Acropora recruits. Incorporating our results into transition matrix models demonstrated that most corals escape mortality once they exceed 50 mm, but for smaller corals mortality in brooders was double those of spawners . For corals on the reef flat, sensitivity analysis demonstrated that growth and mortality of larger juveniles (21–50 mm) highly influenced population dynamics; whereas the recruitment, growth and mortality of smaller corals (<20 mm) had the highest influence on reef slope population dynamics. Our results provide insight into the population dynamics and recovery trajectories in disparate reef habitats, and highlight the importance of acroporid recruitment in driving rapid increases in coral cover following large-scale perturbation in reef slope environments.Tropical reefs are dynamic ecosystems that host diverse coral assemblages with different life-history strategies. Here, we quantified how juvenile (<50 mm) coral demographics influenced benthic coral structure in reef flat and reef slope habitats on the southern Great Barrier Reef, Australia. Permanent plots and settlement tiles were monitored every six months for three years in each habitat. These environments exhibited profound differences: the reef slope was characterised by 95% less macroalgal cover, and twice the amount of available settlement substrata and rates of coral settlement than the reef flat. Consequently, post-settlement coral survival in the reef slope was substantially higher than that of the reef flat, and resulted in a rapid increase in coral cover from 7 to 31% in 2.5 years. In contrast, coral cover on the reef flat remained low (~10%), whereas macroalgal cover increased from 23 to 45%. A positive stock-recruitment relationship was found in brooding corals in both habitats; however, brooding corals were not directly responsible for the observed changes in coral cover. Rather, the rapid increase on the reef slope resulted from high abundances of broadcast spawning  Follod states . AccordiMany key issues connecting how recruitment influences population maintenance and recovery remain unresolved, including the relationship among new recruits, juveniles, and adult stock; differences in the relative importance of recruitment versus post-recruitment processes in determining population size and structure; whether differences in post-settlement survival alter benthic dynamics; and how life-history strategies influence recovery trajectories , 13, 14.Such unresolved variability among the links between coral recruits, juveniles, and adult benthic community composition can partly be explained by three major factors involving differences in: (1) coral life-history strategies; (2) microhabitat availability and selective larval settlement; and (3) coral post-settlement growth and survival. Corals have two major reproductive modes with contrasting scales of larval development, pelagic duration, and settlement behaviour (reviewed in ). BroadcRecent work has defined ratios between the density of coral recruits with juveniles over a 15 year time period for Caribbean and Pacific reefs . Yet, stThis research was conducted in the Great Barrier Reef Marine Park, Australia, in accordance with permits issued by the Great Barrier Reef Marine Park Authority (31597.1). No protected species were sampled.unpublished data).The study was conducted at Heron reef, southern Great Barrier Reef (GBR). Heron reef has a large tidal amplitude ~3.0m at the largest spring tide. This drives water flow that changes direction every 6 hours, where it is highest on the reef crest and slope, and least on the inner and mid reef flat . AccordiHeron reef is frequently subjected to cyclone driven wave disturbances and winter storms , as wellpersonal observation).Coral cover at the reef flat site consistently declined from 20% in 1964 to ~2% in 1992, due to multiple cyclone impacts  and macrWe quantified benthic community cover, rates of coral recruitment, growth and mortality, coral stock-recruitment relationships, and the population dynamics of coral communities found on the reef flat and reef slope. Recruits were defined as corals ≤10 mm, juveniles as corals ≤50 mm in maximum diameter, and adults as corals >50 mm. While such a classification of coral juveniles is somewhat arbitrary, it is a commonly used size classification throughout the coral literature e.g. , 41–43).–43.41–43Initially, in August 2009, we quantified the broad benthic characteristics  of both habitats. Then, we haphazardly marked permanent plots around at least one juvenile at each site and also attached settlement tiles to the plots, which were monitored to quantify coral recruitment, survival, and growth. Plots and tiles were sampled at approximately six month intervals for 2.5 years, from August 2009 to February 2012, resulting in six sampling times and five sampling periods: three from austral winter to austral summer, and two from austral summer to austral winter. Finally, we used the coral demographic rates to parameterise matrix models to understand the functioning of coral juvenile dynamics in two disparate habitats.2 quadrats, each with a 10 x 10 grid (i.e. 100 cells), were placed every second metre (10 quadrats per transect), covering a total area of 100 m2 in each site. In every cell of a quadrat, the benthic cover was quantified according to whether it was dominated by sand/rock, crustose coralline algae (CCA), epilithic algal matrix , fleshy macroalgae, or live coral.The broad benthic community of the reef flat and reef slope was initially quantified at the beginning of the study. In each habitat, 10 replicate 20 m transects were randomly placed and separated by ~4 m. Within each transect, 10 replicate 1 m2) using CPCe [2, and the depth ranged from ~0.1 m to ~3.0 m . On the reef slope, there were 36 permanent plots marked around a total of 210 individual recruits. The plots were located within 1.6–4.6 m depth at the most extreme low tide, and were spaced in an area covering approximately 400 m2. Similar to other studies from the Indo-Pacific region (e.g. [Porites and Faviidae), branching Pocilloporidae , branching Isopora, and branching Acropora (reef slope only).To periodically quantify benthic community structure the permanent plots in both habitats were photographed every sampling time. Community cover was quantified by classifying the substrate under 100 random points per plot , th, th2) usTo assess whether the benthic community in the permanent plots was representative of the benthic community in each habitat, community cover was compared among permanent plots and transects recorded from the reef flat and reef slope in August 2009. Data were plotted using multidimensional scaling (MDS) and a correlation vector based on Spearman ranking (>0.6) was added to visualise the relationship among the benthic categories and ordination axes. The data were analysed using ANOSIM, based on a Bray-Curtis dissimilarity matrix. Habitat was fixed, with four levels that included reef flat plot, reef flat broad, reef slope plot, and reef slope broad. MDS and ANOSIM were conducted using Primer-E v6 software , and oveChanges in benthic community cover of the permanent plots was compared between habitats  over time  using permutational multivariate analysis of variance (PERMANOVA) based on a Bray-Curtis dissimilarity matrix. Significant main effects were investigated using pair-wise comparisons. Following this, data were averaged within each habitat x time combination to visualise similarities amongst habitats over time using principal coordinate analysis (PCO). A correlation vector based on Spearman ranking (>0.6) was overlaid on the PCO to visualise the relationship among the benthic categories and ordination axes. PCO and PERMANOVA were conducted using Primer-E v6 software  with the2 quadrat (10 per transect), the abundance and taxa of every juvenile coral (i.e. <50 mm) was quantified. Juvenile abundances were compared between habitats with transects as replicates using a t-test with heteroscedastic variance structure in Microsoft Excel. Juvenile coral community structure was compared between habitats with transects as replicates using PERMANOVA with 999 permutations based on a Bray-Curtis dissimilarity matrix. Homogeneity of multivariate dispersion was not met prior to or following data transformation, tested using PERMDISP, therefore a conservative α value of 0.01 was used to avoid a type I error [To characterise juvenile coral community structure, we used the same transects previously described for the characterisation of the broad benthic community (10 replicate x 20 m transects per habitat). Within each 1 m I error . PERMANO I error  with the I error .in situ. At subsequent sampling times, known individuals were located and remeasured, or marked as dead. Coral recruit growth was quantified using linear extension (mm per 6 months) according to six different size classes  for the four dominant coral groups. Any transition that included recruitment or mortality was not included in growth calculations.In August 2009, every known recruit in the permanent plots was mapped, identified, and the maximum diameter measured to the nearest mm P value >0.25. Raw data did not conform to homogeneity and could not be transformed due to negative growth values (i.e. shrinkage), so the α value was set to 0.01. Mixed effects ANOVAs were conducted using Primer-E v6 software [Two mixed effect ANOVAs were conducted for comparisons of coral growth. Firstly, average colony linear extension (mm per 6 months) was compared among taxa  and habitats  with time (5 levels) as a random factor to account for the temporal autocorrelation structure resulting from resampling the same individual over time. Secondly, average colony linear extension (mm per 6 months) was compared among taxa and size classes , again with time included as a random factor to account for temporal autocorrelation. Both analyses were based on Euclidean distance, used 999 permutations of the raw growth data, and significant effects were investigated with pair-wise comparisons. Model simplification occurred by pooling any term that had a negative estimate of components of variation or a software  with thesoftware .in situ. In August of each year, approximately three months before the major spawning event, a tile pair was firmly attached with cable ties to a corner peg of most permanent plots. However, some tile pairs were lost and not all were retrieved six months following deployment (see 2 for each pair). Tile pairs were removed from the permanent plots at every sampling time, transported to the laboratory, held in a large outdoor holding tank with flow-through seawater, and scored using a dissecting microscope. Each new recruit was mapped and the maximum diameter was measured to the nearest 100 μm. Tile pairs with recruits were returned to their original plot and resampled to quantify post-settlement survival. Coral recruitment into the permanent plots was also quantified. The smallest recruits found in the plots were 3 mm on the reef flat and 1 mm on the reef slope.Settlement tiles were used to capture coral settlement at a finer scale due to the difficulty of seeing very small recruits The numbers of corals recruiting onto the settlement tiles and permanent plots were analysed using a generalised linear model (GLM) with Poisson distribution. Quasi-Poisson distribution was applied to the settlement tile data to account for overdispersion . The totPocillopora [Stylophora [Seriatopora [Isopora [Porites [Acropora [Stock-recruitment relationships were investigated by comparing the density of reproductive adults in the permanent plots with the density