Viroids are circular noncoding RNAs infecting plants , 2. DuriHere, we describe that 3-dimensional (3D) structural motifs formed by non–Watson-Crick (non-WC) base pairs in viroid RNAs act as a critical constraint for the sequence space of viroid genome evolution. This constraint operates because RNA 3D motifs can play crucial roles by mediating (1) RNA–RNA interactions for the folding of a part or a whole of RNA into a distinct tertiary conformation and (2) RNA–protein interactions. Therefore, mutations in a 3D motif that do not disrupt the structure and function will be retained in the population, whereas mutations that disrupt the 3D structures of motifs, and consequently the function, will be lost.RNA 3D structures, to a first approximation, are composed of helices  and loops, both of which are shown in RNA 2D structures . The loohttp://ndbserver.rutgers.edu/ndbmodule/services/BPCatalog/bpCatalog.html). The Catalog provides a numerical measure of the degree of isostericity among different base combinations for each of the 12 base-pairing geometries, displayed in interactive heat maps, illustrated by AG versus CU trans Hoogsteen/Sugar Edge base pairs in Each RNA base has 3 edges, the WC, Hoogsteen, and Sugar edges, that can potentially hydrogen bond (H-bond) with other base edges in loop motifs  12]. Ac. Ac12]. In a regular RNA helix, only the minor groove is easily accessible to proteins, while the major groove is too narrow for inserting alpha helixes, as occurs in DNA–protein complexes. The minor groove (sugar) edges of the nucleotides display a smaller difference between AU and GC base pairs than the major groove, but some amino groups, such as GN2 in guanine nucleotide, can sometimes constrain RNA sequence variations when H-bonding with proteins. More common functional sites are the loop regions of an RNA that provide specific binding locations for proteins or other molecules. Non-WC base pairs in RNA loops expose WC edges and widen the major groove. The WC edges are more distinct across the 4 bases, which allows for specific interactions critical for function.RNA secondary structures of potato spindle tuber viroid (PSTVd), the type species of Pospiviroidae family, have been well characterized through chemical mapping  14, 15], 1514, 1Three-dimensional non-WC base pair arrangements in several PSTVd RNA motifs were annotated recently. Zhong et al.  analyzedFollowing this study, 3D structural arrangements of 2 additional PSTVd motifs have been shown to play critical roles , 19. U436 possible sequence combinations, but there are only 8 functional variants out of 49 possible isosteric combinations [6/49) and testing for function by an additional factor of 6 (= 49/8), indicating that RNA 3D structural motifs serve as a critical constraining factor.Because some RNA 3D motifs control viral infection, strong selective pressures exist for maintaining the 3D motif structures that constrain the variation in sequence space. Mutational analyses on loop E, loop 6, and loop 7 all support this –19. TakiNicotiana tabacum (tobacco) was observed in nature. However, in planta selection assays identified the C259U substitution in PSTVd loop E that led to the emergence of a new infectious strain for tobacco [While maintaining the 3D structure of RNA loop motifs is pivotal, isosteric base substitutions may allow infection of new tissues or hosts. Previously, no infectious PSTVd strain for  tobacco . A subse tobacco . Both su tobacco . TherefoMaintaining structures of RNA 3D motifs serves as a critical constraint of viroid evolution. In RNA 3D motifs, isosteric base substitutions in noncanonical base pairs are required to maintain 3D motif structure, greatly reducing the range of possible base substitutions. Maintaining functional interactions with proteins reliant on specific nucleotide–residue combinations further reduces the space of possible base changes.RNA 3D motifs may be a constraint for viruses as well. Despite differences in their genetic makeups and unique infection and evolution pathways, different viral and viroid RNAs should all share one common property: RNA 3D motif–based RNA–RNA, RNA–protein, and RNA–small ligand interactions necessary for completing life cycles –27. Ther


The study uncovers RNA and mRNA nitration as an integral event in plant metabolism intensified during immune response. Using potato-avr/vr Phytophthora infestans systems and immunoassays we documented that potato immunity is accompanied by two waves of boosted ONOO- formation affecting guanine nucleotides embedded in RNA/mRNA and protein tyrosine residues. The early ONOO- generation was orchestrated with an elevated level of protein nitration and a huge accumulation of 8-nitroguanine (8-NO2-G) in RNA and mRNA pools confirmed as a biomarker of nucleic acid nitration. Importantly, potato cells lacking ONOO- due to scavenger treatment and attacked by the avr pathogen exhibited a low level of 8-NO2-G in the mRNA pool correlated with reduced symptoms of programmed cell death (PCD). The second burst of ONOO- coincided both with an enhanced level of tyrosine-nitrated proteins identified as subtilisine-like proteases and diminished protease activity in cells surrounding the PCD zone. Nitration of both RNA/mRNA and proteins via NO/ONOO- may constitute a new metabolic switch in redox regulation of PCD, potentially limiting its range in potato immunity to avr P. infestans.Peroxynitrite (ONOO Importantly, ONOO- reacts with the DNA and RNA bases at selected positions (- can mediate oxidation and nitration of guanine which occurs mainly at the C8 position.It is well documented that ONOOhemistry . In planproteins . The preperature . The intd/or RNA . Notablyositions . As indiPhytophthora infestans (Mont.) de Bary is accompanied by changes in the NO metabolic status within the attacked cell  – cv. Bintje (lacking R genes) which is highly susceptible to isolate 1.3.4.7.10.11. P. infestans, and cv. Bzura [carrying the R1 gene (P. infestans. Plants of both cultivars derived from in vitro tissue culture were transferred to soil and grown for 4 weeks in a growth chamber with 16 h of light (180 μmol m-2 s-1) at 18 ± 2°C and 60% humidity.The experiments were conducted on two sterile potato cultivars  and kept at 100% humidity in a growth chamber. The material for further analysis was taken until 96 h post inoculation (hpi). For the point inoculation experiment, 20 μl of the zoospore suspension were drop inoculated on the abaxial leaf surface and kept at 100% humidity in a growth chamber. The material for analysis was taken at the site of inoculation from an area of 0.5 cm in diameter  and from the surrounding area within the radius 0.25 cm;  until 96 hpi.For - detached potato leaves were sprayed with ONOO- donor – 50 μM SIN-1  which gradually decomposed to yield equimolar amounts of NO and -  were used to evaluate the effect of endogenous on 8-NO2-G formation. Control plants were treated with water. After 5 h of incubation leaves were gently dried and inoculated as described above.To estimate the effect of exogenous ONOOPeroxynitrite formation was measured quantitatively using aminophenyl fluorescein (APF). Leaf disks (0.5 g) were incubated in darkness for 1 h in a mixture containing 5 μM APF in 100 mM phosphate buffer (pH 7.4). After the incubation, the probes were transferred into 24-well plates (1 ml per well) and the fluorescence was measured using spectrofluorometer at 485 nm excitation and 510 nm emission filters. Fluorescence was expressed as a relative fluorescence units.The TUNEL assay measures DNA fragmentation using the terminal deoxynucleotidyl transferase (TdT)-mediated deoxyuridine triphosphate (dUTP) nick end labeling method, which involves the TdT-mediated addition of fluorescein-12-dUTP to the 3′-OH ends of fragmented DNA. The samples were studied using TUNEL fluorescein kit  according to Potato leaves were frozen in liquid nitrogen and stored at -80°C until use. For RNA extraction leaves (0.5 g) were ground to a fine powder in liquid nitrogen, and total RNA was extracted using TriReagent (Sigma) according to the manufacturer’s instructions. The Poly(A)-RNA from previously prepared total RNA was prepared using the GenElute mRNA Miniprep Kit (Sigma Aldrich) according to the manufacturer’s protocol. Briefly, the obtained total RNA was mixed with the binding buffer and oligo(dT) beads followed by 3-min incubation at 70°C and 10-min incubation at room temperature. After centrifugation the pellet of the oligo(dT) polystyrene beads: the mRNA complex was mixed with washing buffer and transferred to spin columns. After the second washing the mRNA was eluted at 70°C using an elution buffer . The quantity and quality of obtained mRNA were measured by spectrophotometric methods.TM Nitrosative DNA/RNA Damage ELISA Kit , similar to 2-Gua samples or 8-NO2-Gua standards were first added to an 8-NO2-Gua-BSA conjugate preabsorbed microplate. After a brief incubation, an anti-8-NO2-Gua monoclonal antibody was added, which bind to either the 8-nitroguanine of the samples or standards or to the 8-NO2-Gua-BSA conjugate preabsorbed on the plate. The more 8-nitroguanine in the sample, the less free antibody available to bind to the conjugate on the plate. The absorbance of the