We here introduce a novel method, named Structural Probing of Elongating Transcripts (SPET-seq), that permits single-base resolution analysis of transcription intermediates’ secondary structures on a transcriptome-wide scale, enabling base-resolution analysis of the RNA folding events. Our results suggest that cotranscriptional RNA folding in vivo is a mixture of cooperative folding events, in which local RNA secondary structure elements are formed as they get transcribed, and non-cooperative events, in which 5′-halves of long-range helices get sequestered into transient non-native interactions until their 3′ counterparts have been transcribed. Together our work provides the first transcriptome-scale overview of RNA cotranscriptional folding in a living organism.Defining the  There are two main hypotheses on how it can occur. One possibility is that RNA exists after transcription as a random coil, in which any residue within the RNA chain is virtually available for base pairing. Given that the folding space for an RNA molecule is enormous, so that an n nucleotides long RNA can assume up to 1.8n distinct conformations  suggested cotranscriptional folding to be a cooperative process, in which only correct helical stems are formed during transcription, while no additional or competing helices are produced . This approach, allowed us to capture the structure of individual transcription intermediates at near base-resolution. We applied SPET-seq to 600 ∼0.3) of E. coli strain DH10B using DNeasy Blood & Tissue Kit , following manufacturer instructions. The RNase P gene (rnpB) was amplified by 20 cycles of polymerase chain reaction (PCR) using Phusion® High-Fidelity DNA Polymerase . Oligonucleotides were designed 40-bp upstream of the promoter’s −35 Box to account for the presence of an upstream element, and at the end of the Rho-independent terminator. After PCR, two bands were visible on agarose gel (489- and 601-bp bands), due to the presence of two consecutive terminators with identical sequences. The shorter band (489 bp) was excised, purified, cloned into Zero Blunt® TOPO® vector  and verified by Sanger sequencing. A total of 60 μg of TOPO-rnpB plasmid were digested using EcoRI, insert was gel purified and used for downstream in vitro transcription reaction.Genomic DNA was extracted from a 3 ml culture , 10 μl E. coli RNA polymerase buffer (10×), 1 μl SUPERase• In™ RNase Inhibitor  and 500 ng template DNA. Reactions were incubated at 37°C for 5 min to allow formation of the RNA Polymerase–DNA binary complex. Transcription was started by addition of 1 μl NTPs (25 mM each), and incubated at 37°C for 10 min. Transcription was stopped by addition of 1 μl DNase I (50 U/μl) and Actinomycin D  to 5 μg/μl) to a final concentration of 25 ng/μl. Reactions were diluted by addition of 50 μl E. coli RNA polymerase buffer 1×. DMS  was diluted 1:6 in 100% ethanol to a final concentration of 1.76 M. Diluted Dimethyl sulfate (DMS) was added to reactions to a final concentration of 100 mM. For control samples, an equal volume of a 1:6 dilution of nuclease-free water in 100% ethanol was added. Samples were incubated with moderate shaking (800 RPM) at 25°C for 2 min, after which reactions were immediately transferred to ice. Two volumes of ice-cold RNA binding buffer from RNA Clean & Concentrator™-5 kit  supplemented with DTT  to a final concentration of 0.7 M, were added to quench DMS and samples were vigorously vortexed for 10 s. RNA was purified on RNA Clean & Concentrator™-5 columns following manufacturer instructions, and eluted in 6 μl of nuclease-free water. A total of 6 μl of 2× RNA Loading Dye  were added to purified RNA. Both DMS-treated and -untreated samples were heated to 95°C for 2 min, and immediately placed on ice. Samples were resolved on a 10% TBE-Urea polyacrylamide gel, and a gel slice corresponding to fragments in the range 50–400 nt was cut. Gel slices were crushed by centrifugation through a punctured 0.5 ml tube, and resuspended in 500 μl of diffusion buffer  supplemented with 60 U SUPERase• In™ RNase Inhibitor, then rotated at 4°C for 16 h to allow passive diffusion of RNA fragments into buffer. RNA was precipitated by addition of 1 ml Isopropanol, and 2 μl Glycogen (20 μg/μl) and resuspended in 6 μl nuclease-free water.E. coli strain DH10B was inoculated into 250 ml of LB medium without antibiotics, and grown at 37°C with shaking (150 RPM) for ∼4 h, until OD600 was ∼0.3 (log phase). A total of 25 ml aliquots of bacteria were then pelleted by centrifugation at 1800 g for 15 min (4°C). After centrifugation, medium was decanted and cells from each 25 ml of culture were resuspended in 1 ml of structure probing buffer . DMS was diluted 1:6 in 100% ethanol to a final concentration of 1.76 M. Diluted DMS was added to bacteria to a final concentration of ∼105 mM. Samples were incubated with moderate shaking (800 RPM) at 25°C for 2 min, after which reactions were immediately transferred to ice. DTT was added to a final concentration of 0.7 M to quench DMS, and samples were vigorously vortexed for 10 s. Bacteria were then pelleted by centrifugation at 10 000g for 30 s (4°C), and the supernatant was decanted. Pellets were then washed once with 1 ml Isoamyl alcohol  to remove traces of water-insoluble DMS. Bacteria were then pelleted by additional centrifugation at 10 000g for 30 s (4°C), supernatant was decanted and samples were snap-frozen in liquid nitrogen. Pellets were stored at −80°C.A single colony of 600 ∼0.3) was homogeneously resuspended in 200 μl of buffer A  supplemented with 200 U SUPERase• In™ RNase Inhibitor, by pipetting. A total of 50 μl of buffer B  supplemented with 1 μl Ready-Lyse™ Lysozyme Solution  were added dropwise, and the vial was gently tilted five times to ensure homogenous mixing. The sample was then incubated 1 min at room temperature. A total of 250 μl of buffer C  were immediately added dropwise. The sample was then incubated 5 min at room temperature. At this stage, the solution clears considerably without increasing its viscosity, and nucleoid becomes visible. Using a cut P1000 pipette tip, the whole sample was gently layered on the top of a 5–30% w/v sucrose gradient  and centrifuged at 17 000 Rounds Per Minute (RPM) in a SW55Ti rotor  for 9 min (4°C). After centrifugation, the nucleoid fraction was collected using a syringe with a 18G blunt fill needle, and transferred to a new centrifuge tube. The remaining gradient was assumed to represent the cytosolic fraction. The nucleoid was then resuspended in 2.5 ml Wash & Resuspension buffer  supplemented with 200 U SUPERase• In™ RNase Inhibitor, pulse vortexed for 5 s and then centrifuged at 28 000 RPM in a SW55Ti rotor for 30 min (0°C). After centrifugation the supernatant was decanted, and the nucleoid pellet was washed twice with 2 ml of Wash & Resuspension buffer, taking care not to disturb it. The nucleoid was then resuspended in 500 μl Wash & Resuspension buffer, and solubilized by addition of 0.1 gr acid-washed glass beads , and shaking for 5 min in a TissueLyser (QIAGEN). For each 100 μl of purified nucleoids (or cytosolic fraction), 1 ml of TRIzol® Reagent  was added and RNA was extracted following manufacturer's instructions. RNA was analyzed on a 2100 Bioanalyzer (Agilent). In all experiments, RNA from cytosolic fraction (corresponding to mature RNA species) had RIN > 9.5. Total RNA yield from nucleoid fraction was ∼6% of the total RNA content.Each bacteria pellet from a 25 ml culture (ODin vitro transcription was initiated by addition of 1 μl of NTPs (25 mM each) and incubated at 37°C for 20 min. A parallel reaction was performed without adding NTPs. Reactions were stopped by addition of 1 ml TRIzol® Reagent. RNA was extracted following manufacturer's instructions, and analyzed on a 2100 Bioanalyzer.A 50 μl aliquot of solubilized nucleoid fraction was pre-equilibrated at 37°C for 5 min, then 2]. RNA was fragmented by incubation at 95°C for 8 min. Fragmented RNA was purified using RNA Clean & Concentrator™-5 columns, and eluted in 5.5 μl of nuclease-free water. RNA was heat-denatured at 70°C for 5 min, and reverse transcription (RT) was carried out in a final volume of 10 μl, in the presence of 0.5 mM dNTPs, 5 pmol of RT primer, 20 U RNaseOUT™ Recombinant Ribonuclease Inhibitor  and 100 U SuperScript® III Reverse Transcriptase , by incubation at 50°C for 50 min. Template RNA was degraded by adding 1 μl of 1 M NaOH, and incubating at 95°C for 5 min. Reaction clean-up was performed using RNA Clean & Concentrator™-5 columns, and cDNA was eluted in 6 μl nuclease-free water. cDNA fragments were resolved on a 10% TBE-Urea polyacrylamide gel, and a gel slice corresponding to fragments in the range 40–150 nt was cut. DNA was recovered by passive diffusion in Diffusion buffer for 16 h at 37°C with moderate shaking. cDNA was precipitated by addition of 1 ml Isopropanol, and 2 μl Glycogen (20 μg/μl), and resuspended in 8.25 μl nuclease-free water. A total of 10 pmol of a 5′-phosphorylated adapter were ligated to the 3′-OH of cDNA fragments in a final reaction volume of 25 μl, in the presence of 0.05 mM ATP, 20% PEG-4000 and 100 U CircLigase™ II ssDNA Ligase , by incubation at 60°C for 4 h and 68°C for 2 h. Adapter-ligated cDNA fragments were purified from excess adapter using 1.8 volumes of Agencourt AMPure XP beads , following manufacturer’s instructions. cDNA was eluted in 20 μl of nuclease-free water, and indexed sequencing adapters were introduced by 15 cycles of PCR in the presence of 25 pmol of each primer, and 25 μl NEBNext® High-Fidelity 2× PCR Master Mix .A total of 10 pmol of a pre-adenylated (rApp) adapter  were lig® III Reverse Transcriptase, by incubation at 50°C for 50 min. Template RNA was degraded by adding 1 μl of 1 M NaOH, and incubating at 95°C for 5 min. Reaction clean-up was performed using RNA Clean & Concentrator™-5 columns, and cDNA was eluted in 6 μl nuclease-free water. cDNA fragments were resolved on a 10% TBE-Urea polyacrylamide gel, and three gel slices corresponding to fragments in the ranges of 40–200, 200–400 and 400–600 nt were cut. DNA was recovered by passive diffusion in diffusion buffer for 16 h at 37°C with moderate shaking. cDNA was precipitated by addition of 1 ml Isopropanol, and 2 μl Glycogen (20 μg/μl), and resuspended in 8.25 μl nuclease-free water. A total of 10 pmol of a 5′-phosphorilated adapter were ligated