In this study, HIV-1 Rev response element (RRE) RNA was detected via an Au-coated vertical silicon nanowire electrode array (VSNEA). The VSNEA was fabricated by combining bottom-up and top-down approaches and then immobilized by artificial peptides for the recognition of HIV-1 RRE. Differential pulse voltammetry (DPV) analysis was used to measure the electrochemical response of the peptide-immobilized VSNEA to the concentration and types of HIV-1 RRE RNA. DPV peaks showed linearity to the concentration of RNA with a detection limit down to 1.513 fM. It also showed the clear different peaks to the mutated HIV-1 RRE RNA. The high sensitivity and selectivity of VSNEA for the detection of HIV-1 RRE RNA may be attributed to the high surface-to-volume ratio and total overlap diffusion mode of ions of the one-dimensional nanowire electrodes. During the past decades, many biosensors have been developed for more sensitive and selective detection of target materials under various conditions. Among these, electrochemical sensors have raised interest due to their simplicity, rapid detectability, and low cost together with high sensitivity . AdditioOne of the critical factors that determines the performance of electrochemical sensors is the electrode where the reactions occur. Conventional macroelectrodes cannot access micro- and nanoenvironments due to their size. The development of nanoscale electrodes allows for the detection of single biomolecules  and singSeveral nanomaterials have been studied as electrodes including nanoparticles (NPs) , 10, nanNanowires (NWs), which can solve these problems, have advantages as building blocks for electrochemical sensors, such as relatively simple preparation methods allowing large-scale production, superior stability due to high crystallinity, and very large surface-to-volume (S/V) ratio. The latter are mandatory for fast reaction kinetics and high density loading of a target species and catalyst deposition over the surface for the induction or inhibition of specific reactions . Thus, oIn this study, we propose an electrochemical biosensor by combining the NWs with artificial peptides designed to recognize HIV-1 Rev response element (RRE) RNA. Developing device aspects to enhance sensor properties, we also used artificial peptides instead of intact proteins. Artificial peptides are economic, easy to synthesize, and are small in size. Additionally, it is easy to change their properties by modifying the side chains. Using the advantages of both the NWs and artificial peptides, we demonstrate ultra-sensitive and -selective detection of HIV-1 RRE RNA.−1 cm−1) at 547 nm.Rink amide MBHA resin LL and all Fmoc-protected amino acids were purchased from Novabiochem. Fmoc-Ebes-OH was purchased from Anaspec. N-Methyl-2-pyrrolidone was purchased from Merck. Other solvents were purchased from Sigma-Aldrich. Peptide was synthesized on Rink Amide MBHA resin LL using standard Fmoc protocols via a Tribute™ peptide synthesizer . The peptides were purified by reverse-phase high-performance liquid chromatography . Concentration was determined spectrophotometrically using a molar extinction coefficient of TAMRA  and a SiO2 passivation layer onto the surface of the substrate using a sputtering process at a rate of 5 nm/min. Then, the SiO2 passivation layer was selectively etched out to expose the Au tips at the top of the NWs by a complementary metal-oxide semiconductor (CMOS) process.The Au-coated vertical silicon nanowire electrode array (VSNEA) was fabricated using the procedures described previously , 18 modiThe peptide immobilization on VSNEA was carried out by infusing 50 μL of 40 μM peptide in 25 °C environments. The peptide and Au-coated SiNWs were bound by covalent interaction using cysteine  and differential pulse voltammetry (DPV) measurements were performed with 20 mM potassium ferrycianide in 50 mM phosphate-buffered saline (PBS) solution. CV measurements were carried out in a potential range of 0.8 to −0.8 V and the scan rate was 20 mV/s. Sensitivity and selectivity were measured by DPV with a potential range from −0.2 to 0.6 V, pulse amplitude of 50 mV, and pulse width of 10 ms.Figure The diameter and length of the SiNWs could be controlled by the size of Au colloidal particles and growth time. The density of SiNWs on the substrate could also be controlled by the concentration of Au nanoparticles in the colloidal solution. Previous studies have shown that the density and size of NWs that are electrodes of a device can change the properties like sensitivity and selectivity of the NW-based sensors .S) and capacitive currents  at the VSNEA under a total overlap regime, then we obtain the following equations  with respect to the target RNA concentration. The regression equation for the peak current (I), and the concentration of the target RNA (c), shows a correlation coefficient (R2) of 0.99557 with standard deviation of ± 2.310−7 fM. Detection limit was calculated to be 1.513 fM. Recent HIV-1 biosensors using nanomaterials such as nanopore  were evaluated by DPV measurement. The sequences used were as follows:RRE IIB RNA sequence:GGGCGCAGCGCAAGCUGACGGUACAGGCCAGGUCGACCUGGUAURRE IIB mutant sequence:CGGCGCAGCGCAAGCUGACGGUAGAGGCCAGGUCGACCUGGUAUT7 primer DNA sequence: TAATACGACTCACTATAGGAGFigure Sensitive and selective detection of HIV-1 RNA by using VSNEA combined with artificial peptides was demonstrated. The advantages of vertical, 1D nanowire electrodes such as high surface area to volume ratio and total overlap diffusion of ions make it possible to detect RNA concentrations of up to 1.513 fM and distinguish between wild-type RNA and mutant RNA. Thus, vertical nanowire sensors may be used as sensitive and selective biosensor platforms in many applications.


In eukaryotic cells, inefficient splicing is surprisingly common and leads to the degradation of transcripts with retained introns. How pre-mRNAs are committed to nuclear decay is unknown. Here, we uncover a mechanism by which specific intron-containing transcripts are targeted for nuclear degradation in fission yeast. Sequence elements within these “decay-promoting” introns co-transcriptionally recruit the exosome specificity factor Mmi1, which induces degradation of the unspliced precursor and leads to a reduction in the levels of the spliced mRNA. This mechanism negatively regulates levels of the RNA helicase DDX5/Dbp2 to promote cell survival in response to stress. In contrast, fast removal of decay-promoting introns by co-transcriptional splicing precludes Mmi1 recruitment and relieves negative expression regulation. We propose that decay-promoting introns facilitate the regulation of gene expression. Based on the identification of multiple additional Mmi1 targets, including mRNAs, long non-coding RNAs, and sn/snoRNAs, we suggest a general role in RNA regulation for Mmi1 through transcript degradation.  •“Decay-promoting” introns contain sequences that activate nuclear mRNA decay•Fast splicing of decay-promoting introns prevents recruitment of decay factors•In contrast, increased intron retention under stress results in low gene expression•This mechanism acts to promote cell survival in response to stress Inefficient splicing is common and leads to the degradation of transcripts with retained introns. Kilchert et al. show that “decay-promoting” introns harbor sites that recruit RNA decay factors. Although fast splicing precludes recruitment of the nuclear surveillance machinery, increased intron retention reduces gene expression, which can promote cell survival under stress. To produce functional mRNAs, introns in nascent pre-mRNAs must be removed by splicing. Recent studies suggest that splicing is a highly regulated process and responsible for changes in gene expression during differentiation or in response to environmental cues . In highSaccharomyces cerevisiae Trf4/5-Air1/2-Mtr4 polyadenylation (TRAMP) complex consisting of the RNA helicase Mtr4, the polyA (pA) polymerase Trf4, and the RNA-binding protein Air1 or Air2. TRAMP adds short pA tails to transcripts  associate with the canonical pA polymerase Pla1 and function as central activators of the nuclear exosome , RNA sequences that are enriched for the hexanucleotide motif U(U/C/G)AAAC . This re mutants . Althougmmi1 deletion strain (mmi1Δ). We report that expression of many protein-coding and ncRNAs is significantly changed in mmi1Δ, suggesting that Mmi1 could act as a global regulator of mRNA metabolism. However, based on our data, we conclude that the main role of Mmi1 is the activation of RNA decay. Substrates include pre-mRNAs, ncRNAs, and 3′-extended RNAs that are products of leaky transcription termination.Here, we perform UV cross-linking and analysis of cDNA (CRAC) to identify Mmi1 RNA substrates. Unexpectedly, our results demonstrate that Mmi1 associates with RNAs synthesized by all three RNA polymerases (Pol), I, II, and III. To determine how Mmi1 contributes to the regulation of RNA metabolism, we used RNA sequencing (RNA-seq) to measure changes in RNA expression levels in a dbp2 and rps2202, which encode a conserved essential RNA helicase and the ribosomal protein S15a, respectively, we show that Mmi1 binding to the intron reduces gene expression levels under conditions that favor IR. If conditions allow fast splicing, then Mmi1 is no longer recruited, leading to increased levels of mRNA. Strikingly, deletion of the introns deregulates gene expression and alters cell viability in response to environmental stresses. We propose that these “decay-promoting” introns facilitate the regulation of gene expression in response to environmental cues.Importantly, we show that, in some cases, Mmi1 preferably binds introns, suggesting that Mmi1 function on these transcripts depends on splicing. By studying the regulation of two individual examples, mei4 and crs1 or the regulatory phosphate-responsive ncRNAs prt and nc-tgp1 /small nucleolar RNAs (snoRNAs) and many Pol III transcripts. The long ribosomal precursor generated by Pol I contributed strongly to the total number of reads. 13.5% of total reads mapped to intergenic regions, consistent with a role for Mmi1 in regulating long non-coding RNA (lncRNA) expression and/or quality control of 3′ end processing of RNAs.mei4, crs1, spo5, and rec8 were among the most enriched mRNAs. sme2, a well-characterized ncRNA target of Mmi1   C–S2E. St of sme2 F. In theprt/pho1 D and 2F.le, pre6 F and S3A rps2202 . What prin mmi1Δ C. cDNA rmmi1Δ  . Why specifically these transcripts show splicing defects in mmi1Δ remains unclear. Our data strongly suggest that the main function of Mmi1 is RNA degradation. Only a minor portion of the transcriptome depends on Mmi1 for RNA processing (splicing and 3′ end formation), and it is not clear whether these effects are direct.Interestingly, for ∼100 intron-containing genes, we observed an increase in 5′ ss exon-intron boundary reads in n reads) . These rn reads) D, which pre-mRNA D. Howeveg defect D. This ired1Δ)   were als5; mmi1Δ , which sdbp2 and rps2202, where Mmi1 is bound within intronic regions  but not in pab2Δ or red1Δ  region within the intron for which we had detected the strongest cross-linking to Mmi1  could confer an advantage to the cell; for example, by facilitating regulation of gene expression. This prompted us to investigate whether removal of the intron would be detrimental to growth under certain conditions. To test this, we serially diluted cells expressing WT dbp2 or dbp2-i2Δ onto different growth media. In S. cerevisiae, Dbp2 responds to glucose depletion  S15a   S15a A. Unsplinditions A. To tese (dbp2) B and S6A. As negative controls, we either integrated intron-less ura4 or a construct that contained the rps3 intron, which is spliced efficiently . Intriguingly, the pathway may be conserved. DDX5/p68, for example, the human homolog of dbp2, also contains the conserved intron , suggesting that the gene may be autoregulated . Therefore, autoregulation of dbp2 splicing in S. pombe clearly remains a possibility. If true, then this would suggest that IR could be alleviated under conditions where the pool of free Dbp2 is decreased or its access to the nucleus is restricted, as occurs in S. cerevisiae upon glucose depletion  negatively influences splicing of the other (rpl30-2) . Therefore, Mmi1 could have a broader role in the regulation of ribosome biogenesis.For splicing . Fissionrpl30-2) . Interesin mmi1Δ D. More rin mmi1Δ . Interesrps2202 in nuclear surveillance mutants? Globally, only a handful of genes display hyper-IR in mmi1Δ (mmi1Δ encode proteins that have been linked to RNA biogenesis. One example is Mlo3 (a homolog of the S. cerevisiae export factor Yra1), which associates with TRAMP to suppress RNA transcribed from heterochromatic loci . Error bars represent SEM of at least two biological replicates.Chromatin immunoprecipitation (ChIP) assays were performed as described previously  and quanC.K., S.G., and L.V. designed experiments; C.K., S.W., M.P., S.S., and S.G. performed experiments; C.K. and S.G. analyzed data; C.K., S.G., and L.V. wrote the manuscript. All authors edited the manuscript.