samples was measured at a wavelength of 450 nm using an iMark microplate reader (BioRad). The 8-NO2-G content was determined by comparing with the predetermined 8-NO2-G standard curve. Each sample was analyzed in triplicate on ELISA microplates and values presented are means of three biological replicates (n = 9) ± SD.The level of 8-nitroguanine was determined using a competitive enzyme immunoassay OxiSelectTM Nitrotyrosine ELISA Kit  according to the manufacturer’s protocol. Briefly, nitrated BSA or samples were added to each well of 96-well plates and incubated with an anti-nitrotyrosine antibody at a 1:1000 dilution for 1 h on an orbital shaker. Wells were then washed three times, afterward the secondary antibody was added and the mixture incubated for 1 h at room temperature. Subsequently, the wells were washed three times and the substrate solution was added to each well. Finally after the color development the reaction was stopped and optical density was measured at 450 nm using an iMark microplate reader (BioRad). The 3-nitrotyrosine content in protein samples was determined by comparing with the predetermined 3-nitrotyrosine standard curve. Each sample was analyzed in triplicate on ELISA microplates and the values presented are means of three biological replicates (n = 9) ± SD.3-nitrotyrosine in a protein sample was measured using the OxiSelectProtease activity was determined according to To visualize cell death the trypan blue dye was used according to All results are based on three biological replicates derived from three or four independent experiments. For each experiment, means of the obtained values were calculated along with standard deviations. The analysis of variance was performed (ANOVA) and the mean values were compared by Tukey’s test (α = 0.05).P. infestans challenge provoked an early and transient program of boosted ONOO- formation only in the resistant potato genotype . The potato–vr P. infestans pathosystem revealed ONOO- formation starting from 24 hpi, which reached the highest (ca. 2-fold increase) level at 72 hpi .Based on the folic acid method, we previously reported that the genotype . In the -, its detection was performed in the presence of ebselen, an ONOO- scavenger. The used scavenger strongly suppressed fluorescence at 3rd and 24th hpi , providing evidence that APF is a reliable tool to quantitatively investigate ONOO- formation in potato leaves.Moreover, to verify that the detected pathogen-induced increase in APF fluorescence was caused by endogenous ONOOavr P. infestans interaction resulted in HR-like cell death . Cells of potato leaves treated with avr P. infestans contained green-colored nuclei starting from 24 hpi , while phenotype HR-like symptoms appeared 48 h after the avr P. infestans challenge . The number of TUNEL–positive nuclei indicated that the effect was dependent on the ONOO- presence within potato leave cells, since the sequential treatment with epicatechin, a ONOO- scavenger and P. infestans effectively reduced the number of cells with symptoms of active death from 85 to 45% at 48 hpi . Similarly, potato cells pretreated with ebselen and next elicited by the pathogen at 48 hpi showed 35% of nuclei with TUNEL–positive staining . An independent application of both ONOO- scavengers resulted in reduced phenotype HR-like symptoms .The potato–ll death . To gain- generation in potato leaves challenge-inoculated with P. infestans we examined whether, and to what extent, this RNS is able to target nucleic acids. Firstly, we confirmed that SIN-1 is effective in ONOO- formation within potato cells . Secondly, to verify that ONOO- is able to provoke nitration at the RNA level in cells of potato, we monitored the accumulation of 8-NO2-G, a marker of nucleic acid nitration, in healthy leaves treated with the ONOO- generator. As we expected, SIN-1 was effective in 8-NO2-G RNA formation within potato cells of both genotypes .To explore the functional role of ONOOavr P. infestans interaction resulted in an impressive rise in the level of 8-NO2-G within the RNA pool starting from the 1st hpi . The highest increase of nitrated RNA in relation to mock inoculated leaves was noted at 3 hpi; however, a strong, ca. 5-fold enhancement of 8-NO2-G RNA was observed also between 24 and 72 hpi. Then the level of 8-NO2-G RNA declined. Surprisingly, RNA from cells of the susceptible genotype revealed a time-delayed and definitely lower level of 8-NO2-G than did RNA of the resistant potato . Only a 2-fold increase in RNA nitration was observed starting from 24 hpi and the enhancement was maintained during disease progress.The potato–- targets mRNA in potato leaves as well. Interestingly, the use of poly(A)-RNA purified from total RNA showed a significant 2-fold increase in the level of 8-NO2-G mRNA at 1 hpi, peaking at 3 and 48 hpi, respectively . The amount of 8-NO2-G mRNA in cells of the susceptible genotype was raised at 6 hpi, reaching the highest level at 72 hpi . It should be highlighted that the steady-state level of 8-NO2-G mRNA observed within the first 48 h of the experiment was significantly higher in healthy leaves of resistant potato than in the susceptible one . What is more, cells of both potato genotypes lacking ONOO- due to the scavenger application and next attacked by the pathogen exhibited significantly lower levels of 8-NO2-G both in RNA and mRNA .Since mRNA modifications have the potential to affect most post-transcriptional steps in gene expression , in the avr P. infestans challenge inoculation . Based on the immunoassay  we found the highest, ca. 30-fold increase in the total protein pool undergoing tyrosine nitration at 3 hpi . Then, the level of nitrated proteins was gradually decreased and reached the amount recorded in mock inoculated leaves at 48 and 72 hpi. Interestingly, a ca. 3-fold increase of nitrated proteins at 96 hpi was followed by the second burst of ONOO- formation . Additional experiment involving point inoculation revealed that only cells surrounding the PCD zone showed an elevated level of protein nitration at 96 hpi identified as subtilisin-like proteases . Moreover, changes noted within the PCD distal zone coincided with the significantly reduced protease activity . In contrast, disease progress observed during the potato-vr P. infestans interaction was accompanied by an increase of the nitrated protein pool starting from 24 hpi .The nitrative modification of RNA and mRNA overlapped with the tyrosine residue nitration in proteins within the first 24 h after - in HR has been ambiguous. Although application of 1 mM ONOO- has been reported to cause some necrotic lesions in Arabidopsis  considered as a form of programmed cell death (PCD) is a hallmark of effector-triggered immunity (ETI). According to current knowledge, properly balanced doses of NO and ROS co-operate to trigger PCD in plants . To datebidopsis , high cobidopsis . That is- in plants fulfills an important role in the creation of a cellular redox milieu promoting defense expression . In confirmation, an experiment applying point inoculation revealed nitration of SBT1.7 and SBT5.3 concomitant with reduced protease activity only in cells surrounding the PCD zone. It should be noted that the distal changes were not correlated with nitrative mRNA modification (data not presented).Nitration of both guanine nucleotides embedded in RNA and mRNA and protein tyrosine residues can constitute an early switch of the redox environment facilitating HR establishment. sistance . More ret 48 hpi . Importafunction . Since avia ONOO- is an integral part of plant cell metabolism and is intensified in response to pathogen attack. Although nitrative modification of bases in RNA and mRNA can be simply induced by an enhanced peroxynitrite level in the cellular milieu, the rate of ONOO- formation is dependent on the plant genetic makeup. An early and transient program of boosted ONOO- formation during the potato resistant response accelerated the time-dependent switch of the redox environment via the nitration phenomenon. The observed nitrative modification of RNA and mRNA could regulate the post-transcriptional gene expression and fine-tune cell signaling that contributes to PCD during HR . In confirmation, ONOO- elimination overlapped with a reduced pool of nitrated mRNA and the number of cells that undergo programmed cell death. In contrast, a time-delayed peroxynitrite over-accumulation in the potato-vr P. infestans interaction coincident with a relatively low level of 8-NO2-G in the RNA/mRNA pools resulted in failed resistance. The challenge for the future is to understand the mechanisms of selective mRNA nitration and the physiological consequences of mRNA nitration. To this aim identification of mRNA nitration targets during plant responses to pathogen attack should be experimentally verified.Our study demonstrates that the nitration phenomenon presents a much more complex functionality in plant cells than it was assumed previously. The modification of RNA and mRNA MA-J and JF-W planned and designed the research. KI, JG, BM, and DK performed the experiments. KI collected, analyzed the data and participated in writing the manuscript. MA-J wrote the manuscript.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.