to the 3′-OH of cDNA fragments in a final reaction volume of 25 μl, in the presence of 0.05 mM ATP, 20% PEG-4000 and 100 U CircLigase™ II ssDNA Ligase, by incubation at 60°C for 4 h and 68°C for 2 h. Adapter-ligated cDNA fragments were purified from excess adapter using 1.8 volumes of Agencourt AMPure XP beads, following manufacturer’s instructions. cDNA was eluted in 20 μl of nuclease-free water, and indexed sequencing adapters were introduced by 15 cycles of PCR in the presence of 25 pmol of each primer, and 25 μl NEBNext® High-Fidelity 2× PCR Master Mix.A total of 10 pmol of a pre-adenylated (rApp) adapter were ligated to 1 μg of nascent RNA ) in a reaction volume of 20 μl, using 400 U T4 RNA Ligase 2, Deletion Mutant in the presence of 20% PEG-8000, by incubation at 25°C for 2 h. Reaction clean-up was performed using RNA Clean & Concentrator™-5 columns, and RNA was eluted in 5.5 μl nuclease-free water. RNA was heat-denatured at 70°C for 5 min, and RT was carried out in a final volume of 10 μl, in the presence of 0.5 mM dNTPs, 5 pmol of RT primer, 20 U RNaseOUT™ Recombinant Ribonuclease Inhibitor and 100 U SuperScript® III Reverse Transcriptase, by incubation at 50°C for 50 min. Template RNA was degraded by adding 1 μl of 1 M NaOH, and incubating at 95°C for 5 min. Reaction clean-up was performed using RNA Clean & Concentrator™-5 columns, and cDNA was eluted in 6 μl nuclease-free water. cDNA fragments were resolved on a 10% TBE-Urea polyacrylamide gel, and a gel slice corresponding to fragments in the range of 40–150 nt was cut (corresponding to truncated cDNA products). DNA was recovered by passive diffusion in diffusion buffer for 16 h at 37°C with moderate shaking. cDNA was precipitated by addition of 1 ml Isopropanol, and 2 μl Glycogen (20 μg/μl), and resuspended in 8.25 μl nuclease-free water. A total of 10 pmol of a 5′-phosphorilated adapter were ligated to the 3′-OH of cDNA fragments in a final reaction volume of 25 μl, in the presence of 0.05 mM ATP, 20% PEG-4000 and 100 U CircLigase™ II ssDNA Ligase, by incubation at 60°C for 4 h and 68°C for 2 h. Adapter-ligated cDNA fragments were purified from excess adapter using 1.8 volumes of Agencourt AMPure XP beads, following manufacturer's instructions. cDNA was eluted in 20 μl of nuclease-free water, and indexed sequencing adapters were introduced by 15 cycles of PCR in the presence of 25 pmol of each primer, and 25 μl NEBNext® High-Fidelity 2× PCR Master Mix.A total of 2 μg of RNA from the cytoplasmic fraction were diluted in 20 μl of fragmentation buffer, and fragmented by incubation at 95°C for 5 min. Fragmented RNA was purified using RNA Clean & Concentrator™-5 columns. End repair of RNA fragments was performed in a final volume of 20 μl, in the presence of 20 U T4 Polynucleotide Kinase  and 20 U SUPERase• In™ RNase Inhibitor, by incubation at 37°C for 1 h. End-repaired RNA was purified again using RNA Clean & Concentrator™-5 columns, and eluted in 6 μl of nuclease-free water. A total of 6 μl of 2× RNA Loading Dye were added to end-repaired RNA. RNA was heated to 95°C for 2 min, and immediately placed on ice. Samples were resolved on a 10% TBE-Urea polyacrylamide gel, and a gel slice corresponding to fragments above 200 nt was cut. The gel slice was crushed by centrifugation through a punctured 0.5 ml tube, and resuspended in 500 μl of diffusion buffer supplemented with 60 U SUPERase• In™ RNase Inhibitor, then rotated at 4°C for 16 h to allow passive diffusion of RNA fragments into buffer. RNA was precipitated by addition of 1 ml Isopropanol, and 2 μl Glycogen (20 μg/μl), and resuspended in 6 μl nuclease-free water. A total of 10 pmol of a pre-adenylated (rApp) adapter were ligated to size-selected RNA fragments in a reaction volume of 20 μl, using 400 U T4 RNA Ligase 2, Deletion Mutant in the presence of 20% PEG-8000, by incubation at 25°C for 2 h. Reaction clean-up was performed using RNA Clean & Concentrator™-5 columns, and RNA was eluted in 5.5 μl nuclease-free water. RNA was heat-denatured at 70°C for 5 min, and RT was carried out in a final volume of 10 μl, in the presence of 0.5 mM dNTPs, 5 pmol of RT primer, 20 U RNaseOUT™ Recombinant Ribonuclease Inhibitor and 100 U SuperScriptE. coli str. K-12 substr. MG1655 (GenBank: U00096.2) was used as the reference genome to extract transcripts’ sequences. Forward and reverse reads were independently mapped to the reference transcriptome using Bowtie v1.1.2, by allowing up to seven mapping positions to enable mapping to the seven E. coli rRNA genes . Forward and reverse mapped reads were then re-paired. Using reverse read mapping positions , forward reads were split into separate SAM files for each transcription intermediate. When analysis was performed in deciles of transcription, genes were split into 10 equally sized deciles and reads belonging to transcription intermediates falling in the same decile were pooled. SAM files were then passed to the rf-count tool of the RNA framework to generate RT-stop counts (RC) files. Resulting RC files were normalized using the rf-norm tool of the RNA Framework in 50 nt sliding windows, with a 25 nt offset (parameters: -sm 2 -nm 2 -ec 0 -mc 0 -n 50 -nw 50 -wo 25). Mapping of DMS-seq data (mature RNA) was performed by using the rf-count tool (parameters: -cl 15 -bm 7 -ba -b5 5). Resulting RC files were normalized using the rf-norm tool (parameters: -sm 2 -nm 2 -ec 50 -mc 50 -n 50 -nw 50 -wo 25). The rf-norm tool generates a XML file for each transcript (or for each transcription intermediate/decile in the case of SPET-seq data). XML files for mature RNA were passed to the rf-fold tool of the RNA Framework (using ViennaRNA Package 2.2 with soft constraints (FastQ files were examined using the FastQC tool. All the relevant SPET-seq data (nascent RNA) analysis and normalization steps were performed using a custom wrapper built on top of the RNA Framework . BrieflyTo identify regions undergoing structural changes, a dynamically sized window was sled along RNAs to identify windows containing 40 A/C residues, with a dynamic offset of 20 A/C residues. Regions with median coverage < 100 reads, and <1 RT-stop on average per A/C base were discarded. To compare consecutive deciles, windows falling >200 nt before decile’s start or within 25 nt from decile’s end were discarded to avoid biases due to lowly covered regions. This yielded a final list of ∼34 000 windows. Each window was then split into 2 × 20 nt chunks, that were independently normalized by 90% Winsorising . This half-normalization approach allowed smoothing the effect of coverage decrease toward the 5′-end. A Pearson coefficient cutoff of 0.8 was used to identify windows undergoing structural rearrangements.Sequencing data have been deposited on the NCBI Gene Expression Omnibus under accession GSE95567.in vitro folding pathway of a fluoride riboswitch . RNase P is a ribozyme essential for the maturation of tRNAs and several other non-coding RNAs , and using a sliding-window approach we compared ∼34 000 windows of 40 A/C residues across consecutive transcription deciles using two previously defined metrics, the Pearson correlation coefficient and the Gini index difference (r > 0.8) across transcription stages, and negligible Gini index difference (|Gini difference| < 0.1), suggesting that most structures formed during transcription are stable. We next focused on 29% of windows undergoing structural rearrangements , thus suggesting that these bases get sequestered into transitory interactions until their cognates get transcribed.We then used DMS-seq data from cytosolic fraction to perform whole transcriptome soft constraint-guided inference of mature RNA secondary structures ,12. RNA As an example, by analyzing 23S rRNA folding pathway we detected a structural transition involving residues C678-C680 Figure  and F. TIn this work, we introduced a new method, named SPET-seq, that enables base-resolution analysis of transcription intermediate RNA structures. To our knowledge, this is the first report that evaluates the cotranscriptional RNA structure formation on a transcriptome-wide scale in a living organism.in vitro, we applied this technique to E. coli to gain insights into in vivo cotranscriptional structure formation. By systematically analyzing cellular RNAs, we observed that the majority of them don’t undergo significant structural transitions along their folding pathways. Instead, around 29% of analyzed windows sample different conformations across transcription stages. This fraction probably represents a conservative estimate of the actual number of structure-changing regions, as the intrinsic limitation of SPET-seq is to allow the resolution of the 3′-most portion of nascent RNA chains only  gets sequestered by a nearby cluster of three G residues (G695–G697) to form a transitory helix. As the true cognate bases of G695–G697 get transcribed, residues C678–C680 are immediately released, and remain single-stranded until the proper 3′-cognates G797–G799 have been transcribed.No difference was observed also for unpaired and short-range interacting bases. Notably, the same reactivity profile between mature and nascent RNA was observed at the level of local structured domains , thus suggesting that these hairpins get folded as soon as they get transcribed. So, it appears that the kinetics of transcription plays a major role in regulating the formation of these structures.in vivo is a process in which short-range interactions are immediately formed, while long-range interactions require the formation of non-native transitory structures in order to sequester the 5′ side of long-range helices (Figure Collectively our data suggests that cotranscriptional RNA folding s Figure .in vivo and in vitro. We can anticipate that SPET-seq might be exploited to provide insights into the more complex cotranscriptional folding landscape of eukaryotic RNAs. Although nascent RNA probing of eukaryotic RNAs might be harder due to the presence of intronic sequences long up to hundreds of kilobases, an exon enrichment step based on the use of biotinylated capture probes can be easily inserted at the end of the SPET-seq library preparation procedure to enrich exonic reads.SPET-seq constitutes a solid and straightforward method for the study of RNA cotranscriptional folding pathways both GSE95567.Supplementary DataClick here for additional data file.


