Escherichia coli RidA is a member of a structurally conserved, yet functionally highly diverse protein family involved in translation inhibition (human), Hsp90-like chaperone activity (fruit fly) and enamine/imine deamination . Here, we show that E. coli RidA modified with HOCl acts as a highly effective chaperone. Although activation of RidA is reversed by treatment with DTT, ascorbic acid, the thioredoxin system and glutathione, it is independent of cysteine modification. Instead, treatment with HOCl or chloramines decreases the amino group content of RidA by reversibly N-chlorinating positively charged residues. N-chlorination increases hydrophobicity of RidA and promotes binding to a wide spectrum of unfolded cytosolic proteins. Deletion of ridA results in an HOCl-sensitive phenotype. HOCl-mediated N-chlorination thus is a cysteine-independent post-translational modification that reversibly turns RidA into an effective chaperone holdase, which plays a crucial role in the protection of cytosolic proteins during oxidative stress. Hypochlorous acid generated by neutrophils acts as a potent antibacterial agent. Müller et al. now show that this oxidant directly activates a protective counter-response in E. coli by N-chlorinating the protein RidA and converting it into an effective protein chaperone. Myeloperoxidase converts this H2O2 and chloride ions into HOCl, which together with the other products of the respiratory burst, is highly effective in killing microorganisms.Hypochlorous acid (HOCl) is not only a very potent disinfectant, but functions as a physiological oxidant released by neutrophils to fight infections  and N-chlorotaurine accumulate there as well4As HOCl does not only target microbial proteins, but readily halogenates organic and inorganic primary amines present in the phagosome, chloramines such as monochloramine , activation of calpain protease (cow), adenine-mediated repression of purine biosynthesis (Bacillus subtilis) and mitochondrial maintenance (Saccharomyces cerevisiae)171819Salmonella enterica homologue RidA has recently been described as an enamine/imine deaminase, playing a crucial role in the synthesis of branched-chain amino acidsIn this study, we describe a novel mechanism, which confers protection of E. coli RidA acts additionally as redox-regulated chaperone that is activated on exposure to HOCl stress. Although many proteins are inactivated on reaction with HOCl and precipitate, RidA turns into a potent chaperone, preventing highly abundant cellular proteins from HOCl-induced aggregation. Surprisingly, unlike in the other known redox-regulated chaperone Hsp33, where activation involves reversible disulfide bond formationN-chlorination, a modification that has previously been thought to be deleterious to protein function. We also observe activation of chaperone function via N-chlorination in the Drosophila melanogaster RidA homologue DUK114, as well as in a few unrelated proteins, indicating that this is a novel post-translational modification that induces affinity for aggregation-sensitive folding intermediates. In addition to reduction by glutathione, thioredoxin is capable of reducing N-chlorinated RidA, a hitherto unknown function of this well-characterized thiol-disulfide oxidoreductase.Here, we show that Salmonella enterica RidA has previously been reported to act as deaminase of reactive enamine/imine intermediates, stimulating IlvA-mediated degradation of threonine to 2-ketobutyrateHOCl), potentially through a tight direct protein–protein interaction of the two proteins. We, therefore, hypothesized that RidA, once treated with HOCl, might be a chaperone that effectively binds other proteins.HOCl for chaperone activity, we performed aggregation assays using chemically denatured citrate synthase and untreated and HOCl-treated RidA. RidA was incubated with a 10-fold molar excess of HOCl, a concentration that is sufficient to fully activate the well-described HOCl-activated chaperone Hsp33  is also capable of activating the chaperone function of RidA. Indeed, monochloramine-treated RidA (RidAMCA) also prevented citrate synthase aggregation . Indeed, DTT-reduced RidAHOCl and RidAMCA were no longer able to prevent citrate synthase aggregation  and thioredoxin reductase (TrxB).In bacteria, the reductive redox state of cytosolic proteins is maintained by thioredoxins and glutaredoxins28E. coli TrxA is involved in the inactivation of RidA, we reconstituted the complete thioredoxin system comprising TrxA, TrxB and NADPH in vitro. Activity of TrxA was monitored by the consumption of NADPH (measured at 340 nm). In the absence of RidAHOCl, we did not observe any significant decrease in absorption  blocked its holdase function.To test whether sorption . In contn assays . The ovetathione . While oOn the basis of our previous observation that RidA is oxidized at its single cysteine residue (C107) upon peroxynitrite treatmentN-chlorination to mono- and dichloramines. To analyse whether HOCl-mediated activation of RidA involves the chlorination of side chain amines, RidAHOCl was treated with ascorbic acid. In contrast to DTT, ascorbic acid does not significantly reduce cysteine oxidation products but readily reacts with chloramines allowing the discrimination of these modifications30HOCl with ascorbic acid caused a complete loss of chaperone activity supporting the idea that RidA is a chaperone activated by N-chlorination.Apart from the oxidation of cysteine thiols, HOCl is also known to react with a number of other amino acid side chains, including methionine, histidine, lysine, arginine, tryptophan and tyrosineN-chlorination of lysine and arginine side chains inevitably decreases the total amount of detectable amino groups. RidA possesses 14 potential N-chlorination targets: five arginine residues, eight lysine residues and one terminal amino group. We monitored the accessibility of these potential targets upon HOCl or monochloramine treatment using fluorescamineHOCl , uracil phosphoribosyltransferase Upp and iron sulfur cluster assembly scaffold protein IscUN-chlorinated RidA remains subject to further studies.Substrate proteins were identified by mass spectrometry after tryptic digest . A totalE. coli belongs to the YjgF/YER057c/UK114 family of proteins with members in all domains of life. Although highly conserved, different functions have been assigned to members of this family in different species. In Enterobacteriaceae, RidA is an enamine/imine deaminaseE. coliin vitro. To our surprise, HOCl treatment of RidA did not result in its inactivation but instead RidA bound tightly to IlvA, leading to the inhibition of IlvA’s dehydratase activity.RidA from N-chlorination most likely occurs at the ε-amino groups of lysine and/or the guanidinium groups of arginine as well as the protein amino (N) terminus. The formation of chloramines has previously been regarded as a damaging amino acid modification, which could result in protein inactivation, unfolding, aggregation and even fragmentation due to halogen transfer and formation of cross-links336373839In the present study, we show that this inhibition is the result of a chaperone function of HOCl-activated RidA. Activation of RidA’s chaperone function by HOCl occurs rapidly within sample mixing time, similar to previous reports on Hsp33, another bleach-activated chaperoneIn vivo, the lack of RidA makes E. coli more susceptible to chlorine stress. A knockout mutant in a BL21 strain background shows a significant growth deficiency in the presence of 2 mM HOCl. Expression of RidA from a plasmid could partially restore this phenotype. A full rescue is probably not achieved due to gene dosage effects and an overall increased stress level due to protein overexpression. In this specific assay, the BL21-ΔhslO strain (Hsp33 deficient) behaved wild-type-like and was less sensitive towards HOCl as compared with the BL21-ΔridA mutant. This was surprising, as a ΔhslO strain has been shown before to be hypersensitive towards HOClE. coli MC4100. Various attempts to construct a ΔridA strain in the MC4100 strain, as well as in a ΔhslO background, (ΔhslO–ΔridA double mutant) failed, indicating that the lack of ridA in these strains might be lethal.HOCl substrate proteins from E. coli cell lysates after HOCl treatment revealed that highly abundant cytosolic proteins, such as ribosomal subunit proteins, are bound by RidAHOCl. A pathway analysis assigned the majority of these proteins to the primary metabolism. This finding, although performed in cell lysates, points towards an unspecific binding mechanism of N-chlorinated RidA in vivo. The identification of numerous cysteine-free proteins, such as the trigger factor, demonstrates that binding of N-chlorinated RidA does not depend on cysteine disulfide bond formation.Purification of RidAin vivo mechanism of inactivation of RidA’s chaperone function, we found that catalytic quantities of a reconstituted E. coli thioredoxin system could inactivate RidAHOCl NADPH-dependently in vitro. Rates observed in our assay were 10- to 20-fold lower than rates observed in a classic thioredoxin activity assay, which uses insulin as substrate but substantially higher than rates obtained with oxidized glutathione as substrate+ and Cl− will be released. The disulfide bond formed in this reaction may then become reduced NADPH-dependently by thioredoxin reductase TrxB. To our knowledge, the reduction of chloramines by thioredoxin has not been reported to date and is a new function of this ubiquitous reducing system. However, we also observed glutathione-based inactivation of RidAHOCl. As glutathione concentrations are in the millimolar range in the bacterial cytoplasmin vivo.Looking for an Drosophila melanogaster RidA homologue DUK114. This protein has previously been shown to act as a chaperone, albeit no prior HOCl treatment was reportedE. coli, could not inhibit citrate synthase aggregation  showed HOCl-dependent holdase activity . ChaperoN-chlorination by HOCl has been investigated in more detail for various proteins including low-density lipoprotein, lysozyme and insulin3847N-chlorination differed significantly depending on parameters such as surface accessibility, presence of thiol-containing amino acids, the presence of histidine, and the overall tertiary structure of the proteinN-chlorination of lysine residues and is irreversible. Moreover, HOCl-modified human serum albumin was shown to bind and inhibit proteins from West Nile virus and HIV50Other proteins such as RNaseA and porcine pancreas lipase did not respond to HOCl treatment , while lWe compared the predicted physicochemical properties of these proteins  to identify possible characteristics that are favourable for turning a protein into a HOCl-mediated holdase . HoweverN-chlorinated RidA may protect cells during host invasion from the deleterious effect of the strong physiological oxidant hypochlorous acid.In bacteria, together with other proteins, such as Hsp33, which is activated by disulfide bond formation and the novel HOCl-responsive transcription factors HypT, which is activated by methionine oxidation1214N-chlorination of proteins constitutes a novel activity-modulating post-translational switch that reversibly turns RidA into a holdase. N-chlorinated RidA binds a wide spectrum of proteins and can prevent protein aggregation of model