RNA-seq is a well-established method for studying the transcriptome. Popular methods for library preparation in RNA-seq such as Illumina TruSeq® RNA v2 kit use a poly-A pulldown strategy. Such methods can cause loss of coverage at the 5′ end of genes, impacting the ability to detect fusions when used on degraded samples. The goal of this study was to quantify the effects RNA degradation has on fusion detection when using poly-A selected mRNA and to identify the variables involved in this process.Using both artificially and naturally degraded samples, we found that there is a reduced ability to detect fusions as the distance of the breakpoint from the 3′ end of the gene increases. The median transcript coverage decreases exponentially as a function of the distance from the 3′ end and there is a linear relationship between the coverage decay rate and the RNA integrity number (RIN). Based on these findings we developed plots that show the probability of detecting a gene fusion (“sensitivity”) as a function of the distance of the fusion breakpoint from the 3′ end.This study developed a strategy to assess the impact that RNA degradation has on the ability to detect gene fusions by RNA-seq.The online version of this article (doi:10.1186/s12864-016-3161-9) contains supplementary material, which is available to authorized users. RNA-seq  is a popThe major steps involved in RNA-seq include 1) library preparation where the RNA is converted into small fragments of cDNA, 2) NGS where those DNA fragments are sequenced and 3) bioinformatics analysis where the fragments are aligned to the reference genome and processed to identify features of biological interest.The Illumina TruSeq RNA v2 kit is a popular method for next generation sequencing library preparation from mRNA that uses a “poly-A pulldown” which refers to the use of oligo (dT) coated magnetic beads to capture polyadenylated mRNA. Large projects such as The Cancer Genome Atlas (TCGA)  and the Gene fusions are chimeric transcripts where parts of two known genes are expressed in a single transcript. RNA-seq has been successful in identifying fusions and there are numerous tools with which to do this , 16–19. A recent study  reports BCR-ABL fusion  was assessed on an Agilent 2100 Bioanalyzer with the RNA 6000 Nano Kit and quantified with a Qubit® 2.0 fluorometer with the Qubit® RNA BR Assay Kit. The TruSeq® RNA Sample Preparation v2 Kit  was used for isolation of polyadenylated mRNA with oligo-dT beads, second strand cDNA synthesis and NGS library preparation. Paired-end, 101 bp sequencing was performed on a HiSeq 2500 (Illumina) instrument in Rapid Run mode. Base calling was performed by the instrument computer using Illumina Real Time Analysis (RTA) software that is integrated with HiSeq Control Software (HCS) and provides a summary of quality statistics as per Illumina’s acceptance criteria for sequencing. CASAVA 1.8.2 was used for de-multiplexing and conversion of base calls to paired-end FASTQ files. Sequencing reads for the samples described are available from the Gene Expression Omnibus (GEO) under accession number GSE80126.Individual coverage statistics for the samples analysed are available in Additional file c(x) the median coverage for these genes at a distance x from the 3′ end. A linear model log(c(x)) = mx + b was built in R (version 3.1) and the decay rate per kilobase d was defined as d = m × 1000. Decay rate values and R2 are available in Additional file R2 are reported in Additional file d = m′ × RIN + b ' in R (version 3.1) and the equations and the coefficients of the linear model and R2 are reported in the results section.For the UHR samples read coverage was normalized to 50 million reads per sample. The utility DepthOfCoverage from the Genome Analysis Toolkit (GATK) v.1.6.7 was used to query the alignment files to obtain read depth coverage information. Coverage was normalized to coverage per million reads for each sample but refer to as “coverage” below. Only genes with coverage > 10x at a position 300 bases from the 3′ end were considered. The number of reads for each of these genes was measured starting at a position 300 bases from the 3′ end and every 100 bases after that. We denote by


As cell-invading molecular machinery, coronavirus spike proteins pose an evolutionary conundrum due to their high divergence. In this study, we determined the cryo-EM structure of avian infectious bronchitis coronavirus (IBV) spike protein from the γ-genus. The trimeric IBV spike ectodomain contains three receptor-binding S1 heads and a trimeric membrane-fusion S2 stalk. While IBV S2 is structurally similar to those from the other genera, IBV S1 possesses structural features that are unique to different other genera, thereby bridging these diverse spikes into an evolutionary spectrum. Specifically, among different genera, the two domains of S1, the N-terminal domain (S1-NTD) and C-terminal domain (S1-CTD), diverge from simpler tertiary structures and quaternary packing to more complex ones, leading to different functions of the spikes in receptor usage and membrane fusion. Based on the above structural and functional comparisons, we propose that the evolutionary spectrum of coronavirus spikes follows the order of α-, δ-, γ-, and β-genus. This study has provided insight into the evolutionary relationships among coronavirus spikes and deepened our understanding of their structural and functional diversity. Because of their structural and functional diversity, coronavirus spike proteins represent a model system for studying viral evolution. Their evolutionary relationships and history pose major puzzles in virology. A critical missing piece in understanding the evolution of coronavirus spikes is the unavailability of the tertiary structure of γ-coronavirus spikes. Here we determined the first cryo-EM structure of avian infectious bronchitis coronavirus (IBV) spike from the γ-genus. The IBV spike contains structural features that are unique to different other genera; hence it brings the spikes from different coronavirus genera into one evolutionary spectrum, with itself sitting in the middle of this spectrum. The order of the evolutionary spectrum is α-, δ-, γ-, and β-genus. Importantly, through comparisons between IBV spike and other coronavirus spikes, this study illustrates how coronavirus spikes have achieved structural and functional diversity through evolution to guide viral entry into host cells. As large enveloped RNA viruses, coronaviruses are capable of adapting to new hosts with relative ease through mutations and recombinations –3. As a Traces of protein evolution can often be found more reliably in their tertiary structures and related functions than in their primary structures, because proteins generally need to evolve within certain structural and functional constraints , 17. To In the current study, we determined the cryo-EM structure of avian infectious bronchitis coronavirus (IBV) spike, the first such structure from the γ genus. The IBV spike possesses structural features that are unique to different other genera, suggesting that it falls in the middle of an evolutionary spectrum of coronavirus spikes. We also discuss how the structural evolution of coronavirus spikes has affected their functions as cell-invading molecular machinery. Overall, our study has filled in a critical gap in the structural, functional and evolutionary studies of coronavirus spikes, and deepened our understanding of viral evolutions in general.6 tags. It was expressed in Sf9 insect cells using the Bac-to-Bac system (Life Technologies Inc.) and purified as previously described [6 tag and does not contain the GCN4 tag.IBV spike gene  was synthesized (Genscript) with codons optimized for insect cell expression. Its ectodomain (residues 20–1084) was cloned into pFastBac vector (Life Technologies Inc.) with a N-terminal honeybee melittin signal peptide and C-terminal GCN4 and Hisescribed . BrieflyFor sample preparation, aliquots of IBV spike ectodomain  were applied to glow-discharged CF-2/1-4C C-flat grids (Protochips). The grids were then plunge-frozen in liquid ethane using a FEI MarkIII Vitrobot system (FEI Company).2 fractionated in 50 frames of 200 ms exposure. Data collection statistics are summarized in For data collection, images were recorded using a Gatan K2 Summit direct electron detector in the direct electron counting mode (Gatan), attached to a FEI Titan-Krios TEM, at Arizona State University. The automated software SerialEM  was usedFor data processing, whole frames in each movie were corrected for beam-induced motion and dose compensation using MotionCor2  and ~1,4The initial model of IBV spike ectodomain was obtained by fitting the seven parts  of the porcine delta coronavirus spike structure (PDB ID: 6B7N) individually into the cryo-EM density map of IBV spike using UCSF Chimera  and CootIBV pseudovirus entry assay was carried out as previously described . Briefly6 tag (40 μg/ml) at room temperature for 30 minutes, followed by incubation with phycoerythrin (PE)-labeled anti-His6 antibody for 30 minutes. The cells were then analyzed for the binding of IBV S1-CTD using flow cytometry.Recombinant IBV S1-CTD was assayed for its cell-binding capability using flow cytometry as previously described . Brieflyhttp://www.ebi.ac.uk/pdbe/prot_int/pistart.html) [The total surface area and buried surface area of coronavirus S1-CTDs were calculated using the PISA server at the European Bioinformatics Institute (rt.html) . Specifi6 tag (We constructed the IBV spike ectodomain (from IBV strain M41) in the pre-fusion state by replacing its transmembrane anchor and intracellular domain with a C-terminal GCN4 trimerization tag, followed by a His6 tag . We expr6 tag ; S1 Fig,6 tag . The finThe overall structure of IBV spike ectodomain resembles the pre-fusion structures of coronavirus spikes from the α-, β-, and δ-genera , 18–20. IBV S1-NTD takes the same galectin fold as the S1-NTDs from the other three coronavirus genera, but it contains unique structural features . Its corIt has been known that IBV spike binds sugar . A recenThe structure determination of IBV S1-NTD provides insight into the structural and functional evolution of coronavirus S1-NTDs. We hypothesized that coronavirus S1-NTDs originated from host galectins based on the structural similarities between coronavirus S1-NTDs and host galectins , 24. As The structure of IBV S1-CTD is significantly different from the structures of S1-CTDs from the other genera ; S3 Fig.To investigate the function of IBV S1-CTD, we performed two assays to detect possible interactions between IBV S1-CTD and its potential receptor on the host cell surface. First, we