under positive selection using genome-scan approaches and loci with high discriminatory power from random forest analysis was also observed.In this study, we used a IIb-RAD sequencing approach coupled with advanced and sensitive population genetic analyses to genetically ‘fingerprint’ Salmo salar) or resident  anadromous salmonids1. Previous population genetic studies were consistent with such high levels of dispersal12, finding no significant genetic differentiation in our study region when utilizing a set of neutral microsatellite loci. Inclusion of putatively non-neutral loci can improve population discrimination across the Atlantic (e.g10.). However, the same studies could not distinguish populations on a small geographic scale as our data and approach suggest is possible.Sea lice are known to disperse rapidly among aquaculture sites as part of the larval zooplankton as well as via the movements of migratory , in agreement with patterns that have been found previously13. The use of anti-parasitic drugs has been shown to be a strong selective pressure in sea lice and several genomic regions under selection have been linked to drug treatment13. In particular linkage groups 1 and 5 in the study showed evidence of selective sweeps, with the same region on linkage group 5 being implicated in drug resistance in a QTL analysis13. Other linkage groups, such as 14 also showed signal of positive selection in that study13. Our study similarly found that 11 out of the 19 outlier loci we identified also lay in linkage groups 1 & 14, which represents an important independent validation. Spatio-temporal variation in treatment regimes, such as rotations of different drugs or the alternative use of warm-water or freshwater treatments28, may drive the heterogeneity observed in our and previous studies. This is partly as a result of cost, perceived efficacy, as well as different regulatory conditions in the countries concerned. Even though spatio-temporal variation in drug resistance is likely to be the strongest driver of differential selection among populations, local environmental conditions can constitute further selective pressures driving allele frequency differences among populations. Local environmental variables such as temperature (e.g29.) and salinity (e.g30.), for example, can have profound effects on sea louse survival and development. Furthermore, a combination of drug treatment and increased host density is shown to influence the evolution of reproductive and life history traits31, as well as virulence in sea lice32 among different populations. However, such local adaptation is most likely linked to subtle allele frequency differences, compared to strong selective sweeps caused by drug treatments, as the selective pressure is comparably low. The combination of a few outlier loci under strong positive selection and a wide range of loci showing subtle allele frequency differences fits the expected pattern. Independent of the cause for allele frequency differences among populations, we show that a random forest machine learning approach can be used to cost-effectively distinguish even near-by sea louse populations, even with a low number of samples and genotyping density.More recent genome wide analysis of SNP variation in PSA2). The association of genomic response to selection, natural environmental conditions, and drug treatment profiles will be important considerations for future work.The use of  samples from the same site at different time points, differing in treatment regimes, could be used to disentangle the effects of drug regime and local adaption on allele frequency differences among populations and signatures of selection. Genome-scale population genetic profiling, alongside robust phenotyping, may also eventually reveal the genetic architecture underlying drug resistance and local adaptation. Here we have identified signals of selection across the genome, including markers closely associated with functional genes 34. However, biological (or genetic) confirmation of larger scale dispersal models (i.e. between lochs (=fjord) and loch systems) within and across regions is also required to assess long-range re-infestation risks for aquaculture sites. Such a strategy is of particular relevance in the light of an increasing control focus on loch-wide fallowing practices35. Furthermore, integration of genetic connectivity data with hydrographic larval dispersal models – so called ‘seascape genetics’ (e.g36.) - is likely to be more fruitful in defining any spatial-genetic correlations than crude map distances and represents an interesting further avenue for study. In this context, our data show that it may be possible to genetically ‘fingerprint’ louse populations in nearby regions to understand connectivity between them and provide a valuable tool for disease surveillance.Tools to enable parasite traceability and molecular epidemiology are an important requirement for rational sea louse control. Hydrographic modelling has been successfully deployed to understand short-range dispersal Supplementary Dataset 1