RNA editing is an important co/post-transcriptional molecular process able to modify RNAs by nucleotide insertions/deletions or substitutions. In human, the most common RNA editing event involves the deamination of adenosine (A) into inosine (I) through the adenosine deaminase acting on RNA proteins. Although A-to-I editing can occur in both coding and non-coding RNAs, recent findings, based on RNA-seq experiments, have clearly demonstrated that a large fraction of RNA editing events alter non-coding RNAs sequences including untranslated regions of mRNAs, introns, long non-coding RNAs (lncRNAs), and low molecular weight RNAs . An accurate detection of A-to-I events occurring in non-coding RNAs is of utmost importance to clarify yet unknown functional roles of RNA editing in the context of gene expression regulation and maintenance of cell homeostasis. In the last few years, massive transcriptome sequencing has been employed to identify putative RNA editing changes at genome scale. Despite several efforts, the computational prediction of A-to-I sites in complete eukaryotic genomes is yet a challenging task. We have recently developed a software package, called REDItools, in order to simplify the detection of RNA editing events from deep sequencing data. In the present work, we show the potential of our tools in recovering A-to-I candidates from RNA-Seq experiments as well as guidelines to improve the RNA editing detection in non-coding RNAs, with specific attention to the lncRNAs. Massive transcriptome sequencing through high-throughput platforms has defiantly revealed that in mammals the vast majority of transcripts have little protein-coding potential   to inosine (I), recognized as guanosine (G) by all cell molecular machineries  has enabled the study of entire transcriptomes at single nucleotide resolution offering the unique opportunity to explore and investigate at large scale post/co-transcriptional modifications due to RNA editing  and implementing a variety of filters to provide reliable sets of RNA editing sites overcoming main sequencing biases  using GSNAP program (main parameters were -s known-splicesites -E 1000 -n1 -Q -O --nofails -A sam --split-output = outputGsnap) providing a set of known splice sites from UCSC, RefSeq, Ensembl, and Gencode .RNA editing candidates in lncRNAs were detected using the REDItoolDnaRna.py script that is part of REDItools package . Pre-aligned reads from DNA-Seq and RNA-Seq experiments constitute the input for our REDItools that implement extensive filters to mitigate sequencing biases, thus providing reliable lists of A-to-I RNA editing candidates.RNA-Seq is the The main critical issue in the detection of RNA editing sites by NGS data is the mapping of RNA-Seq and DNA-Seq reads onto the reference genome that, in turn, relies on the type and quality of input data. Indeed, low quality reads lead to numerous non-canonical RNA editing sites while very short reads (<50 nucleotides) are prone to misalignments  due to the use of random hexamers in the library preparation. Also, RNA-Seq reads could include overrepresented sequences due to adaptors, contaminants, or rRNAs not completely depleted. In addition, RNA-Seq experiments from degraded RNA may lead to high read duplication rates . Optionally, we add a quick step to eliminate reads showing high similarity to rRNAs by means of STAR program . Only unique and concordant alignments (in case of paired-end reads) are retained and used for downstream RNA editing calling.An accurate detection of A-to-I editing events relies also on the type of input RNA-Seq reads. Optimal results are expected from experiments generating ultra-deep paired and stranded reads of at least 75 nucleotides. The type of RNA-Seq reads is particularly important for lncRNAs since many of them are natural antisense transcripts or produced from intronic regions of protein coding genes either in the sense or antisense direction. In addition, RNA-Seq libraries should be sequenced at high coverage since lncRNAs are generally expressed at low levels.ad hoc script included in REDItools (REDItoolBlatCorrection.py). Such script identifies reads prone to mismapping and collects them in specific lists, ready to be inspected by main REDItools programs.Although GSNAP works accurately, misalignment errors may occur. The mismapping effect can be mitigated realigning reads carrying mismatches by the classical Blat algorithm through an Uncovering RNA editing in lncRNAs is based on the REDItoolDnaRNA.py script in which single RNA editing modifications are identified by comparing pre-aligned RNA-Seq and DNA-Seq reads from the same sample/individual. Briefly, the script explores genomic positions site by site and applies several filters taking into account the coverage depth, the base quality score, the mapping quality, the bases supporting the variation, the type of substitution and its frequency, and changes in homopolymeric regions (≥5 bases) or in intronic sequences surrounding known splice sites. If stranded RNA-Seq data are provided, the script can infer the strand for each position mitigating biases due to antisense transcription or mapping errors and can facilitate the A-to-I detection in lncRNAs. In the meantime, REDItoolDnaRNA.py interrogates also DNA-Seq alignments to exclude potential genomic SNPs. In addition, the script can work on specific genomic regions providing a valid set of coordinates in the GTF format.2. DNA-Seq data, instead, were provided as pre-aligned reads by the 1000 Genomes Project in BAM format1. The genomic DNA of GM12878 was sequenced at 44× coverage, allowing accurate genotype calls.The above-described workflow has been applied to publicly available DNA-Seq and RNA-Seq data from human lymphoblastoid cell line GM12878. RNA-Seq data (polyA+) were obtained from the ENCODE project. Libraries were strand-specific and deeply sequenced with Illumina HiSeq2000 in two biological replicates, resulting in 235.8 and 263.7 million paired-end 76-base sequencing reads, respectivelyAll transcriptomic reads were mapped onto the complete human genome using GSNAP in combination with a large repertoire of known splice sites. Resulting unique and concordant paired-end alignments were submitted to REDItoolDnaRNA.py as well as lncRNA transcript annotations from NON-CODE , an integrated knowledge database dedicated to non-coding RNAs (excluding tRNAs and rRNAs) . The remaining 11,499 sites were annotated using the RepeatMask table from UCSC and NON-CODE transcripts .Our screen for RNA editing in lncRNAs achieved high specificity Figure . Indeed,The majority of A-to-I modifications  were identified in Alu repeat regions while 1,140 resided in repetitive non-Alu regions  and only 384 in non-repetitive regions. These findings are in accordance with other genome-wide computational screens in which a large fraction of RNA editing sites (> 90%) is located in Alu repetitive elements  were found in non-repetitive regions of lncRNAs. In this reduced pool of sites, we observed several RNA editing clusters that may indicate the presence of secondary RNA structures. Such RNA editing sites may have important functional roles altering the secondary structure of lncRNAs preventing or promoting interactions with proteins or other RNAs.The 11,206 unique A-to-G changes fell in 1649 lncRNA gene loci (3374 lncRNA transcripts) and, of these, a substantial number occurred in intervening sequences. According to the NON-CODE database in which lncRNA genes are classified into four categories depending on their genomic location in relation to protein-coding genes , we valuated the distribution of edited lncRNA genes among these four categories. A consistent amount (62%) of lncRNA genes was predominantly in the sense exonic category and only 51 (3%) belonged to sense non-exonic grouping. The number of lncRNA genes cataloged as antisense and intergenic was roughly equivalent, being 267 and 289, respectively.We finally compared our list of RNA editing changes with that identified in a previous study based on the same NGS dataset by using a slighting different methodology , have markedly revealed the pervasiveness of genome transcription (Consortium, In addition to transcriptional complexity of eukaryotic genomes, the transcriptome landscape is further complicated by co/post-transcriptional mechanisms as alternative splicing and RNA editing (Djebali et al., Nowadays lncRNAs and RNA editing can be profiled at single nucleotide resolution through NGS technologies (Picardi et al., To uncover the RNA editing landscape using NGS data, we have recently developed the package REDItools that includes specific scripts to investigate RNA editing starting from matched RNA-Seq and DNA-Seq data or RNA-Seq data alone (Picardi and Pesole, We tested our pipeline on DNA-Seq and RNA-Seq data from human lymphoblastoid cell line GM12878 using 145,331 lncRNA transcripts from NON-CODE database (Xie et al., The majority of edited lncRNA genes were in the sense exonic category meaning that RNA editing target lncRNA genes were in overlap with known protein coding genes and in the same orientation. In such cases, since lncRNAs and overlapping protein coding transcripts share the same strand, the assessment of RNA editing membership, lncRNA or coding transcript, is very hard. Further checks taking into account the expression levels of involved genes and transcripts are extremely needed before claiming novel discoveries.Although the computational detection of RNA editing events in NGS data is not yet completely optimized, our REDItools are the only available software to explore the RNA editing landscape in complete transcriptomes. Given the explosion of NGS technologies in genomic research, REDItools and derived methodologies, as the one described in this work, will be indispensable to characterize RNA editing in novel experimental conditions as well as in human disorders.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.http://www.frontiersin.org/Journal/10.3389/fbioe.2014.00064/abstractThe Supplementary Material for this article can be found online at Click here for additional data file.