carried out an IBV-spike-mediated pseudovirus entry assay in the presence of recombinant IBV S1–CTD . To thisCoronavirus S1-CTDs represent remarkable examples of divergent evolution of viral proteins. The core structures and the RBM regions of S1-CTDs are both divergent among different genera ; S3 Fig.Curiously, coronavirus S1 from different genera take two types of quaternary packing modes  12–14, –20. IBV We examined whether the quaternary structures of coronavirus S1 can lead to functional differences of coronavirus spikes. First, in both β- and γ-coronavirus spikes, the RBMs of their S1-CTDs and the ceiling of their S1-NTDs have evolved to facilitate the cross-subunit packing. These additional structural features further evolved to gain other functions: the RBMs of S1-CTDs recognize diverse protein receptors, whereas the ceiling of the S1-NTDs either protects the sugar-binding site or recognizes a new protein receptor . Second,The structure and function of IBV S2 are highly similar to those of S2 from the other coronavirus genera. In the pre-fusion structure of IBV S2, HR2 is disordered, whereas HR1 and FP each consist of several α-helices and connecting loops (the exact residue range of FP is not clear) . In the The fast evolutionary rates of viruses, particularly RNA viruses, make it difficult to trace their evolutionary history –3. EnvelOur study compares the structures and functions of coronavirus spikes from the four genera, and illustrates the structural and functional evolution of these proteins. First, coronavirus S1-NTDs from all genera share the same structural fold and possibly evolutionary origins with host galectins. From α- and δ- genera to γ genus and then to β genus, the S1-NTDs have evolved from simple galectin-fold core structure with an exposed sugar-binding site, to having a partial ceiling on top of the core structure, and to having an extensive ceiling to protect the sugar-binding site from host immune surveillance (the outer surface of the ceiling in one β-coronavirus can even bind to a novel protein receptor). The partial ceiling in γ-coronavirus S1-NTDs and the extensive ceiling in β-coronavirus S1-NTDs are also involved in the quaternary packing of S1. Second, coronavirus S1-CTDs from different genera are very diverse, but still form an evolutionary spectrum with α- and β-coronavirus S1-CTDs at two ends and δ- and γ-coronavirus S1-CTDs in the middle. The core structures of S1-CTDs have diverged from β-sandwich to weakened β-sandwich and then to β-sheet, whereas the RBMs have diverged from short loops to extended loops and then to a long subdomain. The functional significance of the core structure evolution is not clear, but the RBM evolution may allow the viruses to expand receptor recognition and also contributes to the quaternary packing of S1. Third, from α- and δ- genera to β- and γ-genera, the quaternary packing of S1 has diverged from simple intra-subunit packing to more complex cross-subunit packing. The cross-subunit quaternary packing of β- and γ-coronavirus S1 may have allowed their S1 to evolve additional functions in receptor recognition. Moreover, the relatively small buried surface area of the S1-CTD from β-genus SARS-CoV may allow the S1-CTD to be more dynamic for receptor binding. Finally, the S2 from all four genera are structurally and functionally conserved, although there are some differences in their S1/S2 packing. Quantitative structural comparisons also demonstrate that in terms of structural distances to α-coronavirus S1, δ-coronavirus S1 is the closest, β-coronavirus S1 is the farthest, and γ-coronavirus S1 is the intermediate. We also calculated the phylogenetic tree using the amino acid sequences of 29 coronavirus spikes from different genera, and the result showed that in terms of amino acid sequence distances to α-coronavirus spikes, δ-coronavirus spike is the closest, β-coronavirus spike is the farthest, and γ-coronavirus spike is the intermediate . Taken tBecause of their fast evolutionary rates, viruses are perfect model systems for studying evolution. Our study has demonstrated that despite structural divergence among coronavirus spikes, particularly in their S1 where low or little structural similarities can be detected, we can still trace the evolutionary relationships among these viral proteins through detailed analyses of their structures and functions. Our study also reveals that coronavirus spikes have evolved to remarkable diversity to expand their receptor recognition, facilitate membrane fusion, and evade host immune surveillance, while conserving basic membrane-fusion mechanisms. The evolutionary approaches that coronaviruses take and the evolutionary edges that they gain are good examples of viral evolution and deepen our understanding of evolution in general.S1 Table(DOC)Click here for additional data file.S2 Table(DOC)Click here for additional data file.S1 Fig(A) Representative micrograph of frozen-hydrated IBV spike ectodomain particles (top) and representative 2D class averages in different orientations (bottom). (B) Gold-standard Fourier shell correlation (FSC) curves. The resolution was determined to be 3.93 Å. The 0.143 cut-off value is indicated by a horizontal red bar.(TIF)Click here for additional data file.S2 Fig(A) Structural topology of the core structure of human galectin-3 (PDB ID: 1A3K). (B) Structural topology of the core structures of α-, γ-, and δ-coronavirus S1-NTDs. (C) Structural topology of the core structures of β-coronavirus S1-NTD. PDB IDs of coronavirus S1-NTDs are the same as in (TIF)Click here for additional data file.S3 Fig(A) Structural topology of the core structures of α- and δ-coronavirus S1-CTDs. (B) Structural topology of the core structure of γ-coronavirus S1-CTD. (C) Structural topology of the core structure of β-coronavirus S1-CTD. PDB IDs of coronavirus S1-CTDs are the same as in (TIF)Click here for additional data file.S4 Fig6 tag. Cells only or cells plus antibody without IBV S1-CTD were used as negative controls. Statistic analyses were performed using two-tailed t-test. Error bars indicate S.E.M. (n = 4). *** P<0.001. ** P<0.01. * P<0.05. N.S.: no statistical significance.(A) IBV pseudovirus entry into cells in the presence of recombinant IBV S1-CTD. Entry efficiency was characterized by luciferase activity accompanying entry. RLU: relative light units. Mock: no IBV pseudoviruses were added. Entry: IBV pseudovirus entry in the absence of any recombinant IBV S1-CTD. (B) Flow cytometry assay for the binding of recombinant IBV S1-CTD to the surface of cells. Cell-bound IBV S1-CTD was detected using antibodies recognizing its C-terminal His(TIF)Click here for additional data file.S5 Fig(A) Structures of monomeric β-genus MHV S2 in the pre-fusion conformation  and post-fusion conformation . Structural elements in monomeric S2 are colored in the same way as in (TIF)Click here for additional data file.S6 FigThe phylogenetic tree was constructed using the neighbor-joining method as previously described . Horizon(TIF)Click here for additional data file.


To allow efficient transcript/gene detection, highly abundant ribosomal RNAs (rRNA) are generally removed from total RNA either by positive polyA+ selection or by rRNA depletion (negative selection) before sequencing. Comparisons between the two methods have been carried out by various groups, but the assessments have relied largely on non-clinical samples. In this study, we evaluated these two RNA sequencing approaches using human blood and colon tissue samples. Our analyses showed that rRNA depletion captured more unique transcriptome features, whereas polyA+ selection outperformed rRNA depletion with higher exonic coverage and better accuracy of gene quantification. For blood- and colon-derived RNAs, we found that 220% and 50% more reads, respectively, would have to be sequenced to achieve the same level of exonic coverage in the rRNA depletion method compared with the polyA+ selection method. Therefore, in most cases we strongly recommend polyA+ selection over rRNA depletion for gene quantification in clinical RNA sequencing. Our evaluation revealed that a small number of lncRNAs and small RNAs made up a large fraction of the reads in the rRNA depletion RNA sequencing data. Thus, we recommend that these RNAs are specifically depleted to improve the sequencing depth of the remaining RNAs. Compared with microarray-based transcriptome profiling, RNA-seq has a wider dynamic range and avoids some of the technical limitations such as varying probe performance and cross-hybridization4. RNA-seq can measure the expression levels of thousands of genes simultaneously and provide insights into functional pathways and the regulatory networks in biological systems. In addition, RNA-seq can provide novel insights into alternative splicing5, unannotated exons, and novel transcripts6.RNA sequencing (RNA-seq) has revolutionized the way biologists examine transcriptomes and has been successfully applied in biological research, drug discovery, and clinical development7. To allow efficient transcript/gene detection, highly abundant rRNAs must be removed from total RNA before sequencing. Standard approaches include selection of polyadenylated RNA (polyA) transcripts using oligo (dT) primers, and depletion of highly abundant rRNAs through hybridization capture followed by magnetic bead separation. However, the polyA+ selection and rRNA depletion methods each have unique advantages and limitations. polyA+ selection is used for most transcriptome studies because the sequencing depth required is relatively low when the focus is mainly on the protein-coding fraction of a transcriptome8. Several recent studies have noted that although rRNA-depleted RNA libraries cost more than polyA+ selection libraries to achieve comparable coverage of protein-coding reads in a transcriptome, it provides more information on polyA- transcripts. Another technical advantage that favours RNA-seq of rRNA-depleted libraries compared with polyA-selected libraries is that its performance is better for degraded RNAs12.Ribosomal RNA (rRNA) is the most highly abundant component of total RNA isolated from animal or human cells and tissues, comprising the majority (>80% to 90%) of the molecules in a total RNA sample16. However, the assessments of different RNA-seq protocols have relied mostly on animal samples or cell-line-derived RNAs. In this paper, we evaluated the two sequencing protocols in clinical settings where RNA samples were collected primarily from human blood and tissues. Blood samples from individuals were pooled prior to data generation to remove any possible association of analytical measurements with a single donor. Four