of recruits in the permanent plots. Colonies were considered reproductive based on size measurements. Brooders were considered reproductive at diameters greater than 70 mm for illopora , 50 mm fylophora , 80 mm fiatopora , and 40 [Isopora . For bro[Porites , 70 mm f[Porites , 55, andAcropora .Recruit density was standardised to area of available settlement substrate in the permanent plot, which included rock, EAM, CCA, and small holes (micro-crevices). Unavailable settlement substrata included sand, dense turf algae, macroalgae, other , and live corals. The data were analysed using linear mixed effects models, with recruit density the dependent variable, adult density the predictor, and habitat a random factor. Brooder data conformed to normality and homogeneity prior to transformation, but the broadcast spawner data needed square root transformation to meet model assumptions. Generalised mixed effect models were conducted using lme4  in R  reef flat and (2) reef slope. The spawner groups combined massive Porites and Faviidae common to the (3) reef flat and (4) reef slope, and a separate group for (5) spawning Acropora unique to the reef slope.Transition matrices were separated into five groups based on life-history traits and habitat. Differences in juvenile growth rates were found among brooders and massives on the reef flat, and brooders, massives and fast growing acroporids on the reef slope , and a recruitment vector (R) incorporates the number of new recruits entering the populations ; cells below the diagonal represent the probability of positive growth; and cells above the diagonal represent the probability of negative growth (shrinkage). The bottom row of the matrices represent the probability of mortality (qns (e.g. ).To investigate the relative importance of life history traits on the population dynamics of juvenile corals, we centred a perturbation analysis on earlier transitions , rather Stock and recruitment in the Results section). For brooders, matrices were modelled as a semi-closed population based on our stock-recruitment data, meaning that 50% of recruits in the first four size classes came from local sexually mature size class (>50 mm), and partial larval supply came from outside our sample population. The exact value of 50% is somewhat arbitrary because of the varying values found in the literature about brooder populations . Ho. HoStockns (e.g. ). MatrixAt the beginning of the study (August 2009), the broad benthic community on the reef flat was characterised by sand (~50%), fleshy macroalgae (~20%), and low coral cover (10%). The reef slope was characterised by high EAM (~70%) and low coral cover (18%). The permanent plots represented the reef-wide benthic composition faithfully , with noCommunity structure varied over time in the permanent plots, but remained distinct between habitats . Both haAcropora, which increased in proportional cover from 46 to 67% of the coral assemblage , with mean abundances 0.6 individuals m-2 (±0.1 SEM) and 3.8 individuals m-2 (±0.4 SEM), respectively (P < 0.001), due to the dominance of massive corals (56%) on the reef flat compared to Acropora (40%) on the reef slope. The proportional abundance of Pocilloporidae juveniles was high in both habitats .Juvenile coral abundance was 6 times higher on the reef slope compared to the reef flat , and Pocilloporidae having the fastest growth (11.2 mm per 6 months). On the reef slope, massive corals also had the slowest growth (3.9 mm per 6 months), Isopora and Pocilloporidae had similar growth rates (8.7 mm per 6 months), and Acropora had the highest growth rates (12.2 mm per 6 months). There were no significant differences in growth rates for each coral group between habitats.Juvenile coral growth rates differed among different taxa within each habitat . The average survival of recruits on settlement tiles was also 2-fold higher on the reef slope (43%) compared to the reef flat  and time clearly did not influence post-settlement survival (P = 0.22).The mean size of corals settled to tiles was 2.6 (± 2.8 SD) mm. Coral settlement to tiles on the reef slope was double that of the reef flat. Mean settlement was 1.2 and 0.5 individuals 100 cmectively . Howeverlat 22%; , yet no -2 per 6 months recruited to the reef flat, whereas 1.0 and 0.8 individuals 100 cm-2 month-6 recruited to the reef slope for taxa common to both habitats and Acropora unique to the reef slope, respectively. A significant habitat x time interaction was detected (P = 0.04), but with no consistent patterns related to time (e.g. post-spawning). The survival of new recruits in the permanent plots (P = 0.004) and time (P = 0.02) on recruit survival in the permanent plots. Mean survival was significantly higher for new Acropora recruits unique to the reef slope (94%) compared to taxa common to both habitats on the reef slope (81%) and reef flat (74%). There were no consistent patterns related to time, apart from significantly lower survival from winter 10-summer 11 compared to summer 11-winter 11.The mean size of corals recruiting to the reef itself  was 15.1 (± 8.6 SD) mm. Coral recruitment was 4-fold higher on the reef slope compared to the reef flat . An avernt plots  was morent plots , and the2 = 0.42; P = 0.03). However, there was no evidence of a stock-recruitment relationship for broadcast spawners .A positive stock-recruitment relationship was found in both habitats for corals that brood their larvae . This stIsopora, Pocilloporidae; Acropora  escaped mortality once they exceeded 50 mm irrespective of life-history and habitat. Nevertheless, when considering the mortality of juveniles (<50 mm), distinct differences were apparent. Mortality in the smaller size classes (<20 mm) of brooders , growth and mortality of the larger size classes from 20–50 mm . In contMassive corals in both habitats had low recruitment, slow growth, and low mortality among all size classes . SensitiAcropora on the reef slope generally had low mortality and high positive growth among all size classes  andn Moorea ), both oP. damicornis in Taiwan). Through the use of demographic models, our study provides unique insight into the dynamics of juvenile coral assemblages and their influence to benthic community structure on Indo-Pacific coral reefs. While we did not explicitly explore site replication within habitats within our study, the frequent sampling periodicity (6 month time periods) and high within habitat quadrat replication  provided a unique insight into the fine scale dynamics of the smallest coral size classes that are often overlooked in previous studies of coral reef population dynamics. Our sensitivity analyses focussed on demographic parameters including recruitment, mortality, and growth of juvenile corals with three distinct life-history strategies in two common reef habitats. In general, the populations of weedy brooder, slow-growing massive, and fast-growing competitive corals showed similar sensitivities to perturbations within each habitat. Assemblages in the reef flat were most sensitive to perturbations on growth and mortality of larger size classes (i.e. 20–50 mm), whereas perturbations on the recruitment, growth and mortality of the smallest size classes (i.e. < 20 mm) were most sensitive on the reef slope. These results suggest that the different stressors that influenced the juvenile assemblages are habitat specific.While many studies have modelled coral population and community dynamics on Caribbean e.g. , 59, 73), 7359, 7Isopora, Pocillopora, Stylophora) with fast growth rates, and the slow growing massive Porites and Faviidae, were common to both habitats, yet these taxa did not increase coral cover. Corals on the reef flat are restricted in growth by physical constraints due to the height of water at low tides [Acropora facilitated the increase in coral cover in the reef slope, and these taxa rapidly increase habitat complexity that supports many reef flora and fauna [Acropora colonies , and large fluctuations in coral cover are common in this exposed reef slope habitat [Three genera of brooders  in both habitats, they hardly altered coral cover in this study. These taxa are highly opportunistic and have rapid generation times, but remain relatively small in size despite their rapid growth [A positive stock-recruitment relationship  is a likely contributing factor to rapid recovery rates . Howeverd growth , 29. It d growth , 10, 11.d growth , 63, 82.d growth , 79. WhiOur study demonstrates how the complexities of recruiting coral assemblages can affect benthic habitat structure and trajectories. Specifically, in the system studied, stock-recruitment partially explained the recovery of brooder coral populations, but these taxa did not affect coral cover. Forces that constrain larval supply, the availability of optimal microhabitats, selective larval settlement, and differences in post-settlement survival all appear to have contributed to divergent recovery trajectories. In particular, recruit identity and life-history appear fundamental in driving the increase in coral cover found in Indo-Pacific reefs. The maintenance and recovery of coral populations may be seriously impaired if the recruitment of new individuals is strongly dependent on the size of the adult stock, and the positive stock-recruitment relationship suggests that the resilience of brooding corals is particularly threatened by both local and regional-scale disturbance. For broadcast spawning corals, however, it is more likely that only regional-scale disturbances would threaten recruitment , 83. UnfS1 FigVector overlay represents correlations >0.6 based on Spearman ranking. Upward facing solid triangles = reef flat permanent plot; upward facing hollow triangles = reef flat broad community; downward facing solid triangles = reef slope permanent plots; downward facing hollow triangles = reef slope broad community. EAM = epilithic algal matrix; CCA = crustose coralline algae; MA = fleshy macroalgae.(EPS)Click here for additional data file.S2 FigTridacna, unknown. Winter = August; summer = February (except summer 2011 = April).EAM = epilithic algal matrix; CCA = crustose coralline algae; Turf = dense turf algae; MA = fleshy macroalgae; Other = soft coral, sponge, (EPS)Click here for additional data file.S1 Table2.The summer sampling time was in February (or April in 2011) each year, approximately 3 months following the major annual spawning event. Each tile pair is 100 cm(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 TableThere were 46 permanent plots on the reef flat and 36 on the reef slope. Monitoring began in August 2009 and continued every 6 months until 2012.(PDF)Click here for additional data file.S4 TableIsopora = Iso; Pocilloporidae = Poc; Acropora = Acr.Monitoring began in August 2009 and continued every 6 months until 2012. Results are based on 999 permuations analysing the raw growth data that did not conform to homogeneity, therefore the α was set at 0.01 to avoid a type I error (Underwood 1997). Only significant post-hoc comparisons are displayed for the Ha x Ta interaction Massive = Mas; (PDF)Click here for additional data file.S5 TableIsopora = Iso; Pocilloporidae = Poc; Acropora = Acr.The recruits were separated into six size classes . Monitoring began in August 2009 and continued every 6 months until 2012. Results are based on 999 permuations analysing the raw growth data that did not conform to homogeneity for any effect, therefore the α was set at 0.01 to avoid a type I error (Underwood 1997). Only significant post-hoc comparisons are displayed for the Si x Ta interaction. Massive = Mas; (PDF)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.