Caenorhabditis elegans are guided by multiple extracellular cues, including UNC-6 (netrin), EGL-20 (wnt), UNC-52 (perlecan), and SLT-1 (slit). How multiple extracellular cues determine the direction of axon guidance is not well understood. We have proposed that an axon's response to guidance cues can be modeled as a random walk, i.e., a succession of randomly directed movement. Guidance cues dictate the probability of axon outgrowth activity occurring in each direction, which over time creates a directional bias. Here we provide further evidence for this model. We describe the effects that the UNC-40 (DCC) and SAX-3 (Robo) receptors and the UNC-6, EGL-20, UNC-52, and SLT-1 extracellular cues have on the directional bias of the axon outgrowth activity for the HSN and AVM neurons. We find that the directional bias created by the cues depend on UNC-40 or SAX-3. UNC-6 and EGL-20 affect the directional bias for both neurons, whereas UNC-52 and SLT-1 only affect the directional bias for HSN and AVM, respectively. The direction of the bias created by the loss of a cue can vary and the direction depends on the other cues. The random walk model predicts this combinatorial regulation. In a random walk a probability is assigned for each direction of outgrowth, thus creating a probability distribution. The probability distribution for each neuron is determined by the collective effect of all the cues. Since the sum of the probabilities must equal one, each cue affects the probability of outgrowth in multiple directions.Axons in  Caenorhabditis elegans provides a model to study how axons are guided by UNC-6/UNC-40 and SLT-1/SAX-3 signaling. These neurons are at different positions on the lateral body wall, but are exposed to the same extracellular guidance cues, which include UNC-6, SLT-1, UNC-52 (perlecan) and EGL-20 (wnt) (In response to multiple extracellular guidance cues an axon is directed towards its target. The extracellular SLT-1 (slit) and UNC-6 (netrin) molecules and the SAX-3 (robo) and UNC-40 (DCC) receptors play a conserved role in the guidance of axons 20 (wnt) . The axounc-40(ur304)unc-6 loss-of-function background. However in these mutants, UNC-40 asymmetric localization is directed to a different side of the neuron, which results in the axon protruding from a different side of the neuron in different animals. In the unc-6 wild-type background, UNC-40 localization and axon protrusion is normal, at the ventral side. The interpretation is that UNC-40 mediates two separate responses. First, UNC-40 mediates a response to the UNC-6 molecule that causes UNC-40 asymmetric localization and, second, UNC-40 mediates a response to the external asymmetric distribution of UNC-6 that orients the asymmetric localization of UNC-40 to a specific side of the neuron. Because UNC-40-mediated axon outgrowth activity can be induced without the UNC-6 extracellular spatial cue, it was hypothesized that the direction of UNC-40 axon outgrowth activity might be stochastically determined It is commonly proposed that netrins and slits function as attractants and repellants unc-40(ur304) phenotypes suggested that random UNC-40 asymmetric localization within the neuron becomes stabilized at one side of the neuron because of the UNC-6 gradient C. elegans provides important evidence that this process occurs in cells i.e. the localization occurs at a determined site that shifts its position according to some defined, but indiscernible, pattern.The We have proposed that the movement of axon outgrowth in response to guidance cues can be mathematically described as a stochastic process i.e., a path made by a succession of randomly directed movement  as well. The directional affect a cue has depends on the effects other cues have on the probabilities.By examining loss-of-function mutants in the context of the random walk model, we can make new interpretations of how the UNC-40 and SAX-3 receptors and the UNC-6, EGL-20, UNC-52, and SLT-1 cues affect axon guidance. We find that SAX-3 is required for the induction of the asymmetric localization of UNC-40 in HSN. This induction does not require SLT-1, which is a ligand for SAX-3. We provide evidence that there are different axon outgrowth activities that are regulated by SAX-3 and UNC-40. These can regulate the directional bias in response to specific cues. We also observe cell-specificity; SLT-1 affects the directional bias of AVM but not HSN, whereas UNC-52 affects the directional bias of HSN but not AVM. Together our results suggest a remarkably flexible guidance system based on the properties of random walk movement.We analyzed how SAX-3 and UNC-40 affect the probability of axon outgrowth in each direction from the HSN cell body. It is common to study genes that are suspected of affecting HSN guidance by assaying whether or not the axon reaches the ventral midline in a mutant. This model assumes that the neurons have a specific directional response to a guidance cue. However, if the directional response to the guidance cue is stochastic and there is random walk movement, then the direction of axon guidance is determined by the succession of randomly directed outgrowth. Therefore, to understand how a gene affects the direction of axon guidance it is important to understand how it affects random walk movement. We therefore measured how a mutation affects the probability of outgrowth in four directions from the HSN cell body . This insax-3. The sax-3(ky123) allele results in a deletion of the signal sequence and first exon of the gene, whereas sax-3(ky200) contains a missense mutation at a conserved proline residue (P37S) in the first immunoglobin-like domain (Ig1) sax-3(ky200) mutation is temperature sensitive and evidence suggests that the SAX-3 protein in the mutant is misfolded and mislocalized at the restrictive temperature (25°C) unc-6 and sax-3 are on the same chromosome and sax-3(ky123);unc-6(ev400) doubles do not grow well, it is easier to use the temperature sensitive sax-3 allele to make doubles with unc-6(ev400).In this study we used two alleles of unc-40(e1430) and unc-6(ev400) loss-of-function mutants, in the sax-3 mutant the probability of ventral protrusion is greater, whereas the probability of anterior protrusion is less (unc-40(e1430); sax-3(ky200) than in either single mutant.We find that SAX-3 regulates the probability of axon outgrowth in each direction differently than UNC-40. In comparison to  is less . The prosax-3 double mutants with unc-52(e444) and egl-20(n585) (unc-52(e444) and egl-20(n585) mutants, the probability of ventral outgrowth decreases in the double mutants. Although SLT-1 is a ligand for SAX-3, we find that in slt-1(eh15) mutants the probability of axon outgrowth in each direction is nearly that of wild-type  . Whereasild-type . This rePreviously we reported that mutations that disrupt contact with a ventral extracellular matrix affect UNC-6 axon guidance for the HSN axon, but not for the AVM axon sax-3 mutants the probability of AVM axon outgrowth in each direction is similar to that observed in unc-6(-) and unc-40(-) mutants (sax-3(ky200); unc-6(ev400) mutants the probability of ventral outgrowth decreases, whereas the probability of outgrowth in other directions increases.We find that in the  mutants . Specifisax-3 mutants, we find that in slt-1 mutants the probability of anteriorly directed AVM axon outgrowth increases relative to wild-type animals whereas the probability of ventral outgrowth decreases. There is a notable difference between sax-3(ky200);unc-6(ev400) and slt-1(eh15);unc-6(ev400); in the slt-1(eh15);unc-6(ev400) mutants there is a higher probability for dorsal and anterior outgrowth and a lower probability for ventral outgrowth. Similarly, unc-40(e1430);sax-3(ky200) and unc-40(e1430);slt-1(eh15) are different. These results suggest that SLT-1 has an effect on the direction of axon outgrowth that is not mediated by SAX-3.A significant difference between HSN and AVM is the effect that SLT-1 has on axon guidance. Similar to To visualize the effects that a mutation has on a directional bias for axon outgrowth, we simulated a random walk of 250 steps based on the probability of dorsal, ventral, anterior, and posterior outgrowth given in unc-40(e1430), sax-3(ky200), and unc-40(e1430);sax-3(ky200) mutants is different ;sax-3(ky200) mutants. Further, for HSN there is no ventral bias in sax-3(ky200);unc-6(ev400) or unc-40(e1430) mutants. Together these results suggest that in the sax-3(ky200) mutants UNC-40 mediates a response to UNC-6 that causes a ventral directional bias.The graphs illustrate that the UNC-40 and SAX-3 receptors mediate a directional bias for AVM and HSN. Whereas in wild-type animals there is a strong ventral directional bias, the directional bias for AVM and HSN in ifferent . In the egl-20(n585);sax-3(ky123) mutant and the unc-40(e1430);egl-20(n585) are not the same as that caused by the loss of EGL-20 alone (We previously found that UNC-52 (perlecan) and EGL-20 (wnt) also control the probability of UNC-40 localizing to the ventral side of the HSN neuron. From our analyses of the directional bias, we observe that the directional bias in the double 20 alone . These psax-3 and unc-52 mutants (sax-3 mutants is shifted right (posterior), whereas the pattern for unc-52 mutants is shifted left (anterior). The directional bias of unc-52 mutants is different from that in the unc-52(e444);sax-3(ky123) mutant, suggesting that the directional bias created by the loss of UNC-52 requires SAX-3 activity. We also find that the directional bias in the double unc-52(e444);sax-3(ky123) mutant is similar to the bias of sax-3 mutants, suggesting that UNC-52 affects the directional bias through something that is missing because of the sax-3(ky123) mutation. We also note that the loss of both UNC-52 and UNC-40 creates a directional bias that is different form the directional bias created by the loss of only UNC-52 , in the egl-20(n585), and in the egl-20(n585):unc-6(ev400) mutant are each different (unc-6(ev400), in the unc-52(e444), and in the unc-52(e444);unc-6(ev400) mutant are each different (unc-6(ev400), in the slt-1(eh15), and in the unc-6(ev400); slt-1(eh15) mutant are each different  mutation encodes an UNC-40 variant, UNC-40 (A1056V), which can induce the asymmetrical localization of UNC-40 in unc-6(-) mutants. In the unc-40(ur304);unc-6(ev400) mutants, there is a probability that UNC-40 will localize to any one side of the neuron (unc-40(ur304);unc-6(ev400) mutants there is no strong bias for ventral localization and UNC-40 is stochastically directed to any one side of the neuron. We find that in the triple mutant, unc-40(ur304);sax-3(ky200);unc-6(ev400) grown at 25°C, UNC-40::GFP remains uniformly dispersed around the periphery of HSN  mutants can also be triggered by mutations in unc-53, which encodes a cytoskeletal binding protein unc-53(n152);sax-3(ky200);unc-6(ev400) grown at 25°C, UNC-40::GFP remains uniformly dispersed around the periphery of HSN  and the unc-53(n152);unc-6(ev400) mutant but doesn't asymmetrically localized in either the unc-53(n152);sax-3(ky123) or the unc-53(n152);sax-3(ky200);unc-6(ev400) mutant. We find that the directional bias created in each mutant is different  mutant. If the loss of SAX-3 inhibits the induction of UNC-40 asymmetric localization in HSN, then UNC-40 localization in the double mutants should be similar to that in the sax-3 mutant. In comparison to wild-type animals, in egl-20(n585) and unc-52(e444) mutants the probability of UNC-40 asymmetrically localizing in the anterior and posterior direction increases, whereas the probability of UNC-40 asymmetrically localizing in the ventral direction decreases. We find that in the double mutants, egl-20(n585);sax-3(ky123) and unc-52(e444); sax-3(ky123), UNC-40::GFP remains uniformly dispersed around the periphery of HSN  mutants and found that the