PHF13 is a chromatin affiliated protein with a functional role in differentiation, cell division, DNA damage response and higher chromatin order. To gain insight into PHF13's ability to modulate these processes, we elucidate the mechanisms targeting PHF13 to chromatin, its genome wide localization and its molecular chromatin context. Size exclusion chromatography, mass spectrometry, X-ray crystallography and ChIP sequencing demonstrate that PHF13 binds chromatin in a multivalent fashion via direct interactions with H3K4me2/3 and DNA, and indirectly via interactions with PRC2 and RNA PolII. Furthermore, PHF13 depletion disrupted the interactions between PRC2, RNA PolII S5P, H3K4me3 and H3K27me3 and resulted in the up and down regulation of genes functionally enriched in transcriptional regulation, DNA binding, cell cycle, differentiation and chromatin organization. Together our findings argue that PHF13 is an H3K4me2/3 molecular reader and transcriptional co-regulator, affording it the ability to impact different chromatin processes.DOI:http://dx.doi.org/10.7554/eLife.10607.001 In human and other eukaryotic cells, DNA is packaged around proteins called histones to form a structure known as chromatin. Chemical tags added to the histones alter how the DNA is packaged and the activity of the genes encoded by that DNA. For example, many active genes are packaged around histone H3 proteins that have “Lysine 4 tri-methyl” tags attached to them. Another protein that is associated with chromatin is called PHF13 and it has several roles, including repairing damaged DNA. However, it was not known whether PHF13 binds to chromatin via the chemical tags, or in another way.Ho-Ryun, Xu, Fuchs et al. used several biochemical techniques in mouse and human cells to explore how PHF13 specifically interacts with chromatin. These experiments showed that PHF13 binds specifically to DNA and to two types of methyl tags (lysine 4-tri-methyl or lysine 4-di-methyl). These chemical tags are predominantly found at active promoters as well as at a small subset of less active promoters known as bivalent promoters. PHF13 interacted with other proteins on the chromatin that are known to either drive or repress gene activity and it’s depletion affected the activity of many genes. Whether PHF13 increased or decreased gene activity depended on whether it was bound to active or bivalent promoters. The active promoters targeted by PHF13 had higher numbers of the tri-methyl tags whereas the di-methyl tags were more common on the bivalent promoters.These findings provide preliminary evidence that a protein binding to different methyl tags in the same place on histone H3 can have opposite effects on gene activity. Ho-Ryun, Xu, Fuchs et al. now intend to find out more about the other proteins that interact with PHF13 on chromatin.DOI:http://dx.doi.org/10.7554/eLife.10607.002 Survival time associated PHD finger in Ovarian Cancer 1 (SPOC1), is a chromatin affiliated protein that is conserved from zebra fish to humans. PHF13 has been shown to modulate various processes including development  and RNA polymerase II (RNA PolII) complexes as novel PHF13 chromatin interaction partners. Consistently, PHF13 ChIP sequencing targets co-occurred with CpG rich DNA, H3K4me2/3, PRC2 and the hypophosphorylated, serine 5 and serine 7 phosphorylated forms of RNA PolII in murine embryonic stem cells (mESCs). PHF13 depletion in mESCs resulted in the reduced binding of SUZ12 and RNA PolII S5P to H3K4me3 and H3K27me3 and altered gene expression of a fraction of PHF13 bound genes. Genes that were up regulated upon PHF13 knockdown were enriched in H3K4me2/3, Polycomb and RNA PolII S5P, while down regulated genes were enriched in H3K4me2/3 and RNA PolII S2P and S5P. Finally, PHF13 target genes were enriched in the functional categories of transcription regulation, cell cycle, chromosome organization and differentiation, consistent with earlier publications describing a role of PHF13 in these processes . TogetheWe have previously demonstrated via differential nuclear fractionation experiments that PHF13 is predominantly affiliated with the chromatin fraction of nuclear lysate, implicating a role in chromatin function . TherefoThe inability of PHF13’s PHD domain to interact with recombinant mono-nucleosomes which are devoid of histone post-translational modifications, suggested that it may selectively interact with modified histone tails. Consistently, previous mass spectrometry and bioinformatic predictions indicated that PHF13 may interact with H3K4me3 . To addrTo further evaluate the specificity of PHF13’s PHD domain for H3K4me2/3 in relation to other methyl lysine residues and to calculate approximate binding constants we employed fluorescence polarization analysis using the PHD domain only of PHF13 and differentially modified histone H3 peptides . PHF13’sTo gain structural insight into this specific recognition of histone H3K4me3 by the PHD domain of PHF13, we determined the crystal structures of both the apo-PHF13 PHD domain (aa 226–280) and the PHF13 PHD-H3K4me3 peptide complex . The difThe surface charge representation shows that Arg2 sits in a shallow negatively charged pocket, reminiscent of the Arg2 binding in the ING2 and BPTF2 PHD domain-H3K4me3 complex structures, but in contrast to the RAG2-H3K4me3 complex structure, in which Arg2 is bound in a relatively hydrophobic pocket . This inTo gain additional information about PHF13 chromatin interactions, we immunoprecipitated PHF13 from the nuclease digested chromatin fraction of mESCs and analyzed the co-immunoprecipitating proteins in comparison to an IgG control by mass spectrometry . Indepenbona fide H3K4me2/3 reader.Our results strongly suggest that PHF13 binds nucleosomes marked by H3K4me2/3 via its C-terminal PHD domain – an interaction that can be further stabilized by PHF13’s ability to bind DNA and chromatin affiliated PRC2 and RNA PolII. Thus, we expect that PHF13 should co-localize with H3K4me2/3, PRC2 (SUZ12 and EZH2), RNA PolII and certain DNA sequence motifs in vivo and genome wide. To test this idea we identified PHF13 bound regions by chromatin immunoprecipitation followed by sequencing (ChIPseq) and quantified their overlap with regions marked or bound by H3K4me1, H3K4me2, H3K4me3, H3K9me3, H3K27me3, SUZ12, EZH2 and the different hypophosphorylated and phosphorylated forms of RNA PolII  obtainedSimilarly, we examined the overlap of PHF13 bound regions with H3K9me3 and H3K27me3 repressive modifications . Not surNext we examined the overlap of PHF13 ChIPseq peaks with the hypophosphorylated, serine 2, serine 5 and serine 7 phosphorylated forms of RNA PolII . We obseFinally, given the in vitro DNA binding activity of PHF13 , we alsoTo better understand these PHF13 chromatin contexts we employed the k-means clustering approach to separate PHF13 bound regions by the patterns of histone modifications and chromatin binding proteins  show twoTo gain insight about the functional role of genes targeted by PHF13, we performed an overrepresentation analysis of associated functional terms. We defined PHF13 target genes  by intersecting a window of +/- 1500 base pairs around their transcription start sites (TSS) with PHF13 bound regions . The oveSince PHF13 interacts with RNA PolII in different constellations, with and without PRC2  and sincTo further explore the relationship between the differentially expressed genes and the ChIPseq levels of PHF13, H3K4me1, H3K4me2, H3K4me3, H3K27me3, EZH2, SUZ12, and the differently phosphorylated forms of RNA PolII we compared their normalized levels to the corresponding levels in the unchanged genes . We founThe observations that PHF13 interacts with PRC2 and RNA polymerase II, that they co-localize together at both active and inactive gene promoters and that PHF13 depletion alters the gene expression of a subset of these has lead us to propose that PHF13 acts as a transcriptional co-regulator. In an effort to better understand how PHF13 is influencing transcription, we employed a Doxycyclin inducible PHF13 shRNA mESC cell line. Using this tool we immunoprecipitated PHF13, SUZ12, RNA PolII S2P and RNA PolII S5P from wild type (- Dox) and PHF13 depleted (+ Dox) nuclease digested chromatin lysates. We then checked whether PHF13 depletion interfered with the co-precipitation of PHF13, SUZ12, RNA PolII S2P, RNA PolII S5P, H3K4me3 and H3K27me3, from each of these immunoprecipitations . As expeIn this manuscript we demonstrate that PHF13 is a novel H3K4me2/3 reader and effector. We show that its affiliation with H3K4me2/3 and CpG rich DNA sequences facilitates its specific recruitment to repressed or active chromatin regions, interactions that are conceivably strengthened by additional interactions with PRC2 and RNA PolII. PHF13 depletion disrupts the interactions between PRC2, RNA PolII S5P, H3K4me3 and H3K27me3, indicating that they are in common complexes and that PHF13 is integral to their localization at active and bivalent promoters. Furthermore, PHF13 depletion alters the gene expression of a subset of genes affiliated with these proteins consistent with a co-transcriptional role.The PHD domain of PHF13 displays a specific albeit weak binding affinity to H3K4me2/3, a phenomenon that is common to many PHD domains  and indiAnalysis of PHF13 ChIPseq targets identified that PHF13 localizes to at least 2 distinct chromatin landscapes . A PolycThe comparative analysis of the ChIPseq and RNAseq experiments  indicateA model depicting PHF13’s ability to affiliate H3K4me3, DNA, PRC2 and RNA PolII to co-regulate gene expression is shown in Together our results demonstrate that PHF13 can contact chromatin in a multivalent fashion via direct interactions with DNA and H3K4me2/3 and indirectly by affiliation with Polycomb and RNA PolII complexes. ChIP sequencing revealed that in vivo PHF13 associates with distinct chromatin landscapes and that it shows a substantial overlap with H3K4me2/3, CpG rich DNA and hypo-, Ser5, and Ser7- phosphorylated RNA PolII, and a modest overlap with a subset of bivalent PRC2 targets. Furthermore, its depletion lead to a reduced interaction of RNA PolII S5P and SUZ12 with H3K4me3 and H3K27me3 and with each other, supporting that there are common complexes between these proteins and that PHF13 may act as a scaffold or bridging factor. Finally, PHF13 depletion in mESCs lead to both the up and down regulation of a subset of target genes functionally enriched in transcription, cell cycle, chromatin organization, DNA binding and developmental processes, explaining in part its ability to modulate such processes. Taken, together these data support that PHF13 is an H3K4me2/3 molecular reader and transcriptional co-regulator of H3K4me3 active and H3K4me3/H3K27me3 bivalent promoters.HeLa cells obtained from ATCC were used for GST-pull down experiments and were grown in DMEM supplemented with 1x Penicillin/Streptomycin, 10% FBS (Biochrome), 1x hepes and 1x sodium pyruvate. All other experiments were performed with mouse embryonic stem cells (mESCs) grown in Glassgow MEM (Sigma G5154), 20% FBS Hyclone – ThermoScientific SV30160.03, 1x Glutamax, 1x non-essential amino acids, 1x sodium pyruvate and 1.2 ml β-mercaptomethanol (Gibco 31350010). E14 mESCs were obtained originally from ATCC and were given to us by Prof. Luciano DiCroce. E14 mESCs were grown on gelatin coated plates. Inducible PHF13 shRNA mESCs are primary mESCs isolated from mice blastocytes and were purchased from Artemis Pharmaceuticals Gmbh. They were grown similar to E14 mESCs except that they were not grown on gelatin coated plates. Early passages required feeder cells but this was gradually eliminated by 10–15 passages. Both mESCs cell cultures were periodically stained for alkaline phosphatase activity (Millipore SCR004) to ensure they were significantly enriched (>90%) in stem cells and contained limited differentiated cells. All cell lines were routinely tested to ensure the absence of mycoplasma (Thermofisher Scientific C7028).Purified GST proteins were generated using standard procedures. In brief, BL-21 bacteria carrying the GST expression vector were grown in 1x LB medium plus ampicillin overnight at 37°C under rotation (180 rpm). The next day the GST protein expression was induced using 1 mM IPTG and incubated for 4 hr at 30°C under rotation. The bacteria were then pelleted and stored at -80°C overnight. The pellet was then lysed in 10 ml of MTTBS buffer  supplemented with 30 mg lysozyme, 100 µl of RNAse A (10 mg/ml), 100 µl of DNAse I (50 µg/ml), 100 µl of complete protease inhibitor (Roche) and 100 µl of PMSF, for 10 min on ice. The bacterial lysates were then sonicated at high frequency for 3 x 15 s on ice, centrifuged at 13,000 rpm for 20 min at 4°C and the supernatant added to Glutathione agarose (500 µl) for 3h under rotation at 4°C. The beads were then washed with MTTBS (1x) and with PBS (2x) and frozen at −80°C until needed.GST proteins were eluted from the beads in 250 µl of 40 mM reduced Glutathione for 2 hr at 4°C. The protein concentration of the elution was calculated. 1 µg of GST protein was incubated with 1 µg of biotinylated peptide and 10 µl Streptavidin Dynabeads (Life Sciences) in 300 µl of binding buffer  overnight at 4°C under rotation. The beads were then washed 5x with binding buffer and resuspended in 2x Laemmli buffer. The interactions were evaluated by SDS-PAGE and detected using a GST specific antibody (Invitrogen 71–7500).2) on ice for 10 min and then lysed with a CSK buffer , 1 mM PMSF) for 15 min on ice. The chromatin was then pelleted by centrifugation and the supernatant (cytoplasm/nucleoplasm) was discarded. The chromatin was then resuspended in a nuclear lysis buffer , 2.5 mM MgCl2,1 mM PMSF, 1x complete protease inhibitor cocktail (Roche), 1 mM Na3VO4 and1 mM NaF) for 30 min on ice. The debris was pelleted and the supernatant collected. The lysate was then diluted 1:10 for GST-pull downs with dilution buffer  and incubated with 3 µg of GST proteins (still on the Glutathione agarose). The lysate bead mixture was incubated at 4°C for 1.5 hr under rotation and then washed 5x with PBS and resuspended in 2x Laemmli buffer. The interactions were evaluated by SDS-PAGE using specific antibodies against H3K4me3 (Millipore 04–745).Nuclei were prepared from HeLa in a hypotonic buffer , 1 mM PMSF) for 10 min on ice to isolate the cytoplasm. After centrifugation at 10,000 x G for 5 min, the supernatant was discarded and the pellet resuspended in a mild chromatin buffer  supplemented with 1x complete protease inhibitor cocktail (Roche) for 10 min on ice to isolate the nucleoplasm. After centrifugation at 10,000 x G for 5 min, the supernatant was discarded and the pellet resuspended in nuclease digestion buffer  and 1 µl of Benzonase (Novagene – 70746–3)) for 10 min at RT and then for 30 min on ice to isolate the chromatin fraction of cellular lysate. The lysate was then centrifuged at 14,000 x G for 5 min and the supernatant collected. IPs were performed overnight at 4°C under rotation and then washed 5x in IP buffer, transferred to a new tube and denatured in Laemmli buffer. IPs were analyzed by immunoblot (4–15% gradient gels – Biorad) for precipitation of rt mAb PolIIS2 (Active Motif - 61083), mo mAb PolIIS5 (Abcam ab5408), rt mAb PolIIS7 (Active Motif - 61087), mo mAb SUZ12 (Millipore – 04–046), mo mAb EZH2 , rt mAb PHF13 , rb mAb H3K4me3 (Millipore 04–745), rb pAb H3K27me3 (Diagenode – pAb-195-050).Immunoprecipitations were performed on nuclease digested lysates from E14 mESC cells using 20 ul of protein A agarose beads (Diagenode) containing 2 µg of either rabbit control IgG immunoglobulins (Diagenode - C15410206), rb pAb SUZ12 (Abcam – ab12073), rb pAb RNA PolIIS2P (Abcam – ab5095), rb pAb RNA PolII S5P (Abcam – ab5131) or rb pAb PHF13 specific antibody CR56. 1x 15cm dish of E14 mESC cells was used per IP and lysed first in a CSK buffer  for 20 min on ice and was analyzed by native PAGE. The label was detected with a Fujifilm Phosphoimager FLA-3000. Mononucleosomes for EMSAs were generated by salt gradient dialysis as previously described  by the addition of 0.05 mg of TEV protease per milligram of PHF13 protein, followed by incubation in ice for 12 hr. The sample was then passed through a Ni-NTA column and the flow-through was collected and concentrated for crystallization. In another PHF13 construct (residues 232–281), the his-tag was not removed.Two fragments of human PHF13 (residues 232–281 and 226–280) covering the PHD domain were subcloned into a pET-28a-MHL vector via ligase-independent cloning. The recombinant proteins were over expressed in Escherichia coli BL21 (DE3) with the pRARE plasmid for codon-biased expression. Cells were grown in minimal media at 37°C to an optical density of approximately 2.5. Protein expression was induced with 1 mM isopropyl-1-thio-D-galactopuranoside and the cell cultures were grown for approximately 16 hr at 15°C after induction. The proteins were purified by affinity chromatography on Ni-NTA resin  and size exclusion chromatography using a Superdex 75 column  in 20 mM Tris-HCl pH 8.0, 200 mM NaCl, 1 mM DTT and 20 µM ZnClD values, the data were fit to a hyperbolic function using Sigma Plot software .The Fluorescence Polarization Assays were performed as described . All pep280 and OD260, respectively. A total of 25–27 injections were performed with a spacing of 180 s and a reference power of 15 μcal s−1. Binding isotherms were plotted and analyzed using Origin Software . The ITC measurements were fit to a one-site binding model.Isothermal titration calorimetry measurements were recorded as described previously  at 25°C Purified PHF13 (residue 226–280) was mixed with methylated histone peptides by addition of a two-fold molar excess of peptide to the protein solution and crystallized at 18°C in 0.1M Tris-HCl pH 7.5, 1.5 M Na-Citrate, PEG400 6%. Crystals of PHF13 (residue 232–281) were obtained at 18°C in of 0.1M CHES pH 9.5 and 20% PEG8000.Diffraction experiments were performed at 100 K. Further experimental details are listed in Chromatography was performed using nuclease digested chromatin lysates obtained from 14 (15cm) plates of E14 ES cells and run on a Superose 6 10/300 GL column  in a buffer composed of 50 mM Tris-HCl (pH 7.5) and 100 mM NaCl with a flow rate of 0.3 ml/min. Fractions were collected every 2 min.The HiPPRTM Detergent Removal Resin Kit  was used to remove remaining Triton X contaminations in magnetic beads samples, according to manufactures instructions. Proteins were subsequently digested with 100 ng trypsin (Roche) at pH 8.0 on a rocking platform at 37°C, overnight. Tryptic digests were acidified by 1% final concentration of formic acid, cleaned by C18 StageTips  and lyophilized.PHF13 and control IPs were run into 4–15% gradient gels (Biorad) for 12 min at 100V and then excised from the gel. Gel samples were reduced in 50 mM DTT at 56°C for 1 hr and alkylated with a final concentration of 5.5 mM chloroacetamide for 30 min. In gel digestion was done by 1 µg LysC (Roche) incubation at 24°C for 24 hr, according to .2O, 0.1% formic acid). Nanoelectrospray was generated by applying 3.5 kV. A cycle of one full Fourier transformation scan mass spectrum  was followed by 12 data-dependent MS/MS scans with a normalized collision energy of 25 eV. MS data were analyzed by MaxQuant (v1.5.3.3)  coupled online to a Thermo Scientific Q-Exactive HF Orbitrap mass spectrometer (nanoLC-MS/MS). Briefly, the LC separation was performed using a PicoFrit analytical column  packed in-house with 3 µm C18 resin  under 50°C controlled temperature. Peptides were eluted using a gradient from 3.8 to 40% solvent B in solvent A over 60 min at 266 nL per minute flow rate. Solvent A was 0.1% formic acid and solvent B was 79.9% acetonitrile, 20% H1.5.3.3) , and sea3 and 0.5% SDS): 0.5 volume of Triton dilution buffer . The lysate was passed through a syringe multiple times and then sonicated in Bioruptor at high frequency for 2 x 6 cycles. The lysate was then centrifuged at 13,000 rpm for 30 min to pellet debris and a protein determination was measured. 1 mg of chromatin was then incubated with 5 µl of PHF13 antibody (CR56) or rabbit IgG control antibody overnight at 4°C under rotation. 30 µl of protein A beads were then added to the reaction and allowed to incubate at 4°C for 2h. The beads were then washed by successive low salt , high salt  and LiCl washes and then eluted for 3h at 65°C with 110 µl of elution buffer (1% SDS and 100 mM NaHCO3). The RNA and proteins were then digested with RNAse A and Proteinase K and the DNA purified and sequenced.Chromatin immunoprecipitation was performed using a standard technique. In brief E14 ES cells were grown in the presence of LIF and 20% FBS. The cells were collected and cross-linked for 15 min with 1% formaldehyde. The fixation reaction was quenched by the addition of glycine (0.125 M) for 10 min. The cells were then lysed on ice for 10 min in 1.3 ml of ChIP buffer, 1 volume of SDS buffer , mixed in a conical tube and plated on 10 cm gelatin coated plates.The next day medium was replaced with fresh E14 mESC medium and the cells were put under Puromyocin selection (4 µg/ml). The cells were split every two days and kept under selection. After 12 days the cells were harvested for RNA sequencing. The shRNA sequence for Mission PHF13 shRNA3 TRCN0000241824 is CCGGCCGGGACTCCAAGTTTGATATCTCGAGATATCAAACTTGGAGTCCCGGTTTTTG.Mission-pLKO.1 lenti-viruses (Sigma Aldrich) for Phf13 shRNA (Mission Phf13 shRNA3 TRCN0000241824) and control scrambled shRNA (Mission ShControl– SHC001) were generated by calcium phosphate co-transfection of 18 ng of DNA  in 15 cm dishes of 293T cells. The next day the cells were washed and the medium was replaced and 48 hr post transfection, the supernatants containing lenti-viruses were collected. 6 ml of lenti-viral medium supplemented with 20% FCS and 20 µl β-mercaptomethanol (Gibco 31350010) was diluted with 3ml fresh medium, 9 µl Pb, 20 µl LIF and 1 ml of E14 mESCs  into the Rosa26 locus. The RNAi sequence that was used is GCACCTTCGTCTTGGCATATG.Total cellular RNA was collected from wild-type E14 ES cells or from PHF13 depleted ES cells using RNeasy plus mini-kit (Qiagen) according to manufactures instructions. 