The molecular dynamics simulations suggest that the presence of divalent cations forces the N-terminus of osteopontin to bind the shell of divalent cations adsorbed over the surface of its RNA aptamer, the latter exposing a high negative charge density. The osteopontin plasticity as a function of the local concentration of Mg is discussed in the frame of the proposed strategies for osteopontin targeting as biomarker and in theranostic.Osteopontin is an intrinsically disordered protein involved in tissue remodeling. As a biomarker for pathological hypertrophy and fibrosis, the protein is targeted by an RNA aptamer. In this work, we model the interactions between osteopontin and its aptamer, including mono- (Na Osteopontin (OPN) belongs to the class of matricellular proteins involved in tissue remodeling . The protein of 300 aminoacids has no three-dimensional structure in water . The protein contains, in the post-processed 17–300 chain, 47 D, 24 E, 18 K, and 9 R, for a total charge at physiological pH of −44. The high polarization of charge in OPN (the charge of first 100 residues is −26) indicates that the interactions of OPN with macromolecular partners is associated to a collective rearrangement of the N-terminus consequent to the approach of the target molecule by OPN.The role of IDPs in biomineralization has been reported . In the second hypothesis, the negatively charged N-terminus is repelled by RNA and the interactions between OPN and RNA are non-electrostatic, even though they can still involve the cations adsorbed on the RNA molecule.The interactions between OPN and heparin, the latter a polysaccharide with high negative charge density, have been recently studied by NMR and EPR techniques  of molecular dynamics simulations performed for the large macromolecular complex , the structural models reported here provide a possible alternative frame compared to that observed with no divalent cations, and assist to a better prediction of the interactions of OPN when it is bound to the aptamer and to the theranostic nanoparticle that supports the aptamer.T = 300 K and P = 0 bar, in a range of physiological salt concentrations conditions, for 10 ns. Two of these configurations were simulated for 55 ns. The system size is summarized in Table We built 6 initial configurations for the OPN-(OPN-R3) complex with a low number of intermolecular contacts  complex, the configuration of RNA with Mg simulated after 18 ns was used. This configuration, as shown by the analysis described in the Supplementary Material, has 9 Mg cations adsorbed. These cations were kept for the construction of the initial complex samples.A random configuration for the OPN protein was built and put in contact with the OPN-R3 selected structure. A set of non overlapping OPN-(OPN-R3) configurations was generated by using random temperature Monte Carlo trajectories  in the simulated body fluid , showed that a high degree of screening of RNA is performed by both Na condensation (within 3 nm) and Mg adsorption over the RNA surface (within 0.28 nm). The analysis shows that Mg replaces Na in the condensation layer, but the Mg ions are more structured around the phosphate groups of RNA.The analysis of the OPN-R3 aptamer simulations, shows that, as expected, the simulation time is not long enough to sample all the structural changes occurring in the RNA fragment during the stabilization of the layer of cations (see discussion of the root-mean square deviation and solvent accessible surface area). We remark that the aim of the model is to probe the general effect of the formed layer on the protein-RNA interactions, regardless of specific interactions that can be adequately sampled with atomistic models of this size only using hundreds of replica in the μs time-scale.Summarizing, the RNA molecule adsorbs 8 ± 2 divalent cations and 2 ± 1 Na cations, on average. The comparison of radial distribution function for OP-M pairs (OP being the non-phosphodiester O atom of RNA phosphate groups) with that for all pairs X-M shows that this number is dominated by OP contacts when M is Mg, but there is a significant difference when M = Na. Even when Mg is absent, the number of X-adsorbed Na is 6 ± 2, while that for OP-adsorbed Na is 3 ± 2. This difference means that Na ions are not really adsorbed, while there is exchange between the first shell and farther shells, allowing short-range interactions with groups different from the phosphate groups. This observation is consistent with a loose structure of short-range Na shells. On the other hand, in case of Mg, the number of adsorbed ions measures the extent of ion adsorption by the phosphate groups as an additional information to the ion condensation, the latter occurring within the larger distance of 3 nm .As a consequence of the ionic behavior summarized above , most of the screening contribution due to Mg is made by phosphate-adsorbed Mg . This adsorption is a very slow process, as already noticed in 100-ns long simulations -OPN complex, the time-evolution of different types of contacts and radial distribution functions have been analyzed. The time-length of 10 ns sampled for 6 models, allows the settling of the ionic atmosphere around each complex. On a longer time-scale (55 ns), sampled for 2 of the models, an initial stage of wrapping of the protein around the RNA fragment is clearly observed (see below). The simulation of a complete wrapping process, as well as of the possible equilibrium between wrapped and unwrapped configurations, is beyond the possibilities of these atomistic models. Therefore, we focus only on discriminating among the two mechanisms described, respectively, in Figures We first measured the number of OPN-RNA non specific contacts using a set-up already used to evaluate crystal and solution structures of several protein-RNA complexes. Then, the number of ions M  is used to eventually correlate time evolution of non specific contacts with the adsorption/release of counterions from the short-range layer discussed for RNA in the absence of the OPN protein partner. Similarly to the analysis of RNA, we also analyze the location of adsorbed cations, selecting O atoms in negatively charged groups of OPN protein (Oδ of D and Oϵ of E). These O atoms will be indicated as OD and OE, respectively, hereafter.d0 value  is the same used for RNA (0.28 nm), because the position of the first peak in the radial distribution function of OP-M distance does not change when OPN is added. The same effect holds for O of carboxylate groups in OPN. As for the first evaluation (non specific contacts), the work reported in . We measured CN with d0 = 0.5 nm and for pairs involving non-hydrogen atoms of protein and RNA, respectively, for the 41 complexes, among the 49 PDB entries, for which it is possible to reconstruct the entire unit cell. This construction was performed with the VESTA program  and the number of nucleotides in the RNA partner (nnt) is in the range of the ratio that holds for the OPN-aptamer 1:1 complex, R = 284/40≃7. Therefore, we selected the 26 structures with ratio between 3 and 15. The CN value obtained for these 26 structures is then normalized to CNn = CN/(naa + nnt), i.e., is divided by the total number of residues naa + nnt in the unit cell. The periodic boundary conditions of the unit cell were used to compute the distances in the evaluation of CN in Equation 1 . The minimal value for CNn in this set of structures is 0.002 and the maximal is 0.07. Among this data-set, the unique structure of a protein-aptamer complex is the structure 1OOA, with CNn = 0.02. Hereafter, we shall use this value as the CNn minimal value for the protein-RNA binding.As for specific O-M contacts, the CNn in the six simulations of OPN-aptamer complex is displayed, while the time evolution of specific O-M contacts  is displayed in Figure In left panels of Figure 5, starts from values lower than the threshold displayed by the aptamer complex reported in the PDB , and ends within the first part of simulations at values higher than the threshold, with the exception of trajectories 2 and 6. Therefore, 2 trajectories over 6 display, in the first part of simulation (Mg ~ 10% of Na), final values of CNn higher than that of the crystallized 1OOA aptamer-protein complex. All trajectories display CNn higher than the minimal value 0.002 (PDB 1B23). Concerning the second part (lower Mg concentration) of simulation, all of the trajectories end with CNn higher than the 1OOA threshold.Left panels of Figure 1 reaches a higher value at high Mg concentration (first 10 ns), then, after a sudden decrease due to the removal of phosphate-adsorbed Mg cations, the complex recovers high values of CNn when other Mg cations are adsorbed by RNA . These two trajectories display a OPN wrapping that becomes less tight, compared to the other trajectories, when no Mg is present. In complexes 3, 4, and 5, higher CNn values are reached in the first stage of high Mg concentration and Mg removal has no apparent effect on the OPN wrapping. In trajectory 3 the partial recovery of phosphate-adsorbed Mg occurs in the second simulation stage  and the complete removal is again not