intracellular localization pattern is similar to wild type; UNC-40 does asymmetrically localize to the ventral side of HSN (slt-1(eh15) allele is a deletion that truncates SLT-1 near the N terminus and is a strong loss of function mutation that has been used to inactivate SLT-1 activity Since SLT-1 is a ligand for SAX-3 we tested whether SLT-1 is also required to induce the asymmetric localization of UNC-40 in HSN. We examined e of HSN . The sltunc-6 and unc-40 mutants axon development is delayed and neurite extension is not confined to the ventral side during the L3 stage. A predominate axon, which protrudes anteriorly, eventually forms however not until the L4 stage By the early L2 larval stage, HSN is polarized ventrally with neurites primarily restricted to the ventral side where a leading edge forms sax-3(ky123) mutant (unc-6(ev400) and unc-40(e1430) mutants, that the neurons don't clearly show ventrally oriented neurites until around the L3–L4 transition (Axon outgrowth activity can fluctuate in the ) mutant  and we oansition .Previously we presented evidence that UNC-6 induces axon outgrowth activity that is stochastically directed towards each side of the neuron and that external cues, such as the UNC-6 gradient, regulate the probability that axon outgrowth activity will be directed to each side of the neuron sax-3 mutants UNC-40 remains uniformly dispersed around the periphery of HSN, as it does in unc-6(-) mutants ;unc-6(ev400) or unc-53(n152);unc-6(ev400). Because of these observations, we deduce that UNC-40-based axon outgrowth does not play a significant role in determining the direction of axon guidance in sax-3 mutants. This further suggests that in sax-3 mutants non-UNC-40-based axon outgrowth activity directs axon outgrowth. We further observe that there is a loss of ventral directional bias in unc-53(n152);sax-3(ky200);unc-6(ev400) mutants (unc-53(n152);sax-3(ky123) mutant. This result is consistent with different UNC-40 signals controlling induction and orientation. In the unc-53(n152);sax-3(ky123) mutant the loss of SAX-3 function interferes with the induction signal and prevents UNC-40 asymmetric localization, but it does not inhibit the UNC-40 signaling that orients axon outgrowth activity in response to the UNC-6 gradient. These observations suggest that SAX-3 could play a role in balancing UNC-40 signaling that induces UNC-40 asymmetric localization with UNC-40 signaling that orients axon outgrowth activity. Possibly, the uniform distribution of UNC-40 at the surface of the cell is required for the orientation signal.We find that SAX-3 is required for the process that induces UNC-40 asymmetric localization. In loss-of-function  mutants . Further mutants , suggestsax-3 mutants required EGL-20 and UNC-6 ;unc-6(ev400) mutants but not in unc-6(ev400);slt-1(eh15) or unc-40(e1430);slt-1(eh15) mutants (sax-3(ky200);unc-6(ev400) is caused by SLT-1 signaling that is mediated by UNC-40. The ability of the UNC-40 receptor to mediate a response to SLT-1 was also hypothesized based on the observation that some slt-1 gain-of-function phenotypes can be suppressed by unc-40 loss-of-function mutations sax-3 mutants, we infer that the directional bias for the AVM axon in sax-3(ky200);unc-6(ev400) mutants is because UNC-40 mediates a response to SLT-1 that orients the non-UNC-40 axon outgrowth activity.We observe a ventral directional bias for AVM in  mutants . These rsax-3(ky123); slt-1(eh15) double mutant is not more defective than the slt-1(eh15) single mutant unc-40 mutants, which must be caused by non-UNC-40-based axon outgrowth activity, is reduced in unc-40(e1430);sax-3(ky200) mutants  mutants but not in slt-1(eh15);unc-6(ev400) mutants, suggesting that SLT-1 causes the ventral directional bias in unc-6 mutants. STL-1 in the unc-6 mutants could cause the directional bias either through the UNC-40 or SAX-3 receptor, however a similar ventral directional bias can be derived for unc-40(e1430);unc-6(ev400) and unc-6(ev400) mutants , unc-40(ur304), zdIs5[mec-4::GFP]; LGII, unc-52(e444), unc-53(n152); LGIV, egl-20(n585), kyIs262; LGX, unc-6(ev400), sax-3(ky123), sax-3(ky200), slt-1(eh15).Strains were handled at 20°C by using standard methods  unless stated otherwise. A Bristol strain N2 was used as wild type. The following strains were used: kyEx1212 .Transgenes maintained as extrachromosomal arrays included: kyIs262[unc-86::myr-GFP]. Synchronized worms were obtained by allowing eggs to hatch overnight in M9 buffer without food. L3 staged nematodes larvae were mounted on a 5% agarose pad with 10 mM levamisole buffer. Larval staging was determined by the gonad cell number and gonad size under differential interference contrast (DIC) microscopy. The ventral neurite was scored if it had any ventral filopodia or if a leading edge formed during the middle L3 stage, and the most prevalent direction of growth was noted. Only one HSN was counted as having a non-ventral neurite per animal. Images were taken using epifluorescent microscopy with a Zeiss 63× water immersion objective.HSN neurons were visualized using the transgenic strain zdIs5 [mec-4::GFP]. Axons were scored as anterior protrusion if the axon extended laterally more than three cell body lengths from the cell body. Axons were scored as having dorsal or posterior protrusion if the axon extended dorsally or posteriorly for a distance greater than two cell body lengths from the cell body. The AVM was considered multipolar if more than one process, greater than one cell body length, was observed. Images were taken using epifluorescent microscopy with a Zeiss 40× objective.For analysis of the AVM axon protrusion phenotype, L4 stage larvae were mounted on a 5% agarose pad with 10 mM levamisole buffer. The AVM axon was visualized in L4 stage with the transgenic strain kyIs262[unc-86::myr-GFP]. An anterior protrusion was scored if the axon extended from the anterior side of the cell body for a distance greater than the length of three cell bodies. A dorsal or posterior protrusion was scored if the axon extended dorsally or posteriorly for a distance greater than two cell body lengths. The HSN was considered multipolar if more than one process extended a length greater than one cell body. Images were taken using epifluorescent microscopy with a Zeiss 40× objective.For analysis of the HSN axon protrusion phenotype, L4 stage larvae were mounted on a 5% agarose pad with 10 mM levamisole buffer. HSN axons were visualized using the transgenic strain kyEx1212 were mounted on a 5% agarose pad with 10 mM levamisole buffer. Staging was determined by the gonad cell number and gonad size under differential interference contrast (DIC) microscopy. Images were taken using epifluorescent microscopy with a Zeiss 63× water immersion objective. The UNC-40::GFP localization was determined by measuring the average intensity under lines drawn along the dorsal and ventral edges of each HSN cell body by using ImageJ software. For analysis of the anterior–posterior orientation of UNC-40::GFP, the dorsal segment was geometrically divided into three equal lengths . The line-scan intensity plots of each of these segments were recorded. ANOVA (http://www.physics.csbsju.edu/stats/anova.html) test was used to determine if there is a significant difference between intensities of three segments. The dorsal distribution was considered uniform if p≥0.05 and was considered asymmetrical if p≤0.05. Within an asymmetric population, the highest percent intensity was considered to localize UNC-40::GFP to either anterior, posterior or central domain of the dorsal surface.For analysis of UNC-40::GFP localization, L2 stage larvae with the transgenic marker A program to simulate a two-dimensional lattice random walk based on the probability of dorsal, ventral, anterior, and posterior outgrowth for a mutant  and 2 wa


Elucidating the consequences of genetic differences between humans is essential for understanding phenotypic diversity and personalized medicine. Although variation in RNA levels, transcription factor binding, and chromatin have been explored, little is known about global variation in translation and its genetic determinants. We used ribosome profiling, RNA sequencing, and mass spectrometry to perform an integrated analysis in lymphoblastoid cell lines from a diverse group of individuals. We find significant differences in RNA, translation, and protein levels suggesting diverse mechanisms of personalized gene expression control. Combined analysis of RNA expression and ribosome occupancy improves the identification of individual protein level differences. Finally, we identify genetic differences that specifically modulate ribosome occupancy—many of these differences lie close to start codons and upstream ORFs. Our results reveal a new level of gene expression variation among humans and indicate that genetic variants can cause changes in protein levels through effects on translation. Deciphering the molecular mechanisms that underlie human variation is essential for understanding human diversity and personalized medicine. To date, genetic variants that affect protein function in humans have been well studied, but those that control protein levels are less well characterized. Yet, misregulation of protein levels can have profound consequences for human health. For example, transcriptional regulatory mutations that increase telomerase gene expression have been identified in ∼70% of melanoma patients  and are Protein expression is determined at many levels, including (1) RNA expression, (2) translation efficiency, and (3) protein stability. Recent studies have begun to unravel the extent of human variation at RNA levels and its control through transcription factor binding sites and chromatin . HoweverHere, we utilized RNA-seq and ribosome profiling to identify ribosome occupancy profiles. Ribosome profiling involves RNase digestion of unprotected RNA and isolation of ribosome-bound mRNA segments. The sequences of ribosome-protected mRNA fragments can then be used to deduce the number of ribosomes per message in conjunction with RNA-seq data. We integrated these measurements with quantitative proteomics to reveal a comprehensive view of the variation in gene expression programs across a diverse set of humans.To measure genome-wide ribosome occupancy of mRNAs, we first improved and adapted the ribosome profiling protocol , 2012 foDrosophila and other systems also reported the importance of optimizing nuclease digestion to generate robust ribosome profiling data  resulted in complete digestion of polyribosomes into monosomes  theing data ; and (3)ing data , therebyWe leveraged replicate measurements to assess data quality and its dependence on several parameters, including alignment strategy, mRNA enrichment method, PCR artifacts, gene length normalization, and batch effects . In addition to verifying the high quality of the data, replicate measurements also enabled modeling of gene-specific variance in RNA expression and ribosome occupancy per individual, allowing robust derivation of individual-specific translation efficiency estimates. Specifically, we developed a linear modeling–based approach to regress out the effects of RNA expression from ribosome occupancy measurements to calculate translation efficiency .Finally, for the 28 individuals studied here, we previously measured relative protein abundances via isobaric tag-based quantitative proteomics using the same cell lines . In totaP-value < 2.2 × 10−16; outlier robust correlation 0.88 using Donoho-Stahel estimator) , albeit still lower than biological replicates of RNA expression data , indicating that control of ribosome occupancy levels is distinct from RNA levels. Importantly, ribosome occupancy correlated better with protein levels than RNA expression correlated with protein levels  . Consistent with previous results , an