replicate samples from pooled blood and four replicates from colon tissue were sequenced, using both protocols. Our analyses showed that the rRNA depletion method captured a wider diversity of unique transcriptome features, whereas the polyA+ enrichment method outperformed the rRNA depletion method in exonic coverage and accuracy of gene quantification. In the rRNA depletion method, many more reads were mapped to intronic regions, which not only significantly reduced the number of usable reads for exon/gene quantification but also led to overestimation of the expression levels for the genes that overlapped with the intronic regions of other genes. For the blood and colon samples, 220% and 50% more reads, respectively, had to be sequenced in the rRNA depletion method to achieve the same level of exonic coverage as the polyA+ selection approach. Our results show that selection of the library preparation protocol in clinical research should be guided by the study objectives, and polyA+ selection is recommended for RNA-seq projects where the main goal is quantification of protein-coding genes.Since 2010, comparisons between polyA+ selection and rRNA depletion methods have been carried out by various groups using different kits, cell lines, and samples17. Four technical replicates per condition were sequenced using both the polyA+ selection and rRNA depletion protocols. The rRNA depletion kits used in this study were Ribo-Zero Gold for colon RNA and Globin-Zero for blood (both abbreviated as RiboZ). After sequencing, 50 M reads were randomly sampled from each replicate library, and then processed by an in-house developed QuickRNASeq.18 pipeline.To fairly evaluate the differences between the two protocols and the reproducibility of the RNA-seq data, we minimized the confounding factors as much as possible. The overall experimental design is depicted in Fig. Unique_Mapped in Table Exonic_Reads in Table The annotations for the blood and colon samples and replicates, the number of reads uniquely mapped to the human reference genome GRCh38, and the number of reads falling in exonic regions in human Gencode Release 25 are summarized in Table Blood_PolyA, Blood_RiboZ, Colon_PolyA, and Colon_RiboZ.The read counts for individual genes across replicates of the same group Table  were mer19. Accordingly, all annotated genes in human Gencode Release 2520 were divided into four groups according to their expression levels in the two protocols, namely, Both (detected by both protocols), PolyA (detected only by polyA+ selection), RiboZ (detected only by rRNA depletion), and None (detected by neither protocol). The numbers of detected genes in each group in blood and colon are shown in Fig. A gene was considered as expressed if its RPKM (reads per kilobase of transcript per million mapped reads) in a sample was >0.1All the genes in Gencode Release 25 can be classified into five biotype categories: protein-coding, lncRNA (long noncoding RNA), pseudogene, small RNA, and TCRs and BCRs (T- and B-cell receptors). The number of genes in each category is shown in Fig. Blood_PolyA in Fig. Blood_PolyA in Fig. Next, we split the expressed genes and the total counted reads in individual samples by gene biotype Fig.  and E, aWe observed that a large fraction of reads mapped to intronic regions Fig. . To fairThe scatter plots of gene expression for blood and colon samples in Fig. 21 and the results are summarized in Fig. Differential gene expression analysis was performed using edgeRDespite the overall high concordance between the two protocols, large discrepancies were observed for thousands of genes Figs  and 5. WMALAT1 (metastasis-associated lung adenocarcinoma transcript 1) is broadly expressed and is among the most abundant lncRNAs in mouse and human tissues22. The dominant isoform is unspliced and expressed at levels similar to or higher than many protein-coding house-keeping genes, including β-actin and GAPDH23. MALAT1 alone contributed about 2.7% of the total counted reads  locus is regulated by alternative 3′-end processing24. The primary transcript of NEAT1 can be cleaved either by the canonical cleavage and polyadenylation machinery to generate a polyadenylated RNA or by the tRNA biogenesis machinery to generate a non-polyadenylated RNA with a mature 3′-end that is protected by a triple helix24. NEAT1 was very highly expressed in both blood and colon, regardless of the sequencing protocols. A recent study demonstrated that high NEAT1 expression was associated with a poor prognosis in cancer patients25.HBA2  expression in the blood sample likely results from differences in the efficiencies of globin removal of the different kits. Because globin transcripts can account for up to 70% of the total whole blood mRNA population26, different globin reduction protocols have been used successfully in gene expression studies27 to improve the sensitivity of gene expression profiling experiments. The Globin-Zero Gold rRNA Removal Kit can remove both globin and mitochondrial and cytoplasmic rRNAs. This kit seemed to deplete globin much more effectively than the Globin-Zero Kit, which likely explains why the reported HBA2 expression was much lower in the rRNA depletion approach than in the polyA+ selection method. We were perplexed by the large expression difference for the pseudogene MTATP6P1  between the two protocols, but found that the expression pattern of MTATP6P1 was nearly the same as its functional counterpart MT-ATP6  across the different samples  involved in the processing of rRNA precursors32. RN7SL2 and RN7SL3 are part of the signal recognition particle complex, which mediates the cotranslational insertion of secretory proteins into the lumen of the endoplasmic reticulum33. The exceptionally high expressions of these small RNAs in HEK293 cell line and extracellular vesicles have also been reported34. The RN7SL2 reads accounted for about 88% (in blood) and 84% (in colon) of reads derived from small RNAs. Sultan et al.16 reported exceptionally high expression of RN7SL1 rather than RNSL2. RN7SL1, RN7SL2, and RN7SL3 are paralogous genes, and sequence comparison revealed they share high sequence identity.The small RNAs, seq Fig. . SNORD3ABlood_RiboZ was 29 RPKM, but was undetected in Blood_PolyA. MiRNA 4459 is only 66-bp long, and overlaps with the intronic region of ARL15. The read coverage profile pattern  in Blood_RiboZ was about 130 RPKM, but the read coverage pattern did not corroborate such a high expression level  J recombination to form a TCR molecule. If TRAJ12 was truly expressed, we should have detected exon–exon junctional reads that spanned TRAJ12 and its associated variable gene, but no such junctional reads were observed . For example, the estimated expression of As shown in Fig. Blood_RiboZ sample, the median intron rates were close to 0.5 for both lncRNA and protein-coding genes, whereas, in the Colon_RiboZ sample, the median intron rates were lower . As shown in Fig. Blood_PolyA and Colon_PolyA were nearly identical, and the majority of protein-coding genes had very low intron rates in the polyA+ selection RNA-seq. However, the distribution of the intron rates displayed different patterns in Blood_RiboZ and Colon_RiboZ. The relationship between intron rate and gene structure was investigated. In general, the intron rate increased with intron length or with the number of introns in a gene, but the trend was more apparent in Blood_RiboZ and Colon_RiboZ than in Blood_PolyA and Colon_PolyA  might represent background oligo(dT) priming to stretches of adenines in primary transcripts, rather than true polyadenylated transcripts36. Or a fraction of polyA-selected intronic RNA may represent transcripts that undergo splicing after polyadenylation. A previous study also suggested that polyA+ purification was not completely efficient38.For the blood sample, a higher proportion of RNA originated from regions outside known exons in the rRNA depletion RNA-seq (78%) compared with the polyA+ selection RNA-seq (29%). Notably, 50% of all mapped sequence reads were located in introns for the rRNA depletion, whereas only 6% were located in introns for the polyA+ selection Fig. . The int29. Replication-dependent histone mRNAs are known to lack polyA+ tails, but this is not universally true. In our study, HIST1H2AC was highly expressed in blood and detected by the polyA+ selection method . Written informed consent was obtained from all volunteer blood donors for the research described and potential publication thereof. 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 volunteers was approved by the Schulman Associates Institutional Review Board . The blood samples were pooled across subjects to create a single pool. The pooled blood was dispensed into approximately 10-mL aliquots. Total RNA was extracted from four of the aliquots using a PAXgene Blood RNA Kit  according to the manufacturer’s protocol. The yield and quality of the isolated RNAs were assessed using a NanoDrop 8000 spectrophotometer  and Agilent 2100 Bioanalyzer , respectively. The RIN of blood RNA was 7.2. An aliquot of 1.5 μg of each RNA was further processed with a GlobinClear kit  to remove globin mRNA. After globin RNA depletion, the yield and quality of the resulting RNA were assessed again using a NanoDrop 8000 and Agilent 2100 Bioanalyzer. Another aliquot of 1 μg of each RNA was processed using a Globin-Zero Gold rRNA Removal Kit  to remove rRNA  and any remaining globin mRNA. Then, 1 μl of each resulting RNA was assessed with an Agilent 2100 Bioanalyzer using an RNA 6000 Pico Chip to verify the depletion of rRNA.We purchased 100 μg Human Colon Total RNA from Thermo Fisher Scientific (cat# AM7986). The RNA was dispensed into 2 μg aliquots and stored at −80 °C before being analysed. Four aliquots of 1 μg colon RNA were treated with Ribo-Zero Gold rRNA Removal Kit  to deplete rRNA . Then, 1 μl of each resulting RNA was assessed with an Agilent 2100 Bioanalyzer using an RNA 6000 Pico Chip to verify the depletion of rRNA.42.To generate polyA+ selection RNA-seq data, eight cDNA libraries were prepared from 300 ng of each GlobinClear-treated blood RNA sample (n = 4) and 300 ng of each colon RNA sample (n = 4) using a TruSeq Stranded mRNA Library Prep kit . To generate rRNA depletion RNA-seq data, eight cDNA libraries were prepared from each Globin-Zero-treated blood RNA sample (n = 4) and each Ribo-Zero Gold-treated colon RNA sample (n = 4) using a TruSeq Stranded Total RNA Library Prep kit . The eight mRNA-seq libraries and eight total RNA-seq libraries were sequenced in two separated runs on a NextSeq. 500 platform (Illumina) using paired-end sequencing (2 × 76 bases). About 50 to 80 million pairs of reads were generated from each library. We used stranded RNA-seq rather than non-stranded RNA-seq because it provides more accurate estimates of transcript expression44. Previously, we evaluated the impact of different annotations on RNA-seq data analysis44. In this paper, we used the human genome GRCh38 and Gencode Release 2520 annotations to map and count sequence reads. The reads were mapped to the GRCh38 reference genome using STAR45 v2.5.2 h. The parameters chosen for the STAR run were “–runThreadN 8;–alignSJDBoverhangMin 1;–outReadsUnmapped Fastx;–outFilterMismatchNoverLmax 0.05;–outFilterScoreMinOverLread 0.90;–outFilterMatchNminOverLread 0.90;–alignIntronMax 1000000;–outSAMtype BAM SortedByCoordinate”. The union-exon based approach was used for gene quantification46, and featureCounts47 was used to count reads mapped to individual genes. The parameter used in featureCounts run was “–minOverlap 25”. Only the reads that uniquely mapped to exonic regions were counted, and reads that mapped to gene overlapping regions were excluded. Differential expression analysis was performed using the edgeR21 package.Gene quantification results are dependent on the choice of gene annotationsAll the raw sequencing reads generated in this study have been submitted to the NCBI Sequence Read Archive and are available under the accession number SRP127360.Supplementary Materials


