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      The Discovery, Distribution, and Evolution of Viruses Associated with Drosophila melanogaster

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          Abstract

          Drosophila melanogaster is a valuable invertebrate model for viral infection and antiviral immunity, and is a focus for studies of insect-virus coevolution. Here we use a metagenomic approach to identify more than 20 previously undetected RNA viruses and a DNA virus associated with wild D. melanogaster. These viruses not only include distant relatives of known insect pathogens but also novel groups of insect-infecting viruses. By sequencing virus-derived small RNAs, we show that the viruses represent active infections of Drosophila. We find that the RNA viruses differ in the number and properties of their small RNAs, and we detect both siRNAs and a novel miRNA from the DNA virus. Analysis of small RNAs also allows us to identify putative viral sequences that lack detectable sequence similarity to known viruses. By surveying >2,000 individually collected wild adult Drosophila we show that more than 30% of D. melanogaster carry a detectable virus, and more than 6% carry multiple viruses. However, despite a high prevalence of the Wolbachia endosymbiont—which is known to be protective against virus infections in Drosophila—we were unable to detect any relationship between the presence of Wolbachia and the presence of any virus. Using publicly available RNA-seq datasets, we show that the community of viruses in Drosophila laboratories is very different from that seen in the wild, but that some of the newly discovered viruses are nevertheless widespread in laboratory lines and are ubiquitous in cell culture. By sequencing viruses from individual wild-collected flies we show that some viruses are shared between D. melanogaster and D. simulans. Our results provide an essential evolutionary and ecological context for host–virus interaction in Drosophila, and the newly reported viral sequences will help develop D. melanogaster further as a model for molecular and evolutionary virus research.

          Abstract

          Sequencing of metagenomic RNA and small RNA identifies more than 20 new viruses associated with the fruit fly Drosophila melanogaster, and large-scale surveys show that many are common in the lab and in the field.

          Author Summary

          The fruit fly Drosophila melanogaster is extensively used as a model species for molecular biology and genetics. It is also widely studied for its evolutionary history, helping us understand how natural selection has shaped the genome. Drosophila research has been particularly valuable in determining how the insect immune system interacts with viruses and how co-evolution between hosts and viruses can shape the host immune system. Understanding insect–virus coevolution is important because some viruses—such as those which cause dengue and yellow fever in humans—also infect their insect vectors, and because the viruses of bees and other pollinators are implicated in pollinator decline. Although we have an increasingly good idea of how flies recognise and combat viral pathogens, we still have much to learn about the viruses they encounter and interact with in the wild. In this paper, we sequence all of the genetic material from a large collection of wild fruit flies and use it to identify more than 20 new viruses. We then survey individual wild flies and laboratory stocks to find out which viruses are common, which are rare, and which species of fruit fly they infect. Our results provide valuable tools and an evolutionary and ecological perspective that will help to improve Drosophila as a model for host–virus biology in the future.

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          MicroRNA Targets in Drosophila

          Additional data files Additional data file 1, 2, 3 and 4. Supplementary Material Additional data file 1 Additional data file 1 Click here for additional data file Additional data file 2 Additional data file 2 Click here for additional data file Additional data file 3 Additional data file 3 Click here for additional data file Additional data file 4 Additional data file 4 Click here for additional data file
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            Diversity and dynamics of the Drosophila transcriptome

            Animal transcriptomes are dynamic, each cell type, tissue and organ system expressing an ensemble of transcript isoforms that give rise to substantial diversity. We identified new genes, transcripts, and proteins using poly(A)+ RNA sequence from Drosophila melanogaster cultured cell lines, dissected organ systems, and environmental perturbations. We found a small set of mostly neural-specific genes has the potential to encode thousands of transcripts each through extensive alternative promoter usage and RNA splicing. The magnitudes of splicing changes are larger between tissues than between developmental stages, and most sex-specific splicing is gonad-specific. Gonads express hundreds of previously unknown coding and long noncoding RNAs (lncRNAs) some of which are antisense to protein-coding genes and produce short regulatory RNAs. Furthermore, previously identified pervasive intergenic transcription occurs primarily within newly identified introns. The fly transcriptome is substantially more complex than previously recognized arising from combinatorial usage of promoters, splice sites, and polyadenylation sites.
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              Transcriptional Silencing of Transposons by Piwi and Maelstrom and Its Impact on Chromatin State and Gene Expression