integrative machine learning method that is robust to noise and allows assessment of all relationships simultaneously . BecauseThe emerging map recapitulated the pairwise relationships between RNA expression, ribosome occupancy, and protein levels across neurons B. We furP-value < 0.05 for all categories using permutation-based multiple hypothesis correction)   enrichments , 2009 acP-value < 0.05 based on a simulation based likelihood ratio test)  . ConsequP < 0.05 permutation-based multiple hypothesis correction)  . These fWithin genes that exhibited significant inter-individual variation in both RNA expression and ribosome occupancy, we identified three subsets. Within the first subset, the variability in RNA expression was comparable to variability in ribosome occupancy . This first subset contained 54% of all genes exhibiting inter-individual variation in both RNA expression and ribosome occupancy. The second subset consisted of genes that had higher RNA-level variability compared to ribosome occupancy variability. This subset encompassed nearly twice as many genes as the third subset, where ribosome occupancy variability was higher than that of RNA expression . These results were consistent with the observation that for many genes, inter-individual RNA expression variation is not reflected in ribosome occupancy. Taken together, our results are consistent with yeast studies that reported translational buffering of divergent RNA expression . HoweverP < 0.05 permutation-based multiple hypothesis correction) . These results suggest that translational control may play important roles in cellular signaling, whereby rapid cellular responses are often required.A small, but interesting fraction of genes (0.7%) exhibited differential ribosome occupancy between individuals with no apparent differences at the RNA level . These were enriched in genes coding for proteins involved in “cellular response to chemical stimulus” and the “Golgi apparatus” (P < 2.2 × 10−16) . We first calculated the correlation between RNA expression and the corresponding protein level across individuals. Consistent with previous results  , indicating a partial correlation between RNA and protein variability.Finally, we tested whether joint measurement of RNA expression and ribosome occupancy improved this correspondence. Specifically, we considered genes that exhibit significant inter-individual variation in both ribosome occupancy and RNA expression. Strikingly, 83% of these genes had statistically significant correlation between differences in protein levels and RNA expression with a median correlation coefficient of 0.67  (cis-quantitative trait loci (cis-QTL) mapping approach. Using the cis-QTL mapping strategy, we identified significant association between single nucleotide polymorphisms (SNPs) and ribosome occupancy for 67 genes (30% FDR) . Although 34 of the 67 ribosome occupancy QTLs (roQTLs) were not associated with significant differences in RNA expression , this analysis cannot conclusively show that these roQTLs are not associated with RNA expression. Overall roQTLs had consistent effects on RNA expression and protein levels  . Consistent with recent work comparing two yeast strains , which can alter protein expression by regulating translation . In humain uORFs .ZNF215 gene result in merging of two uORFs by removing a stop codon (P < 0.001)  (op codon D. Mergin< 0.001) D.P-value < 0.05), indicating the presence of uORFs and RNA levels are often coupled. However, for 16 other genes, the observed effect was solely on ribosome occupancy, suggesting direct modulation of the translation efficiency of the main reading frame  (P < 2.8 × 10−7) . Although previous work using reporter systems anticipated the significance of these features  . Hence, further improvements in proteomics technology will be needed to test the generalizability of our results to lowly expressed proteins. Despite the significant improvements obtained by joint analysis of ribosome occupancy and RNA level measurements, there remains unexplained variability in protein levels. One potential contributor to this discrepancy is variability in protein degradation rates . AnotherImportantly, genes that have individual variability only in RNA expression are less likely to have corresponding differences at the protein level. Among this subset of genes, only 40% had statistically significant covariation between RNA levels and protein levels (5% FDR). An important implication of this result concerns ongoing efforts that aim to identify genetic determinants of RNA expression . These strans-acting genetic effects on RNA expression. Interestingly, 85% of the trans-effects on RNA expression were mediated by the effects of the associated SNP on a nearby gene , have the potential to modulate the translation of a large number of targets . A particularly interesting question is the molecular mechanisms of these sequence-function relationships. An intriguing feature of genetic variants modifying uORFs on translation was the observation that both gain and loss events could lead to increased translation of the downstream open reading frame , consistent with previous work that implicated both positive and negative regulation of translation efficiency by uORFs . WhereasRecent structural analysis of the yeast 48S translation initiation complex permitted an unprecedented view of the molecular environment of the start codon in eukaryotes , revealiTogether, these results demonstrate that genetic alterations in the human population and disease-associated mutations may penetrate to phenotype through changes in translation. In the era of personal genome sequencing, this information is crucial for understanding the role of genetic variants on gene expression, phenotypic traits, and human disease susceptibility.6 cells/mL. Approximately 10 million cells were pelleted at 250g at 4°C and washed with PBS. The pellets were frozen in liquid nitrogen prior to cell lysis. Seven A260 units of the cleared cell lysates were incubated with 300 units of RNase T1 (Fermentas) and 500 ng of RNase A (Ambion). A 34% (Weight/Volume) sucrose cushion was used to isolate ribosomes. Library preparation and sequencing was done as previously described with some modifications  were obtained from Coriell Cell Repository. For replicate ribosome profiling experiments, cells were grown separately to a density of 0.8–1.0 × 105–6 × 105 cells/mL. Total RNA was extracted using TRIzol reagent according to the manufacturer's instructions (Life Technologies), then purified using the Qiagen RNeasy kit (Qiagen) and treated with RNase-free DNase (Qiagen). RNA integrity was checked with a Bioanalyzer (Agilent), and only samples with an RNA integrity number (RIN) of >9.5 were subsequently subjected to either ribosomal depletion or poly-A selection. For ribosomal RNA depletion, 5 μg of purified total RNA was depleted of rRNAs using the Ribo-Zero Magnetic Gold Kit (Human/Mouse/Rat) (Epicentre Biotechnologies). For poly-A selection, 10 μg of purified total RNA were enriched by performing two cycles of selection using the Dynabeads mRNA Purification Kit (Life Technologies). Stranded libraries were prepared following the dUTP protocol  that overlap APPRIS transcripts and retained all phasing information from the VCF files. About 8% of the variants in the pilot data set were unphased; and for these variants, we randomly assigned the phase. For details, see Supplemental Methods.M-values to account for differences in library size  (as implemented in the edgeR package)  greater ary size  and estiary size . We explWhen combined with RNA expression measurements, ribosome profiling enables the estimation of translation efficiency by capturing a snapshot of the transcriptome-wide ribosome occupancy. We treated ribosome profiling and RNA-seq as two experimental manipulations of the RNA pool of the cell. Translation efficiency was calculated using a linear model, in which the normalized expression values are dependent on the treatment (RNA-seq or ribosome profiling) and the individual identifiers (limma R package) . For detWe used SOMs to explore the relationship between protein levels and the three expression measurements: RNA levels, ribosome occupancy, and translation efficiency. SOMs rely on a suitable measure of distance between the transcripts for the clustering. To avoid skewing distance calculation due to difference in scale and variance of the expression measurements, expression levels and protein amounts were converted to percentiles using the empirical cumulative distribution function for each level. The kohonen R package  was usedP-value < 0.05. P-value adjustment was carried out using a permutation method to account for the overlap between the GO terms. We calculated the Kappa Similarity Score between all pairs of significantly enriched GO terms. We retained edges between all pairwise GO terms whose Kappa similarity score was >0.1. Enriched GO terms were visualized with Cytoscape  that were performed.We defined the Kozak region as the 6 nt preceding the start codon and the 2 nt following the start codon. We extracted the nucleotide sequence of this region from all annotated APPRIS transcripts and built a PWM, which recapitulated the known Kozak sequence A. We tesP-value was >0.05 or if the regression coefficient had the opposite sign. For details, see Supplemental Methods.Next, we collected all SNPs that intersect annotated Kozak regions. We scored the variant and the reference Kozak sequence using the PWM matrix obtained above. We coded each variant by the PWM score change and assumed an additive relationship between different positions in the Kozak region and copy number of the allele. We then tested whether the variants in the Kozak regions affect ribosome occupancy of the main coding region using a linear model. For all Kozak variants affecting ribosome occupancy, we conducted the same association test using RNA expression level as the phenotype. As for the uORF analysis, we deemed RNA association to be not significant if the nominal Renilla luciferase. Although the Renilla luciferase translation is cap-dependent, firefly luciferase has a Hepatitis C virus (HCV) internal ribosome entry site (IRES) that enables cap-independent translation.To assay translation efficiency, we used a bicistronic luciferase reporter construct . This coRenilla luciferase using the CloneEZ system (GenScript). The bicistronic constructs were transfected into HEK293 cells. Cap-dependent translation was calculated by taking the ratio of Renilla (cap) to firefly (HCV IRES) luciferase activity and derived from five replicate experiments. The HCV-IRES-dependent translation of firefly luciferase accounted for differences in RNA expression and transfection efficiency. Outlier detection was carried out as described  of the escribed . The difhttp://www.ncbi.nlm.nih.gov/geo/) under accession number GSE65912.The sequencing data from this study have been submitted to the NCBI Gene Expression Omnibus (GEO;