1.5 µg of total RNA was then depleted of ribosomal RNA using the Ribo zero magnetic kit (Epicenter) according to the manufacturer’s directions. The rRNA depleted RNA was then measured by Qubit and 20 ng was used to generate the RNA library using ScriptSeq v2 RNA-Seq library preparation kit (Epicenter) according to the manufacturer’s instructions.The paired-end 50 reads were aligned to the mouse genome (downloaded from ENSEMBL: Mus_musculus.GRCm38.dna.primary_assembly.fa) using STAR . The index was built with the --sjdbGTFfile option set to “Mus_musculus.GRCm38.83.gtf”.Differential gene expression was determined by DESeq2 (PMID: 25516281). Counting was performed using the Bioconductor package bamsignals.The ChIP-seq reads  for eachThe DNase HS reads were individually mapped against the mouse genome (genome version mm10) using Bowtie2 with default parameters. The resulting SAM file was converted into BAM, sorted and indexed using samtools. BigWig files were generated from the individual, unmerged BAM files using igvtools count with the parameters: --minMapQuality 30 –postExtFactor 1, whose output was piped into wigToBigWig using the parameters: -clip--treatment <BAM file> --control <BAM file> --format BAM --gsize mm --nomodel --shift-size 100 --keep-dup auto --call-summits . In case--treatment <BAM file> --format BAM --gsize mm --nomodel --shiftsize 100 --keep-dup 1 --call-summits.The Venn diagrams were generated using the R-package DiffBind and VennDiagram . All PHFPHF13 peak centered heatmaps were generated with an in-house developed script written in R. For each ChIPseq target the read counts (in non-overlapping 51 base pair bins) were obtained in a window spanning +/- 5100 base pairs around PHF13 peak summits inferred by MACS2. Counting was performed directly from the BAM files using the Bioconductor package bamsignals. For all ChIPseq tracks the 5’ ends were shifted 100 base pairs downstream, while the DHS track was left unshifted. The resulting matrices were further processed by identifying the value corresponding to the 99.9% quantile of read counts, setting all matrix entries larger than this value to this value and dividing by the value. The so-obtained matrices except the one corresponding to PHF13 were concatenated and served as input to the k-means clustering algorithm with two cluster centers. The obtained clustering was used to order the PHF13 ChIPseq peaks first by cluster and second by the peak score reported by MACS2.The boxplots were generated from control normalized average ChIPseq signals in a region +/- 1500 base pairs around the transcription start site (TSS). First, reads were counted using bamsignals for each of the TSSs. Second, we used an in house developed R package, normR, to normalized the data. Briefly, normR fits a binomial mixture model, where one component corresponds to the background and the other to signal. Using the background component, we estimate pseudo counts and add these to the observed counts . If where for those promoters as estimate for the signal strength grouped by either down- or up-regulated or unchanged genes upon PHF13 knockdown with an adjusted p-value cutoff of 0.05. The boxplots were generated using the R function boxplot.For each gene we determined the promoter with the highest H3K4me3 enrichment. Shown in the boxplots is 10 was used to determine the word size.ChIPSeq targets of PHF13, RNASeq up or down-regulated genes were collected and served as input to DAVID/EASE. Overrepresented GO terms for the ontologies Biological Process (BP_FAT), Molecular Function (MF_FAT), and Cellular Component (CC_FAT) were downloaded and visualized with the R-package wordcloud, where the -10 * loghttp://www.ncbi.nlm.nih.gov/sra).Accession numbers are from the short read archive  You should carry out anti-PHF13 IPs from fractions 16-20, 22-26, and 27-30 see  and anal2) You need to examine if there is any 'free' PHF13 in sESCs and interpret the ChIPseq data accordingly? Without this knowledge, it is very difficult to understand why there are so few overlapping ChIP-seq binding sites between PHF13 and SUZ12 or between PHF13 and EZH2.3) You need to state what type of RNA was analyzed by RNA-seq. If it was total mRNA, then you need to tone down the interpretations on transcriptional regulation, because the state of the field now is to look at nascent RNA.4) Validation of the ChIP-seq results is required by ChIP-qRT PCR for example.5) Demonstration that similar RNA changes were observed with more than one siRNA is necessary to prove these were not off target effects. This could also be done by qRT PCR.6) Determine whether the interaction between the PHF13 PHD finger and histone H3K4 methylation is required for PHF13's function. 1) You should carry out anti-PHF13 IPs from fractions 16-20, 22-26, and 27-30 see  and analWe have replaced In the original manuscript we analyzed the size exclusion profile of PHF13 in U20S cells and observed a partial overlap of PRC2 with the elution profile of PHF13. Furthermore, we showed that PHF13 could co-immunoprecipitate the PRC2 complex members SUZ12, EZH2 and EED. Through the technique of FACS-FRET we determined that the interaction of PHF13 with PRC2 was likely via SUZ12 as no (“direct”- i.e. less than 10Å) interaction was observed using this approach with EZH2 or EED.In the original manuscript we were unaware of the interaction between PHF13 and RNA PolII. This interaction was identified during this revision due to the request that we perform mass spectrometry from PHF13 immunoprecipitations to help shed some light on “the identification of the different PHF13-containing positive and negative co-regulatory complexes , mid (18-26) and lower (27-33) molecular weight fractions see . The fraImmunoprecipitation of PHF13 from these different fractions indicated that PHF13, RNA PolII and PRC2 (SUZ12 and EZH2) were most efficiently precipitated and co-precipitated from fractions 18-26, indicating that PHF13 interacts with these proteins in this fraction pool (18-26) and supports the possibility of a common molecular complex containing RNA PolII, PRC2, PHF13, H3K4me3 and H3K27me3. Similarly, PHF13 co-precipitated with these proteins in the high molecular weight pool (13-17) albeit with substantially less PRC2. Finally, PHF13 co-precipitated only with RNA PolII S2P and S7P and with substantially reduced PRC2 in fractions (27-33). In this lower molecular weight pool RNA PolII S5P and histones were not detected. It is reported that PRC2 specifically interacts with RNA PolII S5P , arguing that PHF13’s forms independent interactions with RNA PolII S2P, S7P and PRC2 in this lower molecular weight pool. Together, this argues that PHF13 forms different RNA PolII containing complexes.The mass spectrometry analysis has proven to be the most difficult point in this revision, due to technical challenges. We have repeated the column chromatography in E14 ES cells and can show co-elution of PHF13 with PRC2 (EZH2 and SUZ12) in a subfraction of the eluted fractions. Furthermore, from the pooled PHF13 and PRC2 overlapping subfractions we are able to co-immunoprecipitate PHF13 and PRC2 together, supporting that they are in a complex together. Unfortunately, we have failed to visualize this interaction by mass spectrometry from PHF13 immunoprecipitated column chromatography pooled fractions. However, we did manage to see these interactions by mass spectrometry from classical PHF13 immunoprecipitation in E14 mESCs nuclease digested chromatin lysates .We had two major problems contributing to these failures. One problem is that we have had recurrent PEG contamination from an unknown source, causing ion suppression and no peptides being visualized by MS. We were able to partially rescue this problem, by either running our samples first through a 4-15% gradient gel, or by using a detergent removal resin from Pierce prior to loading the samples on the MS. Under these conditions we successfully identified PHF13 interactions by MS , however such cleaning methods also always results in a substantial loss of material. Since we were performing MS on endogenous PHF13 IPs, which is already not a very complex sample, we lost a substantial amount of interacting proteins/peptides, rendering this approach inappropriate for detection of PHF13 interacting proteins from column sorted and pooled PHF13 IPs, which only further reduces the complexity of the sample. Nevertheless, amongst the PHF13 interactions that we did identify from MS runs of PHF13 classical IPs were RNA polymerase II complex members, Polr2a, Polr2b, Polr2C and Polr2h as well as Suz12. Furthermore, we could confirm these interactions with SUZ12 and RNA polymerase II by reciprocal co-IP experiments. Additional interactions were also identified that have been previously observed and published such as ATM and TRIM28  and others which we have not yet validated due to time restrictions.https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/GetProtein?atlas_build_id=446&protein_name=Q8K2W6&action=QUERY) and that to date only 3 peptides have been reported in their data base for PHF13, whereas SUZ12 for example gives thousands of peptides from various studies with 34 unique peptides in total (https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/GetProtein?atlas_build_id=446&protein_name=ENSMUSP00000017692&action=QUERY). Consistently the study by Nikolov et al., 2011, which we have referenced in the manuscript, could only show two PHF13 peptides when allowing for mis-cleavages in their MS studies. When we allow for mis-cleavages in our MS runs we can detect one unique peptide for PHF13 in our IPs, but not in the controls. While this has been a challenging point to address, I am nevertheless appreciative of this information as it explains why PHF13 has not been previously seen in PRC2 mass spec analysis, and because we have identified PHF13 as a novel binding partner of RNA polymerase II.The second major problem that we have had is the ability to see PHF13 in the MS results. We know from our IP/WB experiments that PHF13 was precipitated very efficiently and still we had difficulty to see this in our MS results. Therefore, we analyzed the ability of PHF13 to be proteolytically cleaved by trypsin, using in silico digestion and compare this with previously identified peptides from MS studies using the Protein Atlas website. It turns out that PHF13 is extremely inefficiently cleaved by trypsin in contrast to SUZ12 for example. In silico digestion in Protein Atlas returns the result that majority of the protein is unlikely to be observed by MS (2) You need to examine if there is any 'free' PHF13 in sESCs and interpret the ChIPseq data accordingly? Without this knowledge, it is very difficult to understand why there are so few overlapping ChIP-seq binding sites between PHF13 and SUZ12 or between PHF13 and EZH2.The mass spectrometry analysis of PHF13 immunoprecipitations in combination with the column chromatography elution profiles and immunoprecipitations from pooled column fractions revealed that RNA PolII is a major interaction partner of PHF13 . FurtherFurthermore, PHF13 showed a substantial overlap with promoter affiliated RNA PolII but not with the elongating form of RNA PolII (RNA PolII S2), as is expected due to PHF13’s affiliation with H3K4me2/3 and not with H3K36me3. Finally, PHF13 showed the strongest overlap with RNA PolII S5P, which occurred