Whole-transcriptome sequencing (‘RNA-Seq’) has been drastically changing the scale and scope of genomic research. In order to fully understand the power and limitations of this technology, the US Food and Drug Administration (FDA) launched the third phase of the MicroArray Quality Control (MAQC-III) project, also known as the SEquencing Quality Control (SEQC) project. Using two well-established human reference RNA samples from the first phase of the MAQC project, three sequencing platforms were tested across more than ten sites with built-in truths including spike-in of external RNA controls (ERCC), titration data and qPCR verification. The SEQC project generated over 30 billion sequence reads representing the largest RNA-Seq data ever generated by a single project on individual RNA samples. This extraordinarily ultradeep transcriptomic data set and the known truths built into the study design provide many opportunities for further research and development to advance the improvement and application of RNA-Seq. As demonstrated in many publications, RNA-Seq enables the discovery of new structural elements of genes such as exons, junctions, untranslated regions, and rare isoforms and thus has expanded our understanding of the transcriptome2–52–52–52–5. It provides increased sensitivity compared to the more mature microarray technology6 and has opened new avenues of research in transcriptome work, such as the study of gene fusions and allele-specific expression, or the discovery of novel alternative transcripts, whereas the measurement noise of RNA-Seq was shown to be a direct consequence of the random sampling process7–97–97–9.The recent advancement of next-generation sequencing (NGS) has generated tremendous opportunities and challenges in the communities of biomedical research, public health genomics and personalized medicine. Among the versatile applications of NGS, whole-transcriptome sequencing (‘RNA-Seq’ or WTS) has enabled quantitative profiling with a large dynamic range10, which tested multi-site and multi-platform agreement in gene-expression microarrays, the FDA coordinated again the third phase of MAQC (MAQC-III) as a large-scale community effort to assess the performance of RNA-Seq, testing different sequencing platforms and analysis pipelines. This project is also known as the SEquencing Quality Control (SEQC) project. A complementary effort that utilized the same samples, but different platforms  and library protocols  was coordinated with the Association of Biomolecular Resource Facilities (ABRF) Next Generation Sequencing Study (ABRF-NGS)11.While new platforms and protocols for RNA-Seq have emerged in recent years, the comparability of results across platforms and laboratories has not been extensively examined. With the widespread adoption of RNA-Seq in biomedical and clinical research, a comprehensive, cross-site and cross-platform analysis of the performance of RNA-Seq is essential. Reproducibility across laboratories, in particular, is a crucial requirement for any new experimental method to be relevant for research and clinical applications, and this can only be tested in an extensive multi-site and multi-platform comparison. Just as in the first phase of the MicroArray Quality Control (MAQC-I) project10. With spike-ins of synthetic RNA from the External RNA Control Consortium (ERCC)12, samples A and B were then mixed to construct samples C and D in known mixing ratios, 3:1 and 1:3, respectively  and B (Human Brain Reference RNA) from the MAQC consortium, which had been studied extensively with microarrays in MAQC-Iectively  and 2. Aectively . As depi13, we provided an in depth analysis of these data and found that RNA-Seq was highly reproducible across sites and platforms, particularly in differential gene-expression analysis. However, performance was clearly dependent on data treatment and analysis, and transcript-level profiling showed larger variation. This indicates ample opportunities offered by this unique data set: algorithms and pipelines with better and more consistent performance may be developed for transcripts assembly and quantification, gene expression quantification, and gene fusion detection. The presented data set can thus serve a key resource in the development and validation of novel RNA-Seq data analysis algorithms to advance the maturity and performance of applications of RNA-Seq. In this Data Descriptor, we provide additional information aimed at helping others reuse these data within their own research, including more detailed methods descriptions.Thus different sequencing platforms were tested using four well-characterized reference RNA sample mixtures with built-in truths to test accuracy, precision, reproducibility, sensitivity and specificity in a detailed analysis of over 30 billion reads on these reference samples . The dat13. The SEQC (MAQC-III) study design is based on the well-characterized MAQC-I RNA samples: the Universal Human Reference RNA  and the Human Brain Reference RNA 10. More details about these two RNA reference samples were shown in 12 spike-in mixes  were added  to give: Sample A—UHRR with ERCC spike-in mix #1 (Sample E), and Sample B–HBRR with ERCC spike-in mix #2 (Sample F), following the manufacturer's protocol. The ERCC mixes contain 4 subgroups of transcripts with different molar concentration ratios defined between the two mixes  before samples were distributed. Illumina HiSeq 2000 data were provided by 7 sites : (1) Australian Genome Research Facility (AGR); (2) Beijing Genomics Institute (BGI)*; (3) Weill Cornell Medical College (CNL)*; (4) City of Hope (COH); (5) Mayo Clinic (MAY)*; (6) Novartis (NVS); and (7) the New York Genome Center (NYG), generating 100+100 nt read-pairs. Life Technologies SOLiD 5500 data were provided by 4 sites: (1) the University of Liverpool (LIV); (2) Northwestern University (NWU)*; (3) the Pennsylvania State University (PSU)*; and (4) SeqWright Inc. (SQW)*, generating 51+36 nt read-pairs, except for Liverpool which applied a protocol variant giving single 76 nt reads.These descriptions on RNA-Seq sequencing sites are expanded from descriptions in the related research manuscript13. In addition, data produced by the non-official sites were incorporated in some analyses, e.g., the analysis of gene detection and junction discovery as a function of read depth13.All official sites created 4 replicate measurements (libraries) of each sample A to D (labeled as 1–4 in Roche 454 GS FLX data were provided by: (1) the Medical Genomes Project (MGP); (2) the New York University Medical Center (NYU); and (3) SeqWright Inc. (SQW). At each site, one replicate of samples A and B was sequenced (two runs). Roche 454 sequencing data were used to assess gene models but not for quantitative evaluation.per replicate, for a total of 2,200 million per site  based on the low-throughput protocol laid out in the Illumina TruSeq RNA Sample Prep Guide. Briefly, 250 ng of each total RNA sample was used for polyA mRNA selection and fragmentation; followed by first and second strand synthesis, end repair, adenylation of 3′ ends, and barcoded adapter ligation. Thus each library was made from an independent polyA mRNA selection. Each library was enriched by 15 cycles of PCR and the size distribution was validated on the Agilent Bioanalyzer using a DNA 1000 kit. The libraries were made from a band between 200–500 bp with a peak at approximately 275 bp. The cDNA libraries  were each normalized to 10 nM and then pooled. The template cDNA was then diluted to a concentration of 10 pM prior to cluster generation. Equal amounts of pooled libraries were then loaded to each lane of 2 flow cells and run on HiSeq 2000 Sequencing Systems (Illumina) for paired-end 200 cycles. The Illumina sites produced on average 110 million read-pairs per site .per replicate, for a total of 980 million per site  based on the low input protocol of the manufacturer's SOLiD Total RNA-Seq Kit Protocol . Due to the sample input requirement of Poly(A)Purist MAG kit , two rounds of polyA selection were performed with 50 μg of total RNA from each type of samples A–D. The yield and quality of the polyA mRNA were assessed using the Agilent 2100 Bioanalyzer. Four replicate libraries were prepared with each starting from 25 ng of polyA mRNA, following these major steps: RNA fragmentation, hybridization and ligation, reverse transcription, purification and size selection, barcoding and amplification, and purification. The yield and size distribution of the barcoded libraries were assessed and then pooled with vendor prepared libraries, followed by EZ bead emulsification at the E120 scale, amplification, and enrichment. The beads were deposited on the flow chip and sequenced 51×36 cycles on a SOLiD 5500XL sequencer. The official SOLiD sites produced on average 50 million read-pairs per site . The Livper site . ECC wasOne replicate library for sample A and B was prepared and sequenced on a Roche 454 GX FLX sequencer  at each site following the manufacturer's protocols. The Roche 454 sites produced on average 1 million reads per replicate, for a total of about 2.1million reads per site .After base calling, adapter trimming, and barcode demultiplexing using the specific sequencer manufacturer's software, sequence data with quality scores were submitted from all sequencing sites to the FDA’s National Center for Toxicological Research (NCTR) that coordinated this project for integrity check, labeling check and reformatting. A naming convention was then applied to the uniformly reformatted data with a compact digital signature (MD5 checksum) computed for each data file. The name of each data file coded the following fields with valid values in parentheses: project (‘SEQC’), platform , site (see the above section on RNA-Seq sequencing sites), RNA sample (‘A’–‘F’), replicate number (‘1’-‘5’), lane/sector number (‘L01’–‘L08’), indexing barcode (six letters of A/C/G/T for ILM or ten letters for LIF), flow-cell/flow-chip/slide ID, read direction defined by the platform , and file type . Applicable fields were concatenated in the above mentioned order with underscores (‘_’) as the field separators. Finally, each data file was compressed individually with gzip and attached with the suffix ‘gz’. Sequence data were then duplicated and distributed to data analysis sites of the SEQC consortium.Gene Expression Omnibus (GEO) accession GSE47774 (Data Citation 1) contains files for the sequence read counts mapped to each of the human and ERCC transcripts. These files are in the following tab-separated format: NCBI RefSeq transcript ID and mapped reads count. Data Citation 1 also provides a link to the corresponding NCBI Sequence Read Archive (SRA) accession that contains the raw sequencing data from all sequencing sites. PrimePCR data and additional microarray data profiling the SEQC samples have also been deposited at GEO (Data Citation 2). All SEQC (MAQC-III) data sets are available through GEO (Data Citation 3). Data sets from the ABRF-NGS study using the same samples are also available through GEO (accession number: GSE46876). Microarray and qPCR data from the MAQC-I study are available through GEO (Data Citation 4).16–1816–1816–18 were used to build the QC pipeline: Bowtie v0.12.7  and five in sample B . Mapping of reads to ERCC transcripts and these sample-specific genes was used to detect any deviation caused by data corruption or mislabeling from the built-in truth of ERCC spike-ins (about 1%) and sample titration. These mapping and quantification tools v0.12.7 , samtool v0.12.7 , and Cuf v0.12.7 . The pooThis multi-site study with replicated measurements at each site allows a comparison of the reproducibility of RNA-Seq between replicates intra-site and inter-site. Analysis results show that RNA-Seq is reproducible within sites, between sites, and across platforms for the detection of known genes and junctions, relative expression levels, and differential expression analysis. Furthermore, the built-in truth  allows a number of assays reflecting both accuracy and precision of relative quantitative measurements: (1) titration order consistency, (2) known sample mixture ratio recovery, and (3) recovery of ERCC transcript mixture ratios. We observed that the majority of genes (59%) correctly titrated, with little disagreement between platforms. And the correct ratio was recovered for the majority of genes, with better agreement at higher expression levels (top 25%). Across platforms, we also observed that with sufficiently high expression levels (log2[conc]>3), the expected ERCC ratios  of 1/2, 19 could be used to analyze these RNA-Seq data. Using a variety of tools13, the SEQC consortium conducted a detailed analysis of this rich data set to test the performance of RNA-Seq. This extraordinarily ultradeep data set provides the deepest molecular characterization of any RNA samples published to date. With the known truths built into the study design, it provides ample opportunities for further research and development. For example, efficient quantitative expression profiling takes advantage of known gene models, and the choice of a reference annotation can considerably affect results, as reflected in performance assessments. Particularly quantitative expression profiling of alternative transcripts forms a promising area for future efforts13. The depth of this data and the included long reads from Roche 454 can further be utilized to enrich and refine gene models and annotation, which are critical for effective quantitative profiling.Many publicly available software packagesHow to cite this article: Xu, J. et al. Cross-platform ultradeep transcriptomic profiling of human reference RNA samples by RNA-Seq. Sci. Data 1:140020 doi: 10.1038/sdata.2014.20 (2014).


Mixed-logit models were used to estimate the relative importance of the five lifestyle program related attributes that were included. Willingness to pay (WTP) values were calculated and it was tested whether results differed between respondents who answered the choice tasks with an opt-out option in the first or second part of the questionnaire.21.4% of the respondents always opted out. Respondents who were given the opt-out option in the first part of the questionnaire as well as lower educated respondents significantly more often opted out. For both the forced and unforced choice model, different attributes showed significant estimates, the relative importance of the attributes was equal. However, due to differences in relative importance weights, the WTP values for the PA schedule differed significantly between both datasets.Results show differences in opting out based on the location of the opt-out option and respondents' educational level; this resulted in small differences between the forced and unforced choice model. Since respondents seem to learn from answering forced choice tasks, a dual response design might result in higher data quality compared to offering a direct opt-out option. Future research should empirically explore how choice sets should be presented to make them as easy and less complex as possible in order to reduce the proportion of respondents that opts-out due to choice task complexity. Moreover, future research should debrief respondents to examine the reasons for choosing the opt-out alternative. There seems to be consensus regarding the inclusion of an opt-out option in Discrete Choice Experiments (DCEs) that aim to determine the potential participation in an elective program as such an option is more in accordance with the respondent's choice options in real life Most DCEs in health economics are rooted in the Random Utility Theory (RUT) However, in practice, other motives than achieving the highest personal utility may be more important when people make their decisions The above is of special interest within the public health setting. Decisions about personal health, public health and prevention are by definition complex and difficult and not part of an individual's everyday life decisions This study included participants diagnosed with type 2 diabetes mellitus (DM2), who were 35–65 years of age and who were not suffering from any serious complications due to their DM2. Participants were contacted via their diabetes care group. Within the Netherlands, diabetes care of all diagnosed DM2 patients is arranged in care groups, which are legal entities formed by multiple health care providers centered around general practitioners Based on previously published literature on barriers and facilitators to participate in a lifestyle program among DM2 patients A menu schedule and a PA schedule are plans that will be developed by the participants in the program together with a lifestyle coach. These plans describe the aims of the participants with respect to improvements in their diet and PA behavior. A flexible schedule is a schedule that is based mostly on the participants own initiatives and ideas. A general schedule is a schedule that includes general information on either a healthy diet or PA and provides example recipes or exercises. An elaborate schedule comprises a tailored schedule that is prepared mostly by the lifestyle coach. Consultation structure describes the composition of the consultations with the coach . These are the consultations during which the participants develop their menu plan and PA schedule, and during which they discuss their progress. The expected outcome is meant to describe the results with respect to weight loss  and physical fitness, which the respondent can expect to achieve after completing the lifestyle program. Finally, as the costs of participating in a lifestyle program are not part of the participant's health insurance in general, the participant will have to pay for (part) of the program out-of-pocket. These costs can amount to either €75, €150 or €225 per year.The scenarios in the DCE are constructed by combining different levels of each included attribute. The experiment comprises an unlabeled (generic) design with respect to the lifestyle program options. NGene 1.1 software  was used to create a D-efficient design for the current study. The software was instructed to create a design with two blocks using a panel-mixed-multinomial model with all beta-priors set at zero. It was assumed that there would be no interaction between attributes, while level balance, utility balance and minimal overlap between attribute levels were optimized Finally, the design consisted of a sample of nine choice tasks per block . Within this design, each choice task contained two lifestyle program scenarios. To compare the possible differences in decision making when respondents are forced to make a choice or are offered an opt-out option (unforced choice option) and to obtain insight into the possible influence of the location of the opt-out option within the questionnaire The questionnaire was pilot tested among a subgroup (n = 20) of the study population to ensure that the wording used in the questionnaire was correct and understood by the target population The questionnaire contained two parts. The first section of the questionnaire consisted of 28 questions about the respondent's demographic/background characteristics and on the patient's opinion with respect to lifestyle programs in general, accompanied by the EuroQol-5D health status questionnaire According to the RUT, perceived utility (U) of a person ‘n’ in choice situation ‘j’ is estimated by the sum of the systematic utility component (V)  and the random error term (ε)   [5], [6]0 represents the alternative specific constant, while β1–β8 are the attribute estimates that indicate their relative importance. All included attributes were tested for linearity, the attributes that appeared not to be linear were effects coded . In contrast to dummy coding, effects coding enables one to compare the estimates of all attributes despite their categorization into non-linear levels, because the effects are uncorrelated with the intercept Based on the data retrieved by a DCE, the systematic utility component (V) of After comparing the model fits  of different models including different (sets of) random parameters, two parameters were set at random for the final analysis: the constant and the attribute expressing the expected outcome of the lifestyle program. In addition, different distributions of the random parameters were tested and based on the model fit results, both random parameters were included with a normal distribution. It was expected that