substrates, such as citrate synthase or IlvA. Removal of the chloramine modifications, which can be performed NADPH-dependently by catalytic amounts of the thioredoxin system, leads to substrate release by RidA. Foldases can then refold these proteins  was measured with a JASCO V-650 spectrophotometer  at 292 nm using the extinction coefficient ε280=2,980 M−1 cm−1 for the monomer of RidA, ε280=4,470 M−1 cm−1 for DUK114, and ε280=31,860 M−1 cm−1 for IlvA.Overexpression and purification of RidA, RidA mutant and IlvA was performed as previously described using strains and plasmids listed in Oxidation and reduction was performed by incubation of 700 μM of the respective protein with varying ratios of HOCl, monochloramine, DTT or ascorbic acid for incubation times indicated at 30 and 37 °C, respectively. Removal of stressors was carried out with Micro Bio-Spin Chromatography Columns according to the manufacturer’s instructions .HOCl, 5 mM DTT was added to the cuvette after 250 s of measurement.To monitor aggregation, 12 μM citrate synthase or IlvA were chemically denatured in 4.5 M GdnCl, 40 mM HEPES, pH 7.5 at room temperature overnight. Aggregation was induced by the addition of 0.15 μM citrate synthase or 0.3 μM IlvA to 1,600 μl 40 mM HEPES, pH 7.5. Increase of light scattering was monitored with a JASCO FP-8500 fluorescence spectrometer equipped with a Peltier thermo-holder ‘EHC-813’ at 30 °C for 240 s under continuous stirring. Measurement parameters were set to 360 nm (Ex/Em), 2.5 nm slit width (Ex/Em) and medium sensitivity. The activity of chaperones was determined by the addition of varying amounts of untreated or treated proteins before the addition of citrate synthase or IlvA. Calculation of relative chaperone activities was performed by the determination of initial and final absorbance of individual samples and correlation to maximal inhibition of light scattering. For thermal aggregation assays, 10 μM of untreated or HOCl-treated RidA were added to 1,600 μl of prewarmed 40 mM HEPES, pH 7.5. Measurement parameters were set to 360 nm (Ex/Em), 2.5 nm slit width (Ex/Em) and medium sensitivity. Measurements were performed at 43 °C for 1,200 s under continuous stirring. After 60 s, citrate synthase was added to a final concentration of 0.3 μM. To monitor citrate synthase aggregation upon release by RidATo measure the influence of ATP on chaperone activity, a twofold or eightfold excess of RidA over citrate synthase was used. ATP was added in a 10-fold excess over RidA.Reduction of RidA by thioredoxin was followed spectrometrically at the absorption maximum of NADPH using a JASCO V-650 spectrophotometer equipped with the temperature-controlled cell holder PSC-718. An amount of 50 μM RidA were mixed with 5 μM TrxA  and 100 μM NADPH  in 50 mM Tris-buffer, pH 7.5. After 30 s of stirring, 80 nM TrxB were added. Consumption of NADPH was measured at 340 nm for 2 h.HOCl with a 1-fold or 10-fold molar excess of glutathione (GSH or GSSG) for 30 min at 37 °C before the measurement of chaperone holdase activity.Reduction by glutathione was carried out by incubating RidA−1 of fluorescamine  were dissolved in acetone. One millilitre of 100 μg ml−1 RidA solutions in phosphate-buffered saline were mixed with 334 μl of fluorescamine solution. Emission of fluorescamine was measured from 400 to 600 nm with the excitation set to 388 nm. Relative amino group content was calculated by setting the fluorescence of fully reduced RidA to 100%.The detection of protein amino groups was carried out as previously describedHOCl (1–200 μM) were mixed in 40 mM HEPES-KOH, pH 7.4 with 0.2 μM Nile red. The fluorescence was monitored using a JASCO FP-8500 fluorescence spectrometer equipped with a Peltier thermo-holder ‘EHC-813’. Measurement parameters were set to 550 nm excitation, 570–700 nm emission with 5 nm slit width (Ex/Em) and medium sensitivity. RidA and RidAHOCl concentrations resulting in half-saturation were calculated from these data using Igor Pro Version 6.34A  and Excel for Mac 2011 version 14.3.9 . Data were fitted with Igor Pro using the ‘Sigmoid’ fit function. To obtain the negative inverse of the half-saturation, the reverse protein concentration was plotted against the reverse fluorescence intensity and the ‘Trendline’ linear regression function of Excel was used.Nile red  was dissolved in DMSO to 0.25 mM. Varying concentrations of RidA and RidARidA treated with HOCl and MCA, and/or the reductant DTT was adjusted for pipette tip-based purification to 0.1% TFA. Purification was performed using OMIX C18 Tips  according to the manufacturer’s instructions. Elution of RidA was performed with 100 μl 0.1% FA, 70% acetonitrile. RidA was then directly injected into an Orbitrap Elite mass spectrometer  using an API Source with a static nanospray probe and a 1 μm picoTip emitter from EconoTips . Measurements were performed with a source voltage of 1.2 kV and a capillary temperature of 275 °C. Spectra were deconvoluted and further analysed using Xcalibur software .Analysis of α-ketobutyrate generation was performed as described previously212O2, H2O2/Cu was performed using a 10-fold excess of the oxidants. After 10 min of incubation at 30 °C, oxidants were removed using Micro Bio-Spin Chromatography Columns according to the manufacturer’s instructions .Treatment of RidA with peroxynitrite, H−1.Analytical gel filtration was carried out as described previouslyDrosophila melanogaster) was purified using the same protocol as for RidA2PO4, pH 8.0, 1 mM EDTA and treated with a 10-fold excess of HOCl for 10 min at 30 °C. HOCl was removed using Micro Bio-Spin Chromatography Columns according to the manufacturer’s instructions .Bovine serum albumin (from bovine serum), lipase (from porcine pancreas), ribonuclease A (from bovine pancreas), lysozyme (from chicken egg white), α-amylase (from porcine pancreas) and β-lactoglobulin were purchased from Fluka  or Sigma-Aldrich . DUK114 E. coli strains BL21, CL048 (BL21-ΔridA) and CL053 (BL21-pET11-a_ridA) were grown in LB medium at 37 °C to OD600 of 0.3. Overexpression of RidA in CL053 was induced by the addition of 1 mM IPTG for 30 min. Cells were OD600-normalized and inoculated to OD600=0.1 into microtiter plates filled with serial 1:1 dilutions of HOCl-containing LB starting with 32 mM NaOCl. Growth of strains at 37 °C was monitored in an Infinite 200 PRO Microplate Reader .His) was overexpressed in E. coli BL21_ΔridA . After collecting the flow-through, columns were washed with 20 ml washing buffer . Elution of substrate proteins was achieved by four additional washing steps with washing buffer containing 10 mM DTT. After washing two times with DTT-free buffer, RidAHis was eluted with elution buffer . Samples were analysed on non-reducing SDS gels. After pooling and concentrating of washing fractions four to six, protein identification was performed by mass spectrometry as described below.RidA substrate proteins were purified by Ni-NTA chromatography. For this purpose, His-tagged RidA . After incubation with mild shaking at 37 °C for 5 h, 2 μl of concentrated TFA  was added to the sample and precipitated RapiGest was removed by centrifugation. PhosB peptides as Hi3 quantification standard (Waters) were added to a final concentration of 12.5 fmol μl−1. Samples were injected into a nanoAcquity UPLC system to be loaded on a trap column  and were then eluted using a gradient  of acetonitrile with 0.1% formic acid  from an analytical column at 40 °C .A volume of 25 μl of each sample containing 0.1% RapiGest  was subjected to tryptic digestion. Cysteine reduction using 2.5 mM TCEP  for 60 min at 60 °C was followed by alkylation with 5 mM iodoacetamide  for 15 min at 25 °C in the dark and addition of trypsin to a final concentration of 5 ng μlm/z with 0.5 s per scan using the MSE technology and a trap collision energy ramp of 14–45 V. The following parameters were used for the NanoLockSpray source: capillary voltage, 2.0 kV; sampling cone voltage, 30 V; source offset, 30 V; source temperature, 100 °C; desolvation temperature, 150 °C; cone gas flow; 50 l h−1; nanoflow gas pressure, 0.5 bar; desolvation gas flow, 500 l h−1. Leucine enkephalin serving as lock mass analyte was fed through the lock spray channel . Analysis of the spectra was performed using MassLynx V4.1 SCN813 (Waters).The nanoUPLC column was coupled online to a Synapt G2-S HDMS ESI/ToF mass spectrometer (Waters). Spectra were recorded in positive ionization mode and resolution mode over a mass range of 50–1,800 Escherichia coli MG1655 database (NCBI accession NC_000913.3) containing 4,109 protein entries  was used for protein identification using the following parameters: peptide tolerance, automatic; fragment tolerance, automatic; minimal fragment ion matches per peptide, 2; minimal fragment ion matches per protein, 2; minimal peptide matches per protein, 1; maximum protein mass, 300 kDa; primary digest reagent, trypsin; secondary digest reagent, none; missed cleavages, 2; fixed modifications, carbamidomethyl C; variable modifications, deamidation N and Q, oxidation M, oxidation C, sulfinic acid C, cysteic acid C; false positive rate, 4%; calibration protein, PhosB standard. Only proteins identified with at least three peptides in at least two of three replicates were considered for evaluation.Data were analysed using the ProteinLynx Global Server 2.5.2 (Waters) software. Mass spectra were processed using the following parameters: chromatographic peak width, automatic; MS ToF resolution, automatic; lock mass window, 0.25 Da; low energy threshold, 50 counts; elevated energy threshold, 15 counts; intensity threshold, 500 counts. A non-redundant version of the E.coliProteins that were eluted after DTT treatment were categorized according to biological functions. The EcoCyc GO annotation of A.M. with L.I.O.L. designed and conceived the experiments and wrote the manuscript. A.M. performed the chaperone assays, thioredoxin and glutathione reduction assays, measurement of amino group contents, hydrophobicity assays, purification of RidA substrates, growth experiments and gel filtration. A.M. and L.I.O.L. performed mass spectrometry of full-length RidA. N.L. performed threonine deaminase assays and purified RidA and DUK114. A.M. and S.L. identified RidA substrate proteins by mass spectrometry. J.E.B., S.L. and K.K. evaluated the MS data and set up MS-based experimental procedures. S.L. performed the GO annotation of RidA clients.How to cite this article: Müller, A. et al. Activation of RidA chaperone function by N-chlorination. Nat. Commun. 5:5804 doi: 10.1038/ncomms6804 (2014).Supplementary Tables 1-4 and Supplementary References