in both active and repressed promoter clusters. The larger cluster was enriched for H3K4me2/3, DHS, all forms of RNA PolII and CpG density and was depleted of H3K27me3 and PRC2, indicating active promoters whereas the smaller cluster was enriched for H3K4me2/3, H3K27me3, PRC2, RNA PolII S5 and CpG density and depleted of hypophosphorylated RNA PolII, RNA PolII S2P and RNA PolII S7P, reminiscent of bivalent promoters. Together, we interpret these observations to indicate that PHF13 co-localizes with promoter associated RNA PolII S5P at H3K4me2/3 demarcated active and repressed bivalent promoters. Since bivalent promoters only represent a small portion of H3K4me3, H3K27me3 and PRC2 binding sites genome wide and since they similarly represent only a small fraction of RNA PolII S5P binding sites, we find the approximate 10% representation in our data not inconclusive with this interpretation. Our data indicate that PHF13 localizes only to H3K4me2/3 containing promoters and not to H3K27me3 only promoters or to gene bodies where a substantial fraction of H3K27me3 and PRC2 peaks reside. Therefore, due to the reviewers’ suggestions that we should perform MS, which identified RNA PolII as an interaction partner that led us to analyze this relationship genome-wide with PHF13, we feel that we can now better explain this modest but significant overlap with PRC2 at PolII S5P, H3K4me2/3, H3K27me3 containing bivalent promoters.3) You need to state what type of RNA was analyzed by RNA-seq. If it was total mRNA, then you need to tone down the interpretations on transcriptional regulation, because the state of the field now is to look at nascent RNA.We have analyzed total RNA and not total mRNA in our RNAseq experiments. We used ribo-depletion to remove abundant rRNAs, tRNAs etc. and then sequenced the remaining RNA. This is described in the Materials and methods section. In addition to this, we now provide compelling evidence that PHF13 interacts with RNA polymerase II at H3K4me2/3 containing promoters . Further4) Validation of the ChIP-seq results is required by ChIP-qRT PCR for example.To address the concern that “Experimentally, there is no validation of the results of a single PHF13 ChIP-seq by alternative methods”, we validated several of the PHF13 ChIP sequencing targets that were additionally observed as PHF13 RNA sequencing targets  by ChIP 5) Demonstration that similar RNA changes were observed with more than one siRNA is necessary to prove these were not off target effects. This could also be done by qRT PCR.To address the concern that the RNA-seq was done using only one shRNA, we confirmed several of the genes that were both up and down regulated in our RNA sequencing results by qPCR with a second independent and inducible shRNA  as well 6) Determine whether the interaction between the PHF13 PHD finger and histone H3K4 methylation is required for PHF13's function.In an effort to demonstrate that PHF13 localization at H3K4me3 is required for its proposed function in transcriptional regulation, we employed an inducible PHF13 shRNA mESC cell line. We then evaluated the ability of PHF13 associated complexes, PRC2 (SUZ12) and RNA PolII (S2P and S5P) to interact with PHF13, H3K4me3, H3K27me3 and with each other in wild type or PHF13 depleted lysates . Our resWhile we did not directly address the question of “the role of the DNA interaction of PHF13 and if there is a synergy between DNA binding and histone binding of PHF13 in cells” posed by Reviewer 1, our data indicate 1) that PHF13 can interact directly with both DNA and H3K4me2/3 via independent domains, 2) that PHF13 ChIP seq targets are enriched for CpG rich sequences, and 3) that PHF13 co-localizes at both active and repressed chromatin regions which are enriched for H3K4me2/3 and CpG rich DNA overlapping at PHF13 peaks. These features are common to both active and bivalent promoters and indicate that they could and likely do cooperate in PHF13 recruitment as they co-occur at all PHF13 peaks . It is i


We describe a multiplexed RNA aptamer selection to 19 different targets simultaneously using a microcolumn-based device, MEDUSA (Microplate-based Enrichment Device Used for the Selection of Aptamers), as well as a modified selection process, that significantly reduce the time and reagents needed for selections. We exploited MEDUSA’s reconfigurable design between parallel and serially-connected microcolumns to enable the use of just 2 aliquots of starting library, and its 96-well microplate compatibility to enable the continued use of high-throughput techniques in downstream processes. Our modified selection protocol allowed us to perform the equivalent of a 10-cycle selection in the time it takes for 4 traditional selection cycles. Several aptamers were discovered with nanomolar dissociation constants. Furthermore, aptamers were identified that not only bound with high affinity, but also acted as inhibitors to significantly reduce the activity of their target protein, mouse decapping exoribonuclease (DXO). The aptamers resisted DXO’s exoribonuclease activity, and in studies monitoring DXO’s degradation of a 30-nucleotide substrate, less than 1 μM of aptamer demonstrated significant inhibition of DXO activity. This aptamer selection method using MEDUSA helps to overcome some of the major challenges with traditional aptamer selections, and provides a platform for high-throughput selections that lends itself to process automation. These molecules are capable of binding with high affinity and specificity to a variety of targets ranging from small metal ions to membrane proteins on the surface of living cells15 unique sequences, yet the RAPID selection took approximately one third the time as our previously-optimized SELEX process and significantly reduced the amount of reagents usedThe standard SELEX process tends to be time consuming and uses a large amount of reagents making aptamer selections costly9Previously, we also developed a microcolumn technology designed for aptamer selections using an affinity chromatography approach. The target is immobilized on a resin packed within a microcolumn and the nucleic acid library is pumped through the columnIn this article, we describe the combination of the multi-well format MEDUSA device with the RAPID approach to simultaneously perform multiplexed aptamer selections to 19 different targets, including 4 background targets. The background targets, including maltose-binding protein (MBP), and amylose, nickel-nitrilotriacetic acid (Ni-NTA), and anti-FLAG resins, served as negative selections in the first round with the microcolumns connected in series. This allowed us to identify non-specific sequences that bound to these resins or the MBP-tag. The primary protein targets are associated with regulation of transcription, RNA stability, or localization. Mouse DXO and its yeast homolog (Ag Rai1) are decapping exoribonucleases that possess pyrophosphohydrolase, decapping and 5′-to-3′ exoribonuclease activities, and are thus implicated in RNA surveillance1416D) in the nanomolar range. In addition, further inspection of aptamer sequences enriched for DXO led us to identify aptamers that not only bind, but also resist DXO’s exoribonuclease activity. Furthermore, in studies monitoring the degradation of a 30-nt RNA substrate by DXO, the aptamers demonstrated significant inhibition of DXO activity. These and the aptamers to other proteins could be used for inhibiting target protein function, and the sequencing data provides a rich source of information to understand the biology of these target proteins and perhaps uncover novel functions (e.g. RNA interaction) and specificity.We exploited the reconfigurable design of the MEDUSA device to enable multiple selections to be performed using just 2 aliquots of library, and its microplate-based dimensions to perform downstream processes such as reverse transcription, PCR, quantitative PCR (qPCR), and transcription in a 96-well plate format. Encouraged by our earlier results with RAPID where amplification and non-amplification cycles are alternating, we decided to take our RAPID approach a step further by increasing the number of non-amplification cycles with later rounds of selection to drastically reduce the time and reagents needed for the selection. We performed the equivalent of a 10-cycle SELEX (with only 4 of these cycles requiring amplification) to 19 targets in about the time it would take to perform 4 cycles of conventional SELEX to one target. Analysis of high-throughput sequencing results allowed us to identify potential aptamer candidates, and many of these candidate aptamers were verified to bind the corresponding target proteins with a dissociation constant ] and the pools after Round #3-Cycle #3 (R3C3) for every target were analyzed via high-throughput sequencing. All intermediate pools for four of the targets were also sequenced . This waUpon examination of the high-throughput sequencing results, sequences that could not be identified as an aptamer candidate (i.e. the constant regions were missing) were discarded, though a few still made it through our analysis pipeline . SequencEMSAs were performed to test the binding of candidate aptamers to their respective targets and determine the dissociation constants. Results for 5 of the aptamer candidates can be seen in D (25The copy numbers (multiplicity) of the selected aptamer candidates varied greatly, and did not predict whether the aptamer would bind nor did they correlate with the determined KD . There wTo gain further insight on the selection process and how it could be used to study the biological function of a target protein, as well as provide useful tools that do more than just bind, we turned our focus to the DXO protein, which has 5′-3′ exoribonuclease and is implicated in RNA surveillance1427in vivo function of DXO and can help to identify in vivo targets of DXO exoribonuclease activity.To test whether the determining factor for RNA aptamer resistance against DXO exoribonuclease activity is the sequence of the motif itself or just the stability of the first stem in the aptamer structure, we took the DXO #2 and #5 aptamers with perfect matches to DXO Motif 1, and generated mutant aptamers. In mut1 aptamers, the sequence of the motif was altered but the stability was maintained by changing the original motif (5′-GGATCCC-3′ to 5′-CCTAGGC-3′) and the BamHI site in the forward constant region (5′-GGATCC-3′ to 5′-CCTAGG-3′). In mut2 aptamers, the stability of the first stem was reduced by changing the original motif to 5′-AATATTC-3′ and the BamHI site in the forward constant region to 5′-AATATT-3′. In addition, we generated truncations of DXO #2 and #5 aptamers, which retained the DXO Motif 1 and the first stem-loop structure for each aptamer. A sequence-shuffled version of DXO #5 trunc was used as a control. As shown in Finally, we tested whether these RNA aptamers could inhibit the exoribonuclease activity of DXO. We measured the exoribonuclease activity on a 30-nt RNA substrate that was 3′-Cy5 labeled. DXO was pre-incubated with a range of concentrations (0–1100 nM) of full-length RNA aptamers, and the RNA substrate was subsequently added. As shown in in vivo. In the