especially the constant and the outcome attribute would show high preference heterogeneity among the respondents, due to large differences in general opinions concerning lifestyle programs and the variation in Body Mass Index (BMI) among the respondents.The attribute estimates for the data that included the opt-out option was retrieved via Choice consistency of the study population was checked Differences in opting out between respondents with a lower  and higher  were determined using independent sample t-tests.Marginal willingness to pay (WTP) values were determined for all statistically significant attribute estimates of the main analysis. These results can be compared directly between the forced-choice DCE and the DCE including the opt-out option. In order to calculate the patient's WTP, the negative of the out-of-pocket attribute was used as a measure of the marginal utility of money. The ratio of either attribute estimate with this negative of the out-of-pocket attribute was calculated to estimate the patient's WTP concerning that specific attribute NLogit 5.0  was used to construct the models that were estimated within this study. Results were considered statistically significant when p<0.05.2 and the mean HbA1c was 49.1 mmol/mol . The majority reported no complications due to their DM2 (75.6%) and the mean EQ-5D score was relatively high with 0.91. There were no clinically relevant differences in these demographic and disease-related variables between the respondents who completed the different versions of the questionnaire.Of all 2,500 distributed questionnaires, a total number of 781 (31.2%) questionnaires were returned and included in the analysis. The demographic and disease-specific characteristics of the study population are summarized in With respect to the raw choice percentages, on average over all choice sets, 54% of the respondents were willing to participate in a lifestyle program when the option to opt-out was offered.Respondents did not have a strong tendency to choose either option A or B. This was expected as A and B were unlabeled and therefore did not have specific characteristics which would make one of them more attractive within every choice tasks for the same reason .The percentages of respondents choosing option A and B both dropped when the opt-out option was included. However, in 13 of the 18 choice tasks the difference in percentage of individuals moving from option A to the opt-out option and the individuals moving from option B to the opt-out option was more than 5%.In total 21.6% of the respondents always chose to opt-out, while 22.8% never chose to opt-out within the nine unforced choice sets. Respondents with a lower educational level significantly more often chose to opt out compared to respondents with a higher educational level . Except for choice task one and two, the frequency of choosing the opt-out option was significantly higher among respondents who first had the option to choose to opt-out and then were forced to make a choice, compared to respondents who first were forced to make a choice and later were able to choose the opt-out option  .With respect to the consistency of the respondents when making their choices, overall 83.4% of the population answered consistently on all but one choice task. In total, 65.2% answered consistently on all choice tasks, while only 0.2% was consistent in six out of nine choice tasks (this was the lowest consistency score). The consistency measure was 93% on average per choice task.Within both the forced and unforced model, respondents did not have any specific preferences with respect to a menu schedule . They reLocation was included as a covariate in the analysis and turned out to be statistically significant (p<0.001), indicating differences in attribute estimates and their relative importance based on the location of the opt-out option . Therefore, location was included as an interaction term with all of the attributes, which resulted in a significant estimate with; out-of-pocket costs, expected outcome, consultation structure and menu schedule. Respondents who answered the choice tasks with opt-out first, showed stronger preferences for out-of-pocket costs and menu schedule and less pronounced preferences for the expected outcome and consultation structure compared to the respondents who first completed the forced-choice tasks.Finally, attribute estimates were analyzed separately for these two groups . Except To obtain more insight into the robustness of the retrieved results, three separate additional analyses were performed. First, respondents who answered inconsistently in more than one choice task (16.6%) were excluded from the analysis. Secondly, respondents were excluded if they indicated (in the first part of the questionnaire) that they would probably not or certainly not participate in a future lifestyle program (53.6%), no matter what attributes this lifestyle program would have. Third, all respondents who chose the opt-out option in all choice tasks were excluded (21.6%). The results of these analyses did not show any notable differences from the analysis described above with respect to the results on the forced choice data and opt-out data (results not shown).WTP values differed between the data where the opt-out was offered first or seconds, but these differences were not statistically significant due to the large confidence intervals around these estimates .Results show differences in opting out based on the location of the opt-out option and the educational level of the respondents. The attribute estimates between the forced-choice and opt-out dataset differed, but no notable differences in the relative order of the attributes (as compared to each other) were present. However, because the importance weights of the attributes did differ between the datasets, there is a statistically significant difference in the WTP of patients for a PA schedule. This difference could lead to different conclusions and recommendations with regard to developing a lifestyle program that is most attractive for the target group.Current findings underline the statements of Dahr and colleagues (2003) that the independence of irrelevant alternatives (IIA) assumption for forced-choice data does not hold in unforced data While the direct results of the DCE might not have differed much between the forced and opt-out data, the analysis of the influence of the location of the opt-out option (either in the first nine choice tasks or the second nine choice tasks), showed clearly that the location of the opt-out option in the questionnaire influences the results of a DCE. This was also shown in previous research Additionally to the effect on the data by simply including such an opt-out option, our study results indicate that choice behavior changes which influences DCE results when respondents are given the opportunity to opt-out. Including an extra choice option automatically implies reduced effectiveness, as there are more answering categories included. Specifically an opt-out option does not provide any insight on attribute level trade-offs. This is not an issue, if the choice to opt-out is due to the low perceived personal utility of the other scenarios. However, our analysis showed that it is likely to assume that a considerable number of respondents chose to opt-out for other reasons than a dislike for lifestyle programs. It can therefore be suggested that including an opt-out option in a DCE, leads to an ‘unnecessary’ loss of effectiveness. This is of special interest in the light of designing DCEs in an efficient manner . Such designs strive to create choice sets with an optimal utility balance between the scenarios of each choice task, by optimizing the variance-covariance matrix (22–24). This designing procedure results in a DCE that requires a minimal number of choice tasks per respondent and a minimal number of respondents per experiment ). At the same time, this may induce complexity of the generated choice tasks. Since there are indications for higher levels of opt-out when choice tasks become more complex, the efficiency of designing DCEs in such a way may be at risk. Future research is necessary to identify subgroups among study populations that are most likely to opt-out due to other reasons than solely personal utility. Moreover, it should be explored how DCEs can be designed in an efficient manner while keeping in mind this phenomenon.The current study has some limitations. Although an efficient DCE design was developed, an even more efficient design could have been created if the beta priors retrieved with the pilot-study were more stable. If more informed beta priors would have been used , the expected preferences of the target population would already be included in the design of the DCE. This way, the design varies the attribute levels based on their relative importance as displayed in the beta priors, resulting in more complex choice tasks. Since choice task complexity is expected to drive the choice to opt-out, using a more efficient design would probably have led to even more individuals that chose to opt-out and thereby more pronounced differences between the results of the model with and without an opt-out option.The response rate was 31.2%. Due to confidentiality agreements with the care groups that distributed the questionnaires, no reminder letters could be distributed and a non-response analysis could not be conducted. Non-response is likely to be selective, in the sense that DM2 patients who are not interested in a lifestyle program were less likely to participate in this study. It is therefore expected that this selective response resulted in an underestimation of the differences between the datasets with and without an opt-out option. Although the conclusions of this paper would probably not change, they might have been more pronounced if respondents with a negative attribute towards lifestyle programs had participated.The current study included DM2 patients in the age category 35–65. There is very limited information on the representativeness of the current study population compared to the target population. Additional analysis of DM2 patients aged 35–65 in a large Dutch Cohort study EPIC-NL  showed tIn general, the choice for including an opt-out option in DCEs, depends evidently on the research objective. When the research objective is to determine the potential participation in a health program, an opt-out option should always be included; if in real life ‘not participating’ is an option as well. By doing so, researchers stay as close as possible to the actual choices of their target population. Introducing an additional loss of power, because respondents do not make any trade-offs and chose to opt-out, should then be accepted. However, the number of respondents that opt-out for other reasons than aiming for the highest personal utility should be minimized. Based on the learning effect that was shown in this study, future DCEs that include an opt-out option may want to incorporate multiple forced choice warm-up exercises. However, since DCE questionnaire are already cognitively demanding and time consuming, a more efficient solution might be to use a dual response design. In such a design, respondents are forced to make a choice and immediately after choosing, respondents are asked if they would like to opt out if given the choice