Using purified reaction components, a commercial monoclonal antibody (Ab) specific to RNase inhibitor (RI) was found to interfere with the activity of RI. Total RNA was mixed with a monoclonal Ab specific to either RI (clone 3F11) or glyceraldehyde-3-phosphate dehydrogenase (GAPDH), RNase A, RI, or a combination of the above. Following incubation for 1 h at 22 °C or 37 °C, RNA integrity of the mixtures was assessed using microfluidics-based Bio-Rad Experion RNA electrophoresis. The addition of Ab 3F11 prevented RI from effectively inhibiting RNase A and therefore resulted in extensive RNA degradation. The data presented are associated with the research article entitled “Endogenous RNase Inhibitor Contributes to Stability of RNA in Crude Cell Lysates: Applicability to Reverse Transcription Quantitative PCR (RT-qPCR)”   Specifications TableValue of the data•Using purified reaction components, a commercial monoclonal Ab specific to RI (3F11) was found to interfere with the activity of RI.•This monoclonal Ab may be a useful reagent for exploring structure-function relationships of RI and for probing the interaction between RI and its target RNases.•In addition, this monoclonal Ab may aid in establishing the role of RI as a determinant of RNA stability in complex mixtures (such as crude cell lysates).1Mixtures of purified components  were subjected to incubation for 1 h at various temperatures . Following the incubation, RNA was purified from the mixtures and subjected to microfluidics-based Bio-Rad Experion RNA electrophoresis. Data shown are virtual gel images and RNA Quality Indicator (RQI) values generated by the Experion software.The data presented are associated with the research article entitled "Endogenous RNase Inhibitor Contributes to Stability of RNA in Crude Cell Lysates: Applicability to RT-qPCR" 22.1Total RNA from Vero cells was purified using the RNeasy kit (Qiagen) and diluted in Cell-Lysis (CL) Buffer  Data from this experiment are shown in 2.2Purified total RNA from Vero cells (5 µg in 200 µL of CL Buffer) was mixed with 1 µg (1 µL) of monoclonal Ab specific to either RI  or GAPDH , 1 ng (1 µL) of RNase A , 40 units (1 µL) of human placental RI , or a combination of the above; in reactions containing hpRI, its addition preceded the addition of other components. The mixtures were incubated for 1 h at 22 °C or at 37 °C. Following the incubation, RNA was purified and subjected to Experion analysis. Data from this experiment are shown in


In order to become functionally competent but harmless mediators of the immune system, T cells undergo a strict educational program in the thymus, where they learn to discriminate between self and non-self. This educational program is, to a large extent, mediated by medullary thymic epithelial cells that have a unique capacity to express, and subsequently present, a large fraction of body antigens. While the scope of promiscuously expressed genes by medullary thymic epithelial cells is well-established, relatively little is known about the expression of variants that are generated by co-transcriptional and post-transcriptional processes.Our study reveals that in comparison to other cell types, medullary thymic epithelial cells display significantly higher levels of alternative splicing, as well as A-to-I and C-to-U RNA editing, which thereby further expand the diversity of their self-antigen repertoire. Interestingly, Aire, the key mediator of promiscuous gene expression in these cells, plays a limited role in the regulation of these transcriptional processes.Our results highlight RNA processing as another layer by which the immune system assures a comprehensive self-representation in the thymus which is required for the establishment of self-tolerance and prevention of autoimmunity.The online version of this article (doi:10.1186/s13059-016-1079-9) contains supplementary material, which is available to authorized users. This process is primarily mediated by both negative selection of potentially self-reactive T cell clones and the induction of CD25ng cells . In part1 (APS1) , 5.RNA-processing mechanisms, in particular alternative splicing (AS) and RNA editing, enable the production of multiple mRNA transcripts from the same gene, thereby expanding the diversity and complexity of individual gene products. Consequently, a single gene may give rise to different protein variants with different functional roles in different tissues , 7. CurrTranscript and protein diversities are further increased by various RNA-editing mechanisms, which induce single nucleotide substitution in the RNA, which may thereby alter the protein sequence and function. In mammals, RNA editing involves mainly deamination of adenosine (A) to inosine (I) (recognized as guanosine (G) by all molecular machineries) mediated by the adenosine deaminases acting on RNA (ADARs) protein family , 15. TheRNA processing in the peripheral tissues may increase the repertoire of potentially immunogenic epitopes, which may then be recognized as non-self-peptides by the adaptive immune system. This is particularly true for any protein variant, which was not presented to the developing T cells in the thymus . For insHere, we evaluate the extent of various co-transcriptional and/or post-transcriptional RNA process products expressed in mTECs, assess the intrinsic diversity of the individual self-antigen transcripts, and compare it with that of other cell types and tissues. To this end, we analyzed available RNA-sequencing (RNA-seq) datasets and determined the extent and diversity of gene expression, AS and RNA editing in mTECs in comparison with other tissues and cell types in the body. Indeed, our analyses demonstrate that, on average, mTECs express 3000–6000 more genes than other tissues. Interestingly, the extent of representation of the individual tissues within mTECs is very diverse, ranging from relatively low (e.g. testis or brain) to high (e.g. colon or skin) levels of tissue-specific coverage. Moreover, our results reveal that mTECs display a relatively high level of AS and RNA editing, which thus helps to further expand the already broad repertoire of self-antigens in the thymus. Our results therefore highlight yet another level by which the immune system assures a comprehensive representation of the body’s own proteins in the thymus.lo), which mainly represent an immature mTEC population; (2) MHC-II high mTECs (mTEChi), mainly representing a mature population; and (3) an Aire-deficient mTEChi population (AireKO), which is severely impaired in promiscuous gene expression  expressed approximately 15,000 genes, suggesting that Aire is responsible for the induction of ~3000 genes in mTECs [lo population was also characterized by a strong promiscuous gene expression. However, the levels of many individual transcripts were dramatically lower than in the mTEChi population as clearly illustrated in Fig. The analysis, using the selected cutoff see “”, revealin mTECs –40. InteAlthough mTECs are characterized by a very high level of promiscuous expression of TRA genes Fig. , whetherhi population  were found to undergo RNA editing. In the vast majority of these recoding edited sites in mTECs (73.7 %), RNA-editing levels were lower than 10 %, suggesting that RNA editing in mTECs may follow a similar stochastic pattern as promiscuous gene expression. Moreover, these data imply that accurate assessment of the rate of editing in these sites may require a higher coverage.In addition to AS, protein diversity is further expanded by RNA editing, which can make discrete changes to specific nucleotide sequences within an RNA molecule after it has been generated by RNA polymerase . Two typhi cells demonstrated 4.80 such sites per million aligned reads. This number is comparable to the number observed in the brain (4.75)  Fig. , which hAdar1 since its expression levels in mTEChi and cTEC samples were comparable to the level in the brain tissue, whereas Adar2 expression levels were significantly higher in the brain  ≤ 0.05) . Indeed, the vast majority of statistically significant altered events had a higher A-to-I editing rate in mTECs vis-à-vis other tissues, except for mTECNext, we examined the level of C-to-U RNA editing, which is a less prevalent type of RNA-editing mechanism. The only verified non-synonymous change induced by this type of editing in mouse is ApoB in which a codon is edited to a termination codon in mouse liver and small intestine , 50, 51.lo or cTECs editing rates were much lower than that obtained for mature mTECs. Moreover, Apobec1 expression levels were comparable in mTEChi cells and in the colon and were significantly correlated to the corresponding C-to-U editing levels  already at the stage of immature mTECs or that the analyzed mTEClo population also contains terminally differentiated mTECs, which have downregulated their MHC-II molecules, but still express residual levels of TRA transcripts. Given that Aire becomes expressed only at the mTEChi stage and that the mTEClo population was indeed found to contain a fraction of such post-Aire mTECs [Indeed, comprehensive RNA-seq analysis validated that in contrast to all other analyzed cell types and tissues, mature mTECs are capable of expressing the vast majority of the protein-coding genome Fig. . Specifire mTECs –58, the hi population