Large-scale sequencing experiments are complex and require a wide spectrum of computational tools to extract and interpret relevant biological information. This is especially true in projects where individual processing and integrated analysis of both small RNA and complementary RNA data is needed. Such studies would benefit from a computational workflow that is easy to implement and standardizes the processing and analysis of both sequenced data types.We developed SePIA , a comprehensive small RNA and RNA workflow. It provides ready execution for over 20 commonly known RNA-seq tools on top of an established workflow engine and provides dynamic pipeline architecture to manage, individually analyze, and integrate both small RNA and RNA data. Implementation with Docker makes SePIA portable and easy to run. We demonstrate the workflow’s extensive utility with two case studies involving three breast cancer datasets. SePIA is straightforward to configure and organizes results into a perusable HTML report. Furthermore, the underlying pipeline engine supports computational resource management for optimal performance.http://anduril.org/sepia.SePIA is an open-source workflow introducing standardized processing and analysis of RNA and small RNA data. SePIA’s modular design enables robust customization to a given experiment while maintaining overall workflow structure. It is available at The online version of this article (doi:10.1186/s13040-016-0099-z) contains supplementary material, which is available to authorized users. Massively parallel cDNA sequencing technology (RNA-seq) generates vast amounts of biological data and has become a primary means of transcriptome analysis. A number of RNA-seq protocols exist to produce total RNA, poly(A)-derived RNA and small RNA data. The choice of RNA-seq protocol affects the design principles implemented in the computational analysis of the data . AccordiHere, we present the open-source workflow for automated RNA-seq processing, integration and analysis (SePIA). It provides essential pipeline infrastructure for efficient and reproducible analysis of total RNA, poly(A)-derived RNA, small RNA, and integrated microRNA (miRNA) and mRNA data. It does so by (1) expanding the utility of the pipeline engine Anduril  to incluCurrently, over 20 existing software and packages Table  are incoIncluded in the workflow are two novel processes for improved integrated analysis of miRNA and mRNA datasets. The first is a database creation pipeline able to integrate and query multiple, user-downloaded public resources  or custom lists of regulatory pairs. The second is a pair of target prediction components utilizing sequence complementarity to discover likely targets of putative novel miRNAs and to increase the number of potential mRNA targets of known miRNAs.http://anduril.org/sepia.The SePIA workflow was designed for straightforward execution of pipelines by users of all programming experience. Defining inputs and parameters, for example, is the minimum requirement by both graphical and command-line RNA-seq pipelines, requires no programming, and is done in a single settings file. Installation of the workflow is also simplified to a few copy-and-paste command-lines on a Linux distribution. However, in order to make changes to the pipeline execution and take advantage of other tools available with the workflow, some programming experience is required. Fortunately for method developers and users willing to do some learning while doing, features implemented with the workflow provide a stable environment for straightforward modification and addition of components in a pipeline. Examples, including a fully executable demo case, installation instructions, component documentation, workflow scripts and resources are available on the website, Abstraction of multiple tools and executable scripts written in different programming languages, sparing the user the task of familiarizing with each language.Optimization of available computer resources, including parallelization of independent components.Modularity. Tools and resources are implemented as independently executable components made linkable through the standardization of input and output formats.Verbose logs of executed processes for directive error tracking.Implementation of filters and fail safes, including data type checking, to prevent execution and consequent failure of software with invalid/insufficient input.An ability to pick-up processes from a previous state, in case of failure or other interruption, and avoiding unnecessary re-execution of parts of a pipeline.Complete and easily searchable documentation of all components and their parameters.SePIA is an open-source workflow designed to meet three general aims: (1) to make available a selection of state-of-the-art RNA-seq tools and methods with minimal effort to run, (2) to automatically organize computational results in reproducible, presentable, and easy-to-use formats for in-depth investigation, and (3) to provide a framework for integrated RNA and small RNA processing and analysis. To meet these aims, SePIA relies on the following features supported by the workflow engine Anduril : AbstraAnduril’s Java-based workflow configuration language allows users to set component resources and parameters, and to connect data between otherwise independent components for seamless data flow. The configuration language makes no contribution to low-level processing, providing a clear separation between workflow development and application development. Anduril also contains programming interfaces that support the implementation of custom components written in any command-line executable program such as Bash, Java, R, Python, Perl and MATLAB. Users can thus create custom scripts for a pipeline in their preferred language.SePIA is built on Anduril and thus inherits the necessary infrastructure and features for reliable large-scale data analysis. Computational overhead produced by a workflow built on Anduril versus simple Bash scripting was explored by Rantanen et al. , with thThough the underlying pipeline engine does not enable parallel computing nor regulate computational resources by default, a user can specify these in SePIA execution scripts with normal and locally-defined command-line switches in the header or as input string parameters to select components (e.g. memory-intensive read-mapping software). An example enabling multiple threads is described with performance statistics in the Discussion.SePIA runs on Linux and assumes all software and dependencies are discoverable in PATH prior to script execution. While some tools and libraries are installed with Anduril and the bundle of RNA-seq components (e.g. R and Python packages), it was not possible for all tools incorporated for use with the workflow. Manually setting up a Linux machine to run SePIA, especially one with custom settings, can thus be daunting and time-consuming.http://www.docker.com) is an open-source implementation of lightweight Linux containers in which an ’image’ of an application and all its dependencies are packaged to run in a standardized way. Local folders can also be mounted to Docker containers, which means SePIA can use all the computational resources within the container but access and write files locally. This includes essential log files for workflow process tracking. Docker thus provides a convenient and static environment for pipelines to be executed in with guaranteed result reproducibility. Installation and set up is reduced from multiple required dependencies to Docker and any software for optional execution scripts (e.g. novel miRNA discovery) that a user may wish to run. A demo is available on the website showing how, with a mere six lines of copy-and-paste code, one can install Docker with SePIA and run a complete small RNA pipeline from pre-processing to differential analysis.To simplify installation and enable immediate use of the SePIA workflow, a Docker image was made of a Ubuntu 14.04 distribution with Anduril and essential sequencing tools pre-installed. Docker  for further investigation. SePIA is structured to allow for the execution of components as soon as resources and input becomes available , and components that require multiple inputs to be processed  will wait to execute when all inputs are ready. This prevents downstream analysis with incomplete data, and ensures each component produces valid results in a module.The mandatory input files for SePIA’s first module are tab-delimited text files containing the following information: unique, per-sample IDs and the corresponding path to unprocessed.fq/.fastq file. Two optional columns can be added to provide further identification of ‘treatment’ and ‘sample’ information, which are required for differential analysis and visualization. Example inputs are provided in Additional file Three adapter and quality trimmers are implemented in SePIA to cover different features in processing RNA and small RNA sequences . To deteThe output of this module includes an HTML report summarizing preprocessing statistic, FastQC results organized by patient sample or metric type Fig. a, and anSePIA is equipped to use any of the five available sequence alignment tools listed in Table Mapped reads from RNA data are then quantified for expression at a gene, transcript, and/or exon level using HTSeq and Cufflinks; and for small RNA data at a mature or transcript miRNA level using HTSeq. While Cufflinks produces scaled expression in the form of fragments per kilobase per million (FPKM), HTSeq produces ‘raw counts’ that generally require further normalization to be informative. Three methods are implemented in SePIA for this purpose: counts-per-million with edgeR , libraryhttp://www.mirbase.org). Though they share a lot of the same genomic information, differences in miRBase’s annotation format compared to Ensembl makes it incompatible for use with HTSeq for expression quantification. SePIA solves the problem with a Boolean settings parameter mirbase_gff that, when true, calls a transitional step to reformat the.gff3 file to resemble that of an Ensembl.gtf file. This enables expression quantification on a mature miRNA level not possible with Ensembl transcript annotation.Small RNA annotations can be extracted from Ensembl general transfer format (.gtf) files and referenced for read quantification. Alternatively, miRNA annotation files in general feature format (.gff3) can be directly downloaded from the miRBase website . If a user defines a complex dataset with more than two groups in the sample and treatment information, differential analysis will be performed on all paired combinations of treatment groups. These are particularly useful for bioinformatics developers, who are often required to gain results from multiple approaches or with multiple parameter values for comparison.Novel miRNA and other small RNA discovery is performed on the subset of sequenced small RNA mapping to the genome. MiRanalyzer and miRDeep2 are currently implemented in the workflow. The former features detection of low-abundance candidates and the latter’s conserved approach reduces false positives . Counts To date, miRNAs have the most consistent and well-characterized regulatory function of all known small RNAs. To evaluate this function for differentially expressed miRNAs, however, their mRNA targets must first be identified. One approach that produces encouraging results is to integrate known miRNA- and mRNA-level expression data with in silico target predictions to filter subsets of potentially interesting miRNAs and their target genes in a particular experiment –24. In SFor target prediction, SePIA has an optional pipeline, MirTPdb, to create, query, and maintain an integrated SQLite database of predicted and validated miRNA-mRNA pairs from public resources. MirTPdb consists of two components: one to create the SQLite database, and one to query it. The database produced for our case study is comprised of predicted miRNA-mRNA pairs downloaded from TargetScan , MicroCohttp://anduril.org/sepia.To identify targets of predicted novel miRNA regions or to expand the set of targets for known miRNAs, target prediction based on miRNA sequence complementarity was developed for SePIA’s integration module. All known and novel miRNA-mRNA pairs with highly anti-correlated expression profiles and support for their functional interaction from target databases or sequence complementarity analysis are reported in an easy-to-browse website Fig. d. Users Integrated analysis of miRNAs and mRNAs is a dynamically developing field of RNA-seq research. While correlation is the most common approach incorporated to analyses identifying likely miRNA targets, it is not the only approach. Demonstrating how the workflow’s modular pipeline architecture supports customized application and methods development, we also include an alternative Bayesian approach to identifying functional miRNA and mRNA relationships with GenMir++  as an opWe demonstrate SePIA’s broad utility with three publicly available Illumina RNA-seq datasets consisting of paired-end total RNA, poly(A) RNA, and single-end small RNA. The first dataset comprised of total RNA-extracted mRNA from 17 tumor and 3 normal breast samples, sequenced with Illumina HiSeq, from the Gene Expression Omnibus GSE52194, ). The se194, . ThWe organize the datasets into two case studies: the first to showcase SePIA’s utility for transcript-level sequence analysis; the second to demonstrate integration of mRNA and small RNA data.EIF3L and EEF2 [For this RNA-seq dataset we performed preprocessing, read mapping, expression quantification, and differential expression analysis on a gene and transcript level. One tumor sample SRR1027187) was discarded during preprocessing because less than 60 % of the reads survived quality and adapter trimming. Successfully trimmed reads were mapped to the human genome NCBI38v76 using STAR aligner. A detailed summary of sequence and expression analysis results, as well as parameters used are provided in Additional file 027187 waand EEF2 , 34. DEXVariant calling was implemented using Bambino  and filtFrom the 144 mRNA samples, 138 passed quality control with one sample discarded for having mostly poor quality sequences , and five samples discarded for having less than 60 % of reads surviving trimming, an indicator of poor sequence content. Of the TCGA miRNA dataset, 148 of 149 samples passed quality control, with one sample discarded for having less than 20 % of reads surviving trimming. The low percentage indicates either poor sequence content after trimming or poor miRNA representation in the sample.Preprocessing, read mapping, and expression quantification of poly(A)-derived mRNA data was performed with the same tools as in Case I. 4,170 genes and 2,257 mRNA transcripts were found to be differentially expressed between normal and tumor breast tissue with a conservative false-discovery rate of 0.05. Furthermore, 48 genes had preferential usage of one isoform over others in normal versus tumor samples . On average, 95 % of our preprocessed small RNA map to the genome, with approximately 1–33 % mapping to something other than known miRNAs in the genome. Eipper–Mains et al.  suggestsThough SePIA can quantify miRNA expression on a mature and precursor level, only mature miRNAs were calculated for this study. Lengths of trimmed reads were normally distributed around 22nt, indicating content consistent with mature miRNA sequences. The human annotation file hsa.gff3 was retrieved directly from miRBase to create the reference for HTSeq quantification. 28 %  of all human mature miRNAs were expressed in the normal and tumor breast tissue samples with at least two mapped reads, consistent with tissue-specific expression characteristic of miRNAs .n=408 with false discovery rate <0.05) shared by all three methods.Demonstrating the ease in which SePIA can produce comparable results from different approaches, three methods were used for differential expression analysis: DESeq, EdgeR, and upper-quartile normalized t-test. Bioconductor package edgeR  identifiSNORA56, SNORA69, SNORD95, SNORD49, SNORD82), one overlapping a predicted novel miRNA (AL161626.1), one overlapping a known miRNA not in miRBase (FP236383.10), and one small zinc-finger protein (ZNF813). SnoRNAs that encode miRNAs or have miRNA-like function have been previously reported [Novel miRNA and other small RNA discovery utilizing miRanalyzer produced a matrix of 442 putative novel miRNA regions between 72 and 159nt in length. In comparison, the range of known miRNA transcripts in miRBase v.21 is between 40 and 179nt. Analysis identified 33 of these putative novel miRNA regions to be differentially expressed between normal and tumor breast tissue . Encouragingly, manual inspection of these novel regions revealed five overlapping snoRNAs  and their roles in breast cancer. Integration was performed on 123 tumor and 15 normal samples sharing patient sample IDs in both the TCGA miRNA and mRNA datasets. Target transcripts of the differentially expressed miRNAs were identified with our target prediction database, with anti-correlated expression  , 39 and CCNDBP1 [AHNAK [MYO10 [CCNB2 [SNORD95. These genes are known to play essential roles in breast cancer tumorigenesis, invasion and metastasis [We found 256 differentially expressed transcripts predicted by SePIA’s sequence complementarity components to have binding motifs for our 33 putative novel miRNAs and anti-correlated expression . Stringent binding motifs  were found in 115 of the 373 predicted miRNA-mRNA pairs. These include two down-regulated tumor suppressor genes CCNDBP1  and AHNA1 [AHNAK  targetedK [MYO10  and CCNB0 [CCNB2  targetedtastasis –43.C13orf25 gene at 13q31.3, and are well-known promoters of cell proliferation, cell survival, and angiogenesis [Amongst our list of differentially expressed miRNAs are members of the mir-17/92 cluster, the most frequently activated miRNA cluster in cancers. All six members  are located within the third intron of the ogenesis . Indeed,ogenesis .TGFBR2 [DCN [CAV1 [TGFBR2 and DCN: 9 of the 10 expressed members appear to target both of the known transcripts of TGFBR2, the ligand-binding receptor for all members of the TGF-beta family, while all 10 miRNAs appear to co-regulate four different transcripts of DCN, an anti-angiogenic factor in breast cancer [CAV1, whose role in breast cancer has been extensively studied [Three commonly predicted targets of the miR-17/92 cluster showing anti-correlated expression are tumor suppressors TGFBR2 , DCN [47CN [CAV1  which weCN [CAV1 . Anti-co studied , 49 but The SePIA workflow allows analysis of paired-end total RNA, poly(A)-derived RNA, and single-end small RNA data. All three data types were processed by the same RNA-seq components at common steps in the workflow, such as preprocessing and alignment, to produce standardized outputs. The ease in which SePIA’s pipelines can be extended was demonstrated in Case I, where variant calling was performed as an additional, independently-executed analysis of transcript-level data. Integrated analysis was demonstrated in Case II, identifying biologically interesting miRNA-mRNA pairs by combining expression correlation with in silico target predictions . Though All workflow scripts have a ’.and’ extension and are written in Anduril’s configuration language based on Java. Users of any member of the Java programming language and comparable languages like Python will, therefore, find it relatively straightforward to read the pipeline execution scripts and learn how to further configure them.SePIA’s sequence complementarity components discovered potential target mRNAs for small RNAs with miRNA-like features, providing association to biological function. It was also used to increase the number of predicted targets of differentially expressed known miRNAs. Sequence complementarity is widely accepted as a mechanism in which miRNAs identify targets. In Case II, 174 of the 408 differentially expressed miRNAs had targets from our integrated target database with anti-correlated expression and minimum fold-change of 0.5. Sequence complementarity added 54 miRNAs with potential binding motifs in the 3’UTR regions of their anti-correlated targets. The number includes tumor suppressive (mir-205 and mir-143) and oncogenic miRNAs (mir-187 and mir-27b) reported in breast cancer –56. All Correlation is an intuitive and straightforward approach to integrating miRNA and mRNA expression. Putative miRNA targets supported by anti-correlated expression is valuable information for planning any experimental work with miRNAs. However, to identify functionally interesting networks of miRNAs and mRNAs from expression data, users may prefer more complex integrative approaches such as GenMiR++ . An alteTools available with SePIA are generally free to use. Most dependent software and packages are also included with the Docker installation of SePIA. However, while it is possible to run MATLAB scripts with the workflow, MathWorks license prevents us from including the software. Therefore, to implement a custom MATLAB script in a pipeline or to use the optional integration pipeline with GenMiR++, users must first install the software themselves.SePIA pipeline scripts have been set up for the general process and analysis of a two-case RNA-seq dataset. Users with such a dataset need only to change the inputs and parameters outlined in the master settings file to run the pipeline. A dataset with more than two cases will, however, affect how differential analysis is performed and pipeline modification is required. The amount of modification to be done ultimately depends on the programming experience of the user and willingness to troubleshoot a pipeline script. A user can, at the very least, disable components in the analysis module incompatible with their dataset by simply commenting them out. Similarly, a user who wishes the pipeline to automatically output a ‘result’ file from a ‘componentA’ rather than retrieve the file manually from the execution folder must be able to add to the end of the pipeline’s execution script the line ‘OUTPUT(componentA.result)’. The pipeline’s modular design thus allows for both component execution and exclusion of independent parts.The underlying infrastructure of SePIA is designed to be scalable to large computational resources dedicated to high-throughput sequencing analysis, and for optimal use with batch processing. SePIA is capable of automating a time-memory trade-off by running in parallel independent processes and allocating necessary memory or thread requirements for each individual component. However, for some components the time-memory complexity depends on the underlying implementation. To demonstrate, a subset of 8 samples  was extracted from the TCGA miRNA dataset to run on an Intel Xeon X5650 processor with 90GB RAM and a 24-core. Using eight computational threads, preprocessing, alignment, quantification, and differential expression analysis was performed in less than 70 minutes. Novel miRNA discovery was fast with an average 40 minutes per sample, but the single act of annotating putative novel miRNA regions with information on their relative genomic location was time-intensive and required an additional 24 hours to perform. Performance statistics is available in Additional file High-throughput