In the mid-1980s I remember having the (self-appointed) task of running that most user-friendly of molecular biology tools, the denaturing formaldehyde RNA gel, on whole Drosophila embryo extracts (data sadly unpublished). I was struck at the time by the large amount of RNA I detected, and the wide size range . Thirty years on we understand massively more about the RNA complement of the cell, with new RNA components being discovered at frequent intervals.One of the most prominent of these “new” RNA classes is the microRNAs (miRNAs), which play a key role in gene regulation by binding mRNAs and inducing their cleavage, destablilsation through shortening of their poly(A) tail or blockage of translation. The miRNAs are an excellent example of the new, more complex view we now have of gene regulation, which supplants the traditional view of gene regulation acting primarily at the DNA level. With this in mind, it should have come as no surprise to learn, as we have recently, that miRNAs themselves also undergo complex patterns of regulation, some of them mediated by other RNA species.The idea that there may be circular RNAs in cells go back at least as far as the mid-1970s when  does not take into account any uncertainty in the underlying predictions. Future developments of this resource will likely need to use a more sophisticated statistical approach. This is not to suggest that Circ2Traits has no utility, however. As an example of its utility, the authors present evidence that their approach identifies a potentially important relationship between CDR1as, which is posited to bind miR-7, and Parkinson's disease. This underlines the value of this resource as a hypothesis-generating tool.An understandable concern with such a resource, which relies heavily on combining We are at the earliest stages of understanding any role of circRNAs in disease, and in biology more widely. Circ2Traits is likely the first of a number of attempts to bring together evidence on circRNAs that will facilitate experimental analysis of these RNAs.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In the “footsteps illusion”, light and dark squares travel at                    constant speed across black and white stripes. The squares appear to move faster                    and slower as their contrast against the stripes varies. We now demonstrate some                    second-order footsteps illusions, in which all edges are defined by colors or                    textures—even though luminance-based neural motion detectors are blind                    to such edges.                             Consequently, the squares appear to go faster and slower in alternation, like a pair of            walking feet. So it was called the “footsteps illusion.” In Movie #1,            the contrast hugely alters the apparent speed in real time. For instance, the two            squares in each row seem to be alternately closer together and further apart, although            their actual separation is always constant.        Anstis attributed the footsteps illusion to                 Meanwhile, we now report several second-order versions of the footsteps illusion, which            are presented one after another, all within one large movie file. In Movie #1, the            contrasts that determine apparent speed are defined by luminance : moving yellow squares have high contrast against black stripes but low contrast            against white stripes. The opposite is true for blue squares.        In the second-order Movies #2 to #5, the contrasts are defined by different visual            properties, as shown in the thumbnail sketches the lower part of         Movie #2 (contrast-modulated textures): The low-contrast moving squares have high            modulation-contrast against the high-contrast textured stripes but low contrast against            the low-contrast textured stripes.        Movie #3 (color): The magenta squares have low color-contrast against the red stripes but            high color-contrast against the green stripes. The opposite is true for the cyan            squares.        Movie #4 (fine vs. coarse): The finely textured moving squares have high            spatial-frequency contrast against the fine stationary stripes but low contrast against            the coarse stripes.        Movie #5 (discontinuous abutting gratings): The short horizontal lines in the moving            squares have large  offset-contrast when they are strongly misaligned  with horizontals in the stationary stripes.                 SE for 12            observers), with the footstep effect of Movie #1 being rated “10.” The            illusions are weaker but nonetheless present. Yet, the motion detectors first reported            by In all these movies, the squares always move from side to side at constant speed, but            they seem to vary their apparent speeds and separations as they move across the stripes.


CDC continues to work closely with the World Health Organization (WHO) and other partners to better understand the public health risk posed by a novel coronavirus that was first reported to cause human infection in September 2012 . GeneticThree of the confirmed cases of novel coronavirus infection were identified in the United Kingdom (UK) as part of a cluster within one family . The indhttp://www.cdc.gov/coronavirus/ncv). Persons who develop severe acute lower respiratory illness within 10 days after traveling from the Arabian Peninsula or neighboring countriesThis recent cluster provides the first clear evidence of human-to-human transmission of this novel coronavirus, coinfection of this novel coronavirus with another pathogen (influenza A), and a case of mild illness associated with this novel coronavirus infection. In light of these developments, updated guidance has been posted on the CDC coronavirus website , case investigation, and specimen collection and shipment for testing, are available at the CDC coronavirus website. Additional information and potentially frequent updates will be posted on the CDC coronavirus website. State and local health departments with questions should contact the CDC Emergency Operations Center (770-488-7100).


Moreover, an engineered human cell line harboring the NTF2L domain mutation in the NXF1 gene showed a synthetic growth phenotype and severe mRNA export defect under Aly/REF and Thoc5 depleted condition. These data suggest that Tap-p15 recognizes bulk mRNAs through combinatorial use of the distinct RNA binding domains.Metazoan Tap-p15  and yeast Mex67-Mtr2 heterodimers are the general mRNA export receptors. The RNA binding activity of Tap-p15, which is essential for mRNA nuclear export, has been attributed to the amino-terminal RNA binding module of Tap consists of RNA recognition motif (RRM) and leucine-rich repeat. In this study, we identified a novel RNA interaction surface in the NTF2-like (NTF2L) domain of Tap, which is analogous to the rRNA binding platform of Mex67-Mtr2. Tap-p15 uses the three domains to tightly bind the retroviral constitutive transport element. The RNA binding through the NTF2L domain is functionally relevant as introduction of mutations in this region reduced CTE-containing mRNA export activity. In contrast, only when the RRM and NTF2L domains were mutated simultaneously, bulk poly (A) Soluble macromolecules, such as RNAs and proteins, are transported through the nuclear pore complexes (NPCs), huge protein assemblies that span the nuclear envelope. Transport receptors specifically recognize the nuclear import and export signals of cognate cargoes and facilitate their translocation through NPCs using interactions with phenylalanine-glycine (FG-) repeats of nucleoporins, which form a meshwork inside NPCs ,2. ImporThe importin/karyopherin-β family members Exportin-5 and exportin-t transport microRNAs and tRNAs from the nucleus to the cytoplasm, respectively ,9. Expormex67/mtr2 double knockout, indicating that the mechanism of mRNA export is evolutionarily conserved . Bo. Bomex67onserved –19. As dactivity . Howeveractivity ,22, and,activity . A yeastAs a compensation mechanism for the mostly non-specific nature of the intrinsic RNA binding activities, Tap-p15 and Mex67-Mtr2 interact with a series of adaptor proteins, which are recruited to mRNAs during transcription and processing ,13,23,24cis-acting RNA sequence called constitutive transport element (CTE) of the D-type retrovirus, which forms a 2-fold symmetrical stem-loop structure, recruits Tap-p15 to facilitate nuclear export of unspliced viral mRNA Escherichia coli expression vectors have been reported previously (128K>EE mutations in the RRM (R) expression vector was constructed by introducing silent mutations to the siRNA target site (TCT ATC ATC ATC > agc ATt ATa ATt: lower case letters indicate mutations) of the pEGFP-Tap vector using the QuikChange kit. All the mutations were verified by DNA sequencing. A mammalian expression vector of p15-FLAG has been described previously  were cloned to the pNT1.1 vector (A cDNA fragment encoding Tap (96–551) was amplified by polymerase