              Introduction A major selection force during evolution is the maintainance of genomic integrity over generations. Transposable elements (TEs) are threatening genomic stability due to their mobile character and their creating repetitive sequence islands that can initiate ectopic recombination (Kazazian, 2004). Small-RNA-based silencing pathways are universally employed by eukaryotes to silence TEs (Slotkin and Martienssen, 2007). In animals, this is of particular importance in germ cells. The PIWI-interacting RNA (piRNA) pathway serves as the main line of defense in the animal gonad, and defects in it result in TE derepression, genomic instability, and sterility (Malone and Hannon, 2009; Siomi et al., 2011). At the core of the pathway is the piRNA-induced silencing complex (pi-RISC) that consists of a single-stranded piRNA bound by a PIWI family protein. piRNAs are typically processed from TE RNAs and so-called piRNA cluster transcripts that are enriched in TE sequences. Thus, by virtue of their sequence, piRNAs guide the specific silencing of TEs. (Senti and Brennecke, 2010; Siomi et al., 2011). Conceptually, two major silencing modes are distinguished, namely transcriptional silencing (TGS) and posttranscriptional silencing (PTGS). Most animals express multiple PIWI proteins, and these might employ different silencing modes. The Drosophila genome encodes the PIWI proteins Piwi, Aubergine (Aub), and Argonaute 3 (AGO3). Aub and AGO3 piRISCs are cytoplasmic, possess slicer activity, and are the major players in a piRNA amplification loop that requires reciprocal cleavage of TE RNAs and piRNA cluster transcripts (Senti and Brennecke, 2010; Siomi et al., 2011). As TE sense RNAs are consumed during this amplification loop, Aub/AGO3-mediated silencing represents a PTGS process. The third family member Piwi, however, is enriched in the nucleus, and its silencing mode is much less understood. Genetically, Piwi-mediated TE silencing depends on its nuclear localization, but not on its slicer activity (Klenov et al., 2011; Saito et al., 2010). These observations indicate that Piwi might induce TGS via triggering repressive chromatin modifications. Indeed, changes in chromatin marks and nascent RNA levels have been observed for some TEs in piRNA pathway mutants (Klenov et al., 2011; Shpiz et al., 2011; Wang and Elgin, 2011). On the other hand, a specific chromatin association of PIWI proteins in germ cells has not been demonstrated. Some studies even challenge a role for Piwi in chromatin regulation. For example, no significant changes in heterochromatin protein 1 (HP1) occupancy on TEs were observed upon Piwi knockdown (KD) (Moshkovich and Lei, 2010), and a genetic study in flies concluded that Piwi triggers PTGS rather than TGS (Dufourt et al., 2011). A systematic understanding of the silencing mode employed by nuclear PIWI proteins is therefore a major open question in the field. All three Drosophila PIWI family proteins are coexpressed in germline cells. Due to their interdependence in terms of piRNA biogenesis and TE silencing, the precise genetic and mechanistic dissection of Piwi's nuclear role is challenging. Somatic support cells of the ovary, however, express a simplified piRNA pathway based exclusively on nuclear Piwi. Importantly, a stable cell line derived from ovarian somatic cells (these cultured cells are called OSSs or OSCs) has been established (Niki et al., 2006; Saito et al., 2009). These cells harbor a piRNA pathway that, in every aspect analyzed, mirrors the pathway acting in ovarian somatic cells. In OSCs, piRNAs antisense to TEs are derived from piRNA clusters such as flamenco. piRNA biogenesis depends on several cytoplasmic factors, and defects in it result in loss of Piwi, presumably due to destabilization of unloaded Piwi (Siomi et al., 2011). Upon loss of Piwi-RISC, several TEs, which are normally silenced by the piRNA pathway, are derepressed. We took advantage of this linear piRNA pathway and dissected the underlying silencing process in detail. Our data demonstrate that Piwi-RISC mediates TE silencing at the transcriptional level and that this is accompanied by local heterochromatin formation. Remarkably, most euchromatic H3K9me3 islands are due to piRNA-mediated silencing of TE insertions, and spreading of this heterochromatic mark into flanking genomic regions has striking effects on the expression of nearby genes. Results Maelstrom Is Required for Piwi-Mediated Silencing, but Not for piRNA Biogenesis While the process of piRNA biogenesis within the somatic pathway is being increasingly dissected at the molecular level and multiple involved factors are known, not a single protein has been linked to Piwi-mediated silencing in the nucleus. To identify such factors, we utilized an assay system based on transgenic RNAi and a lacZ reporter that monitors silencing of the gypsy TE in follicle cells (Figure 1A; Olivieri et al., 2010; Sarot et al., 2004). The evolutionarily conserved maelstrom (mael) gene scored strongly in this assay (Figure 1A). This came as a surprise, as Mael levels are low in ovarian somatic cells (Findley et al., 2003) and a recent study indicated that mael is dispensable for TE silencing in ovarian somatic cells (Klenov et al., 2011). To support the gypsy-lacZ results, we induced tissue-specific mael RNAi in soma or germline and analyzed RNA levels of several marker TEs. In both cell types, mael KD resulted in desilencing of TEs to extents comparable to KD of the essential piRNA biogenesis factor Armitage (Armi) (Figures 1B and 1C). Ovaries from mael loss-of-function flies also exhibited derepression of soma and germline transposons (Figure S1A available online; note that Klenov et al. [2011] did not use mael null alleles). To identify the level at which Mael acts in the piRNA pathway, we monitored Piwi in clones of mael KD cells in the follicular epithelium. Defective piRNA biogenesis (e.g., armi KD) triggers loss of Piwi, presumably as unloaded Piwi is