HIV-1 Gag selects and packages a dimeric, unspliced viral RNA in the context of a large excess of cytosolic human RNAs. As Gag assembles on the plasma membrane, the HIV-1 genome is enriched relative to cellular RNAs by an unknown mechanism. We used a minimal system consisting of purified RNAs, recombinant HIV-1 Gag and giant unilamellar vesicles to recapitulate the selective packaging of the 5’ untranslated region of the HIV-1 genome in the presence of excess competitor RNA. Mutations in the CA-CTD domain of Gag which subtly affect the self-assembly of Gag abrogated RNA selectivity. We further found that tRNA suppresses Gag membrane binding less when Gag has bound viral RNA. The ability of HIV-1 Gag to selectively package its RNA genome and its self-assembly on membranes are thus interdependent on one another.DOI:http://dx.doi.org/10.7554/eLife.14663.001 HIV-1 is the virus that causes AIDS – short for acquired immune deficiency syndrome – in humans. When HIV-1 infects a person, it targets cells of the immune system, which normally act to defend the body against infections. As the virus spreads from one immune cell to the next, it weakens the immune system so that individuals become more vulnerable to other illnesses. A cell infected with HIV-1 creates new virus particles at its surface and then releases the particles so that they can infect other cells.HIV-1 viruses encode their genetic information as molecules of ribonucleic acid (RNA). However, the host cell also makes many other RNA molecules that do not contain virus genes so there must be a mechanism in place to ensure that the new virus particles only contain viral RNA. An HIV-1 protein called Gag is responsible for assembling new virus particles and several Gag proteins come together on the cell membrane to form a honeycomb-like structure called the immature lattice. However, it is not clear how Gag is able to select the right RNA molecules.To study how RNA is packaged into new HIV-1 particles, Carlson et al. used artificial versions of the cell membrane, viral RNA and the virus protein Gag to create a simple cell-free system. This system shows that all that is needed for viral RNA to be correctly packaged into new HIV-1 particles is for Gag to be attached to the cell membrane in such a way that the lattice forms correctly. Disturbing the immature lattice by altering the Gag proteins can result in a drastic loss of RNA selectivity. Further experiments show that other molecules in host cells called transfer RNAs enhance the ability of Gag to select the RNAs that encode virus genes.Carlson et al.’s findings reveal a link between the formation of the Gag lattice and the packaging of virus genes into new virus particles. Drugs that inhibit this process could have the potential to be used as therapies against HIV-1. A future challenge will be to re-create the entire process of HIV-1 assembly in a cell-free system, which would make it easier to develop new drugs that target the process.DOI:http://dx.doi.org/10.7554/eLife.14663.002 Lys-3 as a primer for reverse transcription (PBS), and genome dimerization (DIS)  .Single-virion fluorescence assays indicate that >90% of released HIV-1 particles have packaged genomic RNA , and pacA single released HIV-1 virus contains one dimeric genomic RNA  and 2400Here, we have reconstituted the selective genome packaging of HIV-1 in a minimal system using giant unilamellar vesicles (GUVs), recombinant myristoylated HIV-1 Gag and purified RNAs. Gag assembling on GUV membranes selectively packages the HIV-1 5’UTR at subnanomolar concentrations in the presence of excess competitor RNA. We find that the RNA selectivity of Gag is abolished by mutations that subtly affect its ability to cluster on a membrane. Using SHAPE analysis, we find that membrane recruitment of the 5’UTR by Gag does not lead to drastic changes in its secondary structure, and we further demonstrate that the association of soluble Gag with tRNA confers additional RNA packaging selectivity.2-containing giant unilamellar vesicles (GUVs) Ps (GUVs) . We shows (GUVs) . RNAs werization , 2 mM MgIn order to quantitate the RNA occupancy of assemblies, ten confocal z-stacks were recorded at random positions on each sample, with each data set containing an average of 50–100 GUVs. Strong RNA fluorescence was consistently observed at Gag clusters on GUV membranes, regardless of which RNA was used . This RNTo further characterize the sensitivity of the in vitro system, we created a set of altered 5’UTR constructs . 5’UTR 32-containing GUVs recapitulates the selective RNA packaging of an assembling HIV-1 particle at RNA concentrations far below reported solution affinities of NC for 5’UTR. The 5’UTR is strongly selected for in a competition assay, whereas the RRE is only partially selected for after addition of competitor. 5’UTRs that are either monomerized, have packaging-critical guanines mutated to adenines, or represent a spliced viral RNA are less favored in the competition assay. Given the low concentrations at which this selectivity is observed, below the reported 17 nM affinity constant for 5’UTR-NC association Pociation , we turnWe next sought to determine whether there was a connection between RNA selectivity by Gag, and its assembly into the membrane-bound hexagonal 'immature' lattice found in viral budding sites and released immature virions  Figure . To thisHaving characterized the membrane binding of the mutant Gag proteins, we proceeded to study their RNA binding and selectivity. We first compared the membrane binding of Gag proteins in the presence of RNA to Gag proteins alone as characterized above. We observed that the presence of RNA increased the clustering of all Gag proteins, and increased the fraction of GUVs which had bound Gag . First, To further understand how membrane association of Gag affects its interactions with the HIV 5’UTR, we performed selective 2’-hydroxyl acylation analyzed by primer extension (SHAPE) analysis  on the 52  domain of cytosolic Gag has been shown to bind primarily tRNA in cells . Several2 . We want2 . Since t nM tRNA . At 20 µ nM tRNA . Next, l nM tRNA . In the  nM tRNA . RNA378  nM tRNA . In summ2-containing GUVs is capable of selective RNA packaging in the presence of an excess of non-specific RNA. We find a strong correlation between the ability of Gag to multimerize on a membrane and its selective packaging of viral RNA. Mutations in the lattice-forming CA domain that lessen Gag clustering on membranes PLive-cell fluorescence microscopy has delivered important constraints on the stoichiometry and the location of the RNA selection event. Genomic RNA is present at the onset of HIV-1 assembly at the plasma membrane, at which point the number of Gag molecules was reported to be below the detection limit of ~12 molecules . CytosolThe SHAPE analysis focused on the secondary structure of the 5’UTR RNA in situations representing different stages of packaging . When co2 P2 . The RNA2 . Our rec2 . We showIn summary, our study leads to a model for HIV-1 genome selection where the NC domain of Gag is necessary but not sufficient for the selective packaging of the HIV-1 genomic RNA . LatticeE. coli as a C-terminal, TEV-protease cleavable, maltose-binding protein(MBP)-(His)6 fusion protein and purified analogously to full-length Gag, using NiNTA- and Heparin affinity columns followed by TEV protease digestion and size exclusion chromatography on a Superdex 75 16/60 column . ΔMA Gag (residues 120–500) was based on the same synthetic gene as used for full-length Gag, expressed in E. coli as a TEV-cleavable (His)6-MBP- ΔMA Gag-MBP construct, and purified as full-length Gag.Full-length, N-terminally myristoylated HIV-1 Gag was purified as previously described . Gag wasH. sapiens AIP1/ALIX (GenBank: NM_013374.5), respectively. The integrity of the RNAs was monitored by denaturing formaldehyde-agarose gel electrophoresis. Fluorophore labeling of RNA was carried out using ULYSIS Alexa Fluor 488  at 90°C for 10 min in the presence of 3 mM MgCl2 followed by rapid cooling to 4°C to refold the labeled RNAs. Labeled RNAs were buffer-exchanged using Bio-Spin 6 spin columns , and the final degree of labeling was measured using UV-VIS spectrometry.Yeast tRNA was purchased from Invitrogen. All other RNAs were generated by in vitro transcription using T7 polymerase, and purified either by sequential LiCl and ethanol precipitation, or polyacrylamide gel electrophoresis. The 5’UTR and RRE RNAs consisted of nucleotides 1–345 and 7259–7612 of the NL4-3 (GenBank: AF324493.2) and ARV-2/SF2 (GenBank: K02007.1) unspliced RNAs, respectively . RNA378 2) and 0.1% ATTO647-dioleoyl-phosphatidylethanolamine were electroformed as described previously . They were subjected to ten freeze-thaw cycles on liquid nitrogen, followed by eleven times extrusion through a Nuclepore Track-Etched Membrane with 1.0 µm pore size .Lipids were acquired from Avanti polar lipids , except the fluorophore ATTO647-DOPE which was acquired from ATTO-TEC GmbH . GUVs with a molar composition of 69.9% palmitoyl-oleoyl-phosphatidylcholine (POPC), 25% cholesterol, 5% brain-extracted phosphatidylinositol-4,5-bisphosphate Peviously . For LUV2, pH = 7.4) and then diluted 1:1 with GUVs in a Lab-Tek II chambered coverglass . Ten minutes after mixing, the reaction was imaged using an LSM 5 LIVE confocal microscope . The membrane fluorophore was imaged using the 635 nm laser and a 650 nm long-pass emission filter, Gag using the 532 nm laser and a 550–615 nm emission filter and RNA using the 488 nm laser and a 500–525 nm emission filter. Membrane, protein, and RNA were imaged using separate tracks, and cross-excitation was found to be minimal with the chosen imaging parameters. On each reaction, ten z-stacks (each consisting of ten slices with 1.0 µm spacing) were acquired at positions selected solely on the basis of containing GUVs, without observing the fluorescence channels prior to acquisition. All experiments shown in the same figure were done within 24 hr using the same GUV batch for comparability. Each such experiment series was repeated on three separate occasions with different GUV preparations.GUVs electroformed in 600 mM sucrose were stored at room temperature and imaged within 24 hr. Protein and RNAs were preincubated for 5 min at room temperature in GUV dilution buffer  dissolved in DMSO was added to a final concentration of 100 µM, followed by a 15 min incubation at 37°C prior to imaging.Statistics on RNA recruitment to Gag clusters was performed as described previously . BrieflyFor SHAPE analysis, SHAPE handles were added to both ends of the 345-nt and 356-nt 5’UTR construct as previously reported . Purifie2 and incubated for 5 min at room temperature. 40 µl 1.0 mg/ml freshly prepared LUVs were added and the samples were incubated for 10 min at 37°C. The reactions were mixed with OptiPrep solution (Sigma-Aldrich) and LUV buffer to 700 µl and 30% (w/v) iodixanol. This solution was placed at the bottom of ultracentrifuge tubes and overlayed with 900 µl 18% (w/v) iodixanol in LUV buffer and 700 µl LUV buffer. The gradients were subjected to ultracentrifugation in an Sw55.Ti rotor for 3 hr at 45,000 rpm at 4°C. At the end of the run the vesicles fractions were collected from the interface between the 0% and 18% iodixanol fraction, and the amount of Gag associated with the vesicles was measured in a GloMax-Multi microplate reader  using 490 nm excitation light and a 510–570 nm emission filter. All experiments shown in the same panel were conducted with the same preparation of LUVs and each experiment series was repeated on three separate occasions with different LUV preparationsGag and RNA were mixed in a total volume of 60 µl LUV buffer containing 1.67 mM MgCl In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been favorably evaluated by Wenhui Li as the Senior editor and three reviewers, one of whom, Wes Sundquist, is a member of our Board of Reviewing Editors.Thank you for submitting your article "Reconstitution of selective HIV-1 RNA packaging in vitro by membrane-bound Gag assemblies" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.General assessment:The authors describe a reconstituted HIV RNA "packaging" system featuring specific interactions between the viral RNA and membrane-associated Gag polyprotein. Attractive features of the system include: 1) dependence upon the 5'UTR core packaging site, 2) a requirement for immature Gag assembly, and 3) tRNA inhibition of Gag-membrane interactions that is specifically overcome by RNAs that contain the HIV-1 5'UTR. In addition to describing the system, the authors point out that their study is the first to show that the structure of the dimeric RNA does not change significantly during the different