sufficient to trigger the OPN unwrapping away from RNA, even though a partial unwrapping is displayed (last 10 ns).The second feature is more specific for each trajectory. Trajectory i − j pairs, i running over OPN atoms and j running over RNA atoms. The time evolution of this contribution is displayed in the right panels of Figure 2 and 6, that are those not reaching the threshold for CNn. According to this observation, the value of −50 kcal/mol for the Lennard-Jones complexation energy appears as a threshold for an irreversible wrapping. The correlation between the behavior of CNn in the absence of Mg cations and that of the Lennard-Jones complexation energy is displayed by trajectories 3, 4, and 5, i.e., those trajectories where the OPN wrapping is more evident and Mg cations are adsorbed by both OPN-R3 and OPN. In trajectories 3 and 4 there is partial unwrapping when no Mg is present, while in trajectory 5 the wrapping is recovered and the energy becomes lower than −100 kcal/mol.The behavior described above is also displayed by the non-electrostatic and non-solvation energy contribution to the formation of the OPN-RNA complex, i.e., the Lennard-Jones complexation energy. This is the sum of Lennard-Jones pair contributions to energy, with the sum extended to The O-Mg contacts account for all the OPN and OPN-R3 contacts with Mg in the second half of trajectories. Differences between X (with X any atom in the solute) and O = OP, OD, OE  as Mg adsorber, are displayed only in the first half of the trajectories, while in the second half there is no difference between the calculation including the addressed O  atoms and all the atoms in the OPN protein and RNA aptamer, respectively. This behavior shows that the OPN protein arranges its conformation to adapt to the constraint of possible Mg-OP and Mg-OD/OE salt bridges. A detailed analysis of the salt bridges and charge neutralization for the 6 OPN-RNA complexes simulated for 10 ns is reported in Table n1 , by decreasing Mg concentration (from top to bottom). The charge of OPN and RNA reported in the last two columns is computed by assigning one half of the ionic charge to OPN and RNA, respectively, when the ion is involved in a salt bridge of type OPN-M-RNA (the fifth column). It must be noticed that the negative charge of both partners in the complex, OPN and OPN-R3 aptamer, is minimal in the presence of 10% of Mg. A significant decrease of charge (the charge becomes more negative) occurs when Mg cations are removed from the system. The exchange of divalent cations between binding sites and the bulk is very slow in RNA, as it has not been observed in 200-ns long MD simulations  over six simulations during the following 2 simulations with decreasing number of Mg cations. In this case we notice that no efficient replacement of Mg by Na occurs in the adsorbed layer.Summarizing, in all of the simulated trajectories 10-ns long and in the presence of about 10% of Mg cations among the positive counterions, the cations trigger the collapse of OPN onto the RNA aptamer, as shown by the initial time evolution of n Figure . Then, u1 and 3, have been extended to 55 ns in the presence of Na and Mg counterions in the conditions of the simulated body fluid. In both simulations, the wrapping of OPN around OPN-R3 increases. The extent of wrapping is measured via the R quantity in Equation 2 in the Supplementary Material. In Figure 3 it is more extended than for 1.Two selected simulations, 1), the wrapping is moderate also over a longer time-scale (R~0.9). On the other hand, when the initial wrapping is large  wrapping is moderate  is displayed. Even if in both cases the number of Mg cations that is adsorbed is almost the total number of Mg ions in the sample (18 over 20 in both cases), the time evolution is markedly different in the two cases. In the first case (1) the Mg adsorption is not yet completed at 55 ns and consequently the number of adsorbed Na ions is still kept at the initial level (n1 = 15–16). In the second trajectory (3) the number of adsorbed Na ions is significantly decreased when the number of adsorbed Mg ions is stable at 18. This exchange between Mg and Na in the adsorbed layer (d0 < 0.28 nm) occurs when the wrapping of OPN around OPN-R3 displays values of R lower than 0.9.In Figure 3 is significantly larger than for 1, thus indicating a partial RNA unfolding upon the larger wrapping of 3. The time evolution of RMSD  for OPN and OPN-R3 is also different in the two cases (data not shown). The SASA of RNA within the first 20 ns of simulation is very similar in the two cases, but during the last 20 ns the SASA(OPN-R3) for D Figure  displaysd0 = 0.28 nm. For trajectory 1, 18 Mg ions are adsorbed among the total amount of 20, while only 16 Na ions among the total amount of 366 are adsorbed. In case of trajectory 3, the number of adsorbed Mg ions is the same (see above), but that of Na ions is significantly smaller (11) when averaged over the last ns. Nevertheless, the final values of n1 are similar, 16 and 15 for Na in, respectively, 1 and 3.Following the analysis performed for the OPN-OPN-R3) complex for the first 10 ns of simulation  gives −17.5 and −13.5 for OPN and OPN-R3, respectively, in case 1, and −16.5 and −27.5 for 3, respectively. In both cases the negative charge of the assembly is strongly screened at short distances: 37 and 53%, respectively, of the total bare charge of −44−39 = −83.The calculation of 1 and 3  obtained at the end of trajectories 1 and 3). No Cl anion is adsorbed over the assembly. The loop in the stem-loop topology of RNA has different orientations, since in 1 the loop is directed toward the C-terminal domain of OPN (left panel), while the opposite is for 3 (right panel). This shows that the role of RNA sequence is in keeping the stem-loop RNA topology and the density of negative charges of phosphate groups, while the positions of bases have not a strong effect in the assembly topology.Both final configurations display the formation of two nascent protein domains. One involves the N-terminal region (first 110 residues), strongly interacting with the RNA aptamer via extended salt bridges involving both Na and Mg cations. Among the 20 available Mg cations, 15 (left panel) and 14 (right panel) ions are concentrated in the assembly of OPN-R3 with the N-terminal OPN domain. The second domain  contains a few cations  contacts Figure . The strThis two-domain behavior of OPN, when the latter is exposed to its RNA aptamer OPN-R3, can be summarized as a folding-upon-binding mechanism, opposite to that proposed for the interaction between OPN and heparin in the absence of divalent cations. Nevertheless, the type of interaction between the OPN N-terminus and RNA is consistent with the few structural results reported in the literature for isolated OPN. The most recent NMR data for non-phosphorylated OPN  the rough model of divalent cations; (ii) the short length of MD trajectories due to the slow conformational change of the disordered protein; (iii) the slow process of exchange of divalent cations. Despite these limitations, the two longer simulations, started from different initial configurations, describe the assembly as two nascent domains: (i) the ternary complex between OPN-R3, the N-terminus of OPN rich in negatively charged residues (D and E), and most of the divalent cations recruited in the solution; (ii) the C-terminus of OPN adopting a collapsed structure where cations are not involved and that points oppositely to the first domain.This two-domain topology can be altered by OPN phosphorylation, the latter providing a negative charge to other residues  that are more distributed among OPN regions (Lenton et al., All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Scientific Reports6: Article number: 3904310.1038/srep39043; published online: 12232016; updated: 04052017Affiliation 1 was incorrectly listed as ‘Group of Clinical Genomic Networks, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Shanghai 200031, P.R. China’ in the original version of the Article. The correct affiliation is listed below:CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Shanghai, 200031, P.R. China.In addition, an additional affiliation for all the authors was omitted. The correct affiliation is listed below:University of Chinese Academy of Sciences, Beijing, 100049, P.R. China.As a result, the affiliation list and affiliated authors now read:Affiliation 1CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Shanghai, 200031, P.R. China.Christine Nardini, Valentina Devescovi, Yuanhua Liu, Xiaoyuan Zhou, Youtao Lu & Jennifer E. DentAffiliation 2University of Chinese Academy of Sciences, Beijing, 100049, P.R. China.Christine Nardini, Valentina Devescovi, Yuanhua Liu, Xiaoyuan Zhou, Youtao Lu & Jennifer E. DentAffiliation 3CNR IAC “Mauro Picone”, Via dei Taurini 19 00185-Roma, Italy.Christine NardiniAffiliation 4Bioinformatics Platform, Institut Pasteur of Shanghai, CAS, Shanghai 200031, P.R. China.Yuanhua LiuAffiliation 5NORSAS consultancy limited, Norwich (NR12 8QP), Norfolk, UK.Jennifer E. DentThis has now been corrected in the HTML and PDF versions of this Article.