unstable (Figure 1D). In contrast, depletion of Mael had no impact on nuclear Piwi levels (Figure 1E). Similarly, levels and localizations of all PIWI proteins were unaffected in soma and germline of mael null ovaries (Figure S1B). This suggested that Mael does not act in piRNA biogenesis. To test this, we monitored TE expression and piRNA levels in OSCs upon mael KD or armi KD (Figures 1F–1I). Both KDs resulted in derepression of the TEs mdg1 and 412, but not of the germline-specific element HeT-A (Figure 1G), demonstrating an essential role for Mael in the OSC piRNA pathway. However, whereas loss of Armi resulted in reduced Piwi protein (Figure 1F; but not mRNA: Figure S1C) as well as in reduced piRNA levels (Figure 1H), loss of Mael did not. The size of Piwi-bound piRNAs was also unaffected upon mael KD (Figure 1I). We finally sequenced piRNAs from mael mutant ovaries and compared them to heterozygous controls. In agreement with the OSC data and in contrast to known primary biogenesis factors, loss of Mael did not affect piRNAs derived from the soma-dominant flamenco cluster or the traffic jam 3′UTR (Figure 1J). For the global pool of ovarian piRNAs (soma and germline), we observed a slight shift toward sense piRNAs, probably due to abundant derepressed TE messages (Figures S1D and S1E). piRNAs derived from the germline-dominant 42AB cluster were moderately reduced (Figure S1F). At the level of most individual TEs, loss of Mael had only mild impacts on antisense piRNA populations from soma-dominant, intermediate, and many germline-dominant TEs (Figure S1G). The most notable exceptions were the telomeric TEs HeT-A, TAHRE, and TART that exhibited strong piRNA losses. We speculate that desilencing of these TEs interferes with piRNA precursor transcription at the same loci, therefore blocking piRNA production. Taken together, Mael is not required for biogenesis or nuclear accumulation of the Piwi-RISC yet is essential for Piwi-mediated TE silencing. Piwi/Mael-Mediated Silencing Is a Nuclear Process The subcellular localization of the Piwi-RISC suggests a nuclear silencing process. Indeed, experiments in OSCs indicated that Piwi's nuclear localization, but not its slicer activity, is required for silencing (Saito et al., 2010). Also in flies, N terminally truncated Piwi is cytoplasmic and piwi[ΔN] flies are defective in TE silencing (Klenov et al., 2011). We reconstructed these findings in vivo by complementing piwi[1]/piwi[2] mutant flies with various GFP-tagged genomic piwi rescue constructs. A nine-amino-acid deletion at the N terminus (ΔNLS) largely prevented nuclear accumulation of Piwi-GFP, whereas both slicer mutant GFP-Piwis (ADK or DAK) localized like wild-type GFP-Piwi to the nucleus (Figures 2A and 2B; efficient loading of all variants with piRNAs verified by IP-CIP-kinase experiments). Real-time quantitative PCR (RT-qPCR) analysis of TE RNA levels showed derepression of soma- and germline-specific TEs in piwi[ΔNLS], but not in piwi[ADK] or piwi[DAK] ovaries (Figure 2C). Moreover, both slicer mutant flies resembled wild-type flies in fertility, whereas only some eggs laid by piwi[ΔNLS] flies developed into larvae and adults. An involvement of Mael in the silencing process predicts a nuclear localization for this protein. In ovaries, endogenous Mael, as well as GFP-tagged Mael expressed under the mael control regions, is abundant in germline cells and localizes to cytoplasm, nuage, and nucleus (Figures 2D and 2E, upper left; Findley et al., 2003). As levels in follicle cells were low, we turned to OSCs in which endogenous Mael, as well as N- and C-tagged GFP-Mael, localized throughout the cell but were clearly enriched in the nucleus (Figure 2E, upper right; data not shown). Based on Mael's domain architecture, we tested the requirement of HMG and MAEL domains for nuclear localization and TE silencing in complementation assays using mael loss-of-function alleles and GFP-tagged mael rescue constructs. Whereas the wild-type construct rescued sterility and TE derepression nearly completely, two constructs harboring point mutations in conserved residues of the MAEL domain (Zhang et al., 2008a) did not (Figures 2F and 2G). In both cases, nuclear accumulation of mutant Mael was strongly reduced in ovaries and OSCs (Figure 2E). Loss of the HMG domain had only mild effects on Mael's subcellular localization (Figure 2E). mael[ΔHMG] flies did lay eggs, but these displayed defects in egg asymmetry, presumably as TE silencing was only partially rescued in these flies (Figures 2F and 2G). We conclude that Piwi-mediated silencing is a nuclear process that is independent of Piwi's slicer activity but requires Mael and, in particular, its MAEL domain. Piwi Silences TEs at the Transcriptional Level in a Mael-Dependent Manner To dissect at which step of TE expression Piwi mediates silencing, we took advantage of cultured OSCs. These cells express a functional linear piRNA pathway and allow gene knockdowns using siRNAs. We profiled gene expression at three hierarchical levels in cells treated with GFP siRNAs (control KD) or with siRNAs targeting key pathway factors (piwi KD, armi KD, mael KD; Figure 3A). We first defined the set of TEs that are repressed by the piRNA pathway by comparing steady-state RNA levels (RNA-seq) between control KD and piRNA pathway KD cells. We then determined transcription rates by measuring RNA polymerase II (Pol II) occupancy (Rpb3 chromatin immunoprecipitation sequencing (ChIP-seq); Adelman et al., 2005) and nascent RNA polymerase output via global run-on sequencing (GRO-seq; Figures S2A and S2B; Core et al., 2008). To determine steady-state RNA levels, we sequenced total RNA after removal of ribosomal RNA. Reads per kilobase per million mapped reads (RPKM) values for annotated genes were highly correlated between piwi KD and control KD cells (Pearson correlation coefficient 0.95). In contrast, several out of the 125 annotated D. melanogaster TE families showed strong increases in RNA levels (Figure 3B). For example, the LTR elements mdg1 or gypsy increased by >200- or >30-fold upon piwi KD, respectively. With RPKM values of >1,000, both TEs were among the most abundant coding transcripts in OSCs (Figure S2C). Almost identical results were obtained upon knockdown of Armi (Figure S2D; Pearson correlation coefficient piwi KD/armi KD 0.99). Thus, loss of the Piwi-RISC led to highly reproducible increases in the RNA levels of a subset of TEs. Based on these data, we classified TEs into four groups (Figure 3B). Group I elements exhibited RNA increases >10-fold; group II elements. 