chain reaction (PCR) and cloned to the BamHI-XhoI site of the pGEX6P3 vector. All the other eviously ,29. A cD the RRM  were inteviously . A CTE cescribed . A plasm1 vector  to obtai1 vector . An anneE. coli BL21(DE3) codon plus strain and purified by glutathione-Sepharose gel chromatography  as described previously (GST and GST-Tap fusion proteins were expressed in the eviously . UntaggeTAATACGACTCACTATAGGGTCACTAACCTAAGACAGGAGGG-3′ and 5′-TCACTTTTACCCGTCTTTGGATTAGGC-3′ (underline indicates the T7 promotor sequence). A template for an RNA probe encoding the polylinker sequence was prepared by linearizing the pBluescript (pBS) vector by NotI. Run-off transcription was performed in the presence of [32P]-UTP using Riboprobe Combination system according to the manufacturer's protocol (Promega). The radio-labeled RNA probes were purified by gel electrophoresis. RNA gel shift assays were performed as previously described  were cultured in Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum. Transfection of plasmids and siRNAs was done as previously described . Three independent transfections were done for each combination. Significance of difference was tested by Student's Cell growth was examined by luminescent-based cell viability assay using CellTiter-Glo assay kit according to the manufacturer's protocol (Promega).The Cas9 and guide RNA expression vector pX330-Tap6 and the two linearized donor vectors pNT1.1-m8 and pNT1.1-Puro-m8 were simultaneously transfected to 293 cells. Selection for G418- and puromycin-resistance was started at 48 h after transfection. Genomic DNA was obtained from individual colonies and successful homologous recombination was confirmed by PCR. Total RNA obtained from the wild-type and m8 clone 8 cells was subjected to RT-PCR. Successful introduction of m8 mutation was further confirmed by directly sequencing the RT-PCR product.The sequences of siRNAs against dsRed, Thoc5, Aly/REF and Tap have been described previously .UV-crosslinking experiments were performed as previously described ,32. OligIn situ hybridization using Cy3-labeled oligo-dT50 probe was performed as previously described  Mex67-Mtr2 + RNAs was blocked (R) was resistant to the siRNA treatment + RNAs  and with double mutations in both sites (i.e. m8+ R128K>EE) were expressed in similar levels + RNA export block, the double mutant m8+ R128K>EE did not do so efficiently + RNA fraction as efficiently as the wild-type protein  blocked . As expet Figure . Overexps Figure . GFP-Taps Figure . Althougy Figure . Previouslinking ,32. Therd Figure , lane 8.+ RNA export, we established a 293F cell line expressing the NTF2L domain mutant. A fragment of the Tap cDNA containing the m8 mutation was introduced into the NXF1 allele by CRISPR/Cas9-mediated genome engineering + RNA export defect under Aly/REF-depleted condition + RNA was less pronounced than Aly/REF depletion ineering  + RNA export nor bind to poly (A)+ RNAs in vivo. In addition, a 293F cell line expressing the NTF2L domain mutant of Tap showed a synthetic growth phenotype and poly (A)+ RNA export defect on Aly/REF and Thoc5 depletion. Moreover, double knock down of Thoc5 and Aly/REF in wild-type 293F cells induced nuclear accumulation of poly (A)+ RNA as previously reported . Intriguingly, the formation of the platform also allows alignment of the FG-repeat binding sites on the other side of the complex. Moreover, introduction of mutations to the residues critical for the dimer formation reduced the CTE binding activity of Tap-p15, suggesting a connection between the CTE binding and formation of the dimer. Therefore, the specialized dimeric platform configuration formed on CTE may promote the FG-repeat binding and accelerate the NPC translocation of CTE containing mRNA. The CTE export activities of the NTF2L domain mutants varied in the range from nearly 0 (m3 and m8) and 50% (m7) to 70% (m1 and m9) of the wild-type Tap-p15 activity (Figure in vitro assay conditions. Although speculative, another plausible explanation for the observation could be that the mutations in the different sites of the NTF2L domain might have distinct effects on the formation of the dimeric platform that occurs upon binding to CTE and thus affect the CTE export activities in variable extent.Retroviral CTE is thought to directly recruit Tap-p15. CTE contains internal loops arranged in 2-fold symmetry. Each symmetrical unit with a conserved AAGACA sequence in the internal loop accommodates one molecule of the Tap-p15 heterodimer ,47. The y Figure . These mSupplementary Data are available at NAR Online.


Disappearance of the Barr body is considered a hallmark of cancer, although whether this corresponds to genetic loss or to epigenetic instability and transcriptional reactivation is unclear. Here we show that breast tumors and cell lines frequently display major epigenetic instability of the inactive X chromosome, with highly abnormal 3D nuclear organization and global perturbations of heterochromatin, including gain of euchromatic marks and aberrant distributions of repressive marks such as H3K27me3 and promoter DNA methylation. Genome-wide profiling of chromatin and transcription reveal modified epigenomic landscapes in cancer cells and a significant degree of aberrant gene activity from the inactive X chromosome, including several genes involved in cancer promotion. We demonstrate that many of these genes are aberrantly reactivated in primary breast tumors, and we further demonstrate that epigenetic instability of the inactive X can lead to perturbed dosage of X-linked factors. Taken together, our study provides the first integrated analysis of the inactive X chromosome in the context of breast cancer and establishes that epigenetic erosion of the inactive X can lead to the disappearance of the Barr body in breast cancer cells. This work offers new insights and opens up the possibility of exploiting the inactive X chromosome as an epigenetic biomarker at the molecular and cytological levels in cancer. There is increasing evidence that epigenetic modifications, such as changes in DNA methylation, chromatin structure, noncoding RNAs, and nuclear organization, accompany tumorigenesis . Even tuBRCA1, a major hereditary factor predisposing to breast and ovarian cancer development and a key player in the maintenance of genome integrity  subtype, a high-grade, genetically unstable, invasive ductal carcinoma. Indeed, when the genetic status of the X chromosome was explored in BLCs  and HER2 (encoded by ERBB2) amplified molecular subtypes of invasive ductal carcinoma are more genetically stable and show less frequent loss of the inactive X chromosome (The inactive X chromosome (Xi), also known as the Barr body, provides an outstanding example of an epigenetic nuclear landmark that is disrupted in cancer. The disappearance of the Barr body in breast tumors was noted many decades ago . To datetability . Howevertability . BRCA1-d in BLCs , geneticromosome . HoweverXist (X-inactive-specific-transcript), which becomes up-regulated on one of the two X chromosomes, coating it in cis and inducing gene silencing. Xist RNA accumulation on the future inactive X rapidly creates a silent nuclear compartment that is depleted of RNA Polymerase II (RNA Pol II), transcription factors, and transcription (as detected by Cot-1 RNA). X-linked genes become repressed during the early stages of XCI , and the loss of di- and trimethylation at histone 3 lysine 4 (H3K4me2/3)  ensures dosage compensation for X-linked gene products between XX females and XY males . It is as of XCI . Xist RNK4me2/3) . Promotehylation . The Xi Xist RNA . FurtherXist RNA . In somachanisms . Howeverchanisms . In cancchanisms . HoweverAMER1 , FOXP3 , PHF8, HDAC8 (KDM6A), but most are normally stably repressed on the inactive X. In the cases of AMER1 and FOXP3, tumorigenesis has been linked to clonal expansion of cells in which the wild-type copy is on the inactive X in female patients heterozygous for a mutation  genes ; tumor s), FOXP3 ; chromat8, HDAC8 . A few omutation .Xist was reported to lead to hematological dysplasia and leukemia in mice; however, the allele-specific transcriptional activity of the inactive X chromosome and its heterochromatin structure were not examined . WI-38 (embryonic lung fibroblasts) and Human Mammary Epithelial Cells (HMECs) were analyzed in parallel as nonmalignant  female primary cells. Using RNA FISH, we found that ZR-75-1 and MDA-MB-436 cell lines possess one XIST RNA domain, whereas SK-BR-3 cells have two domains. X-chromosome paint DNA FISH combined with XIST RNA FISH, and 3D microscopy revealed that XIST RNA signals overlapped to a great extent with the X chromosome DNA in both normal and tumor cell lines. However, punctate XIST RNA signals beyond the X-chromosome territory could be detected in the tumor cell lines, particularly in ZR-75-1 and MDA-MB-436 . Importantly, all of the tumor cell lines revealed a markedly weaker DNA enrichment of the Barr body .To evaluate the status of the inactive X chromosome in different types of breast cancer, we selected three cell lines that represent the main breast cancer molecular subtypes: ZR-75-1 , SK-BR-3 (HER2+), and MDA-MB-436 . SK-BR-3 possesses four X-chromosome fragments, each with an XIC locus, but only two are associated with XIST RNA. MDA-MB-436 displayed the most complex situation, with six X-chromosome fragments visible by DNA FISH on metaphase spreads, but with only two XIC loci and one XIST RNA domain . Thus, all three tumor cell lines contain at least one fragment of an Xi chromosome.Given the complex genomes of breast cancer cells, we investigated the precise genetic constitution of the active and inactive X chromosomes using single nucleotide polymorphism array (Human SNP Array 6.0) analysis and DNA FISH  .We next investigated whether heterochromatic hallmarks of the Xi were preserved. Detection of H3K27me3 by IF combined with ll lines A. In HMEpy (SIM) C. IndeedXIST RNA FISH, we found that H3K9 and H4 acetylation were present within the XIST RNA domain in tumor cells in contrast to normal cells . SIM of RNA Pol II also revealed substantial intermingled overlap with XIST RNA domains . Thus, there is a major disruption of chromatin hallmarks over the XIST