Bioorthogonal cleavable fluorescent oligonucleotides have been developed to enable highly multiplexed single-cell in situ RNA and DNA analysis. in situ is essential to advance our understanding of normal physiology and disease pathogenesis. Here we report a highly multiplexed single-cell in situ RNA and DNA analysis approach using bioorthogonal cleavable fluorescent oligonucleotides. In this approach, oligonucleotides tethered to fluorophores through an azide-based cleavable linker are used to detect their nucleic acids targets by in situ hybridization. After fluorescence imaging, the fluorophores in the whole specimen are efficiently cleaved in 30 minutes without loss of RNA or DNA integrity. Through reiterative cycles of hybridization, imaging, and cleavage, this method has the potential to quantify hundreds to thousands of different RNA species or genomic loci in single cells in situ at the single-molecule sensitivity. Applying this approach, we demonstrate that different nucleic acids can be detected in each hybridization cycle by multi-color staining, and at least ten continuous hybridization cycles can be carried out in the same specimen. We also show that the integrated single-cell in situ analysis of DNA, RNA and protein can be achieved using cleavable fluorescent oligonucleotides combined with cleavable fluorescent antibodies. This highly multiplexed imaging platform will have wide applications in systems biology and biomedical research.The ability to profile transcripts and genomic loci comprehensively in single cells  Comprehensive analyses of the copy number and spatial organization of transcripts and genomic loci in single cells promise to transform our understanding of many heterogeneous biological systems, such as brain tissues, solid tumors and developing embryos.in situ nucleic acids analysis, a number of methods have been explored. For example, in situ sequencingin situ hybridization (MER-FISH)To enable multiplexed single-cell in situ RNA and DNA analysis approach using bioorthogonal cleavable fluorescent oligonucleotides (BoCFO). In this method, oligonucleotides (ON) conjugated to fluorophores through an azide-based chemically cleavable linker are applied to detect their nucleic acids targets by in situ hybridization. Upon continuous cycles of target hybridization, fluorescence imaging, and fluorophore cleavage, this approach has the potential to quantify hundreds to thousands of different RNA species or genomic loci in individual cells at the optical resolution. To demonstrate the feasibility of this approach, we designed and synthesized BoCFO by coupling oligonucleotides with different cleavable fluorophores. We show that the fluorophores conjugated to oligonucleotides can be efficiently cleaved within the cellular environment in 30 minutes at 37 °C without loss of RNA or DNA integrity. We also demonstrate that different nucleic acids species can be detected in each hybridization cycle by multi-color staining, and at least ten continuous hybridization cycles can be carried out in the same set of cells. Additionally, we show that integrated single-cell in situ analysis of DNA, RNA and protein can be achieved by using cleavable fluorescent oligonucleotides together with cleavable fluorescent antibodies. Applying this approach, we studied RNA expression heterogeneity in a population of genetically identical cells, and performed the expression correlation analysis between different RNA species and also between RNA and protein.Here, we report a highly multiplexed single-cell in situ. For example, by staining different nucleic acids in sequential hybridization cycles, an overall M × N nucleic acids can be quantified in individual cells in situ, where M is the number of varied fluorophores used in each analysis cycle, and N is the number of hybridization cycles. When the same set of nucleic acids are stained in sequential hybridization cycles , V-akt murine thymoma viral oncogene homolog 1 (AKT1), transferrin receptor (TFRC), breast cancer 1 (BRCA1), breast cancer 2 (BRCA2), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), polymerase II polypeptide A (POLR2A), actin beta (ACTB) and PR domain containing 4 (PRDM4). Each library of the direct staining probes is composed of about forty 20 mer BoCFO. The indirect staining probes target mRNA GAPDH and marker of proliferation Ki-67 (MKI67), along with a 5 kb genomic locus at 4p16.1. Each library of the indirect mRNA and DNA staining probes is composed of ∼40 and 100 predecoding oligonucleotides, respectively. These predecoding oligonucleotides include one target binding site, one or multiple decoding oligonucleotides binding sites, and poly-T linkers inserted between the binding sites. Each library of the predecoding oligonucleotides can recruit a corresponding decoding oligonucleotide, which is conjugated with cleavable fluorophores and function as BoCFO.N-hydroxysuccinimide (NHS) ester phosphine (TCEP) for 15, 30 and 60 minutes at 37 °C . In comparison, only 78% of the spots reoccur in the third cycle when DNase is applied to remove the all the probes in every analysis cycle.To quantify hundreds of RNA species simultaneously in single cells by sequential staining,3-Quasar 570 and ON-N3-Cy5, respectively. The obtained expression patterns  for integrated DNA, RNA and protein  Fig. S8A. After r Fig. S8A and geno Fig. S8A were seqTo study whether the copy numbers of transcripts can be used to predict the abundances of the corresponding proteins, we performed the single-cell RNA-protein expression correlation analysis. This analysis of mRNA MKI67 and protein Ki67 yields the correlation coefficient value of 0.54 . These rin situ nucleic acids profiling. Compared with the existing technologies, our approach has the following advantages. (i) In this method, nucleic acids targets are detected directly by in situ hybridization without target sequence amplification. Therefore, transcripts and genomic loci can be visualized at the single-molecule sensitivity. (ii) Our technology has high multiplexing capacity as it allows a large number of the same or different nucleic acids to be detected in different analysis cycles by sequential staining or reiterative hybridization, respectively. (iii) The TCEP treatment simultaneously cleaves all the different fluorophores in the whole specimen within 30 minutes. Thus, our method has high sample throughput, and permits a large number of cells to be analyzed in a short time. (iv) As BoCFO has high signal removal efficiency and avoids the cross-reactions with endogenous biomolecules and other probes, our approach has enhanced signal to noise ratio and analysis accuracy. (v) Rather than re-hybridizing the expensive target-binding oligonucleotide library in every analysis cycle, our technology only applies this time-consuming hybridization in the first cycle. Therefore, our method is more time- and cost-effective. (vi) As the small cleaved fluorophores diffuse out faster than the large stripped oligonucleotide probes, our technology facilitates the analysis of intact tissues. (vii) By cleaving the fluorophores while maintaining the integrity of almost all the biomolecules, our approach can be applied for the integrated single-cell in situ DNA, RNA and protein analysis.We have designed and synthesized BoCFO, and applied them for multiplexed single cell in situ. Additionally, the BoCFO probes developed here integrated with cleavable fluorescent antibodies we reported previously enable the comprehensive and integrated DNA, RNA and protein profiling at the optical resolution in single cells. This highly multiplexed imaging platform will bring new insights into cell signaling network, gene expression regulation, molecular diagnosis and cellular targeted therapy.The number of nucleic acids that can be quantified in single cells using this BoCFO-based approach depends on two factors: the number of hybridization cycles and the number of fluorophores applied in each cycle. As we have shown, TCEP can efficiently remove the fluorophores within 30 minutes, while the integrity of RNA and DNA is preserved after the treatment with TCEP for at least 24 hours. This suggests that the cycling number can be further increased significantly. Additionally, classical fluorophores with four or five varied colors can be applied simultaneously to visualize different nucleic acids in one hybridization cycle. And multispectral fluorophores coupled with the hyperspectral imaging methodJ. G. is an inventor on a patent application filed by Arizona State University that covers the method of using bioorthogonal cleavable fluorescent oligonucleotides for multiplexed nucleic acids analysis.Supplementary informationClick here for additional data file.Supplementary informationClick here for additional data file.


Crinivirus, family Closteroviridae) encodes an RNA silencing suppressor, the protein p22, that has been described as having one of the longest lasting local suppressor activities when assayed in Nicotiana benthamiana. Since suppression of RNA silencing and the ability to enhance disease severity are closely associated, we analyzed the effect of expressing p22 in heterologous viral contexts. Thus, we studied the effect of the expression of ToCV p22 from viral vectors Tobacco rattle virus (TRV) and Potato virus X (PVX), and from attenuated suppressor mutants in N. benthamiana plants. Our results show that although an exacerbation of disease symptoms leading to plant death was observed in the heterologous expression of ToCV p22 from both viruses, only in the case of TRV did increased viral accumulation occur. The heterologous expression of ToCV p22 could not complement suppressor-defective mutant viruses.To counteract host antiviral RNA silencing, plant viruses express suppressor proteins that function as pathogenicity enhancers. The genome of the Tomato chlorosis virus (ToCV) (genus  Potyvirus, family Potyviridae) and Potato virus X  in tobacco [RNA silencing acts as an effective antiviral defense in plants . To coun tobacco ,5,6,7,8. tobacco . Similar tobacco ,10.Crinivirus) belongs to the family Closteroviridae, in which the largest RNA genomes among plant viruses have been reported [Bemisia tabaci and has a bipartite single-stranded, positive-sense RNA genome [Closteroviridae, ToCV adopts the strategy of encoding multiple RNA silencing suppressors [Nicotiana benthamiana, suppressing local RNA silencing induced either by sense RNA or dsRNAs [Tomato chlorosis virus  and PVX have been frequently used as viral vectors for both, gene expression and gene silencing studies [Tobacco rattle virus  35S promoter have been described . To consA. tumefaciens strain GV3101 by electroporation.Plasmid pgR107 containing the cDNA of PVX (pPVX) under the CaMV 35S promoter has been described . The genN. benthamiana plants (3–5 leaf growth stage) were agroinfiltrated with A. tumefaciens strain GV3101 carrying pTRV1 + pTRV2p22 or pTRV2, pTRV1Δ16K + pTRV2p22 or pTRV2, and pPVXp22, pPVX, pPVXΔP25p22, or pPVXΔP25. For co-infiltration of pTRV1 and pTRV2 and its derivatives, equal volumes of individual A. tumefaciens cultures  were mixed prior to infiltration. Plants were maintained in a controlled temperature chamber at 25 °C with a 16 h/8 h light/dark cycle.For northern blot analysis, total RNA was extracted from leaf tissues as described previously . Total RN. benthamiana plants were co-infiltrated with A. tumefaciens strains harboring a mixture of TRV1 and TRV2p22 or TRV2, and TRV1∆16K plus TRV2p22 or TRV2 , and from a 16K suppressor deficient TRV mutant (TRV∆16Kp22 construct), comparing the results with those obtained with the wild type TRV and a 16K suppressor deficient TRV mutant .  