sequencing of RNA is powerful but it is not without its challenges . With inWith changes in RNA-seq technology and research comes changes in softwares and algorithms used in pipelines. Often, these changes can break pipelines and prevent reproducibility of results. Implementation of SePIA with a Docker container ensures a reliable and static environment with all the required tools in compatible software versions is always available to run SePIA. Furthermore, we will update the Docker container with new releases of the SePIA workflow to ensure continued usability.Though users can install and update software manually, there is no guarantee components using the software will continue to work correctly. Troubleshooting components requires some programming skills and because SePIA is built on Anduril, online support is available to the user. The bundle of RNA-seq components uses versioning and it is possible to update to development versions of the RNA-seq components.Like RNA-seq, the SePIA workflow continues to be developed. Future releases will provide ready execution of additional tools, like the previously mentioned RSEM, as new RNA-seq components. Version updates of STAR, Picard, and other listed tools may alter component implementation in the workflow. Currently, version information is printed to log files by most components available for use with SePIA but not by all. This will be resolved in a future release to further ensure reproducibility of results. Fusion gene detection will be a standardized process supporting the use of ChimeraScan , FusionCWe developed SePIA to address the need for a high-throughput workflow that provides a standard framework to process and analyze multiple RNA-seq data types, primarily small (<200nt) and long (>200nt) RNA. The built-in pipeline infrastructure, modular design, and range of independently executable components of the workflow supports advanced methods-development and benefits users who regularly process RNA-seq data. Pipeline scripts can be minimally configured with basic programming knowledge. For novice users, the master settings file and ready-to-use computational environment in Docker makes it easy to input data and run pipelines with minimal effort.Pipeline extensibility is a key advantage of the modular framework. It supports implementation of alternative computational approaches and the development of further downstream analysis. To assist with functional studies of regulatory small RNAs, SePIA features tools to query and maintain a SQLite database of annotated interactions, and to discover putative novel miRNA-mRNA interactions.The versatile design of the SePIA workflow makes it straightforward to incorporate developments in RNA-seq technology and customized applications. Components for use with the workflow are constantly being developed for further user convenience. SePIA thus achieves a ’middle-ground’ approach to RNA-seq processing and analysis that harnesses the flexibility and computational resource management of bash scripting with the approachable usability and ease of one-command pipeline execution of more graphical user interfaces.


The centromere is a specialized chromatin region marked by the histone H3 variant CENP-A. Although active centromeric transcription has been documented for over a decade, the role of centromeric transcription or transcripts has been elusive. Here, we report that centromeric α-satellite transcription is dependent on RNA Polymerase II and occurs at late mitosis into early G1, concurrent with the timing of new CENP-A assembly. Inhibition of RNA Polymerase II-dependent transcription abrogates the recruitment of CENP-A and its chaperone HJURP to native human centromeres. Biochemical characterization of CENP-A associated RNAs reveals a 1.3 kb molecule that originates from centromeres, which physically interacts with the soluble pre-assembly HJURP/CENP-A complex in vivo, and whose down-regulation leads to the loss of CENP-A and HJURP at centromeres. This study describes a novel function for human centromeric long non-coding RNAs in the recruitment of HJURP and CENP-A, implicating RNA-based chaperone targeting in histone variant assembly. Before a cell divides, it copies its chromosomes. Initially, the two copies of each chromosome remain linked via their centromeres. These regions also serve as the attachment sites for the proteins that pull these two copies apart, and eventually segregate the chromosomes equally between the two newly formed cells.Chromosome segregation is the main function of centromeres; and in most organisms, the DNA in these regions is highly repetitive and is not thought to encode any proteins. However, it has been observed that cells need enzymes called RNA polymeraseswhich transcribe stretches of DNA into RNA moleculesto be able to separate the copies of their chromosomes correctly. This suggests that RNAs transcribed from centromeres might be required for cell division, but the identity and function of these RNAs remained elusive.Quénet and Dalal have now discovered that an RNA polymerase localizes to the DNA in human centromeres and produces RNA molecules during the early stages of the cell cycle. Two proteins–one called CENP-A and another that functions as its chaperone–that normally bind to the centromere and determine its structure were found less often in this region of the chromosome if the activity of the RNA polymerase was inhibited. Qunet and Dalal identified a specific RNA molecule that is transcribed from the centromeric DNA, which directly binds to the CENP-A protein and its chaperone before CENP-A is assembled onto the centromeric DNA. Reducing the levels of this RNA within the cells made them unable to separate their chromosomes correctly during cell divisions. Qunet and Dalal also demonstrated that this centromeric RNA is needed to specifically target both the CENP-A protein, via its chaperone, to the centromere.The findings of Qunet and Dalal demonstrate that RNAs produced from a specific part of the chromosome can help target DNA-binding proteins back to that region's DNA sequence. Following on from this work, the next challenge will be to determine if other RNA molecules are used for the same purpose in humans and other species. Specialized chromatin domains called centromeres play an essential role in chromosome segregation, serving as a platform for kinetochore complex formation, which in turn binds spindle microtubules at mitosis . AlthougIn this study, we report that centromeric RNAs play a critical role in the maintenance of the human centromere in vivo. Using chromatin immunoprecipitation (IP), and immunofluorescence (IF) on chromatin fibers, we find that RNAPII, in conjunction with TATA-box binding protein (TBP) localizes to, and actively transcribes native human centromeres from late mitosis to early G1 (eG1). Biochemical purification and sequencing of the RNA associated with human centromeric chromatin at eG1 reveals a 1.3 kb long transcript. This RNA physically interacts with CENP-A and its chaperone HJURP (Holliday junction recognition protein) in the pre-assembly soluble state in vivo. Targeted sequence-specific knockdown of the transcript results in the formation of multipolar spindles and lagging chromosomes in subsequent mitoses, leading to chromosome instability. IF analysis of centromeric chromatin fibers demonstrates that these cellular and nuclear phenotypes arise specifically from the abrogation of CENP-A and HJURP localization to the centromere. Together, our data describe a direct physical role for a centromeric long non-coding RNA (lncRNA) in HJURP targeting, subsequent CENP-A loading, and the maintenance of centromere integrity. Our study supports the possibility that an lncRNA-based mechanism is involved in targeting CENP-A and its chaperone HJURP to the centromere.S2P) on centromeric chromatin fibers throughout the cell cycle by IF  to block RNAPII activity as above, and quantified potential changes in intensity for CENP-A or CENP-B IF signal using ImageJ. Consistent with its role as a constitutive centromeric DNA-binding protein , CENP-B S2P, and HJURP on chromatin fibers, with or without α-amanitin treatment at eG1. Consistent with the known effect of α-amanitin on RNAPII   , inhibitic fiber , after Ric fiber  combinedic fiber , suggestPrevious data have documented the existence of ncRNA at centromeres in multiple species . In humaThe inhibition of transcription was accompanied by the loss of CENP-A and HJURP at the centromere during eG1 , and ourAfter a brief MNase digestion of eG1-synchronized cells, we immunoprecipitated CENP-A and HJURP from both, the soluble fraction , and the chromatin fraction   or shRNAcenRNA#1 constructs, and selected with puromycin Cells transfected with shRNAscram displayed no changes in morphology and density, whereas cells treated with shRNAcenRNA#1 displayed significant loss of cell density , and presented aberrant morphology , that was necessary for HJURP and CENP-A targeting to the centromere at eG1. To distinguish between these hypotheses, we further examined functional consequences arising from the targeted loss of total centromeric α-satellite RNA, without inhibiting RNAPII transcription. sat1 and shRNAsat2) to destroy centromeric transcripts  or shRNAsat constructs and puromycin selection, the expression level of centromeric α-satellite transcript was analyzed by qtPCR. Compared to control shRNAscram, cells transfected with shRNAsat constructs displayed a significant decrease (∼70%) of the centromeric α-satellite transcript  and exhibited abnormal morphology . Transfection of HeLa cells with LNA ASO were conducted as in . Briefly, cells were seeded 24 hours before transfection to allow no more than 75% confluency, and transfected using Lonza’s Amaxa Cell Line Nucleofector Kit R (Cat #VCA-1001) with Amaxa Biosystems Nucleofector II electroporation system using program O-005. After transfection, cells were grown on coverslips with fresh DMEM medium.The day following LNA ASO transfection, cells were synchronized with a double thymidine block and released for 10.5 hours on the third day, to enrich for anaphase-cytokinesis staged cells. IF experiments were performed as described previously . Cells wIF slides were observed with a DeltaVision Elite RT microscopy imaging system  controlling an interline charge-coupled device camera (Coolsnap) mounted on an inverted microscope . Images were captured by using a 60X objective at 0.2μm z-sections and analyzed with Image J .Antibodies are commercially available, except the custom CENP-A antibody (available upon request) used for CENP-A detection on Western blot. 2, in Dulbecco's modified Eagle's medium high in glucose and L-glutamine  supplemented with 10% Fetal Bovine Serum  and 1X Pen/Strep solution .HeLa cells were grown at 37°C in a humidified atmosphere containing 5% COAll synchronizations were done by double thymidine block . After a first block of 19 hr, cells were released for 9 hr, followed by a second thymidine block of 16 hr. Cells then were released for the appropriate time  according to manufacturer protocol. Briefly, cells were resuspended in Trizol, and following 5 min incubation at room temperature (RT), 200 μl of chloroform  was added. After centrifugation at 12,000 rpm for 15 min at 4°C, the clear phase was mixed with 500 μl of isopropanol  