RNA-coated chromosome, most strikingly in ZR-75-1 and MDA-MB-436 cell lines. We confirmed that XIST is always expressed from only one allele, excluding the possibility of aberrant XIST expression and coating of the Xa instead of the Xi . In summary, the heterochromatic structure of the Xi is disrupted in the three tumor cell lines to variable extents. The variability in Xi perturbation between cells was not found to be linked to a specific stage of the cell cycle . Furthermore the global levels of histone modifications in the different cell lines did not correlate with the aberrant chromatin status of the Xi .Depletion of euchromatic histone modifications is another hallmark of the Xi. Using IF combined with r nuclei F, whereaXIST-coated) fragment is enriched for H3K27me3   . Due to r KDM6A) , confirmr KDM6A)  to definr KDM6A) C. With tr KDM6A) D. We valMAGEA6), we used RNA FISH to show that this aberrant expression usually originated from the active rather than the inactive X in tumor cells .The preceding allele-specific analysis could not identify genes that are fully silenced in somatic cells and reactivated from only one allele in cancer cells, such as members of the C/T antigen family that show aberrant expression in cancer cells . By asseHDAC8 is expressed from the XIST RNA-associated Xi chromosome only in MDA-MB-436 and SK-BR-3 cells , whereas TBL1X was expressed from Xi only in ZR-75-1 (APOOL and SYTL4 are only escaping from XCI in MDA-MB-436 by RNA FISH (data not shown). ATRX was used as a control gene that is subject to XCI in all five cell lines .In order to assess allelic expression of specific genes at the single-cell level, we developed RNA FISH probes for several X-linked escapee genes, bypassing the issue of uninformative SNPs. We confirmed that  ZR-75-1 A. We alsTBL1X, one of the “cancer-specific” escapees in ZR-75-1 cells. Using IF against TBL1X combined with RNA FISH, we correlated the protein levels of TBL1X to its expression from the Xi  in this cell line. To determine whether the higher protein levels in ZR-75-1 are due to reactivation of TBL1X on the Xi or to overexpression of the active alleles on the Xa (as in MDA-MB-436), we quantitated the IF signal in cells that do, or do not, show TBL1X transcriptional reactivation on the Xi . However, we noted that some genes subject to XCI  in cancer cell lines, nevertheless showed low promoter methylation . This suggests that they might be more prone to reactivation in a cancer context, with outright reexpression from the Xi in only some cell lines.To investigate further the underlying causes of Xi gene reactivation in cancer cells, we investigated the chromatin status of “cancer-specific” escapees at the molecular level. First, the DNA methylation status of multiple X-linked gene promoters was investigated using EpiTYPER analysis (Sequenom) . All escapees  showed low levels of DNA methylation at their promoters  on normal and cancer cell lines to assess Xi chromatin status. We investigated H3K27me3 (associated with the inactive state of the Xi), H3K4me3 , and RNA Pol II. The comparison of quantile-normalized H3K27me3 profiles revealed major changes for the X chromosomes between normal and tumor cells A. In HMECFP, FLNA, and MOSPD1 in MDA-MB-436 cells displayed no obvious differences in TSS profiles) . We also noted that “cancer-specific” escapees, such as HDAC8 or NXT2, exhibit additional and/or enlarged H3K4me3 sites in tumor cells when compared to HMEC .Next, we assessed patterns of H3K4me3 and RNA Pol II around the TSS of X-linked genes, and noted that the escapees identified in each cell line displayed a generally higher enrichment of RNA Pol II and H3K4me3 than X-linked genes that were expressed only from the Xa . Similarly, “cancer-specific” escapees generally exhibited higher enrichment at their TSS in the cell lines where they escaped compared to HMECs (KDM5C and SMC1A) or are silenced on the Xi  and found them to display the expected low and high enrichments, respectively , no obvious systematic correlation between local H3K27me3 levels and escape/silencing could be seen , whereas escapees SMC1A and DDX3X and several “cancer-specific” escapees displayed biallelic H3K4me3 and RNA Pol II, with monoallelic H3K27me3 . Thus, for informative escapees in the three cancer cell lines, H3K27me3 is observed on one allele, whereas both alleles show signs of active transcription (H3K4me3 and RNA Pol II occupancy).Finally, we monitored allele-specific enrichment of H3K4me3, RNA Pol II peaks, and H3K27me3 enrichment across genes with informative SNPs. XIST RNA accumulation  . Taken together, these results demonstrate that the Xi shows significant chromosome disorganization and chromatin disruption in primary breast tumors, similarly to the tumor cell lines described above and that suggesting that disappearance of the Barr body in certain breast cancers is indeed due to epigenetic instability.We next assessed whether epigenetic disruption of the Xi also occurs in primary breast tumors. Due to the cellular heterogeneity in such samples, as well as the variable presence of normal stromal cells, we focused on single-cell techniques (IF and RNA FISH) to investigate the Xi. We analyzed seven tumors using a tumor stamp technique with fresh samples (see Methods) to evaluate the degree of enrichment of H3K27me3 at sites of XIST together with HDAC8, ATRX, MAGEA6, and TBL1X expression on fresh tumor stamps  or tumor-tissue cryosections. These genes were chosen because (1) they are robustly detected by RNA FISH; (2) they are “cancer-specific” escapees in some tumor cell lines (except ATRX and MAGEA6); and (3) HDAC8 and ATRX lie in proximity to each other and to XIST (within a few megabases), thus minimizing their chances of being separated by translocations and facilitating RNA FISH analysis in tumors. We analyzed 41 primary breast tumors with corresponding normal tissue for 15 of them . For X-linked genes, aberrant reactivation from the Xi was considered to occur if ≥5% of nuclei harbored a nascent RNA FISH signal at or within a XIST RNA domain of a given sample. With these criteria, we found 28%, 20%, and 29% of tumors displayed aberrant HDAC8, ATRX, and TBL1X expression from the inactive X, respectively . We identified no obvious correlation between the degree of “cancer-specific” escape from XCI and the BRCAness of the tumor .XIST RNA coating was often found to be highly dispersed, with variable H3K27me3 enrichment, and a marked absence of an RNA Pol II-depleted nuclear compartment. Based on ChIP-seq, abnormal presence of both RNA Pol II and H3K4me3 was observed at “cancer-specific” escapees, reminiscent of the chromatin organization of the normally escapees from XCI in noncancer cells .Epigenetic instability of the Xi appears to occur across a broad spectrum of breast cancer types with no obvious specificities for particular molecular subclasses. For example, elevated genetic instability, such as in t tumors  cannot eic cells , as wellHDAC8, which is implicated in cellular transformation , pointing to differences in the stability of silent genes on the active versus the inactive X chromosomes in cancer.Importantly, in addition to the aberrant reactivation of genes on the inactive X, aberrant silencing of several genes that normally escape XCI, such as  cancers . Aberran cancers . FinallyXIST RNA . This is presumably because other marks, such as hypoacetylation of H4, hypomethylation of H3K4, and promoter DNA methylation, are not fully perturbed and can propagate the inactive state. Thus, although XIST RNA and PRC2-associated chromatin changes may participate in maintaining the inactive state, they do not appear to be essential in the context of this particular cell line. We also made the intriguing observation that in X:autosome translocations involving an Xi fragment still carrying an XIC and expressing XIST RNA, H3K27me3 enrichment could be found to spread into the autosomal sequences adjacent to the XIC (for example in the SK-BR-3 and MDA-MB-436 cell lines)  to the Xi via XIST RNA . Howeverl lines) C. Althoul lines)  could clIn conclusion, the perturbed transcriptional and chromatin status of the inactive X chromosome that we have identified in the context of breast cancer opens up several important clinical perspectives. Today, there is still no rapid and efficient way to evaluate the epigenetic instability of tumor cells in a clinical context . In theoXIST RNA FISH, a combination of two probes covering 16 kb of XIST mRNA was used (HDAC8 (RP11-1021B19), TBL1X (RP11-451G24), ATRX (RP11-42M11), HUWE1 (RP11-155O24), and KDM5C (RP11-258C19). The correct chromosomal location of BACs was first verified using DNA FISH on metaphase spreads. A FISH probe for MAGEA6 was generated by cloning the genomic sequence in pCR-XL-TOPO vector. Human Cot-1 DNA (Invitrogen) was used for Cot-1 RNA FISH. Probes were labeled by nick translation (Vysis) with Spectrum Red-dUTP, Spectrum Green-dUTP, or Cy5-dUTP following the manufacturer's instructions. RNA and DNA FISH were performed as described previously  was performed using a DeltaVision OMX microscope .http://bioinfo-out.curie.fr/projects/snp_gap/ and have been previously reported . Human SNP Array 6.0 data were mined using the previously described and validated GAP method . Segmentreported . For morPreparation of samples and analysis of nascent RNA were performed as described previously . BrieflyA hematein-eosin-safran (HES)–stained tissue section was made in each primary tumor to evaluate tumor cellularity and diagnosis. Characterization of the tumor samples was completed by the determination of estrogen receptor, progesterone receptor, ERBB2, cytokeratin 5/6, and epidermal growth factor receptor (EGFR) status determined by immunohistochemistry done according to previously published protocols . All expFor details on experimental procedures used for cell culture, DNA methylation analysis, Sanger sequencing, real-time PCR, allele-specific PCR, pyro-sequencing, RNA sequencing analysis, and chromatin immunoprecipitation analysis, see Supplemental Methods.http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE62907.All high-throughput data from this study have been submitted to the NCBI Gene Expression Omnibus (GEO;