absence of a highly specific small molecule inhibitor of DXO, DXO-specific inhibitory RNA aptamers, which can in principle be expressed transgenically at specified levels, time points, and durations29in vivo. Likewise, compared to RNAi knockdowns, inhibitory RNA aptamers offer unique advantages for the following in vivo functional studies: i) a specific function or interaction of the target protein (e.g. exoribonuclease activity of DXO) could be targeted rather than complete elimination of the entire protein as with RNAi, and ii) distinction between primary and secondary effects is possible, since inhibitory concentrations of RNA aptamers could be attained relatively quickly (i.e. minutes to tens of minutes) upon induction, whereas significant knockdown of most proteins by RNAi requires hours to days.Given their moderately-high affinity and DXO exoribonuclease inhibitory effect, these full-length aptamers could potentially be used as DXO inhibitors for studying DXO function These studies have shown that different properties of the target proteins can also be discovered and studied through an aptamer selection. Furthermore, selecting sequences for more than just affinity, such as nuclease resistance and inhibition, can be accomplished. Some of the targets in this selection possess nuclease activity, which affected the affinity testing, and likely the selection process itself, as sequences with a higher resistance to the nuclease activity would be selected over those that may have higher affinity. DXO possesses 5′-3′ exoribonuclease activity, and we have shown that the selected aptamers resist degradation significantly longer than the random RNA library . We alsoWe have demonstrated that a highly multiplexed aptamer selection can be performed using the MEDUSA device, and its reconfigurable microcolumn design allows for substantial reduction in reagent consumption. Most notably, a single aliquot of the starting random library can be used to select aptamers to multiple different targets, and just 6 μg of protein target is needed for each cycle. Additionally, a modified SELEX process incorporating increasing numbers of consecutive non-amplification cycles significantly reduced the time required to perform the selections. Most importantly, high-throughput aptamer selections are particularly beneficial because they increase the rate of success per selection.High-throughput sequencing allows for the identification of top sequences, as well as their multiplicity in the pool. Performing multiplex aptamer selections is also beneficial when analyzing sequencing results, since background binding and non-specific sequences can be recognized when they appear in multiple pools with significant abundance. However, after identifying potential aptamer candidates and performing binding experiments, it was obvious that sequence multiplicity could not predict binding or the affinity of the aptamer.Finally, through these selections, we demonstrated that useful tools, other than high-affinity ligands, can be discovered. Aspects of a target protein’s function were studied, and aptamers capable of resisting exoribonuclease activity and acting as inhibitors were determined. Therefore, aptamer selections can produce very useful tools that can be used in many different applications, and the device and selection process presented here provides advantages over other processes that improve the efficiency and success of aptamer selections.E. coli Bl21 (DE3) RIPL strain using amylose resin (NEB) according to the manufacturer’s recommendations. Briefly, for each protein the bacteria were grown to an OD600 of 0.6 in 1 L of LB media supplemented with 100 μg/mL of ampicillin, and induced with 200 μM IPTG. Induction cultures were then incubated at 18–22 °C for 12–14 hours with 250 rpm shaking. Bacteria pelleted at 3500 rpm and 4 °C for 30 min were lysed in MBP buffer  supplemented with 0.1% NP-40, 1 mg/mL Lysozyme, and 1X Protease Inhibitor Cocktail (Roche). After a 15-min incubation on ice, lysates were sonicated on ice 3 times for 30 s each using a Branson Cell Distributor with a microtip, and cleared by centrifugation at 20,000 × g and 4 °C for 45 min. Cleared lysates were applied to 1 mL of pre-washed amylose resin and allowed to pass through the column under gravity flow. Protein-bound resin was washed 3 times with 10 mL of MBP buffer, and the proteins were eluted in five 1-mL fractions using MBP buffer supplemented with 10 mM maltose. Protein concentration was measured by Bradford Protein Assay (Bio-Rad) against a BSA standard, and the purity of each protein preparation was determined by SDS PAGE and Coomassie Blue staining. One or two fractions that had the highest protein concentration (often >1 mg/ml) and were relatively pure (>80% pure estimated from Coomassie stained gel) were supplemented with 10% glycerol, aliquoted, and stored at −80 °C. The maltose used for elution was not dialyzed, since most proteins precipitated out during dialysis, and re-binding of these proteins was done at very dilute conditions , which permitted efficient re-binding of the purified proteins to the amylose resin for the aptamer selections.MBP-tagged protein domains were purified from Hexahistidine (6xHis)-tagged NELF-E was purified as described previously2) prior to target immobilization. MBP-, 6xHis-, and FLAG-tagged protein targets were immobilized onto amylose, Ni-NTA, and anti-FLAG resins, respectively, by incubating the proteins at a concentration of 0.6 μg/μL of resin with the resin in a 10% slurry in binding buffer. The target resins were incubated for 1 h at 4 °C with constant mixing. A list of targets and the order in which they were connected in series for the first round of selection can be found in For each cycle of the selection, fresh protein-immobilized resins were prepared. Amylose, Ni-NTA, and anti-FLAG resins were washed thoroughly with binding buffer  as described elsewhereGATAATACGACTCACTATAGGGAATGGATCCACATCTACGA-3′, and AptLibConsREV, 5′-AAGCTTCGTCAAGTCTGCAGTGAA-3′, respectively. The forward oligo contains the T7 promoter sequence (underlined) used for transcription of the DNA template to RNA, and a shortened version of the original forward constant region to reduce primer dimer formation. These primers and other oligos, including the gBlock templates of the candidate aptamers, were purchased from Integrated DNA Technologies.The random RNA library used in this selection contained ~5 × 102 laser at 10.6 μm  to cut the poly(methyl methacrylate) (PMMA) and silicone layers. Each microcolumn had a 10-μL volume capacity, and fluidic connections were made using standard microfluidic connectors purchased from IDEX. This device was assembled such that it could be reversibly configured between the microcolumns connected in series or in parallel. Between rounds of selection, the device was washed extensively with 1 M KOH, 1 M HCl, and 1x binding buffer separated by DEPC water washes, and reused for all subsequent rounds.A description of MEDUSA’s components, as well as its fabrication, is described in detail elsewhereAll solutions were degassed and introduced into the device via a standard syringe pump (Harvard Apparatus). The selection consisted of 4 rounds ending in pool amplification, with a total of 10 binding cycles by incorporating increasing numbers of non-amplification cycles in each round . The floIn the first round of selection, MEDUSA was configured with microcolumns connected in series, and two aliquots of the N70 RNA library were used for 19 total targets. The order of target proteins in each series is shown in The standards and resuspended pools were reverse transcribed using Moloney Murine Leukemia Virus Reverse Transcriptase (MMLV-RT). Residual RNA was digested using a cocktail of RNases, including RNase H, A, and T1 (Invitrogen). Approximately 3% of the cDNA from each pool was used to determine the RNA recovery compared to the initial quantity of RNA at the beginning of the round via qPCR. The remaining cDNA was PCR-amplified, and purified using DNA Clean & Concentrator spin columns (Zymo Research). A fraction of the purified DNA was used for transcription with T7 RNA polymerase (Invitrogen) to produce the RNA pools for the next round of selection. Following transcription, the template DNA was digested using DNase I (Ambion), and the RNA was phenol-chloroform extracted and ethanol precipitated. The purified RNA pools were verified by denaturing PAGE for purity and length, and quantified via Qubit BR RNA assay (Invitrogen).In rounds 2, 3 and 4 of the selection, 50 μg of the enriched RNA pools were used, and an increasing number of non-amplification cycles were incorporated in which the eluted RNA was purified by phenol-chloroform extraction, ethanol precipitated, and directly subjected to the next binding cycle without amplification. The flowrates were increased slightly to allow multiple binding cycles to be performed within a single day see .http://hannonlab.cshl.edu/fastx_toolkit/). The resulting library of sequences was first clustered using UCLUST software, part of the USEARCH softwareA small amount of cDNA from each final pool was PCR-amplified using primers containing unique 6-nt barcodes and the necessary adapters for the HiSeq 2500 (Illumina) sequencing platform operated in Rapid Run Mode. The sequencing data was analyzed with readily available software. Briefly, the sequencing data was quality filtered, barcode split, trimmed of constant regions and barcodes, and collapsed using programs included in the FASTX toolkit (http://meme-suite.org/)27To identify motifs, 5–20 nt RNA sequences that were enriched in a selected RNA pool and present in the top 3000 aptamer clusters were subjected to analysis by the MEME program of the MEME Suite (2), and imaged using a fluorescence scanner. The gel images were quantified using ImageJ, and the resulting curves were fitted to the Hill Equation using Igor Pro 5.04A to determine the KD. For many of the aptamers, a second EMSA was performed, and the data from both experiments were merged and fitted to the Hill Equation.To test the binding capability of the aptamer candidates and determine the dissociation constant, EMSAs were performed for each candidate. The RNA aptamer candidate was fluorescently labeled at the 3′ end with fluorescein isothiocyanate (FITC)in vitro transcription with T7 RNA polymerase and [α-P32]UTP (Perkin Elmer), and purified by RNase-free Micro Bio-Spin P-30 columns (Bio-Rad). Exoribonuclease assays were performed as described elsewhere2, 20 mM Tris-Cl pH 8.0, 0.5 mM DTT, 0.025 mg/mL UltraPure BSA) were first heat-denatured at 70 °C for 5 min and renatured at room temperature for 15–30 min. They were then incubated with 1 μM recombinant DXO enzyme (in DXO buffer) for various durations at 37 °C. For testing the inhibition of DXO 5′-3′exoribonuclease activity by RNA aptamers, DXO was first equilibrated with RNA aptamers at room temperature for 5 min. 3′-Cy5-labeled 30-nt RNA substrate (Dharmacon)Body-labeled RNA aptamers were generated by How to cite this article: Reinholt, S. J. et al. Highly Multiplexed RNA Aptamer Selection using a Microplate-based Microcolumn Device. Sci. Rep.6, 29771; doi: 10.1038/srep29771 (2016).