and centrifuged. The pellet was washed with 75% ethanol  and resuspended in water complemented with DNase I buffer, DNase I , and RNase inhibitor  to avoid genomic DNA contamination. After incubation for 30 min at 37°C, the DNase I activity was inhibited by addition of 5 mM EDTA  and incubation at 65°C for 10 min. RNAs were purified a second time by phenol:chloroform:isoamyl alcohol  method and ethanol precipitated. RNAs were conserved at −80°C until further analysis.After quantification by UV-spectroscopy  and verification of RNA quality on 1.5% agarose gel, equivalent concentrations of RNA were subjected to retro-transcription, using the SuperScript III First-Strand Synthesis System with random hexamer primers (#18080 – 051), and amplified with Takara PCR kit . Control reactions without the reverse transcriptase or complementary DNA were performed to rule out DNA contamination and non-specific amplification, respectively. Primer sequences are included in 2). Following three washes with cold PEM, soluble proteins were extracted for 5 min on ice with 0.5% Triton X-100  in CSK . Few drops of 4% PFA in PEM were added for 5 min. Slides were then incubated with fresh 4% PFA in PEM for 40 min on ice. After three washes with PEM, cells were permeabilized with 0.5% Triton X-100 in PEM for 30 min at RT, washed again three times, and blocked in 1×— TBS, 3% Bovine Serum Albumin, 5% normal goat serum  for 1 hr at RT. Finally cells were incubated with the primary antibody diluted in 1×— TBS, 1% Bovine Serum Albumin, 5% normal goat serum over-night (O/N) at 4°C in a humidified chamber. Slides were washed three times for 5 min at RT with 1×— TBS, 0.1% Tween 20 , and incubated with secondary antibody for 1 hr at RT. After washing, the same protocol was repeated for co-IF, and then cells were stained with DAPI  in 1×— TBS and mounted with mowiol solution  in PEM  O/N at 37°C in a humidified chamber. Slides were washed 5 min at 45°C three times with 50% formamide, 2X SSC solution, and four times with 2X SSC, 0.05% Tween 20 solution. Slides were blocked in 4X SSC, 0.1% Tween 20, 3% Bovine Serum Albumin for 30 min at RT. Following the incubation with the secondary antibody at 37°C for 1 hr, slides were washed four times for 5 min each at 45°C with 4X SSC, 0.1% Tween 20, stained with DAPI in 2X SSC, and mounted coverslip with mowiol solution.When FISH was performed, antibody protein complexes were crosslinked  for 10 min at RT, denaturated in 70% formamide , 2X SSC buffer  for 8 min at 78°C, and then incubated with denaturated probed  controlling an interline charge-coupled device camera  mounted on an inverted microscope . Images were captured by using a 60×— at 0.2 μm z sections for cell and 100×— objective at 0.1 μm z sections for chromatin fiber, deconvolved, and projected by using softWoRx (Applied Precision). Three independent experiments were performed and in each, 5–10 chromatin fibers or 30–50 cells were analyzed per slide.To quantify IF signals, the acquisition of pictures for all samples of an experiment was performed with the same time of exposure during the same day to avoid variability from the instrument. Using ImageJ (ImageJ 1.43U), signal intensity of each CENP-A or CENP-B spot inside of the nucleus (as defined by the DAPI staining) was extracted. The background level of the nucleus was subtracted from the average value of the spot intensity per cell. For each experiment, the average value of the spot intensity per cell and the ratio signal intensity in treated condition vs signal intensity in non-treated condition was measured. The mean and standard deviation values of three experiments are presented in the 2, 0.5 mM PMSF) complemented with 0.5% NP40 substitute , and washed once with TM2 buffer. Chromatin was digested 2 min at 37°C with 0.2 unit/ml of MNase  in 0.1 M TE buffer  complemented with 2 mM CaCl2. The reaction was stopped by addition of 10 mM EGTA and transferred to ice. After centrifugation for 5 min at 800 rpm at 4°C, the nuclear pellet was resuspended in 1 ml low-salt buffer , and the chromatin was extracted O/N at 4°C in an end-over-end rotator. An aliquot of the supernatant obtained after centrifugation for 5 min at 8000 rpm at 4°C was saved as input (1.5%). At 4°C, sample was precleared with protein A/G Plus agarose beads  for 30 min at 4°C in an end-over-end rotator, incubated with the primary antibody for 4 hr, followed by IP with protein A/G Plus agarose beads for 2 hr. After centrifugation, an aliquot was saved as unbound , and the bead-associated IP was washed three times with low-salt buffer, and stored at −20°C in Laemmli buffer (30 μl) for Western blot analysis.Five F175 flasks of HeLa cells (70–80% of confluence) were used for IP. Cells were trypsinized  and washed three times with cold 1× PBS, 0.1% Tween 20 coupled with 5 min centrifugation at 800 rpm at 4°C. Nuclei were isolated in TM2 buffer . The supernatant was saved and the elution step was repeated once more. All samples  were denatured at 65°C for 2 hr with 200 mM NaCl. Proteins were digested with 20 μg of proteinase K  in the presence of 40 mM Tris HCl pH6.5, 10 mM EDTA at 42°C for 45 min. To avoid genomic DNA contamination, samples were treated with DNase I for 30 min at 37°C. The reaction was stopped by addition of 5 mM EDTA, and RNAs were purified by phenol:chloroform:isoamyl alcohol method and ethanol precipitation. Samples were stored at 80°C until further use in retro-transcription PCR and Northern blot.For RNA study, RNA protein complexes were eluted from protein A/G Plus agarose beads by incubation in an end-over-end rotator for 15 min at RT with 250 μl elution buffer  for separation in 1×— Tris Glycine SDS Running Buffer , and transferred to Whatman nitrocellulose membrane  in Tris Glycine transfer buffer  diluted in 20% ethanol. Membrane was blocked in Odyssey blocking buffer  diluted in 1× PBS (1:1) at RT for 1 hr, and incubated with primary antibody diluted in blocking buffer complemented with 0.1% Tween 20 O/N at 4°C. After three washes in 1× PBS, 0.1% Tween 20, the membrane was incubated with the secondary antibody conjugated to IRDye680  diluted in blocking buffer complemented with 0.1% Tween 20% and 0.05% SDS for 1 hr at RT. The membrane was washed in the same conditions than previously and proteins were detected by scan on Odyssey CLx scanner (Li-Cor).eG1-synchronized HeLa cells treated with α-amanitin (as described in Cell culture and RNA Polymerase inhibition) were resuspended in lysis buffer . After three cycles of freezing and thawing, extract was centrifuged for 20 min at 12,000 rpm at 4°C. The cleared suspension was quantified by UV spectroscopy, and 50 μg of proteins were resuspended in Laemmli buffer. After separation into a 4 20% SDS-PAGE, transfer to a nitrocellulose membrane and incubation with primary and secondary antibodies (as described in Western Blot), proteins were detected by scan on Odyssey CLx scanner and quantified with ImageStudioLite software (Li-Cor). For each experiment, the ratio of signal intensity in treated condition vs signal intensity in non-treated condition was measured. The means and standard deviations of three experiments are presented in the figure.http://archive.bio.ed.ac.uk/ribosys/protocols/website_Northern_blotting.pdf. 5 μg of Trizol extracted RNAs or 1 μg of immunoprecipitated RNAs was separated on 4% urea PAGE against 0.5×— TBE buffer at 25 W for 90 min. RNAs were transferred to Amersham Hybond-NX membrane  for 2 hr at 65 V, UV-cross-linked, blocked for 1 hr in SES buffer , and hybridized O/N at 37°C with radiolabelled α-satellite probes  diluted in SES buffer. Membrane was washed in 6×— SSPE  two times for 15 min at 37°C and two times for 30 min at 42°C. Blot was exposed to P32-sensitive film  at −80°C to reveal the potential interaction for a short (<24 hr) or long (>24 hr) period of time. The sequences of the radiolabeled probes are indicated in Northern blotting protocol was adapted from + program (http://bioinfo.ggc.org/bindn+/) with a specificity equal to 85% . Two shRNA sequence cassettes were designed from the centromeric α-satellite consensus sequence . Two controls were used for each experiment: the empty vector (#TR30007) and the vector expressing scrambled sequence cassette  following manufacturer instruction. 48 hr after transfection, the medium was replaced every 2 days with fresh medium complemented with 0.5 μg/ml puromycin  to select transfected cells. At day 6, cells were either treated for IF or RNA extraction.To detect α-satellite expression level in shRNA-transfected cells, RNAs were extracted, quantified by UV-spectroscopy, and equal quantities were retro-transcribed using Superscript III First-Strand Synthesis kit as described above . To perform qtPCR, complementary DNAs (cDNAs) samples were prepared using the iQ SYBR Green supermix  following manufacturer's protocol. Control reactions without the cDNA were performed to rule out non-specific amplification. The qtPCR was run on Step one plus Real time PCR system . Primer sequences are available on T) method was used to analyze the expression level of α-satellite transcripts. CT values were normalized against the average CT value of the housekeeping gene β-actin. The ΔΔCT values were determined from the scrambled shRNA samples. Relative fold differences (2− ΔΔCT) are indicated on figure.The comparative cycle threshold (C2) O/N at 37°C. After rinsing, cells were observed under a light microscope for blue color, indicator of senescence.Senescent cells were detected using the protocol developed by t test was performed. For all tests α was assumed to be 0.05. The p-value is indicated on the figures or tables each time it was evaluated.Standard deviation was determined for all quantification measures. To test the significance of these measures, a two-tailed, paired Student's  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.Your manuscript titled, A centromeric long non-coding RNA is required for CENP-A targeting to the human centromere, was reviewed by three experts in the field and by a member of the Board of Reviewing Editors (BRE). After a full discussion of the study and the reviews, I am happy to report that the reviewers and the BRE member found the study of great interest to the journal, and therefore, we are happy to consider a revised manuscript addressing the following issues. I have also included the full-reviewers' comments at the bottom of this letter, which contain minor issues and textual modifications that need to be addressed in the revised manuscript.1) Your report of the 665 bp portion of the 1.3 kb sequence lacks alpha satellite, even though it was picked up on a Northern blot using alpha satellite as probes. Please provide more details concerning the probe and also repeat a Northern that is reprobed with this 665-bp sequence to determine whether the sequence identified is actually present in the RNA. This point needs to be clarified for our reviewers and readership.2) Provide negative and positive controls for the experiments as suggested by all three reviewers (the detailed reviews are included below).3) Provide quantification for the results for Figures 1a, 3c, 4c, and 6.4) Identify the genomic region from where the centromeric RNA originates. One reviewer has suggested that you could clone the full length RNA, and validate more carefully its