3- to 10-fold; group III elements, 100 F insertions, and only 18 map to euchromatin. We speculate that euchromatic F insertions are transcriptionally inactive (supported by very low RNA-seq RPKM levels) and devoid of H3K9me3 marks (hence no H3K9me3 spreading) and that the high H3K9me3 levels for the average F-element stem from the abundant heterochromatic insertions. A few euchromatic F and roo insertions did, however, trigger H3K9me3 spreading in a Piwi-dependent manner (Figures S3B and S3C). Interestingly, these were typically in sense orientation within introns of transcribed genes and thus provide targets for antisense piRNAs that do exist in OSCs. Thus, Piwi-guided H3K9me3 depends on transcription. Intrigued by the strong correlation between H3K9me3 and group I TE insertions, we determined all euchromatic H3K9me3 peaks (n = 466; FDR 10−4) and displayed H3K9me3 signals, as well as Pol II occupancy, in a 50 kb window centered on the peak summit in control, piwi, or mael KDs (Figures 6B and 6C). We sorted these peaks according to their loss in H3K9me3 signal upon piwi KD and divided them into five equally sized bins (I–V). Strikingly, piwi KD led to significant reductions in the H3K9me3 signal for most peaks, whereas mael KD did not and instead resulted often in a broadened H3K9me3 domain (Figures 6B and 6C). At the same time, piwi or mael KDs triggered increased Pol II occupancy in proximity to the H3K9me3 summit for those peaks that depended on Piwi (Figure 6B). We compared these results to randomly chosen euchromatic 50 kb windows (Figure 6B) and to all heterochromatic H3K9me3 peaks (n = 655; Figure S3D; analysis based on genome unique reads). Though the repetitive nature of heterochromatin complicates the analysis, Piwi seemingly impacted H3K9me3 patterns predominantly in euchromatic areas. Of note, Pol II occupancy was highly similar in euchromatic H3K9me3 windows and random control windows (Figure S3E), and it was not reduced within H3K9me3 domains compared to their surroundings. Thus, H3K9me3—a mark typically implicated in condensed chromatin state—is compatible with transcription, at least within euchromatic domains. The data in Figures 6B and 6C suggested that most euchromatic H3K9me3 islands are linked to the piRNA pathway. We investigated whether this correlated with TE insertions or whether this indicated a TE-independent role of Piwi. Strikingly, nearly all (88%) H3K9me3 peaks had a TE insertion within 5 kb up- or downstream of the summit (Figure 6B, right; random expectation: 14%). Sixty-one percent of these insertions belonged to group I TEs (random expectation 11%), and these exhibited a very pronounced enrichment at H3K9me3 summits (Figure 6D). On average, ∼80% of all group I TE insertions were found within 5 kb of H3K9me3 summits compared to only ∼5% of group III TE insertions (Figure 6E; p  5 in any of the RNA-seq libraries into three groups: genes with no TE insertion, genes with a group I TE insertion, and genes with no group I but with a group III TE insertion. We plotted their mean RPKM levels versus their fold RPKM change upon piwi KD (Figure 7B) or mael KD (Figure S4B). Strikingly, whereas genes with no insertion (gray) and those with group III insertions (yellow) were distributed symmetrically around the baseline, the population of genes with group I insertions (red) was significantly skewed to increased RNA levels upon Piwi or Mael loss. As a whole, the set of genes with group I TE insertions had significantly increased RNA-seq RPKM values over genes with group III TE insertions, as well as over genes with no TE insertions (Figure 7C). Reciprocally, we selected the set of genes with the most consistent changes upon piRNA pathway KD (4-fold in piwi and armi KD and 2-fold in mael KD cells). Thirty-four genes were upregulated, with no gene being downregulated (Figure 7D). Eighty percent of these genes were associated with a TE insertion within 5 kb, and 85% of these TEs belonged to group I (Figure 7E). In comparison to an average set of random control genes, group I elements were highly enriched in derepressed genes (p value  30 kb. Maybe H3K9me3 spreading is generally more pronounced in heterochromatic areas, as local concentrations of required factors are higher in these chromatin environments. A key feature of the OSC system is that hundreds of TEs and H3K9me3 domains can be monitored simultaneously upon loss of silencing. This strengthens experimental conclusions but also allows unexpected discoveries. For example, three euchromatic mdg1 insertions did not show increased Pol II bleeding upon Piwi loss and nucleated only low H3K9me3 levels in their vicinity. These insertions might simply lack essential promoter features. However, we found that all three insertions reside in H3K27me3 domains (red arrows in Figure 5C; G.S. and J.B., unpublished data), suggesting that H3K27me3 is dominant over Piwi mediated TGS. The Impact of TEs and the piRNA Pathway on Gene Expression Many links have been made between TEs and the regulation of genes (Feschotte, 2008; Slotkin and Martienssen, 2007). The unique aspect of our work is that hundreds of TE insertions can be studied upon loss of their repression. This shed considerable light on two key modes of how TEs impact gene expression. On the one hand, TEs act positively on gene expression by providing transcriptional competence to certain genomic regions (promoter addition). On the other hand, TEs act negatively on flanking genes if they are silenced transcriptionally as the “silencing character” spreads into flanking domains (repressive chromatin influence). In both cases, loss of TE silencing will often lead to increased expression of the neighboring gene. Considering the promoter addition model, we were surprised to see how far Pol II transcription can bleed into sequences flanking the insertion. All piRNA-repressed TEs in OSCs are LTR elements, and it is unclear whether the 3′ LTR serves as an independent TSS or whether transcriptional bleeding implies a large transcript encompassing the entire TE plus flanking sequences. Transcriptional bleeding often traversed genic transcription units and triggered legitimate splice events, suggesting synthesis of stable hybrid mRNAs. Whether a TE insertion dampens transcription of a nearby gene via the chromatin influence model seems to depend on the distance between TE insertion and gene TSS, as well as on the strength of the gene's promoter (we rarely observed impacts on highly expressed genes). Our data therefore show that piRNA target sequences within introns can impact host gene transcription. Whether cotranscriptional splicing lowers the impact of intronic target sequences remains to be determined. All in all, the piRNA pathway does considerably impact gene expression via TGS of TEs. An important open question is to what extent TE insertions display transcriptional bleeding or nucleate chromatin changes also in nongonadal cells in which the piRNA pathway is thought to be not active. Experimental Procedures Drosophila Stocks Fly stocks are listed in Table S1. Cell Culture OSCs were cultured as described (Niki et al., 2006) and transfected with Cell Line Nucleofector kit V (Amaxa Biosystems; program T-029). Antibodies α-Piwi and α-Armi (rabbit) were described in Olivieri et al. (2010) and mouse α-Piwi and mouse α-Armi in Saito et al. (2010). Rabbit α-Mael was raised against the SDNDFSVNGADGKLKK peptide. α-Rpb3 was described in Adelman et al. (2005), and α-H3K9me3 was from Abcam (ab8898). CIP-Kinase Labeling of Small RNAs Cells lysates were prepared from respective knockdowns. Piwi-RISC was isolated with α-Piwi. Small RNAs were extracted, dephosphorylated (CIP), and radioactively labeled (T4 PNK). For details, see the Extended Experimental Procedures. Northern Blot Total RNA was isolated from respective knockdowns and separated on a 15% Urea-PAA gel. After transfer onto a membrane, radioactively labeled probes were hybridized overnight. Probe sequences are shown in Table S3, and details are provided in the Extended Experimental Procedures. Small RNA Cloning Small RNA cloning and sequencing was performed as in Brennecke et al. (2007). RT-qPCR Analysis Primer sequences and details are given in the Extended Experimental Procedures. RNA-Seq Total RNA from siRNA-treated OSCs was rRNA depleted using RiboZero (Epicenter), fragmented, and reverse transcribed with random hexamers. Strand-specific libraries were prepared using the UDG-digestion-based strategy, cloned with NEBNext ChIP-Seq Library Prep Reagent Set for Illumina (NEB), and sequenced on HiSeq2000 (Illumina). GRO-Seq Global nuclear run-on procedure was according to Core et al. (2008). In brief, ∼10 million OSC nuclei were isolated per experiment and subjected for nuclear run-on in the presence of Br-UTP followed by purification of fragmented RNA with anti-deoxyBrU beads. RNA fragments were cloned and sequenced on HiSeq2000 (Illumina). Detailed description is provided in the Extended Experimental Procedures. ChIP-Seq Chromatin immunoprecipitation (ChIP) was carried out according to Lee et al. (2006). In brief, ∼10 million OSCs or 50 ul dissected ovaries were fixed with 1% or 1.8% formaldehyde, respectively. Prepared chromatin was sonicated and used for respective immunoprecipitation followed by decrosslinking and purification of DNA. Recovered DNA fragments were cloned with NEBNext ChIP-Seq Library Prep Reagent Set for Illumina (NEB) and sequenced on HiSeq2000. Detailed description is provided in the Extended Experimental Procedures. DNA-Seq Genomic DNA of OSCs was fragmented and cloned with NEBNext ChIP-Seq Library Prep Reagent Set for Illumina (NEB) followed by sequencing on HiSeq2000 (Illumina). Detailed description is provided in the Extended Experimental Procedures. Computational Analyses Detailed information is provided in the Extended Experimental Procedures. Extended Experimental Procedures X-Gal Staining Ovaries from 5-7 day old flies were dissected into ice cold PBS (max 30 min), fixed in 0.5% Glutaraldehyde/PBS (RT, 15 min), and washed with PBS. The staining reaction was performed with staining solution (10mM PBS, 1mM MgCl2, 150 mM NaCl, 3 mM potassium ferricyanide, 3 mM potassium ferrocyanide, 0.1% Triton, 0.1% X-Gal) at room temperature over night. CIP-Kinase RNA Labeling Cells were transfected twice with respective siRNAs. Cells were lysed in Lysis buffer (20mM HEPES-NaOH (pH7.0), 150mM NaCl, 2.5mM MgCl2, 250mM sucrose, 0.05% NP40, 0.5%Triton). 4mg total protein was used from each sample for immunoprecipitation. Immunoprecipitation was performed using Dynabeads Protein G (Invitrogen) and rabbit anti- Piwi antibody (Brennecke et al., 2007). Lysates were incubated 2 hr at 4°C with antibody crosslinked to beads and washed 3 times with 300mM NaCl and 0.2%NP-40 and 3 times without detergent. Beads were treated with Proteinase K (Roche) and RNA was isolated with TRIzol (Invitrogen). Isolated RNA was dephosphorylated using CIP (Alkaline Phosphatase, Calf Intestinal), phosphorylated using PNK and radio-labeled gamma-ATP. RNAs were separated on a 15% PAA-Urea Gel. Transposon QPCR Analysis cDNA was prepared via random priming of 1μg total RNA isolated from ovaries of 5-7 day old flies. Quantitative PCR was performed using BioRad IQ SYBR Green Super Mix. Each experiment was performed in biological triplicates with technical duplicates. Relative RNA levels were