or TRV2 A.N. benthamiana plants developed large necrotic lesions on the stems, leaf petioles, and young leaves; these spread rapidly to the whole plant, resulting in plant death by 19–21 dpi (By 7–9 days post inoculation (dpi), a striking enhancement of symptoms was only observed in plants infected with TRVp22. TRVp22-infected 9–21 dpi A. In con9–21 dpi A—althougIn contrast to this, the expression of the suppressor p22 from a 16K suppressor deficient TRV mutant (TRV∆16Kp22 infections), in either agroinfiltrated patches or non-inoculated leaves, did not result in increased viral RNA accumulation, with levels always lower than that observed for wild type TRV-inoculated plants. In fact, by 19 and 26 dpi, the viral RNA accumulation in plants infected with TRV∆16Kp22, when p22 was present, were similar to the viral RNA levels observed for TRV∆16K infections, when neither 16K and p22 suppressors were present B. The diTaken together, these results indicate that the expression of the suppressor p22 from the heterologous vector TRV when the 16K suppressor is present enhances both symptomatology and viral RNA accumulation, but it cannot functionally complement a 16K defective virus at either the local and systemic level.We examined whether the rapid spread of the necrotic symptoms observed in plants infected with TRVp22 was linked to faster systemic spread of the virus. Tissue blot analysis was performed for cross-sections of non-inoculated tissues in four different locations of the plants , left coThese results show that the enhanced disease symptoms and virus accumulation in TRV2p22 infected plants do not seem to be linked to a faster spread of the chimeric virus across the plant. In addition to this, the presence of the suppressor p22 could not complement the absence of 16K in a defective TRV mutant virus.N. benthamiana plants at 7–8 dpi, plants inoculated with PVXp22 exhibited evident leaf curling in young non-inoculated leaves that rapidly progressed into generalized necrosis that led to plant death by 10–12 dpi  2b, Pea early browning virus  12K, Soil-borne wheat mosaic virus  19K, and Barley stripe mosaic virus  γb suppressors from a 16K deficient mutant TRV virus were able to compensate the absence of the TRV 16K protein [The expression of ToCV p22 from TRV vector resulted into an enhancement of the pathogenicity and accumulation of the viral RNAs in plants. This enhancement, however, was not linked to a faster virus systemic spread. The fact of finding that the p22 expression increased the viral accumulation of TRV could be explained, at least in part, by the combined action of different suppressors on multiple steps of the RNA silencing pathway ,43. ThisV vector . Althoug protein . AlthougN. benthamiana. Similar results have been reported in N. benthamiana infected with PVX or PVX expressing the silencing suppressor HC-Pro either derived from PVY or TEV [N. benthamiana could be due to the existence of a threshold above which viral accumulation cannot increase in this host, despite the inhibition of antiviral defenses by HC-Pro [N. benthamiana do not have any effect in the PVX genomic RNA levels, but enhance and/or stabilize PVX subgenomic RNAs. In our case, we did not see a remarkable increase in the accumulation of subgenomic RNAs. However, at 4 dpi, the longest of the subgenomic RNAs show a higher intensity in the case of the recombinant viruses that express p22, even though these subgenomic RNAs are longer than in wild type PVX (Nicotiana spp. could carry a matching R gene that can specifically recognize P25 and mount an HR, where the suppressor activity of the P25 protein is necessary. Similar to what we observed for TRV, expression of p22 was unable to compensate for the impaired infection of a P25-suppressor defective mutant PVX. Although it can be argued that the frameshift mutation introduced in P25 would affect the RNA silencing suppression and movement functions, both necessary for PVX movement [In the case of the heterologous expression of ToCV p22 from PVX, although we observed an enhancement of symptoms leading to plant death at 10–12 dpi, no differences in the viral RNA accumulation were observed in either agroinfiltrated or non-inoculated leaves of Y or TEV ,8. It way HC-Pro . Previouy HC-Pro  indicatetype PVX B. Thus, movement , this domovement , it is nmovement ,17,46, nmovement ,48,49,50In summary, the results presented here show that the combined action of non-related RNA silencing suppressors leads to an enhancement in disease severity, providing additional evidence for the existence of a link between viral suppression of RNA silencing and virulence.


Alzheimer’s disease (AD) is characterized by neuronal loss and astrocytosis in the cerebral cortex. However, the specific effects that pathological mutations and coding variants associated with AD have on the cellular composition of the brain are often ignored.We developed and optimized a cell-type-specific expression reference panel and employed digital deconvolution methods to determine brain cellular distribution in three independent transcriptomic studies.APP, PSEN1, or PSEN2 presented lower neuron and higher astrocyte relative proportions compared to sporadic AD. Similarly, the APOE ε4 allele also showed decreased neuronal and increased astrocyte relative proportions compared to AD non-carriers. In contrast, carriers of variants in TREM2 risk showed a lower degree of neuronal loss compared to matched AD cases in multiple independent studies.We found that neuronal and astrocyte relative proportions differ between healthy and diseased brains and also among AD cases that carry specific genetic risk variants. Brain carriers of pathogenic mutations in These findings suggest that genetic risk factors associated with AD etiology have a specific imprinting in the cellular composition of AD brains. Our digital deconvolution reference panel provides an enhanced understanding of the fundamental molecular mechanisms underlying neurodegeneration, enabling the analysis of large bulk RNA-sequencing studies for cell composition and suggests that correcting for the cellular structure when performing transcriptomic analysis will lead to novel insights of AD.The online version of this article (10.1186/s13073-018-0551-4) contains supplementary material, which is available to authorized users. APP) and Presenilin genes (PSEN1 and PSEN2) [APOE ε4 is the most common genetic risk factor, increasing the risk in three- to eightfold [TREM2 [PLD3 [ABCA7 [SORL1 [APOE ε4 allele. Besides age at onset, the clinical presentations of LOAD and ADAD are remarkably similar with an amnestic and cognitive impairment phenotype [Alzheimer’s disease (AD) is a neurodegenerative disorder characterized clinically by gradual and progressive memory loss and pathologically by the presence of senile plaques (Aβ deposits) and neurofibrillary tangles  in the brain . AD has d PSEN2)  cause aud [TREM2 , 6, PLD3M2 [PLD3 , ABCA7 [3 [ABCA7 , 9, and 7 [SORL1 , 11 thathenotype , 13. A mhenotype , 13.Altered cellular composition is associated with AD progression and decline in cognition. Neuronal loss in the hippocampus is characteristic in the initial stages of AD, which could explain early memory disturbances , 15. As Recently there has been a growing interest in understanding the transcriptomic changes attributed to AD –25, as tDigital deconvolution approaches enhance the interrogation of expression profiles to identify the cellular population structure of individual samples, alleviating the requirement of additional neurostereology procedures. These approaches have been developed, tested, and applied to ascertain cellular composition altered in many traits –29. HowePLD3, TREM2, and APOE ε4. Our findings indicate that cell-type composition differs among carriers of specific genetic risk factors, which might be revealing distinct pathogenic mechanisms contributing to disease etiology.We sought to investigate the cellular population structure in AD by analyzing RNA-seq from multiple brain regions of LOAD participants. To do so, we assembled a novel brain reference panel and evaluated the accuracy of digital deconvolution methods by analyzing additional cell-type-specific RNA-seq samples and by creating synthetic admixtures with defined cellular distributions. Then we analyzed large cohorts of pathologically confirmed AD cases and controls (n = 613) and verified that our model predicts cellular distribution patterns consistent with neurodegeneration. Finally, we generated RNA-seq from the parietal lobe of participants from the Charles F. and Joanne Knight Alzheimer’s Disease Research Center (Knight-ADRC) , includiTREM2 . RNA-seq paired-end reads with read lengths of 2 × 150 bp were generated using Illumina HiSeq 4000 with a mean coverage of 80 million reads per sample  following the manufacturer’s instruction. RIN (RNA integrity) and DV200 were measured with RNA 6000 Pico Assay using Bioanalyzer 2100 (Agilent Technologies). The RIN is determined by the software on the Bioanalyzer taking into account the entire electrophoretic trace of the RNA including the presence or absence of degradation products. The DV200 value is defined as the percentage of nucleotides > 200 nt. RIN and DV200 for all the samples can be found on Additional file Mayo Clinic Brain Bank RNA-seq was accessed from the Advanced Medicines Partnership – Alzheimer’s Disease (AMP-AD) portal   and temporal cortex  of Caucasian subjects 9 samples. RNA wasThe Mount Sinai Brain Bank (MSBB) RNA-seq study was downloaded from the AMP-AD portal  ; superior temporal gyrus ; parahippocampal gyrus ; and inferior frontal gyrus . RNA-seq was generated using the TruSeq RNA Sample Preparation Kit v2 and Ribo-Zero rRNA removal kit  .Dermal fibroblasts were obtained from skin biopsies from research participants in the Knight-ADRC . Human fibroblasts were reprogrammed into iPSCs using non-integrating Sendai virus carrying OCT3/4, SOX2, KLF4, and cMYC , 41. iPSIn addition, we accessed RNA-seq data generated for iPSC-derived neurons from the Broad iPSC study  (synapsefsTRAP mice (Gt(ROSA)26Sortm1Wtp) [Cre mice Arbr) [All animal procedures were performed in accordance