implication in CENP-A deposition (further information regarding the reviewer's suggestion is included below).Reviewer #1How typical centromeres, such as those of humans, are targeted for function remains poorly understood. Whereas it might be assumed that alpha satellite is necessary and sufficient, because all human centromeres are embedded in alpha satellite, the occurrence of neocentromeres implies that alpha satellite is not sufficient for localization of the centromere foundation protein, the histone H3 variant CENP-A. A chaperone protein, HJURP, has been identified as being a requirement, and it is known that at least some alpha satellite sequences are transcribed, but these finding do not provide a clue as to how centromeres are maintained throughout the cell cycle. Now the authors show that transcription during early G1 is necessary for correct localization of CENP-A and describe an RNA sequence component of the CENP-A assembly complex that is required for normal centromere function. The data supporting these major novel points are convincing, making this study an exciting advance in understanding how centromeres are maintained.eLife, there were some issues that need to be addressed.Although the importance of the central findings justify publication in 1) The authors report that a search for a 665 bp portion of the 1.3 kb sequence failed to find it in the human or any other genome. Oddly, this portion lacks alpha satellite, even though it was picked up on a Northern blot using alpha satellite as probe. Some details are needed concerning exactly what the probe was. It would seem surprising if the 665-bp sequence is also absent from human short-read archives, as there is no reason to expect there to be a bias against a centromere-linked sequence in unassembled short reads produced from whole genomic DNA. If the sequence is not also found there, then some rational explanation for the phantom nature of this sequence would be welcome. For example, perhaps it derives from an as-yet unidentified endogenous RNA virus particle. In any case, the Northern should be reprobed with this 665-bp sequence to determine whether the sequence identified is actually present in the RNA.2) Figure 1B: All 3 IPs show enrichment relative to Input and Unbound. A negative control is needed for this experiment to verify that some of the enrichment isn't a technical artifact, such as background chromatin preferentially sticking to beads or some other systematic bias.Reviewer #2This manuscript presents an interesting idea, namely that a long noncoding RNA associates with HJURP and CENP-A and facilitates CENPa nucleosome assembly in early G1. I think the idea is consistent with the data, but I think there are many aspects of the experiments that could be significantly improved to be more convincing. In general, the experiments need more controls and quantification to be interpretable.1) Its not clear if the shRNAs actually target the novel RNA sequence that the authors identified, they target a-sat which colocalizes with the novel RNA sequence. This seems like a crucial piece of data. Why not design a shRNA to knock down the identified RNA sequence, show it is depleted by northern, and then perform the functional assays? The significance of the sequence identified is otherwise unclear.2) No negative controls for the western blots in Figure 1b. Also, there are multiple bands detected for CENP-C and TBP, which bands are the relevant bands?3) No positive control for Figure 4figure supplement 1B (-RNAse).4) Background is too high in mock IP Figure 4figure supplement 4 to know if there is any significant pull down in the mock IPs. There is a lot of smearing in part B that does not seem to be as prevalent as Figure 4 S1C.5) In Figure 4B there is no mock control.6) A lot is riding on the chromatin fiber experiments. In the figure legends, there are indications of repeats and number of fibers, but there is no quantification of the results for Figures 1a, 3c, 4c, and 6. Colocalization is very difficult/impossible to see in the figures as presented.Reviewer #3In this study, Quenet and Dalal investigated the mechanism of human CENP-A deposition at centromeres during early G1 phase of the cell cycle. Using immuno-precipitation and chromatin fibers imaging, they were able to identify a single 1.3 kb centromeric RNA that is claimed to play a critical role in CENP-A targeting and deposition at centromeres. The identified RNA molecule interacts with the soluble pre-assembly HJURP and CENP-A complexes in vivo. Its down-regulation is suggested to result in the loss of CENP-A and HJURP from centromeres and to subsequent chromosome instability. The 1.3 kb molecule is transcribed by RNA Polymerase II from centromeric DNA. Accordingly, inhibition of RNA Polymerase II transcription abrogates the recruitment of both CENP-A and its chaperone HJURP to centromeres.Overall, I think this work provides some interesting observation but they are too preliminary and contradictory to deserve publication in the present form. The authors need to seriously revise the paper, strengthen the biochemistry experiments and to validate their model in vivo and in vitro.The manuscript in its present form exhibits several caveats. The most important issues are listed below:1) Figure 1B, the Western Blot. The signal with the anti-CENP-C antibody is very weak (just slightly above the background). The authors should present a more convincing blot.2) Mounting evidence supports the hypothesis that an RNA component is involved in centromere function; although how RNAs participate in centromere formation in mammals has remained unknown. This study identifies a novel RNA component but fail to provide convincing evidence for its implication in CENP-A deposition. The proposed model for RNA guided CENP-A deposition is interesting but the identified 1.3 kb RNA molecule is very questionable. In Figure 4B, the size of the RNA identified in the HJURP complex (1.3 Kb) is different from the one identified in the CENP-A complex (1.1 Kb). How do the authors explain this discrepancy?3) To be convincing, the authors need to identify the genomic region from where this centromeric RNA originates. They should clone the full length RNA and validate more carefully its implication in CENP-A deposition. The two shRNAs presented in Figure 5 are useless since they were designed against the ?-satellite RNA and not against the 1.3 Kb RNA identified in this study. What is the relationship between ?-satellite RNA and the 1.3 Kb RNA? Why the authors are not able to detect the association of CENP-A with ?-satellite RNA as previously described by others :5071-80 ; Wong et al., Genome Res. 2007;17:1146-1160).4) Figure 4figure supplement 2 predicts the presence of several RNA binding motifs in HJURP and CENP-A but fail to provide a single experimental evidence for such interaction. The authors should reconstitute this interaction in vitro and map the interaction motifs either in CENP-A or HJURP. They should next mutate these motifs and validate their functional relevance in vivo.5) Is it possible that the overexpression of the 1.3 kb RNA affects the centromere function and CENP-A deposition? We thank the senior editor and all 3 reviewers for their positive comments and excellent experimental suggestions, both of which helped us strengthen the conclusions of our manuscript, namely that cell-cycle regulated non-coding 1.3kb centromeric RNAs participate in targeting HJURP and CENP-A to the human centromere, and that one such CENP-A-associated lncRNA (cenRNA#1) is important for cell integrity.Reviewer #1 Although the importance of the central findings justify publication in eLife, there were some issues that need to be addressed.1) The authors report that a search for a 665 bp portion of the 1.3 kb sequence failed to find it in the human or any other genome. Oddly, this portion lacks alpha satellite, even though it was picked up on a Northern blot using alpha satellite as probe. Some details are needed concerning exactly what the probe was. It would seem surprising if the 665-bp sequence is also absent from human short-read archives, as there is no reason to expect there to be a bias against a centromere-linked sequence in unassembled short reads produced from whole genomic DNA. If the sequence is not also found there, then some rational explanation for the phantom nature of this sequence would be welcome. For example, perhaps it derives from an as-yet unidentified endogenous RNA virus particle. In any case, the Northern should be reprobed with this 665-bp sequence to determine whether the sequence identified is actually present in the RNA.The reviewer has a valid point. We note that DNA FISH experiments  demonstrate the centromeric origin of cenRNA#1, which we originally cloned from CENP-A IPs. The logical assumption we made is that this transcript is one of the 1.3 kb RNA species observed by Northern blot using centromeric ?-satellite probe .However as the reviewer notes, the identified CenRNA#1 is curious because no significant alignment to the current human genome build can be detected (nor to any other DNA sequences extant in the literature). Similarity to ?-satellite and chromosome X-HOR is just under the 60% cutoff used in Hasson et al. (2013) . However, a weak similarity to canonical CENP-B box has been observed within this sequence for four periodically spaced 28-bp repeats (data not shown). Thus, the lack of robust identification of this sequence relative to known centromeric sequences remains a major puzzle.There are several possibilities that might contribute to this issue:Gill, Walkiewicz et al, in revision));i) Low frequency centromeric repeats from which cenRNA#1 arises were either not enriched efficiently in the Hasson et al study  As the reviewer suggests, this RNA arises from currently unidentified low frequency centromeric sequences.iii) The RNA is spliced, but we think this unlikely.iv) The RNA is unstable, making full cloning technically challenging .We probed Northern blots with the cenRNA#1 sequence, and despite our best efforts were unable to obtain a robust signal. This supports the possibility that full length cenRNA#1 is either at low abundance or is unstable. Instability in the full-length construct could also explain why alpha-satellite consensus sequence (through which the cenRNA was cloned) are lacking in the 665bp. Thus, we specifically state in the main manuscript that we were unable to detect this RNA using Northern blots.CEN#1 construct . Reassuringly, after shRNACEN#1treatment, cells display far lower density (reduced by 70%), and abnormal morphology, with very few mitotics relative to control shRNA . This phenotype is reminiscent to cells depleted of all centromeric ?