calculated by the 2-ΔΔC T method (Livak and Schmittgen, 2001) and normalized to rp49 levels. Fold enrichments were calculated in comparison to respective RNA levels obtained from heterozygous flies or from flies not harboring a knockdown hairpin. For ChIP-qPCR experiments, enrichments were calculated over input and normalized to an intergenic region. Northern Blot Total RNA was isolated from respective knockdowns and separated on a 15% polyacrylamide urea gel. RNA was transferred to Amersham Hybond-NX (RPN303T) membrane and crosslinked by EDC (1-ethyl-3-(3- dimethylaminoprophy) carbodiimide) for 1 hr. The membrane was pre-hybridized in Church Buffer and hybridized to probes overnight at 37°C. The membrane was washed 3 times 10 min with 2xSSC, 0.1% SDS and exposed. Small RNA Cloning Small RNA cloning and sequencing was performed as described in (Brennecke et al., 2007). In brief, 20 ug of total RNA was isolated from mael M391/mael Df or respective heterozygous ovaries by TRIzol and Phenol/Chloroform extraction, was resolved on a denaturing polyacrylamide gel and RNAs corresponding to 18-28 nt were isolated and subjected to ligations of 3′-, and 5′-adaptors followed by reverse transcription and PCR amplification; libraries were sequenced on GAII or HiSeq2000 platforms (Illumina). For sequencing of piRNAs (Figure 3D) we purified Piwi-bound piRNAs from OSCs and followed the protocol described above. GRO-Seq 10 millions of OSC nuclei were isolated per experiment and subjected to the nuclear run-on reaction in the presence of Br-UTP for 5min at 30 degrees according to (Larschan et al., 2011). The reaction was terminated with Trizol LS (Invitrogen) followed by extraction of total RNA. RNA was fragmented by base hydrolysis with 1M NaOH on ice to ∼20-150nt and remaining DNA was removed with DNaseI (Promega). Br-UTP-containing RNA fragments were enriched and purified using anti-deoxyBrU beads (Santa Cruz Biotech). RNA was then end-repaired and ligated to 3′- and 5′-adaptor used in the small RNA cloning procedure. Each ligation step was followed by purification with anti-deoxyBrU beads. Cloned library of nascent RNA fragments was reverse transcribed, PCR amplified and sequenced on HiSeq2000 (Illumina). ChIP-Seq 10 million OSCs were fixed with 1% formaldehyde for 10 min followed by quenching with glycine. For ChIP from tissue, 50 ul of ovaries were dissected into ice cold PBS, washed and crosslinked with 1,8% formaldehyde for 10 min, quenched and dounced to disrupt the tissue. Nuclei were isolated, washed and lysed; isolated chromatin was fragmented using tip sonicator (Omni-Ruptor) to fragment sizes of 200-400nt. Immuno-precipitation was done overnight with specific antibodies. Intensive washing steps removed non-specific background and beads were eluted with 1% SDS. For de-crosslinking of protein-DNA complexes eluates were incubated 6h at 65 degrees and remaining proteins were digested with proteinase K and RNA with RNase A. DNA fragments were extracted with phenol/chloroform and used as template either for qPCR or library preparation. Libraries were cloned with NEBNext ChIP-Seq Library Prep Reagent Set for Illumina (NEB) and sequenced on HiSeq2000 (Illumina). DNA-Seq OSCs were lysed in RIPA buffer (Tris 50mM pH7.5, NaCl 150mM, SDS 0.1%, sodium deoxycholate 0.5%, Triton X-100 1%) followed be o/n incubation with proteinase K (Roche). RNA was removed by digestion with RNase A (Fermentas) and genomic DNA was extracted with phenol/chloroform. DNA was fragmented, cloned with NEBNext ChIP-Seq Library Prep Reagent Set for Illumina (NEB) and sequenced on HiSeq2000 (Illumina). Cellular Fractionation OSCs were resuspended in buffer I (10 mM HEPES, pH 7.9, 10 mM KCl, 0.1 mM MgCl2, 0.1mM EDTA, 0.1mM DTT, 0,1% NP40) and incubated on ice followed by mechanical isolation of nuclei with syringe and centrifugation. Supernatant was pre-cleared and taken as cytoplasmic fraction. Pellet was washed twice with buffer I and washed twice in lysis buffer (10 mM Tris (pH 5 7.5), 2 mM MgCl2, 3 mM CaCl2, 10% glycerol, 0.5% NP40). Nuclei were next pre-cleaned in buffer F (50mM Tris-Cl (pH 5 8.3), 40% glycerol, 5 mM MgCl2, 0.1 mM EDTA) and opened with triton lysis buffer (50 mM Tris pH 7.5, 0.5% Triton, 137.5 mM NaCl, 5mM EDTA, 10% glycerol). Supernatant was collected as nuclear fraction. Remaining pellet was washed twice with the same buffer and afterward mechanically disrupted by extensive pipetting (saved as soluble chromatin). Remaining pellet was collected as insoluble chromatin. Computational Analyses Mapping of Short Reads All the Illumina short reads were quality controlled (filtering out N-containing reads, sequencing artifacts) and mapped to the Drosophila melanogaster genome (dm3) excluding chromosome Uextra with bowtie 0.12.7 (Langmead et al., 2009). For piRNA-seq as well as for GRO-seq we allowed up to 1 mismatch due to short fragments, whereas up to 3 mismatches were allowed for RNA-seq and ChIP-seq reads. Genome-aligned reads were mapped to transposon consensus sequences (obtained from BDGP (http://www.fruitfly.org/data/p_disrupt/datasets/ASHBURNER/D_mel_transposon_sequence_set.fasta) and Repbase (Jurka, 1998) and available upon request) allowing up to 1 mismatch for GRO-seq and up to 3 mismatches for the other methods used in this study. To avoid cross-contamination between highly similar TE sequence stretches, only reads mapping to one transposon in our list were retrieved. For reads mapped twice within one entry (as the TE consensus sequences contain always two LTRs for LTR-containing TEs) we applied a weighting scheme. Number of reads mapped to each TE was normalized to its length and total number of genome-aligned reads (RPKM value, Reads Per Kilobase of exon model per Million mapped reads) (Mortazavi et al., 2008). Processing of Small RNA Sequencing Reads and Global Nuclear Run-On Sequencing Reads Our RNA cloning strategy introduces 4 random nucleotides at 3′ end of 5′ linker and 5′ end of 3′ linker, which reduces ligation