steps of packaging, as evidenced by a relative lack of SHAPE reactivity changes. This report marks the next progression in the excellent studies from the Hurley lab in which they have reconstituted different stages of HIV-1 assembly in vitro.In principle, these studies represent a significant advance, but several additional controls are necessary to ensure that the system is accurately mimicking viral RNA packaging as it occurs in the context of a cellular infection. Specifically, recent studies from other laboratories support a model in which HIV RNA packaging occurs through a stepwise pathway in which the viral RNA begins in a translatable conformation (Gag AUG codon exposed), converts into a dimerizing conformation , interacts specifically with a small number of Gag molecules that bind guanosine clusters in the 5'UTR, and then interacts with a much larger number of Gag molecules along its entire length as the immature virion lattice assemblies at the membrane. Unspliced, dimeric RNA is selectively packaged against a background of cellular RNAs, spliced RNAs, and monomeric RNAs. Thus far, the in vitro system has been shown to recapitulate some, but not all of these features.Substantive concerns to be addressed:1) The authors argue that their work "will stand as the definitive in vitro study of HIV genome packaging". This is certainly a worthy goal, but to achieve it the authors need to do more to demonstrate that their system really does recapitulate all of the key aspects of specificity seen in the cellular context.Specifically, the authors need to show:A) Selective packaging of dimeric vs. mutant HIV RNAs  that have been shown to reduce packaging).B) Selective packaging of unspliced RNAs .C) That NC mutants (or AT-2 treatment) reduce binding specificity.D) That the membrane actually plays an important role in helping to generate RNA selectivity .E) That Gag proteins actually bind along the length of the viral RNA when the RNA-Gag-membrane complex forms.This is not just a list of "extra" experiments to do – we consider them essential for validating the in vitro packaging system.2) The authors should show tests of statistical significance throughout. Some of the changes shown appear to be rather modest, and it is important to know that they are statistically significant (or discover that they are not).3) Molecular interpretations of the fluorescence data are very difficult. Of what the "Gag clusters" really consist remains unknown, and the fluorescence visualization in some cases is marginal. The lipid dye sometimes shows what GUV experts would interpret to be "junk" and does not track uniformly around the periphery of the GUV e.g. . While tOther points for the authors' consideration:1) In 2) In 3) In the subsection “SHAPE-based mapping of interactions between the HIV-1 5’UTR and Gag in solution and on membranes”: It was not clear why different protein:RNA ratios were used in the solution vs. GUV reactions.4) A related question is how the authors know that most of the RNA is bound by Gag proteins under these conditions.5) In the subsection “RNA selectivity depends upon intact CA domain lattice contacts”, last paragraph: It appears that the authors meant to refer to 6) 7) The authors should probably discuss the point that the RNA structure from the Summers' laboratory that they use to interpret their SHAPE data is actually of the monomeric core packaging element. They argue, based upon the SHAPE data, that their RNA is dimeric even before it interacts with Gag, and this may well be true (though it should be shown more directly), but even if they're right it seems possible that the structures of the monomeric and dimeric core packaging RNA elements may differ in significant ways (beyond simply forming or not forming a DIS kissing loop). Substantive concerns to be addressed:1) The authors argue that their work "will stand as the definitive in vitro study of HIV genome packaging".We admit that the word “definitive” might have sounded too strong. This word was only used in the cover letter and is absent from the manuscript.This is certainly a worthy goal, but to achieve it the authors need to do more to demonstrate that their system really does recapitulate all of the key aspects of specificity seen in the cellular context.Specifically, the authors need to show:A) Selective packaging of dimeric vs. mutant HIV RNAs  that have been shown to reduce packaging).B) Selective packaging of unspliced RNAs .These are excellent suggestions and very important points that concerned us as well. We address points A and B together in the new C) That NC mutants (or AT-2 treatment) reduce binding specificity.The new data in D) That the membrane actually plays an important role in helping to generate RNA selectivity .Our data, and specifically the findings that specificity is lost in 2-fold and 6-fold axis mutants , stronglE) That Gag proteins actually bind along the length of the viral RNA when the RNA-Gag-membrane complex forms.The best evidence we have to support binding along the length of the viral RNA is the gain in specificity relative to tRNA competition for the HIV1947 construct as compared to the 5’UTR construct . It woul2) The authors should show tests of statistical significance throughout. Some of the changes shown appear to be rather modest, and it is important to know that they are statistically significant (or discover that they are not).These have now been added throughout and support the significance of all the conclusions drawn previously.3) Molecular interpretations of the fluorescence data are very difficult. Of what the "Gag clusters" really consist remains unknown, and the fluorescence visualization in some cases is marginal. The lipid dye sometimes shows what GUV experts would interpret to be "junk" and does not track uniformly around the periphery of the GUV e.g. .2-containing GUVs, myristylated Gag, and in vitro transcribed RNA, so we do not feel it would be correct to state that the contents of the Gag clusters are “unknown”. With respect to the unevenly distributed membrane dye referred to as "junk": Mg2+ was added to the buffers in this study in order to ensure proper folding of the RNAs. We noticed that the presence of Mg2+ made the GUV morphology more variable, which is consistent with literature on the effect of Mg2+ on membranes containing anionic lipids. We now point this out in the text. The more variable GUV morphology does not influence the assignments of Gag and RNA clusters and causes no difficulty apart from a cosmetic one.The components the in vitro system are fully defined: PIPWhile the bar graph showing that Gag mutants (esp. the two-fold WM mutant) are defective in clustering seems quite convincing , the actWe have now increased their brightness in all figures by a uniform and equal amount. In addition, we marked the GUVs having diffuse Gag binding in the former Other points for the authors' consideration:1) In It is likely that the amount of fluorescent RNA at Gag clusters is reduced to 10%, not 0%. But our image analysis script makes a binary decision if a Gag cluster is positive for RNA based on an intensity threshold, and the reduction of the amount of fluorescent RNA by 90% apparently lowers the RNA fluorescence to below this threshold for all Gag clusters.2) In We assume that the reviewers referred to 3) In the subsection “SHAPE-based mapping of interactions between the HIV-1 5’UTR and Gag in solution and on membranes”: It was not clear why different protein:RNA ratios were used in the solution vs. GUV reactions.The SHAPE experiments on GUVs were conducted at lower RNA and protein concentrations. To ensure that little unbound RNA was present during those experiments (see below), we chose to use a higher protein:RNA ratio.4) A related question is how the authors know that most of the RNA is bound by Gag proteins under these conditions.In solution, the protein concentration of 7.2 µM is far in excess of the reported affinities between NC and 5'UTR . For the studies on GUVs, we measured the RNA concentration in the GUV supernatant after a 5 min spin to pellet GUVs, and found that it was ~10-20% of added RNA, dependent on the presence of both Gag and GUVs. This is now stated in the Results section.5) In the subsection “RNA selectivity depends upon intact CA domain lattice contacts”, last paragraph: It appears that the authors meant to refer to Corrected.6) We have corrected these omissions.7) The authors should probably discuss the point that the RNA structure from the Summers' laboratory that they use to interpret their SHAPE data is actually of the monomeric core packaging element. They argue, based upon the SHAPE data, that their RNA is dimeric even before it interacts with Gag, and this may well be true (though it should be shown more directly), but even if they're right it seems possible that the structures of the monomeric and dimeric core packaging RNA elements may differ in significant ways (beyond simply forming or not forming a DIS kissing loop).Heng et al. & Summers (2012) showed that the construct used was dimeric in the absence of Gag, and this is now referenced. The point that


In 1842 Charles Darwin claimed that vertical growth on a subsiding foundation caused fringing reefs to transform into barrier reefs then atolls. Yet historically no transition between reef types has been discovered and they are widely considered to develop independently from antecedent foundations during glacio-eustatic sea-level rise. Here we reconstruct reef development from cores recovered by IODP Expedition 310 to Tahiti, and show that a fringing reef retreated upslope during postglacial sea-level rise and transformed into a barrier reef when it encountered a Pleistocene reef-flat platform. The reef became stranded on the platform edge, creating a lagoon that isolated it from coastal sediment and facilitated a switch to a faster-growing coral assemblage dominated by acroporids. The switch increased the reef's accretion rate, allowing it to keep pace with rising sea level, and transform into a barrier reef. This retreat mechanism not only links Darwin's reef types, but explains the re-occupation of reefs during Pleistocene glacio-eustacy. Darwin's coral-reef hypothesis, that fringing reefs transform first into barrier reefs and then atolls during subsidence, came into focus in November 1835 when HMS Beagle visited the South Pacific island of TahitiDrilling to test these hypotheses subsequently showed that atolls were not formed by uninterrupted accretion of a single reef unit, but by stacked units formed during SL highstands, separated by laterally extensive subaerial-erosion surfaces formed during lowstands5Although data from drilling confirmed Darwin's postulate that atolls had subsided11Evidence supporting a link between reef types, however, had already started to emerge from last-Interglacial reef-terraces on the Huon Peninsula, where fringing and barrier reefs developed over apparently simple sloping substrates with no apparent karst rimsHere we present evidence supporting the conversion of a fringing to a barrier reef on Tahiti, where Darwin first visualized his reef-types hypothesis. By integrating new sedimentological and paleo-depth data with coral and coralline-algal assemblages, we reconstruct the structure and shallow zonation of the postglacial reef sequence recovered in drill-cores from the north (Tiarei) and south (Maara) coasts of Tahiti during the Integrated Ocean Drilling Program (IODP) Expedition 310. Using published dates, we calculate rates of vertical accretion and determine reef development between 15 and 10 ka. When combined with cores from the modern barrier reef, these data provide a complete picture of postglacial reef development and allow us to identify, for the first time, a viable mechanism controlling the conversion between reef types during glacio-eustatic sea