-satellite transcripts . Combined with the DNA FISH experiments , this new piece of data lends support to the interpretation that cenRNA#1 is indeed involved in centromeric integrity. Further genetic and biochemical characterization of cenRNA#1, and other cenRNAs is currently a major effort in the lab.Importantly, reviewer comments prompted us to directly assay the functional biological relevance of cenRNA#1 by performing a targeted knockdown using shRNA2)Figure 1B: All 3 IPs show enrichment relative to Input and Unbound. A negative control is needed for this experiment to verify that some of the enrichment isn't a technical artifact, such as background chromatin preferentially sticking to beads or some other systematic bias.We agree and apologize for leaving it out in the original version. A mock IP control showing the specificity of the three immuno-precipitations in Figure 1B, is presented in Figure 1figure supplement 2C.Reviewer #2This manuscript presents an interesting idea, namely that a long noncoding RNA associates with HJURP and CENP-A and facilitates CENPa nucleosome assembly in early G1. I think the idea is consistent with the data, but I think there are many aspects of the experiments that could be significantly improved to be more convincing. In general, the experiments need more controls and quantification to be interpretable.We thank the reviewer for finding the data support our interpretation that ncRNAs are involved in CENP-A/HJURP targeting.1) Its not clear if the shRNAs actually target the novel RNA sequence that the authors identified, they target a-sat which colocalizes with the novel RNA sequence. This seems like a crucial piece of data. Why not design a shRNA to knock down the identified RNA sequence, show it is depleted by northern, and then perform the functional assays? The significance of the sequence identified is otherwise unclear.We agree this is an excellent experiment. An shRNA designed specifically against cenRNA#1  can be seen to strongly affects cell viability and morphology , 70% lower cell density and abnormal cell morphology, as well as very few mitotics. These phenotypes are similar to those we obtained with the total centromeric ?-satellite RNA knockdown. Further biochemical and genetic characterization of cenRNA#1 (as well as other cenRNAs) is currently underway, but outside the scope of this manuscript.2) No negative controls for the western blots inFigure 1b. Also, there are multiple bands detected for CENP-C and TBP, which bands are the relevant bands?To test the specificity of the immuno-precipitations in Figure 1B, we present a mock IP control in which the different proteins involved in this study were examined . As can be seen, these proteins are observed in input and unbound, but not in the mock IP.To detect TBP protein on Western Blot, we used the Abcam TBP antibody (ab63766), which, according the manufacturer reveals two bands- the lower band (?45kDa) is TBP, whereas the upper band (?50kDa) has an unknown identity.A perfect antibody to reveal endogenous CENP-C protein on Western Blot does not currently exist, a problem known to plague the field. However, a band ?140kDa has been observed with several CENP-C antibodies including the Santa Cruz antibody (sc11285) presented in Figure 1B. Thus, the expected band for CENP-C is the lower band on each blot presented.3) No positive control forFigure 4figure supplement 1B(-RNAse).In the initial version of the manuscript, Figure 4figure supplement 1B only presented the RNA sample treated with RNAse. This experiment was performed with the same RNA sample used in Figure 4, but treated with RNAse before loading on a separate gel. This choice of running two different gels was to avoid the possibility that RNAse may diffuse and degrade RNAs loaded in neighboring wells. We performed new Northern Blot and loaded both samples on the same gel. The result is presented on Figure 4figure supplement 1B.4) Background is too high in mock IPFigure 4figure supplement 4to know if there is any significant pull down in the mock IPs. There is a lot of smearing in part B that does not seem to be as prevalent asFigure 4S1C.The HJURP Western blot for the soluble fraction mock control in Figure 4figure supplement 4A was changed, for a new one with a lower background. However, we constantly observe this higher background in the IP lane after incubation with HJURP antibody. We hope that this new blot will convince Reviewer 2.There is also this smearing signal in the Northern blot for the mock control. We were unsuccessful to have a better blot to present in the new version of the manuscript, as the quantity of DNA extracted after IP is low and we performed the experiment with 1?g of RNA.5) InFigure 4Bthere is no mock control.The mock control for Figure 4B is presented in Figure 4figure supplement 4B. This control shows the absence of 1.3kb RNA molecule in IP lane when CENP-A or HJURP are not immuno-precipitated.6) A lot is riding on the chromatin fiber experiments. In the figure legends, there are indications of repeats and number of fibers, but there is no quantification of the results forFigures 1a, 3c, 4c, and 6. Colocalization is very difficult/impossible to see in the figures as presented.Optical chromatin fiber mapping experiments constitute a powerful technique to examine the localization of proteins at the chromatin level. However, quantification of the immuno-fluorescent signal on chromatin fibers is unlikely to be informative as the signal intensity often varies from one experiment to another. In our view, the information gained from such experiments is qualitative, providing a binary observation of the presence/absence or the distribution of the studied protein(s), not absolute amount . This is why we quantified CENP-A and CENP-B signals on whole cells in Figure 3, and not on chromatin fibers. We suggest a more meaningful measure is the number of analyzed centromeric chromatin fibers that display a particular phenotype, thus yielding a quantifiable index of the robustness of the given observation. These data are added on each figure legend presenting chromatin fibers .Reviewer #3 Overall, I think this work provides some interesting observation but they are too preliminary and contradictory to deserve publication in the present form. The authors need to seriously revise the paper, strengthen the biochemistry experiments and to validate their model in vivo and in vitro.The manuscript in its present form exhibits several caveats. The most important issues are listed below:1)Figure 1B, the Western Blot. The signal with the anti-CENP-C antibody is very weak (just slightly above the background). The authors should present a more convincing blot.Several CENP-C antibodies were tested, but none of them gave us a better result than the one presented in Figure 1B. Although the CENP-C signal is admittedly, weak, this result is reproducible, and therefore we are convinced by our interpretation.2) Mounting evidence supports the hypothesis that an RNA component is involved in centromere function; although how RNAs participate in centromere formation in mammals has remained unknown. This study identifies a novel RNA component but fail to provide convincing evidence for its implication in CENP-A deposition. The proposed model for RNA guided CENP-A deposition is interesting but the identified 1.3 kb RNA molecule is very questionable. InFigure 4B, the size of the RNA identified in the HJURP complex (1.3 Kb) is different from the one identified in the CENP-A complex (1.1 Kb). How do the authors explain this discrepancy?In Figure 4B, the observed difference in size results from a smiling migration, that is evident upon looking at the standard ladder at the far right . Thus, there is no discrepancy in the size of the RNA identified in the HJURP complex and in the CENP-A complex. Both are ?1.3 kb long.3) To be convincing, the authors need to identify the genomic region from where this centromeric RNA originates. They should clone the full length RNA and validate more carefully its implication in CENP-A deposition. The two shRNAs presented inFigure 5are useless since they were designed against the ?-satellite RNA and not against the 1.3 Kb RNA identified in this study. What is the relationship between ?-satellite RNA and the 1.3 Kb RNA? Why the authors are not able to detect the association of CENP-A with ?-satellite RNA as previously described by others :5071-80 ; Wong et al., Genome Res. 2007;17:1146-1160).et al. in mouse cells . To the best of our knowledge, Wong et al.  did not revealed an association of CENP-A and ?-satellite RNA, but instead used CENPC, INCENP and ?-satellite transcript. We did not test this possibility in our model, as we focused on the role of centromeric transcript in CENP-A and HJURP localization at centromeres.Using RNA-immuno-precipitation followed by Northern blot, we revealed an interaction between CENP-A and 1.3kb RNAs which are detected by centromeric ?-satellite primers. These data suggest that in human cells this histone variant binds ?-satellite RNAs whose size is 1.3 kb, as previously described by Ferri Figures 5 and 7 show the importance of these centromeric ?-satellite transcripts in the targeting of CENP-A and HJURP, as their down-regulation affects the centromeric localization of these two proteins. These data are key results, because they show, for the first time, the importance of centromeric RNAs in CENP-A and HJURP targeting.cenRNA#1 (data not shown). Ongoing studies are focused on a complete biochemical and genetic characterization of this and other ncRNAs associated with CENP-A, but this is outside the scope of this initial first description of RNA involvement in CENP-A/HJURP deposition.In order to better characterize these transcripts, we sequenced RNAs co-purified with CENP-A, and identified one sequence (cenRNA#1). We agree with Reviewer 2 and 3 that showing it is involved in cell integrity, is important. We provide this experiment in Figure 5figure supplement 2, designing an shRNA against cenRNA#1, and observing dramatic loss of cell density (by 70%), and abnormal cell morphology. We also see the number of mitotic cells is reduced in cells transfected with shRNA4)Figure 4figure supplement 2predicts the presence of several RNA binding motifs in HJURP and CENP-A but fail to provide a single experimental evidence for such interaction. The authors should reconstitute this interaction in vitro and map the interaction motifs either in CENP-A or HJURP. They should next mutate these motifs and validate their functional relevance in vivo.in vitro characterization of RNACENP-A and RNA-HJURP interaction. Indeed, we are very excited about these next series of experiments. However, we believe this set of experiments constitutes an obvious follow-up project, and is outside the scope of this manuscript.We agree with Reviewer 3 about the importance of 5) Is it possible that the overexpression of the 1.3 kb RNA affects the centromere function and CENP-A deposition?e.g., chromosome missegregation, loss of centromeric proteins) . However, the over-expression of centromeric RNAs observed after stress suggests this alteration may also be a response to environmental conditions . Indeed, the over-expression of cenRNAs is currently being analyzed in the lab, but is outside the scope of this manuscript.Several lines of evidence in the literature suggest that the expression level of centromeric RNAs in essential for the integrity of the centromere . For instance, Bouzinba-Segard et al.  showed the forced accumulation of the mouse centromeric repeat transcripts (120 nucleotide long) leads to chromosome missegregation. In addition, it has been shown using human artificial chromosome, the alteration of the post-translational modifications associated with centromeres, may increase or decrease the level of transcription at this chromatin domain. In both situations, the structure and function of centromere are affected (