Staphylococcus aureus, many sRNAs have been identified and some of them have been functionally studied. Since it is difficult to identify RNA-binding proteins (RBPs), very little has been known about the RBPs in S. aureus, especially those associated with sRNAs.It has been widely recognized that small RNAs (sRNAs) play important roles in physiology and virulence control in bacteria. In S. aureus. The tethered RNA was successfully captured by the streptavidin magnetic beads, and proteins binding to RNAIII were isolated and analyzed by mass spectrometry. We have identified 81 proteins, and expressed heterologously 9 of them in Escherichia coli. The binding ability of the recombinant proteins with RNAIII was further analyzed by electrophoresis mobility shift assay, and the result indicates that proteins CshA, RNase J2, Era, Hu, WalR, Pyk, and FtsZ can bind to RNAIII.Here we adopted a tRNA scaffold streptavidin aptamer based pull-down assay to identify RBPs in S. aureus, and may be involved in RNA III function. And tRSA based pull-down assay is an effective method to search for RBPs in bacteria, which should facilitate the identification and functional study of RBPs in diverse bacterial species.This study suggests that some proteins can bind to RNA III in The online version of this article (doi:10.1186/s12866-015-0435-3) contains supplementary material, which is available to authorized users. Staphylococcus aureus is a human and animal pathogen that can cause multiple infectious diseases. The high infection ability of the bacterium depends on the production of many virulence factors, which are under control of multiple regulatory pathways. These regulators include transcriptional regulator proteins, two-component systems, and small RNAs (sRNAs).sRNAs in bacteria range from 50 to several hundred nucleotides, and function through RNA-RNA base pairing to activate or block the translation, or to induce the degradation of target mRNA. RNAIII is a 514 nucleotides sRNA, and can regulate the expression of many virulence genes as well as some regulators at the post-transcriptional level. Binding between RNAIII and its targets can lead to translation activation, translation blocking, or mRNA degradation mediated by RNase III [S. aureus, but very little has been known about RNA-binding proteins (RBPs) involved in sRNA regulation in this bacterium. It has been widely recognized that Hfq acts as an important chaperone in many Gram-negative bacteria [S. aureus, Hfq can bind to specific short RNA [hfq has no significant impact on sRNA stability or regulation, suggesting that Hfq is not an RNA chaperone in S. aureus [S. aureus, and it degrades double-stranded RNA formed by base-pairing of RNAIII with its mRNA targets such as spa, coa, and rot [S. aureus [Nowadays, hundreds of sRNAs have been identified in bacteria . In S. ahort RNA , 4, but . aureus . RNase I and rot . sRNA re and rot . Thus, i. aureus , 8.RBPs are difficult to identify due to the lack of effective tags. In previous studies, RNA affinity chromatography was used to purify a c-myc binding protein ; biotin S. aureus. Further research on these RBPs should shed light on the function and mechanisms of sRNA regulation in S. aureus.Aiming to identify proteins binding to RNAIII, we carried out pull-down assay using tRSA as a tag in this study. The tethered RNAs were successfully captured by Streptavidin MagneSphere Paramagnetic Particles (SA-PMPs), and those proteins binding to RNAIII were isolated and analyzed by mass spectrometry (MS). Using this method, 81 proteins were identified, and RNA-binding abilities of 9 proteins were further determined by electrophoresis mobility shift assay (EMSA). Our data indicate that some proteins can bind to RNAIII, and that tRSA based pull-down assay is an effective method to identify RBPs in Aiming to identify RNAIII-binding proteins, we performed the pull-down assay using tRSA as a tag. Streptavidin aptamer fusion in the anti-code site of tRNA was captured by streptavidin PMPs, while RNAIII attached to the 3′ site of tRNA can act as a bait for RBPs Fig. A. About S. aureus NCTC8325 proteomics. In contrast, cell wall/membrane biogenesis proteins  and replication-, recombination- and repair-related proteins  were enriched in the RNAIII pull-down system  data in NCBI. The percentage of most kinds of proteins in the pull-down sample was similar to that in the tem Fig. , suggesttem Fig. . Those pE. coli, including four proteins with RNA-binding motifs, two proteins with DNA-binding motifs, and three proteins with no nucleic acid-binding motif. PNPase and Enolase were suggested to be the components of RNA degradosome, but they were not found in our pull-down assay  medium with suitable antibiotics: ampicillin (100 μg/mL) or kanamycin (50 μg/mL). S. aureus strains were grown at 37 °C in tryptic soy broth . The media were solidified with 1.5 % (wt/vol) agar as needed.Strains and plasmids used in this study are listed in Table The template for tRSA-RNAIII transcription was digested from plasmid pSXZ06. The RNAs were produced by in vitro transcription as described , using aRNA was extracted using the Trizol method (Invitrogen), and residual DNA was digested with 10 U of DNaseI (Takara) at 37 °C for 1 h. Reverse transcription was carried out with the PrimeScript 1st Strand cDNA synthesis kit (Takara) and real-time PCR was performed with SYBR Premix Ex Taq (TaKaRa) using a StepOne real-time system (Applied Biosystems). The RNA abundance of input sample was indicated as 1,000 and the relevant quantification of captured RNA was analyzed by calculating the difference of CT in the real-time PCR.S. aureus cultures (2 ml) were collected and bacterial cells were resuspended with lysis buffer  containing protease inhibitor cocktail (Sangon), and 40 U/ml lysostaphin (AMBI). The suspension was incubated at 37 °C for 10 min to lyse the cell. After centrifugation at 12,000 g for 30 min, the supernatants were collected and the concentration of total protein was quantified by BCA assay. Avidin , Yeast RNA , and RNase inhibitor  were added as the final pre-cleared lysates. About 2 mg protein was applied for each pull-down assay.RBPs pull-down assay was performed as previously described , with so2, and denatured at 65 °C for 5 min and then cooled to room temperature. SA-PMPs  was rinsed twice with 0.5 × SSC, and then rinsed twice with lysis buffer. The RNA sample was incubated with the prepared SA-PMPs at 4 °C for 20 min on a rotating shaker, then pre-cleared lysates were added and incubated for another 1.5 h on a rotating shaker. The SA-PMPs was then washed 3 times with fresh lysis buffer. SDS-loading buffer (50 μl) was added and the proteins captured were incubated at 95 °C for 10 min. Samples from each step were collected for RNA assay.At the same time, 10 μg of bait RNA sample was dissolved in buffer containing 10 mM HEPES, pH 7.0, 10 mM MgClThe proteins were separated by SDS-PAGE and silver or Coomassie brilliant blue staining, and the gel bands were excised and in-gel digested with trypsin (0.6 mg), and the tryptic peptides were subjected to LC-MS/MS analysis with a LTQ mass spectrometer . Sequence and peptide fingerprint data were analyzed using the NCBI database.S. aureus NCTC8325 genomic DNA with primers listed in Table E. coli DH5α, and then the expression strains. The plasmids, expression strains, and conditions for proteins were listed in Tables Open reading frames of different proteins were amplified from EMSA between RNA and RBPs was performed as previously described . The lab


Suakwa aphid-borne yellows virus (SABYV), from East Timor. The isolate sequenced came from a virus-infected pumpkin plant. The East Timorese genome had a nucleotide identity of 86.5% with the only other SABYV genome available, which is from Taiwan.We present here the first complete genomic RNA sequence of the polerovirus  Suakwa aphid-borne yellows virus (SABYV) was obtained from a single pumpkin plant sample (DL76) from the Dili district of East Timor, but no SABYV was detected in any of the Australian samples. SABYV is a single-stranded RNA virus in the genus Polerovirus, family Luteoviridae. It causes leaf interveinal yellowing in cucurbit plants and occurs in the Philippines and Thailand in Southeast Asia, and in China and Taiwan in East Asia  cards. In October 2015, 21 leaf samples were collected from cucurbit plants growing in the Broome district of the West Kimberley region of tropical northwest Australia. RNA extracts from 15 East Timorese and 21 Australian samples were subjected to next-generation sequencing. A complete genome of st Asia 13. Currenst Asia 1.Total RNA was extracted from the samples using a ZR Plant RNA MiniPrep kit (Zymo Research), treated with RNase-free DNase (Invitrogen) and measured using Qubit (Invitrogen). RNA integrity was confirmed using RNA screen Tape . Libraries were prepared from total RNA extracts using a TruSeq Stranded Total RNA sample preparation kit with Ribo-Zero Plant . Final size and concentration of each library was verified using Qubit and D1000 ScreenTape (TapeStation 2200). Sequencing was done with the MiSeq platform reagent kit version 2 (Illumina) using 2 × 251 paired-end runs with a 1% PhiX spike. Reads were assembled and genomes annotated using CLC Genomics Workbench (CLC Bio) and Geneious version 8.1.7 (Biomatters) .De novo assembly generated 11 contigs; 13,045 reads were mapped to the contig of interest, giving a coverage of 330×. The assembled full-length genome from DL76 was 5,845 nucleotides in length with the six open reading frames typical of the genus Polerovirus. A BLAST-based search through the Pairwise Sequence Comparison (PASC) tool .The new sequence was deposited in GenBank under the accession number