is higher (~15 k) than in most of the other analyzed tissues (~12–14 k), except for the testis (~15 k). These results validate some of the previous observations suggesting that a large fraction of TRA genes (i.e. 30–40 %) is expressed by mTECs in an Aire-independent manner [Moreover, even in the absence of Aire, the extent of genome expression in the mTECt manner . The molhi population. The only exception is the brain and testes, which display relatively little tissue-specific overlap with mTECs . Interestingly, both tissues are considered to be immunologically privileged sites, which are not under conventional immunological surveillance. Thus, our data suggest that the stringency of central tolerance mechanisms to these two immunoprivileged sites is lower than to other peripheral tissues with conventional immunological surveillance. Consequently, more brain-reactive and testes-reactive T cell clones may escape from the thymus to the periphery, where they must be kept in check by other tolerance mechanisms, such as antigen sequestering. A good example is myelin oligodendrocyte glycoprotein (MOG), which is not expressed by mTECs, yet it is tolerated by the immune system, due to its sequestering in the brain. However, when MOG peptides become exposed (e.g. after tissue damage or immunization), the peripheral MOG-specific T cells, that escaped central tolerance, may become activated and mount an autoimmune attack on the brain [Importantly, our analysis also reveals that most of the analyzed peripheral tissues are, to a large extent ~60–100 %), mirrored in the mTEC–100 %, mhe brain .Furthermore, our analysis also revealed that mTECs are characterized by a high rate of alternatively spliced variants in comparison to other cell types and tissues Fig. . This isNevertheless, our results also show that there is a considerable portion of tissue-restricted splice variants that are not expressed in mTECs. These results therefore suggest that although mTECs demonstrate a dramatically higher rate of AS than other tissues, it seems to be less robust and comprehensive than their promiscuous gene expression capacity. This observation may be partly explained by the stochastic nature of the splicing machinery which gives rise to splice variants that probably represent “noise” , 64. StiOur data suggest that Aire, which induces expression of many tissue-restricted antigen genes, does not seem to have a direct role in increasing the variety of tissue-restricted RNA-splicing products. Recently, it has been suggested that Aire regulates the inclusion of thousands of exons , 42. HowWe also found, for the first time, a high RNA-editing level (A-to-I and C-to-U) in mature mTECs Fig. . Global A high capacity of RNA editing was observed in Aire-deficient mTECs, but not in immature mTECs. Thus, another regulator (or regulators) probably mediates high RNA processing, specifically in mature mTECs. Moreover, it is reasonable that similar A-to-I editing regulation (but not C-to-U) probably occurs in cTECs.The high transcriptome complexity and high rate of RNA-processing TRAs we found to be expressed in mature mTECs ensure lower rate of differences of antigens between mTECs and any peripheral tissue. Specifically, our results demonstrate a high level of two types of RNA editing, A-to-I and C-to-U editing, together with a high rate and diversity of AS in mTECs. Moreover, our results suggest a limited role of Aire in the regulation of this high variability. Thus, our study highlights another layer by which mTECs expand the already broad repertoire of self-representation, which is required for the establishment of self-tolerance and prevention of autoimmunity.The AireKO population was isolated from individual strains (wild-type B6) with a FACSAria III cell sorter (BD), RNA was extracted and sequenced as detailed before . BrieflyWe used RNA-seq data from 13 different cell or tissue types. Details and sample sources are provided in Additional file http://www.r-project.org/)).The statistical analysis was done using R . The procedure was performed by first excluding a given i-th sample, finding genes that are expressed uniquely in each sample (and not in the other remaining tissues), and then assessing the ratio of these genes expressed in the i-th sample. A gene was considered expressed if the normalized read counts was > 0.01.To determine AS frequency differences between the examined tissue and cell types, we used STAR splice junctions output file. In order to obtain comparable samples, reads longer than 80 nt were trimmed and 50 million random reads were selected from each sample. For each coding gene expressed in the tissue, we inferred whether it was alternatively spliced by finding at least two splice junctions that either start or end at the same position. We also compared the number of all the junctions detected within each gene expressed both in mTECs and each of the other tissues examined. Comparison of alternatively spliced variants was also conducted using rMATs . The numThe TRA junction was defined as a junction that is supported by at least ten reads in a specific tissue and not in any of the other tissues examined (mTECs excluded) and is found inside a gene that was expressed in at least additional tissue. In order to assess the extent of TRAs that result from tissue-restricted AS, we calculated the ratio of TRA junctions of each of the tissues examined expressed in mTECs and AireKO samples out of the TRA junctions expressed in the tissue. The same ratio was calculated for splice junctions of known events  from the AltEvents track of the UCSC genome browser .To evaluate the global extent of RNA editing (A-to-I) in mTECs versus other cell types, we estimated the extent of hyper-editing, the editing rate of each RNA-seq sample, and compared the number of sites significantly altered between mTECs and other cell types.Hyper-edited sites were identified as described in  using thThe editing rate was computed as the percentage of editing sites in which the edited variant was expressed out of all known editable sites expressed in a given tissue. We randomly sampled 30 million aligned reads from each RNA-seq for comparable sample size. RNA-editing levels were calculated using the REDItoolKnown.py script that is part of REDItools package . Mouse AAdar1, Adar2, and Apobec1 normalized reads counts were calculated using DEseq [ng DEseq . Mappingng DEseq .


Analogous to DNA methylation and histone modifications, RNA modifications represent a novel layer of regulation of gene expression. The dynamic nature and increasing number of RNA modifications offer new possibilities to rapidly alter gene expression upon specific environmental changes. Recent lines of evidence indicate that modified RNA molecules and associated complexes regulating and “reading” RNA modifications play key roles in the nervous system of several organisms, controlling both, its development and function. Mutations in several human genes that modify transfer RNA (tRNA) have been linked to neurological disorders, in particular to intellectual disability. Loss of RNA modifications alters the stability of tRNA, resulting in reduced translation efficiency and generation of tRNA fragments, which can interfere with neuronal functions. Modifications present on messenger RNAs (mRNAs) also play important roles during brain development. They contribute to neuronal growth and regeneration as well as to the local regulation of synaptic functions. Hence, potential combinatorial effects of RNA modifications on different classes of RNA may represent a novel code to dynamically fine tune gene expression during brain function. Here we discuss the recent findings demonstrating the impact of modified RNAs on neuronal processes and disorders. An estimated 1–2% of all genes in a given organism contribute to nucleic acid modification systems, suggesting biological importance of modified nucleotides , pseudouridine (Ψ) and ribose 2′-O-methylation (Nm) on mRNA  and 5-methylcytidine (m5C) is still debated   Figure . VariousAsp, tRNAVal, and tRNAGly, has been described in yeast, Drosophila, mouse, and human cells  differentiation and motility  is a common nucleoside modification of RNA, where a methyl group is added to the 2′ hydroxyl of the ribose moiety Figure . Nm incrFTSJ1 gene (OMIM:300499)  and mutations in the Ser . Two families of snoRNAs have been described, based on their structure and function: C/D box snoRNAs are responsible for 2′-O-methylation , memory default, and shorter lifespan , are viable but exhibit memory impairment and shortened lifespan  from 3′ → 5′ exonuclease degradation  particule catalyzes the uridine modification  is the first discovered     and ALKBH5 , which strongly impaired neuronal differentiation  are m6A modified in human, but not in mouse cultures of neuronal progenitor cells (NPC), raising the possibility that m6A regulates specifically these human diseases  were elevated, which led to increased translation during the time of axon regeneration, via the specific m6A reader protein Ythdf1. Mettl14 and Ythdf1 conditional KO mice displayed strong reduction of sensory axon regeneration, resulting from reduced protein synthesis, revealing the critical role of m6A modification in response to injury , was recently shown to control axon outgrowth, branching and to regulate synaptic bouton formation via the activity of the CCAP/bursicon neurons , to repress translation of a subset of FMRP target mRNAs  and AlkB homolog 5 (ALKBH5) , suggesting an involvement of ALKBH5 in conferring risk of MDD  that may carry distinct functions (Briggs and Dinman, 6A clearly demonstrate the involvement of mRNA modification in different aspects of neuronal development and regulation. The large diversity of RNA processing events in the brain, including the high rates of alternative splicing and recursive splicing (Duff et al., 6A modification was recently found on circRNAs, and enables their translation (Yang et al., 6A brain functions may rely on circRNA-mediated regulation. Thus, because neurons face distinct challenges with regards to localisation of RNAs to distal processes and to localized translation it is likely that in the brain, more than in any other tissues, a combinatorial effect of RNA modifications on different classes of RNAs represents a critical informational layer that dynamically fine-tunes gene regulation (Figure That being said, the general picture is probably more complex. For instance, recent reports show that tRFs not only interfere with translation but can also affect transposon regulation and genome stability (Durdevic et al., n Figure . ExcitinAll 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.