biases (Jayaprakash et al., 2011). Reads were first stripped of the 3′ adaptor and then the introduced 4 random nucleotides at each end of the read were removed. Only reads larger than 22 nt were selected to increase mapping specificities. Potential contaminants and degradation products of abundant cellular RNAs were removed (reads mapping to rRNA, mitochondrial RNA, microRNAs (all from Flybase) and Drosophila C virus (highly expressed virus present in OSCs)). Next, reads were mapped to Drosophila genome release 5 (excluding Uextra) with bowtie 0.12.7 (Langmead et al., 2009). RNA Sequencing We sequenced rRNA-depleted total RNA in a strand-specific manner from OSCs upon different siRNA-mediated knockdowns. This yielded ∼14-32 million genome- and transcriptome-mappable reads. For the computational analyses, we first extracted high quality bases from every read (6-56 nt) and mapped these to the Drosophila genome as well as to the FlyBase transcriptome. Uniquely aligned reads were used for quantification of gene expression levels according to coordinates in the Flybase gene annotation (r5.31) by calculating RPKM values. For computing TE expressions we used genome-mapped reads, aligned them to the TE consensus and proceeded as described above. Chromatin Immunoprecipitation followed by Sequencing We performed ChIP-seq analysis of Pol II, histone H3K9me3 and their inputs in two replicates. Based on the high correlations between replicates we pooled them together and used these pooled libraries throughout this study. The ChIP-seq reads were mapped to the genome and TE consensus sequence to compute RPKM values. H3K9me3 Peak Calling For calling H3K9me3 peaks we used findPeaks from HOMER software (Heinz et al., 2010). Enrichments of H3K9me3 signal were calculated against input (false discovery rate (FDR) of 10−4). We merged all the euchromatic peaks within 8kb distance from each other, which yielded in total 1121 peaks (466 euchromatic and 655 heterochromatic). Identification of TE Insertions For TE insertion calling, we used genomic DNA-seq reads (single-end, 100bp) from OSCs. We took advantage of reads spanning boarders between TE insertions and neighboring genomic regions. First, we removed all the reads, which map to the Drosophila C-virus genome or human rRNA (both 0 mismatches), which we expect to be contaminants. Second, to validate the presence of already annotated insertions, we filter out reads aligning to the assembled Drosophila melanogaster genome with masked repeats, then aligned separately to genomic repeats only and finally to TE consensus sequences (all up to 3 mismatches). Next, we took 25nt of 5′- as well as 3′-end of each remaining read (∼17.8% of total reads) and used these for further analyses. We examined if one part of those reads maps to a TE consensus sequence (up to 2 mismatches) while the other part maps to the genome (uniquely; up to 1 mismatch; ∼0.06% of all reads). To bias the identification of insertions toward full-length elements, we required the TE mapping part to map to the LTR of LTR-elements and to the first 500nt of non-LTR elements. Only insertions covered by ≥ 2 reads were used further (1852 in euchromatin). Data Visualization For preparation of meta-plots representing an average signal distribution around H3K9me3 peaks or TE insertions we identified the summit of every peak and used ± 25kb windows to display signal of H3K9me3 or Rpb3 ChIP-seq in 100nt bins. All heatmaps generated in this study were prepared with Java Treeview (Saldanha, 2004). For visualization of sequencing tracks we used the UCSC Genome Browser (Kent et al., 2002). Coordinates of Heterochromatin The borders for heterochromatin used throughout this study are depicted in Table S6. These were mostly informed by H3K9me3 densities and the location of piRNA clusters, which map typically at the borders between euchromatin and heterochromatin (Brennecke et al., 2007). Statistical Analysis We used statistical packages implemented in R 2.15.0 for all calculations and plots in this study. For data visualization in box plot format we used the standard features: horizontal bar represents median, the box depicts 25th and 75th percentile (lower and upper quartile respectively), whiskers represent sample minimum (lower) and maximum (upper); outliers are shown as circles. Statistical significance in Figures 3–7 (Figures 3F–H, 4D, 5A, 6E, 7E) was computed with Mann-Whitney U test, whereas p-values in Figures 6E and 7E, F were calculated over random control using binomial test. Random control for peaks in Figure 6E and genes in Figure 7E was generated by calculating an average transposon recovery in random peaks from 100 simulation (Figure 6E); or by selecting a random set of genes and checking for presence TE insertion within ± 5kb (an average of 100 simulations, Figure 7E).
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                14 July 2015
                July 2015
                : 13
                : 7
                : e1002210
                Affiliations
                [1 ]Institute of Evolutionary Biology and Centre for Immunity Infection and Evolution, University of Edinburgh, Edinburgh, United Kingdom
                [2 ]Institute of Immunity and Infection Research, and the Centre for Immunity Infection and Evolution, University of Edinburgh, Edinburgh, United Kingdom
                [3 ]Institute of Cell Biology, University of Edinburgh, Edinburgh, United Kingdom
                [4 ]Department of Entomology, Cornell University, Ithaca, New York, United States of America
                [5 ]Department of Parasitology, Noguchi Memorial Institute for Medical Research, University of Ghana, Accra, Ghana
                Fred Hutchinson Cancer Research Center, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: DJO. Performed the experiments: CLW FMW SR DC GF JMB JFQ EHB. Analyzed the data: DJO. Contributed reagents/materials/analysis tools: CLW PRH JA BPL EHB BL DJO. Wrote the paper: DJO AHB FMW BPL.