with the guidelines of Washington University’s Institutional Animal Care and Use Committee. The Rosa26irA)Wtp)  (The Jacre)Arbr)  (The Jac3 in RNase-free water). Barrel cortex was rapidly dissected and flash-frozen in liquid nitrogen, and then stored at − 80 °C until use. Affinity matrix was prepared with 150 μL of Streptavidin MyOne T1 Dynabeads, 60 μg of Biotinylated Protein L, and 25 μg of each of GFP antibodies 19C8 and 19F7. The tissue was homogenized on ice in 1 mL of tissue-lysis buffer (20 mM HEPES KOH (pH 7.4), 150 mM KCl, 10 mM MgCl2, EDTA-free protease inhibitors, 0.5 mM DTT, 100 μg/mL cycloheximide, and 10 μL/mL rRNasin and Superasin). Homogenates were centrifuged for 10 min at 2000×g, 4 °C, and 1/9 sample volume of 10% NP-40 and 300 mM DHPC were added to the supernatant at final concentration of 1% (vol/vol). After incubation on ice for 5 min, the lysate was centrifuged for 10 min at 20,000×g to pellet insolubilized material. Then 200 μL of freshly resuspended affinity matrix was added to the supernatant and incubated at 4 °C for 16–18 h with gentle end-over-end mixing in a tube rotator. After incubation, the beads were collected with a magnet and resuspended in 1000 μL of high-salt buffer (20 mM HEPES KOH (pH 7.3), 350 mM KCl, 10 mM MgCl2, 1% NP-40, 0.5 mM DTT, and 100 μg/mL cycloheximide) and collected with magnets as above. After four times of washing with high-salt buffer, RNA was extracted using Absolutely RNA Nanoprep Kit (Agilent Technologies) following the manufacturer’s instructions. RNA quantification was measured using Qubit RNA HS Assay Kit (Life Technologies) and the integrity was determined by Bioanalyzer 2100 using an RNA Pico chip (Agilent Technologies). The cDNA library was prepared with Clontech SMARTer and then sequenced by HiSeq3000. Single-end reads of 50 base pairs were generated for an average of 29.2 million reads per sample (24 samples).Purification of cell-type-specific messenger RNA (mRNA) by TRAP was described previously  with mod2+ transients that asserted their microglia identity as opposed to macrophage. Single-ended RNA-seq data were generated with the Illumina HiSeq 2500 platform following the Illumina protocol.The data were accessed from the AMP-AD portal (synapse ID: syn7203233). This dataset comprises iPSC-derived microglia (n = 10) from human primitive streak-like cells . Within FastQC was applied to DIAN and Knight-ADRC RNA-seq data to perform quality checks on various aspects of sequencing quality . The DIAAligned and sorted bam files were loaded into IGV  to perfoHomo sapiens included in the GENCODE reference genome (GRCh37.75). Similarly, we quantified the expression of the mice samples included in the reference panel using the Mus musculus reference genome (mm10).We applied Salmon transcript expression quantification (ver 0.7.2)  to inferWe assembled a cell-type-specific reference panel from publicly available RNA-seq datasets comprising both immunopanning collected or iPSC-derived neurons, astrocytes, oligodendrocytes, and microglial cells from human and murine samples. For immunopanning collected cells, antibodies for cell-type-specific antigens were utilized to bind and immobilize their targeted cell types in order to immunoprecipitate and purify each cell type from the suspensions . cDNA syBoth human adult TC tissue, collected from patients receiving neurological surgeries, and mice cells were disassociated, sorted and sequenced as described elsewhere , and depprcomp (version 3.3.3) to verify that the expressions of these gene were clustering samples by their cell types . Based on accuracy and robustness evaluation results, we compared and reported the following three algorithms that outperformed the others: Digital Sorting Algorithm (named “DSA”) , which eares fit , quadratares fit ) or no sares fit  or latenares fit ) to the We selected the reference samples that provide the most faithful transcriptomic profile for their respective cell types by following a LOOCV approach. We trained iteratively deconvolution models using all but one of the samples that was tested. Only samples predicted with a composition > 80% were kept for the reference panel  contributed from the human reference samples  chimeric libraries. We evaluated the accuracy using the root-mean-square error  vs the defined proportion of reads specific to each of the chimeric libraries:We also tested whether the deconvolution results were dominated by the expression of any specific marker gene and ascertained the robustness of the inferred cellular population structure to any possibly altered expression of marker genes. To do so, we performed the deconvolution analysis discarding each of the marker genes one at a time and evaluated how these distributions differed in comparison to the full gene reference panel.We employed linear regression models to test the association between cell-type proportions and disease status . We used stepwise discriminant analysis  to determine significant covariates and to correct for confounding effects. We included RIN, batch, age at death, and post-mortem interval (PMI) as covariates for the Mayo Clinic analyses. For MSBB analyses, we corrected for RIN, PMI, race, batch, and age at death. We also used linear-mixed models to perform multiple-region association analysis, employing random slopes and random intercepts grouping by observations and by donors , and corTo analyze the DIAN and Knight-ADRC studies, we applied linear-mixed models , clustering at family level to ascertain the effect of the neuropathological status in the cell proportion and corrected for RIN and PMI. For late-onset specific analyses we also corrected for age at death.Cellular composition shown as proportions were plotted using R package ggplot2 (ver 2.2.1).To infer cellular composition from RNA-seq, we first assembled a reference panel to model the transcriptomic signature of neurons, astrocytes, oligodendrocytes, and microglia. The panel was created by analyzing expression data from purified cell lines. We evaluated alternative digital deconvolution methods and selected the best performing for our primary analyses. We tested the digital deconvolution accuracy on iPSC-derived neurons/microglia cells and neuronal TRAP-seq Fig. . Finallyn = 191) [n = 300) [APP, PSEN1, PSEN2) , PLD3 (n = 33), and APOE ε4 allele.Once the deconvolution approach was optimized, we calculated the cell proportion in AD cases and controls from the different brain regions of Mayo and MSBB datasets. The RNA-seq data for the Mayo Clinic study n = 191)  and MSBBn = 300)  are depo91  and MDue to limited availability of brain cell-type-specific transcriptomic data, we compiled reference samples from different sources, including single-population RNA-seq from mice and human .We first tried to create a transcriptome-wide reference panel by selecting the genes that are differentially expressed among cell types , 60, 62.Once we identified the optimal approach to perform digital deconvolution from brain RNA-seq, we benchmarked it by using three sets of independent pure cell populations and simulated chimeric libraries.We first validated the accuracy to predict neuronal composition by generating RNA-seq for eight iPSC-derived cortical neurons see “”. We obsTo evaluate the accuracy of digital deconvolution for measuring cell-type proportion from cell-type admixtures, we simulated RNA-seq libraries by pooling reads from individual cell types into well-defined proportions. We combined randomly sampled reads from neurons, astrocytes, oligodendrocytes, and microglia to create chimeric libraries that mimic bulk RNA-seq from brain, but with a range of pre-defined cell-type distributions . In this way, we verified that the proportions inferred using the reference panel are not driven by the expression of a single gene. This reassured us the inference should be robust to any bias introduced by the potential association of a single gene included in the reference panel with a particular trait.Pathologically, AD is associated with neuronal death and gliosis specifically in the cerebral cortex. We evaluated whether we could exploit deconvolution methods using our reference panel to detect altered cellular population structure from the bulk RNA-seq and whether this corresponded to known pathological alterations.p = 5.01 × 10−09; Table p = 1.58 × 10−07; Table p = 1.8 × 10−02; Table We initially analyzed the RNA-seq from the Mayo Clinic Brain Bank that includes bulk RNA-seq from the TC and CB for 191 participants   . Replicating our findings from the Mayo dataset, we observed a significant lower relative proportion in neurons and increase in astrocytes in all four areas  and DIAN (19 ADAD) studies (Table p = 2.66 × 10−02) and significant higher relative proportion of astrocyte in AD  for the combined LOAD and ADAD brains compared to controls  to match the Braak staging distribution of ADAD brains (n = 17). The ADAD brains showed a significant lower relative neuronal proportion compared to LOAD brains  and increase of relative proportion of astrocytes  from the TC included in the Mayo Clinic dataset  to non-carriers (n = 40) we also observed a decreased relative neuronal proportion  and marginally significant association for the relative astrocytic relative proportion (p = 0.09).We initially ascertained the effect of PLD3 carriers (n = 33). PLD3 carriers exhibited significantly lower relative proportion of neurons compared to controls  and significant higher relative proportion of astrocytes .Next, we analyzed the cellular composition in TREM2 carriers. TREM2 is involved in the immune response and its role in amyloid-β deposition or clearance remains controversial [TREM2 carriers (n = 20) compared to controls . We also investigated the cellular proportion of the 11 TREM2 carriers in the MSBB dataset. The multi-region analysis showed TREM2 carriers do not show a significant difference in relative neuronal proportion compared to controls  and significantly lower relative astrocyte proportion than the AD non-carries . This indicates that the difference of the relative proportion between ADAD and LOAD are not driven by TREM2 carrier brains. Based on our results, we would hypothesize that this change in Aβ processing of ADAD would lead to more direct to neuronal death than the pathological processes of LOAD. Similarly, decreased neuronal and increased astrocyte relative proportions were significantly associated with APOE ε4 allele. It has been reported APOE ε4 allele increases the risk for AD by affecting APP metabolism or Aβ clearance [We generated RNA-seq data from brains carrying pathogenic mutations in learance , 70, sugTREM2 carriers than in other LOAD cases. We replicated this finding in a multi-area analysis from the MSBB dataset. These results may implicate that TREM2 risk variants lead to a cascade of pathological events that differ from those occurring in sporadic AD cases, which is also consistent with