level cycles.Acropora spp18Acropora assemblage found in the Papeete barrier-reef21Pocillopora and massive Porites spp1721Tahiti, a slowly subsiding high volcanic island, is perhaps the ideal site at which to identify conversion between reef types because its modern reef and fore-reef slope have been extensively sampled by drilling, and rigorously dated in order to reconstruct postglacial SL history16Montipora sheets, or ridged or compact-branched forms of Pocillopora sp. with close interbranch spacing and large basal attachments  and 7 drill sites at Maara (20 holes) during IODP-Expedition 310 (Methods). At the base of each site we identified a 1–3 m thick fringing-reef unit characterized by a densely packed framework of heavily bioeroded, shallow-water corals with a distinctive interstitial matrix of mixed skeletal/volcanic sand-and-gravel. The coral assemblage is dominated by small encrusting colonies of either irregular achments . In the 230Th age of corals in these layers decreases progressively upslope, signifying that substrate colonisation was time-transgressive. The lower layer formed over a ~1600 year interval between 16.13 ka (site 9) and 14.51 ka (site 15). This age decrease is reflected in the upper layer, but it is only dated at site 23, where it formed after 14.31 ka . It shows that, rather than reflecting a gradual deepening-up sequence, framework units overlying the fringing reef deepen abruptly at all sites . At Tiarof ~20 m . SimilarReconstruction of paleo-water depths at all Maraa sites with radiometric ages shows that the non-depositional zone in the reef front consistently extends to depths of 15–20 m, and that this zone is represented in core by a small gap in the sequence filled with a poorly-recovered sediment interval . At Tiar−1 (site 9) to 5.0 mm yr−1 (site 23) and average 3.6 mm yr−1 . Transient accretion rates for the fringing-reef unit range from 1.2 mm yr mm yr−1 . By cont mm yr−1 . These dPossible causes of low vertical-accretion rates in the fringing-reef unit become clear on examination of its biotic and sedimentary characteristics . The uni30To determine when the fringing reef stopped retreating upslope and transformed into a vertically-accreting barrier reef, we compare the unit's stratigraphic relations and substrate types. The last dated fringing-reef unit occurs at Tiarei site 23, where it was present until 14.27 ka at a depth of 91 m, a little more than 300 years before the Papeete barrier sequence was initiated at 13.93 ka at 90 mImportantly, both the Papeete barrier-reef sequence and the fringing-reef unit at Maraa overlie a late Pleistocene reef-flat substrate , which f32Acropora and thick crusts of coralline algae. This, in turn, would allow the reef to increase its vertical accretion rate and keep pace with SL rise, transforming it from a fringing reef into a barrier reef.The development of a lagoon would have had several important consequences: first, the fringing reef would be rapidly isolated from coastal sediment flux as the lagoon width increased by shoreline retreat during SL rise. Second, terrigenous sediment would now be trapped in the lagoon, reducing the sediment flux in the reef-front zone. Both of these effects would reduce the sediment flux that previously inhibited vertical accretion, and allow the fringing-reef and reef-front zone to be colonized by rapid-growing species of This lagoonal sediment-trapping conjecture is consistent with taxonomic analysis of reef-front coral and coralline-algal assemblagesThese data imply that the fringing-reef retreat stalled on the Pleistocene reef-flat platform at about the same time as the basal sequence was initiated at Papeete ~14 ka ago. If the appearance of acroporids ~13 ka ago at the Maraa sites signifies that the newly-formed lagoon started to function as a sediment trap, then it apparently took ~1000 years for the sediment flux to reduce sufficiently to allow the fringing reef to start accreting vertically and form a barrier reef at Maraa. However this young barrier may still have been permeable where coastal sediment flux was highest, given that it took a further thousand years for acroporids to dominate reef-front assemblages.In Darwin's genetic model of reef types, fringing reefs maintain a fixed position and accrete vertically in response to relative SL rise. However, during glacial-interglacial cycles, this simple mechanism would produce a barrier reef each time SL rose, and lead to the development of multiple reefs at the same location. Data from Tahiti show that, rather than accreting vertically, the postglacial fringing reef was forced to retreat upslope during SL rise due to the exclusion of rapidly-growing acroporids by sedimentation. The transition into a barrier reef seems to have been initiated when the fringing reef encountered a laterally extensive platform which prevented further retreat. Its newly fixed position on the edge of the platform ‘instantaneously’ created a lagoon that trapped sediment, reducing sediment flux, and eventually allowed acroporids to recolonize the reef. This restored its vertical-accretion potential, enabling it to keep-up with rising SL and become a barrier reef.The key element in the transition between reef types is therefore the presence of a platform with a horizontal or low reverse slope. Evidence from Tahiti implies that this platform was generated by reef-flat development, possibly during the Last Interglacial, although no ages exist for the upper surface of the platform . Neverth34The tendency for fringing reefs to retreat upslope during postglacial SL rise, and to form horizontal platforms during interglacial highstands, provides the key requirements for a genetic link between reef types. In −1 for the last 120 m lowstand). However, on islands where subsidence cannot displace this platform below the SL lowstand , a barrier reef will develop. This is because the second-cycle fringing-reef will retreat upslope with the postglacial rise until it encounters the subsided platform , it is worth noting that many island chains, including the Hawaiian chain, have a wide range of subsidence rates which encompass such thresholds, implying that a lack of acroporids and the resulting reduced accretion potential is more important in preventing the transition between fringing- and barrier-reefs.A second limitation may stem from the biogeographic distribution of corals, particularly prolific reef-builders such as In addition to these absences, the fringing-reef retreat model may also help explain reef development on the edges of more stable continental shelves. For example, highstand fringing reefs likely developed platforms close to shelf edges during mid-Pleistocene interglacials, when the amplitude of the SL cycle was reduced43434445While Darwin stressed the simple up-growth of fringing reefs fixed in position during relative SL change, data from both oceanic islands such as Tahiti and continental shelves like the GBR imply that fringing reefs are not fixed, but are inherently mobile systems forced to retreat and advance during transgressive and regressive stages of glacio-eustatic SL cycles. The anchoring of these mobile reefs by interglacial reef-flat platforms liberates them from coastal sediment flux thereby allowing acroporids to restore their vertical accretion potential and initiate the transition into a barrier reef. This process is optimal on young oceanic islands because thermotectonic subsidence lowers platforms so that they anchor retreating fringing reefs early during postglacial SL rise, giving rise to barrier reefs within a single SL cycle .Darwin's intuition about the genetic link between reef types was right, but his mechanism of vertical growth during subsidence is untenable under glacio-eustasy. The transition between reef types, as finally revealed at Tahiti, stems from a multicyclic interaction between postglacial SL rise, fringing-reef retreat, and former interglacial reef-flat platforms. This interaction, and the transition it causes between reef types, can be prevented by the absence of important reef-building corals, or by excessive rates of subsidence or SL rise. Thus our model not only provides an explanation of why fringing reefs transform into barrier reefs then atolls, but also why this transition is absent in certain island chains, and why shelf-edge barriers and other reefs were repeatedly re-occupied during glacio-eustatic SL cycles.The archive core-half of each hole was logged and scanned in the IODP Gulf Coast Repository at TAMU in College Station, Texas in November 2010. Core logging followed a sedimentary Reef Core Analysis Protocol  online, 230Th ages reported by ref. 14C ages reported by refs. 14C ages for the ocean reservoir effect, and then calibrated the ages to calendar  years using the curve reported in ref. 525314C and 230Th ages reported by ref 69. This calculation stems from the difference between 14C ages calibrated in ref. 230Th. Corals growing during the Late Glacial (>14.5 ka) and Younger Dryas (11.2 to 12.9 ka) have significantly reduced reservoir values of 280 years, whereas warmer Bolling/Allerod and Holocene stages have modern reservoir values of 400 years or greater14C ages using these two reservoir variations . Value differences between consecutive ages provides a measure of transient accretion, which can be used to determine the vertical accretion potential of a unit. Where more than one transient-accretion value is available from each unit, they are combined to provide an average accretion rate for that unit. Transient values are plotted on the core logs in P.B. initiated the study, collected and analysed the data with M.G.-C. and wrote the manuscript. All authors  contributed to the editing of the manuscript.Postglacial Fringing-Reef to Barrier-Reef conversion on Tahiti links Darwin's reef types


RNA:DNA hybrids represent a non-canonical nucleic acid structure that has been associated with a range of human diseases and potential transcriptional regulatory functions. Mapping of RNA:DNA hybrids in human cells reveals them to have a number of characteristics that give insights into their functions.We find RNA:DNA hybrids to occupy millions of base pairs in the human genome. A directional sequencing approach shows the RNA component of the RNA:DNA hybrid to be purine-rich, indicating a thermodynamic contribution to their in vivo stability. The RNA:DNA hybrids are enriched at loci with decreased DNA methylation and increased DNase hypersensitivity, and within larger domains with characteristics of heterochromatin formation, indicating potential transcriptional regulatory properties. Mass spectrometry studies of chromatin at RNA:DNA hybrids shows the presence of the ILF2 and ILF3 transcription factors, supporting a model of certain transcription factors binding preferentially to the RNA:DNA conformation.intrans. The results of the study indicate heterogeneous functions of these genomic elements and new insights into their formation and stability in vivo.Overall, there is little to indicate a dependence for RNA:DNA hybrids forming co-transcriptionally, with results from the ribosomal DNA repeat unit instead supporting the intriguing model of RNA generating these structures The online version of this article (doi:10.1186/s13072-015-0040-6) contains supplementary material, which is available to authorized users. The complex regulatory process leading to gene expression involves, as a major upstream influence, the effects of transcription factors (TFs) binding to specific DNA motifs. The targeting of TFs to specific locations is an informational puzzle, as the number of potential binding sites represented by their generally short sequence binding motifs vastly exceeds the minority used in vivo. This observation suggests that there is additional information present in genomic organization that determines the selection of this subset of sequence motifs. Studies aiming to identify these extra layers of genomic information have revealed influences of chromatin organization –4 and DNFormation of an R-loop has multiple potential consequences in terms of local organization of transcriptional regulatory elements. The helical conformation of the RNA:DNA hybrid differs from the B-form typical of double-stranded DNA (dsDNA), instead creating a conformation intermediate with the A-form associated with dsRNA . A locusin cis with the DNA from which it was transcribed, but there is evidence in yeast that Rad51 can facilitate RNA molecules intrans also forming RNA:DNA hybrids [C. elegans and human HeLa cells [Formation and maintenance of an RNA:DNA hybrid is subject to many influences , 18–21.  