While RNA-sequencing (RNA-seq) is becoming a powerful technology in transcriptome profiling, one significant shortcoming of the first-generation RNA-seq protocol is that it does not retain the strand specificity of origin for each transcript. Without strand information it is difficult and sometimes impossible to accurately quantify gene expression levels for genes with overlapping genomic loci that are transcribed from opposite strands. It has recently become possible to retain the strand information by modifying the RNA-seq protocol, known as strand-specific or stranded RNA-seq. Here, we evaluated the advantages of stranded RNA-seq in transcriptome profiling of whole blood RNA samples compared with non-stranded RNA-seq, and investigated the influence of gene overlaps on gene expression profiling results based on practical RNA-seq datasets and also from a theoretical perspective.Our results demonstrated a substantial impact of stranded RNA-seq on transcriptome profiling and gene expression measurements. As many as 1751 genes in Gencode Release 19 were identified to be differentially expressed when comparing stranded and non-stranded RNA-seq whole blood samples. Antisense and pseudogenes were significantly enriched in differential expression analyses. Because stranded RNA-seq retains strand information of a read, we can resolve read ambiguity in overlapping genes transcribed from opposite strands, which provides a more accurate quantification of gene expression levels compared with traditional non-stranded RNA-seq. In the human genome, it is not uncommon to find genomic loci where both strands encode distinct genes. Among the over 57,800 annotated genes in Gencode release 19, there are an estimated 19 %  of overlapping genes transcribed from the opposite strands. Based on our whole blood mRNA-seq datasets, the fraction of overlapping nucleotide bases on the same and opposite strands were estimated at 2.94 % and 3.1 %, respectively. The corresponding theoretical estimations are 3 % and 3.6 %, well in agreement with our own findings.Stranded RNA-seq provides a more accurate estimate of transcript expression compared with non-stranded RNA-seq, and is therefore the recommended RNA-seq approach for future mRNA-seq studies.The online version of this article (doi:10.1186/s12864-015-1876-7) contains supplementary material, which is available to authorized users. RNA-sequencing (RNA-seq) is a next-generation sequencing technique that allows an in-depth look into the transcriptome –3. CompaWhile RNA-seq is emerging as a powerful technology in transcriptome profiling, one significant shortcoming of the standard RNA-seq protocol is that it loses the strand of origin information for each transcript. Synthesis of randomly primed double-stranded cDNA followed by the addition of adaptors for next-generation sequencing leads to the loss of information on which strand the original mRNA template is coming from, and without that information it becomes difficult to accurately determine gene expression from overlapping genes , i.e., tIt is now possible to retain the information pertaining to strand origin by modifying the standard RNA-seq protocol; this is known as strand specific RNA-seq, or stranded RNA-seq. Recently, multiple protocols for stranded RNA-seq have been published –10. SeveThis new methodology is now emerging as a powerful tool for transcript discovery, genome annotation, and expression profiling , 12. PreTo allow for efficient transcript/gene detection, highly abundant ribosomal RNAs (rRNAs) must be removed from total RNA before sequencing . One staSample_Protocol” to label each RNA-seq dataset. For instance, PFE1_S represents the sample PFE1 sequenced by stranded RNA-seq. For RNA-seq data analysis, we implemented an in-house pipeline in the Pfizer High Performance Computing environment as shown in Fig. The sample preparation, sequencing, and data analysis are detailed in the Each replicate sample was sequenced by both non-stranded and stranded RNA-seq. The summaries for sequencing depth, mapping, and counting are shown in Fig. As shown in Fig. st quartile, median, and 3rd quartile are approximately 0.77 RPKM, 3.0 RPKM, and 9.6 RPKM, respectively. The gene expression distribution plot in Fig. The correlation for gene expression levels among the eight samples studied is plotted in Fig. Every gene in Gencode Release 19 has genomic coordinates, and the frequency of overlapping genes can thus be calculated  was calculated by dividing the length of overlapping exons by the exon length of the shorter gene. A total of 6582 overlapping gene pairs were identified from opposite strands and the number was 3718 at the same strand. The histograms and cumulative distributions of overlaps are shown in Fig. The scatter plots of the gene expression profiles for the four replicate samples are shown in Fig. To identify genes with large expression differences between stranded and non-stranded RNA-seq, we performed a differential expression analysis using R packages edgeR  and Limmprop.test in R was used. The calculated p values are smaller than 2.2E-16 for both gene categories, indicating the enrichment is not by chance.A gene is considered to be expressed if its maximal expression across all eight samples is greater than 1 CPM (count per million), and accordingly, a total of 16,443 expressed genes survived this filtering. All genes that have appreciable expression and those 1751 DE genes in Fig. chisq.test R function was called to evaluate the significance of the test. All tests report a P value lower than 2.2E-16, except for pseudogene .Next, we explored the association between differential analysis results and sequencing protocol. Every gene (dot) in Fig. As observed in Fig. The ENCODE project recently performed a survey of publicly available expression data to identify transcribed pseudogenes and found over 800 pseudogenes with strong evidence of transcription .Recent studies have shown that some pseudogenes are transcribed and contribute to cancer when dysregulated . In partIL24, ICAM4, and GAPDH) to demonstrate this point. The expressions for these three genes are shown in bar charts in Fig. For a given gene, if stranded and non-stranded RNA-seq report different expression levels, which one is more reliable? In principle, the stranded RNA-seq should be more accurate because additional information  is used in gene quantification, and the ambiguous reads in overlapping genes transcribed from opposite strands are resolved and counted. Below, we selected a few example genes  24 is a secreted protein of the soriasis . In non-IL24, an obvious question is why so many reads are mapped to the genomic region of IL24. As we know, our current gene annotation is neither complete nor comprehensive, and it is likely that such reads originate from a novel gene at the opposite strand of IL24. We currently do not have a good explanation for these mapped reads. However, the scenario in Fig. Because those reads in Fig. ICAM4 (intercellular adhesion molecule 4) shows moderate expression in whole blood [ICAM4 is encoded on the “+” strand, and it has three alternative splicing transcripts. It overlaps with another gene CTD-2369P2.8 in the “−“ strand. CTD-2369P2.8 is a manually annotated gene from the Havana  project, and it is longer than ICAM4. As observed in Fig. ICAM4 is 100 % contained within CTD-2369P2.8. In non-stranded RNA-seq, a read mapped to ICAM4 is simultaneously aligned to CTD-2369P2.8 as well. The ambiguous reads in overlapping regions are thus excluded from counting in FeatureCounts, and this explains the lack of expression for ICAM4 on non-stranded RNA-seq. The ambiguous reads in overlapping genes in Fig. ICAM4 (but not CTD-2369P2.8), because they are all reverse complementary to ICAM4. According to our sequencing protocol, it is impossible for such reads to originate from CTD-2369P2.8. The gene expression in stranded RNA-seq also agrees with other supporting evidence [le blood . Howeverevidence , and is For the scenario in non-stranded RNA-seq in Fig. GAPDH  is a well-known housekeeping gene with very high expression in most cell types and tissues. Compared with stranded RNA-seq, its expression in non-stranded RNA-seq is in fact underestimated . Blood was pooled across subjects to create a single pooled sample. This pooled blood was dispensed into a set of approximately 10-mL aliquots. Total RNA was extracted from four aliquots of pooled blood using the PAXgene Blood RNA Kit  according to the manufacturer's protocol. The yield and quality of the isolated RNA were assessed using a NanoDrop8000 Spectrophotometer  and Agilent 2100 Bioanalyzer , respectively. An aliquot of 1.5 mg of each RNA was further processed with a GlobinClear kit  to remove globin mRNA. After globin mRNA depletion, the quality and yield of the RNA were assessed again using an Agilent 2100 Bioanalyzer. Six hundred nanograms of RNA (post-GlobinClear) were divided into two 300 ng aliquots, with one aliquot submitted to stranded RNA-seq processing and the second aliquot submitted to non-stranded RNA-seq processing.For stranded RNA-seq, cDNA libraries were prepared with a TruSeq stranded mRNA library prep Kit . For non-stranded RNA-seq, cDNA libraries were prepared with a TruSeq RNA sample preparation kit v2 . The resulting eight libraries were sequenced on a HiSeq 2000 (Illumina) using a paired-end run (2 × 100 bases). A minimum of 60 M reads were generated from each library. The clean raw sequence reads in FASTQ format were analyzed using the pipeline in Fig. http://www.gencodegenes.org/releases/19.html. The reads were mapped to the hg19 reference genome using STAR v2.4.0h [--runThreadN 8 --alignSJDBoverhangMin 1 --outReadsUnmapped Fastx --outFilterMismatchNoverLmax 0.05 --outFilterScoreMinOverLread 0.90 --outFilterMatchNminOverLread 0.90 --alignIntronMax 1000000 --outSAMtype BAM SortedByCoordinate”. The mapping was performed on the Pfizer High Performance Computing cluster. The mapping summaries, such as the percentage of reads that were uniquely mapped, multiple mapped, or unmapped, were then collected from the log files of STAR runs . Only uniquely mapped reads are used in the counting step. Like the mapping step above, the counting metrics were collected from the summary file of each featureCounts run. Genes that have expression levels less than 1 CPM were labeled as low expressed. If a gene had zero or low expression across all eight samples, it was omitted from the correlation and differential expression analysis. This filtering step was included to reduce the false positives in the differential analysis [To count reads mapped to individual genes in Gencode, the program featureCounts  was usedanalysis .A counts table was generated by featureCounts and then used for the DE analysis. The differential analysis was performed by R packages edgeR 3.8.5  and LimmmakeTranscriptDbFromGFF. We then extracted all exons from TxDb and grouped them by gene. According to strand information, the genes in each chromosome were divided into two groups. The overlaps at the same and opposite strands were quantified at both gene and nucleotide base levels . Written informed consent was obtained from all volunteer blood donors for the research described and potential publication thereof. A copy of the written consent is available for review by the Editor of this journal. Samples from individuals were coded at the time of collection and then pooled prior to data generation, removing any possible association of analytical measurements with a single donor.The protocol for the Pfizer Research Support Program to collect blood samples from volunteer donors was approved by the Schulman Associates Institutional Review Board (IRB#201065670; All the raw sequencing reads have been submitted to the NCBI Sequence Read Archive and are available under accession SRP056985.The R script to estimate the gene overlap is attached as Additional file