                [¤a]

                Current Address: School of Life Sciences, University of Sussex, Brighton, UK

                [¤b]

                Current Address: School of Life Sciences, University of Nottingham, UK

                [¤c]

                Current Address: Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Canada

                [¤d]

                Current Address: Institute of Evolutionary Medicine, University of Zurich, Zurich, Switzerland

                [¤e]

                Current Address: i2a Diagnostics, 401 Avenue du Walhalla, CS173406, 34060 Montpellier Cedex 2, France

                [¤f]

                Current Address: Department of Genetics, University of Cambridge, Cambridge, UK

                [¤g]

                Current Address: Centre for Forensic Science, Department of Pure and Applied Chemistry, University of Strathclyde, Royal College, 204 George Street, Glasgow G1 1XW, UK

                Article
                PBIOLOGY-D-15-00927
                10.1371/journal.pbio.1002210
                4501690
                26172158
                6fd242e3-2f47-4b5d-ad6a-2cd3ec3fb0a8
                Copyright @ 2015

                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

                History
                : 29 March 2015
                : 26 June 2015
                Page count
                Figures: 6, Tables: 1, Pages: 33
                Funding
                This work was funded by a Wellcome Trust Research Career Development Fellowship (WT085064; http://www.wellcome.ac.uk/) to DJO, and work in DJO’s and AHB’s labs is supported by a Wellcome Trust strategic award to the Centre for Immunity, Infection and Evolution (WT095831; http://www.wellcome.ac.uk/). PRH was supported by a fellowship from the UK Natural Environment Research Council (NE/G013195/1; http://www.nerc.ac.uk/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Custom metadata
                All of the relevant data can be found within the paper and its Supporting Information files, with the exception of raw metagenomic sequence data, which are deposited at NCBI Sequence Read Archive (SRP056120), and sequence data, which are deposited at Genbank (KP714070-KP714108, KP757922-KP757936, and KP757937-KP757993).

                Life sciences
                Life sciences

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