the known biology of TREM2. Further longitudinal neuroimaging analysis is required to validate our findings. TREM2 is involved in AD pathology through microglia mediated pathways, implicated on altered immune response and inflammation [TREM2 knock-out animals showed that fewer microglia cells were found surrounding Aβ plaques with impaired microgliosis [TREM2 deficiency was reported to attenuate tauopathy against brain atrophy [TREM2 affects AD risk through a slightly different mechanism to that of ADAD or LOAD in general. Therefore, other pathogenic mechanisms should contribute to disease. We believe that a detailed modeling of immune response cells, reflecting the alternative microglia activation states, will generate more accurate profiles to elucidate the immune cell distribution in AD.In contrast, the analysis of the Knight-ADRC brains showed that the neuronal relative proportion decrease is less pronounced in ammation . Recent ogliosis . Further atrophy . We founTREM2, APOE, APP, PSEN1, and PSEN2; (3) correct the effect that altered cell composition and genetic statuses have in addition to downstream transcriptomic analyses and lead to novel and informative results; and (4) help the analysis of highly informative frozen brains collected over the years.There is a large interest in the scientific community to use brain expression studies to try to identity novel pathogenic mechanisms in AD and to identify novel therapeutic targets. These efforts are generating a large amount of bulk RNA-seq data, as single-cell RNA (scRNA-seq) from human brain tissue in large sample sizes is not feasible. Single-cell sorting needs to be performed with fresh tissue , which rIn conclusion, our study provides a reliable approach to enhance our understanding of the fundamental cellular mechanisms involved in AD and enable the analysis of large bulk RNA-seq data that may lead to novel discoveries and insights into neurodegeneration.Additional file 1:Supplementary results, tables and figures. (DOCX 2353 kb)


In this study, Shimada et al. use fission yeast to investigate how small RNA-loaded Argonaute protein complexes target chromatin to mediate silencing and show that transcription of the target locus is essential for RNA-directed formation of heterochromatin. Their results provide new mechanistic insights into small RNA-directed chromatin silencing. Small RNAs regulate chromatin modification and transcriptional gene silencing across the eukaryotic kingdom. Although these processes have been well studied, fundamental mechanistic aspects remain obscure. Specifically, it is unclear exactly how small RNA-loaded Argonaute protein complexes target chromatin to mediate silencing. Here, using fission yeast, we demonstrate that transcription of the target locus is essential for RNA-directed formation of heterochromatin. However, high transcriptional activity is inhibitory; thus, a transcriptional window exists that is optimal for silencing. We further found that pre-mRNA splicing is compatible with RNA-directed heterochromatin formation. However, the kinetics of pre-mRNA processing is critical. Introns close to the 5′ end of a transcript that are rapidly spliced result in a bistable response whereby the target either remains euchromatic or becomes fully silenced. Together, our results discount siRNA–DNA base pairing in RNA-mediated heterochromatin formation, and the mechanistic insights further reveal guiding paradigms for the design of small RNA-directed chromatin silencing studies in multicellular organisms. Schizosaccharomyces pombe have provided a conceptual framework for studying the molecular mechanisms of these conserved small RNA-directed chromatin silencing pathways (RNAi broadly refers to silencing pathways that depend on conserved Argonaute family proteins to repress gene expression . A unifypathways . HoweverS. pombe Argonaute protein (Ago1) is at the core of the S. pombe RISC, known as the RNA-induced transcriptional silencing (RITS) complex . Paf1C mutant strains are highly susceptible to de novo assembly of heterochromatin and stable gene silencing by synthetic, trans-acting primary siRNAs complementary to the coding sequence of protein-coding genes .To test this directly, we used ic genes . In addipromoter . This co hairpin A. Besideonstruct . The haiade6+ gene is highly susceptible to siRNA-directed assembly of heterochromatin in Paf1C mutant but not wild-type cells . Insertion of a GFP sequence of similar length but devoid of any canonical splice sites (target 3) served as the control for siRNA specificity , similar to our previous findings when providing ade6 siRNAs  as well as a GFP fragment of similar length as a specificity control (target 6) and confirmed splicing by RT–PCR  (ade6+ mRNA levels were reduced only twofold for the spliced target gene (target 4) . These observations allow two alternative interpretations: (1) Rapid splicing of the target sequence decreases the overall silencing potential and therefore only mildly reduces target expression in all cells. (2) Alternatively, due to early splicing, the initiation of silencing becomes stochastic, and only a fraction of cells trigger the full response, while others do not initiate silencing at all. Notably, H3K9me2, siRNAs, and RNA expression were all measured at the population level, integrating over millions of cells. Thus, if 50% of cells expressing the spliced target remained in a euchromatic state while the other half became heterochromatic, maximal twofold repression can be expected from such a population average. Therefore, we decided to address this question at single-cell resolution by growing cells on limiting adenine indicator plates, which leads to red colonies when ade6+ is fully repressed. Indeed, when single cells were seeded, ∼50% formed red colonies, indicative of complete ade6+ repression  E. Howevearget 4) E, indicapression G, left. pression .In sum, siRNA-binding sites close to the 5′ end of a transcript perform as well as more distal sites unless removed by RNA splicing. Although heterochromatin is established with low frequency when promoter-proximal introns are targeted, it is less stably maintained through mitosis G. These trans-acting siRNAs are expected to assemble heterochromatin also in the absence of transcription at the target locus. Therefore, we investigated to what extent the deposition of H3K9 methylation is dictated by promoter strength of the target locus. For consistency, we chose the endogenous ade6+ gene as the siRNA target locus and first replaced its promoter with the strong but tunable nmt1 promoter , preventing us from distinguishing between the DNA and nascent RNA targeting models at the endogenous ade6+ locus. We reasoned that the residual ade6+ activity emanated from neighboring gene activities. Therefore, we examined the S. pombe genome for a region with no apparent sign of transcription to serve as a neutral landing site where transgenes would be less influenced by transcription occurring in the vicinity. Inspecting RNA sequencing (RNA-seq) and small RNA-seq data generated from wild-type S. pombe cells, we found a 17-kb region on chromosome III with no discernible RNA production. Furthermore, H3K9 methylation was not significantly enriched in this area  technology. This revealed that the full-length ade6+ transgene driven by its own promoter gives rise to ∼17 RNA molecules per cell. For the transgene lacking the promoter, we counted only 1.8 RNA molecules per cell, while the transgene without transcription termination signals produced 3.6 ade6+ RNA molecules per cell. For the solitary ade6+ ORF (solo ORF) inserted at the same position, we never counted more than one molecule per cell  Transcription above a certain threshold is an absolute requirement for the formation of heterochromatin. Silencing does not initiate if less than one RNA molecule is present per cell  Splper cell . If siRNDrosophila, where piRNAs in follicle cells originate almost 100% from the antisense strand of transposable elements. These trigger silencing only if the target harbors complementary piRNA sites in sense orientation. Because silencing of reporters and endogenous loci is strictly dependent on the orientation of the piRNA targets, direct DNA targeting as well as targeting of ssDNA exposed in R loops can be excluded also in flies , a major small RNA-mediated epigenetic pathway in plants, requires a specialized transcriptional machinery that comprises the two plant-specific RNA polymerases: polymerase IV (Pol IV) and Pol V . Pol IV of siRNA . The nas elegans . Whethertrans-acting primary siRNAs in S. pombe highlight a key feature of epigenetic gene regulation. Rather than inducing gradually decreasing mRNA levels, siRNAs initiate gene silencing in an “all or nothing” fashion in Paf1C mutant cells. Once established, the off state is stably propagated (FLOWERING LOCUS C (FLC) in Arabidopsis. FLC expression is gradually repressed by prolonged cold exposure when measured at the level of a tissue. However, this seemingly “analog” silencing response is the net result of digital silencing events that occur in individual cells of that particular tissue over time. Thereby, a population of cells as a whole can respond quantitatively to an environmental change by simply switching individual cells from an on state to an off state  as described in Supplemental Table 1.Real-time PCR on cDNA samples and chromatin immunoprecipitation (ChIP) DNA was performed as described in Supplemental Table 1.PCR on cDNA was performed using a fast-cycling PCR kit (Qiagen). PCR products were analyzed by agarose gel electrophoresis. Primer sequences are in 7 cells were used to isolate RNA using the MasterPure yeast RNA purification kit (Epicenter). Two-hundred nanograms of total RNA isolated from mouse embryonic stem cells was added to the yeast samples in lysis buffer to estimate the recovery rate of RNA. cDNA was synthesized using the PrimeScript RT Master Mix (Takara). cDNA corresponding to 20 ng of RNA was used as template for ddPCR. The PCR reaction was prepared with QX200 ddPCR EvaGreen supermix (Bio-Rad) and Droplet generation oil for EvaGreen (Bio-Rad). Droplets were generated with a QX200 droplet generator (Bio-Rad). After amplification to the end point, droplets were quantitated by QX200 droplet reader (Bio-Rad).Exponentially growing cells were harvested, and the cell number was determined with a hemocytometer (Thoma). Typically, 1.2 × 10ChIP experiments were performed as described previously in Small RNA libraries were prepared as described previously . In brieS. pombe genome (ASM294 version 2.24) using Bowtie  was used to remove the 3′ adaptor form the raw reads. Reads <18 nt and untrimmed reads were removed. Trimmed reads were aligned to the g Bowtie . No mismstem see A. To mapSmall RNA-seq data have been deposited at the NCBI Gene Expression Omnibus (GEO) database and are accessible through GEO series number GSE87672.