hybrids . Mutatio hybrids , which s hybrids  and topo hybrids  have bee hybrids  to human hybrids , for whi hybrids , 30, reg hybrids , constit hybrids  and at tLa cells , centromLa cells , and forLa cells . The funSETX) mutations are implicated in the dominant juvenile form of amyotrophic lateral sclerosis type 4 (ALS4) and a recessive form of ataxia oculomotor apraxia type 2 (AOA2) [RNASEH2) mutations are among those associated with Aicardi-Goutières syndrome, in which the accumulation of unusual nucleic acids triggers inflammatory and autoimmune responses [RNA:DNA hybrids are being increasingly associated with human diseases, with a major concern that their presence predisposes a locus to chromosomal breakage. For example, it has been shown that R-loops are processed by the nucleotide excision repair endonucleases XPF and XPG into double strand breaks , and bot2 (AOA2) . RNase Hesponses . In addiesponses  to mammaesponses . Trinuclesponses , 41, 42 esponses . We refeesponses , 44, 45.intrans, a set of results that combines to provide new insights into these non-canonical nucleic acid structures in human cells.To establish a foundation for understanding their function, we mapped RNA:DNA hybrids genome-wide in vivo in two human cell lines with parallel transcriptional and proteomic studies. These studies provide new insights into how specific loci are preferentially selected as sites of formation of these structures, and allow the inference of some of their likely functional properties. These non-canonical nucleic acid structures occur in ribosomal DNA and at tens of thousands of loci in the remainder of the genome, with sequence characteristics indicating a polypurine-richness of the RNA in the hybrid that is likely to increase the thermodynamic stability of these structures. RNA:DNA hybrids appear to have heterogeneous and context-dependent properties, with subgroups showing relationships with local transcription and chromatin structural features, and a general trend towards decreased DNA methylation. On a more regional scale of hundreds of kilobases, RNA:DNA hybrids are enriched in regions of the genome with a greater abundance of L1 LINEs and CpG islands, and the chromatin modifications indicative of heterochromatin organization. These findings also support the possibility that the RNA generating these RNA:DNA hybrids is frequently generated We optimized an assay previously published as DNA:RNA immunoprecipitation (DRIP)  to map Rn probe to confirm enrichment of the telomeric TERRA-associated R-loop [The in vivo studies were focused on the primary, non-transformed, diploid IMR-90 lung fibroblast cell line because of the substantial genome-wide data available from the Roadmap Epigenomics Program . For comd R-loop   throughout the human genome  data generated previously for IMR-90 cells . We founn sequence, which has been associated with binding by the FLI1 transcription factor [To begin to infer any transcriptional regulatory function of the RNA:DNA hybrids from their genomic locations, studies were performed correlating RNA:DNA hybrid locations with enrichment or depletion for other chromatin and transcriptional regulators directly overlapping the RNA:DNA hybrids. Using IMR-90 bisulphite sequencing data from the Roadmap Epigenomics Project (accession number NA000020923.1), a modest decrease in DNA methylation within RNA:DNA hybrids was found compared with genome-wide levels, a finding which is consistent with the hypomethylation of DNA previously observed for RNA:DNA hybrids at CpG islands   is a protein known to be involved in resolving RNA:DNA hybrids [Finally, characterization of chromatin located at RNA:DNA hybrids was performed to identify the proteins enriched at these loci. Chromatin from HEK 293T cells was sonicated and a fraction immunoprecipitated with the S9.6 antibody, eluting the protein complexes using RNA:DNA hybrid oligonucleotides, and identifying local proteins through mass spectrometry Fig. a. These  hybrids  and is a hybrids , while D hybrids  which sh hybrids , with ouThe presence in local chromatin of RNA helicases and topoisomerases is consistent with prior reports that these enzymes are involved in the removal of RNA:DNA hybrids , 42. Thecis-regulatory loci 1). Additoci Fig. . The mapin yeast .intrans with their genes of origin, but this is not reflected by over-representation of RNA:DNA hybrids in RefSeq genes , and the hybridoma line was grown in Integra Flasks by our institution’s monoclonal antibody core facility in serum-free medium. The S9.6 antibody was then purified by the macromolecular therapeutics core facility using a Protein-G column and size exclusion. The antibody was validated using an electrophoretic mobility shift assay (EMSA) and southwestern blotting to test for specificity to RNA:DNA hybrid oligonucleotides. A full description of these experiments is provided in the Additional file HEK 293T cells were fixed in 4 % paraformaldehyde for 10 min at room temperature, and then permeabilized for 10 min with 0.5 % Triton-X-100. The cells were immunostained with anti-S9.6 antibody and anti-Fibrillarin antibody  for 1 h, washed three times with phosphate buffered saline (PBS), and incubated with Alexa Fluor 488-labeled anti-mouse IgG antibody and Alexa Fluor 568 labeled anti-rabbit IgG antibody (Invitrogen) for 30 min at room temperature. Finally, cells were mounted in mounting solution ProLong Gold with DAPI (Invitrogen).XY control metaphases. The slides were denatured with 50 % formaldehyde/2× SSC at 80 °C for 1.5 min and then dehydrated with serial ethanol washing steps . The probes were denaturated in the hybridization solution (50 % dextran sulfate/2× SSC) at 85 °C for 5 min, applied to the slides, and incubated overnight at 37 °C in a humidified chamber. The slides were then washed 3 times for 5 min with 50 % formamide/2× SSC, 1× SSC and 4× SSC/0.1 % Tween. Slides were dehydrated with serial ethanol washing steps (see above) and mounted with ProLong Gold antifade reagent with DAPI  for imaging. Image acquisition is described in Additional file Fluorescence in situ hybridization (FISH) was performed using our previously published approach . For theThe cell culture conditions for IMR-90 and HEK 293T cells are described in the Additional file Using RDIP and input material, directional RDIP-seq libraries were prepared using elements of a directional RNA-seq protocol modified from a previously published approach . StartinPrior to sequencing, the libraries were analyzed for quality of preparation using an Agilent Bioanalyzer high-sensitivity chip. Libraries were multiplexed and combined for sequencing using Illumina HiSeq 2500 150 bp paired-end sequencing in our institutional Epigenomics Shared Facility. FASTQ files were generated through the Illumina CASAVA pipeline (v1.8). Sequencing reads were then run through the Wasp System (WASP v3.1.5 rev. 6632) hosted pipeline for primary data processing, as follows. The reads were aligned to the hg19 reference genome using Bowtie (v0.12.7), using non-default parameters of –tryhard , -I 50  and -X 650 . Alignments were generated in SAM format, which were then transformed into BAM files using Samtools (version 0.1.8). The aligned sequences in BAM format had PCR duplicates removed, and peaks were called based on input and IP files using MACS v1.4.2 . RDIP-seRDIP-seq peaks were validated through non-denaturing bisulphite conversion. Whole cell nucleic acid was isolated from HEK 293T cells through a modified salting out extraction protocol as outlined in the Additional file http://broadinstitute.github.io/picard/). The second read in the pair, representing the sequence derived from the RNA strand following degradation using UNG of the dUTP-incorporated complementary strand, has the bit flag identifiers of 163 or 147, indicating that it maps to the top or bottom strand of the reference genome, respectively. By measuring the number of RNA reads aligned to the top or bottom reference strand for each peak, we could assign each RDIP-seq peak a “strandedness” value, with +1 being all RNA-derived reads aligned to the top strand and −1 all RNA-derived reads aligned to the bottom strand. We removed the small minority (10 %) of peaks with intermediate values of strandedness to decrease what we presumed to be experimental noise in our data set. Custom code for this analysis can be found on our GitHub resource in the file “Determining RNA Strand and Minus10 files”.Due to using directional sequencing through the incorporation of dUTP, we were able to determine the RNA-derived sequence of the RNA:DNA hybrids. To do this, we used the BAM flag information describing our aligned sequences  was utilized for three of the four RNA samples, leaving a non-Ribo-Zero depleted sample for rRNA expression analysis. RNA-seq libraries were prepared using a directional RNA-seq protocol modified from a prior published approach  and detaAfter sequencing, FASTQ file generation was completed using the Illumina CASAVA pipeline (v1.8). Post-sequencing analysis was performed using the WASP pipeline (v3.1.5 rev. 6632), involving read alignment using gsnap (2012-07-20), with htseq (v0.5.3p3) used to determine read quantitation. Biological replicates were normalized using DESeq (Bioconductor) and RefSeq gene identifiers were assigned using biomaRt. Only gene expression assigned a RefSeq identifier was used for further analysis. Custom code for this analysis can be found on our GitHub resource under the file “RNAseq analysis”.In order to align our RDIP-seq reads to the rDNA repeating unit, we used the alignment approach of Zentner and colleagues . We addeLARS R package, was applied to determine the Lasso path. This algorithm provides the optimal values of the regression coefficients as the constraint on the sum of the coefficients is progressively relaxed [covTest R package, was used to test the significance of each additional covariate when it enters the model.We used LASSO regularized linear regression to explore the relationship between the density of RNA:DNA hybrid peaks in 500 kb windows and genomic features associated with transcription and regulation. LASSO regression fits a linear model subject to a constraint on the sum of the regression coefficients . The LAR relaxed . Tight c relaxed , implemeNative chromatin was isolated using a sucrose gradient from HEK 293T cells. Chromatin was incubated overnight with S9.6 antibody or a non-specific control antibody , following which immunoprecipitation was performed on each sample using immunomagnetic precipitation (Dynabeads M-280 Sheep anti-mouse IgG). RNA:DNA hybrid-binding protein complexes were then eluted using RNA:DNA hybrid oligonucleotides, with DNA:DNA oligonucleotides as a control. The oligonucleotide sequences used in this assay are provided in Additional file https://github.com/GreallyLab/Nadel-et-al.-2015.Analysis of RDIP-seq, RNA-seq, and code for all figures are included and annotated at: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE68953).The data generated are all available through the Gene Expression Omnibus, accession number GSE68953 (