Fusarium graminearum that revealed extensive A-to-I editing of mRNAs in sexual structures (fruiting bodies). Here, we searched for putative RNA editing events in RNA-seq data from Sordaria macrospora and Pyronema confluens, two distantly related filamentous ascomycetes, and in data from the Taphrinomycete Schizosaccharomyces pombe. Like F. graminearum, S. macrospora is a member of the Sordariomycetes, whereas P. confluens belongs to the early-diverging group of Pezizomycetes. We found extensive A-to-I editing in RNA-seq data from sexual mycelium from both filamentous ascomycetes, but not in vegetative structures. A-to-I editing was not detected in different stages of meiosis of S. pombe. A comparison of A-to-I editing in S. macrospora with F. graminearum and P. confluens, respectively, revealed little conservation of individual editing sites. An analysis of RNA-seq data from two sterile developmental mutants of S. macrospora showed that A-to-I editing is strongly reduced in these strains. Sequencing of cDNA fragments containing more than one editing site from P. confluens showed that at the beginning of sexual development, transcripts were incompletely edited or unedited, whereas in later stages transcripts were more extensively edited. Taken together, these data suggest that A-to-I RNA editing is an evolutionary conserved feature during fruiting body development in filamentous ascomycetes.RNA editing is a post-transcriptional process that modifies RNA molecules leading to transcript sequences that differ from their template DNA. A-to-I editing was found to be widely distributed in nuclear transcripts of metazoa, but was detected in fungi only recently in a study of the filamentous ascomycete  Doryteuthis pealeii, the majority of investigated protein-coding genes undergo recoding through RNA editing . Furthermore, A-to-I RNA editing in this species is important for sexual development, as it was demonstrated that only an edited version of the protein kinase PUK1 is functional and allows wild type-like ascospore morphology and discharge  of the S. macrospora developmental mutants. These data suggest that A-to-I RNA editing is conserved during sexual development in filamentous ascomycetes.Here, we analyzed the potential for RNA editing in several developmental stages of two additional filamentous ascomycetes, g bodies . FusariuS. macrospora wild type (strain S133143 from our laboratory collection) and developmental mutant pro1 (P. confluens wild type strain (CBS 100304). Unless stated otherwise, standard growth conditions for S. macrospora were as described  based on the genome annotation of S. macrospora and P. confluens  or three reads , at least 3% and two reads coverage of the alternative base, similar to the conditions used in a previous study for F. graminearum  are not due to strain differences or errors in the genome sequences.The strain used for the RNA-seq analysis was identical to the one used for genome sequencing in the case of onfluens . For S. quencing ; howeverquencing . TherefoS. macrospora with P. confluens and F. graminearum, respectively, orthologs between S. macrospora and the other two fungi were identified by reciprocal BLAST as described  fragments from five and six genes covering 12 and 16 putative editing sites of F. graminearum, it was found that A-to-I RNA editing occurs specifically in fruiting bodies, but not in vegetative hyphae and conidia . The sexual mycelium samples were grown as surface cultures that induce fruiting body formation and therefore contain protoperithecia; however, the bulk of the harvested tissue consists of vegetative hyphae that do not themselves participate in fruiting body formation. For the protoperithecia samples, young fruiting bodies were separated from the surrounding nonsexual hyphae by laser microdissection. Therefore these samples consist only of hyphae that participate in the formation of fruiting body structures and asci, but do not contain mature asci. Under the applied thresholds , a total of 3848 positions occurring in at least one of the conditions or strains were identified where a nucleotide base in the transcript differed from the genome sequence , the presence of the A-to-G change was confirmed in all cases . However, this might be due to technical issues, specifically a 3' bias in the protoperithecia samples, because these were prepared by laser microdissection and subsequent linear RNA amplification, which leads to overrepresentation of sequences at the 3' end of mRNAs , thereby leading to an extension of the respective protein. Another 172 would lead to a nonsynonymous codon change. Thus, the majority (53%) of A-to-I editing sites observed in wild type protoperithecia are predicted to lead to a different gene product than the one encoded in the genomic DNA see  online.S. macrospora. However, it was not clear if similar editing events might occur also in other groups of filamentous ascoymcetes. Therefore, we analyzed RNA-seq data from a previous study of the Pezizomycete P. confluens  is the one with the lowest percentage of variant base coverage in the RNA-seq data, so it might be edited at rather low frequency. In general, higher editing frequencies in the RNA-seq data correlated with higher frequencies in the sequenced cDNA clones  of A-to-I editing sites in sexual development are predicted to lead to a different gene product compared to the one encoded in the genomic DNA of P. confluens.Similar to the analysis in plicates . In P. cange see  online. F. graminearum, puk1-like editing)  . We therpora see  online. S. macrospora and P. confluens for those cases where editing leads to an amino acid change and the editing site in the corresponding gene has at least 10% of edited bases in the RNA-seq data. This resulted in 24 editing sites in 21 genes , few significantly enriched functional categories could be identified in S. macrospora, and a direct comparison of the results is difficult. When comparing the expression of edited genes, similar trends were observed in S. macrospora and P. confluens , no differences in overall expression levels are observed between the different groups of edited genes. Thus, the data show a correlation between A-to-I editing that leads to changes in protein sequences, and therefore possibly to functional changes, and an increase in gene expression during sexual development in S. macrospora and P. confluens.To analyze if there are general trends in function or expression of edited genes in different groups of fungi, functional classification and transcript levels of genes with editing sites were analyzed. Functional classification of genes that show A-to-I editing only in wild type protoperithecia of pora see  online ouens see  online ronfluens . In bothonfluens . This inS. pombe (S. pombe (meiotic samples); however, in contrast to S. macrospora and P. confluens, there was no preference for A-to-G changes  for funding .Supplementary DataClick here for additional data file.


Actual European pathological classification of early-stage endometrial cancer (EC) may show insufficient accuracy to precisely stratify recurrence risk, leading to potential over or under treatment. Micro-RNAs are post-transcriptional regulators involved in carcinogenic mechanisms, with some micro-RNA patterns of expression associated with EC characteristics and prognosis. We previously demonstrated that downregulation of micro-RNA-184 was associated with lymph node involvement in low-risk EC (LREC). The aim of this study was to evaluate whether micro-RNA signature in tumor tissues from LREC women can be correlated with the occurrence of recurrences.MicroRNA expression was assessed by chip analysis and qRT-PCR in 7 formalin-fixed paraffin-embedded (FFPE) LREC primary tumors from women whose follow up showed recurrences (R+) and in 14 FFPE LREC primary tumors from women whose follow up did not show any recurrence (R−), matched for grade and age. Various statistical analyses, including enrichment analysis and a minimum p-value approach, were performed.The expression levels of micro-RNAs-184, -497-5p, and -196b-3p were significantly lower in R+ compared to R− women. Women with a micro-RNA-184 fold change < 0.083 were more likely to show recurrence  compared to those with a micro-RNA-184 fold change > 0.083 , p = 0.016. Women with a micro-RNA-196 fold change < 0.56 were more likely to show recurrence  compared to those with a micro-RNA-196 fold change > 0.56 , p = 0.001.These findings confirm the great interest of micro-RNA-184 as a prognostic tool to improve the management of LREC women. Endometrial cancer (EC) is the most common gynecologic cancer in women in developed countries. The highest estimated incidences in 2012 are in the USA and Canada  and northern  and Western Europe  . Women aAmong various risk stratification systems, the European Society for Medical Oncology (ESMO) system, with its four risk groups classification for early stage EC, provides the highest discrimination for both recurrence free survival (RFS) and the risk of nodal metastases . HoweverMicro-RNAs are short non-coding RNA, ~ 22 nucleotides in length, which work at post-transcriptional level as epigenetic regulators. They can inhibit the translation process, or initiate the process of mRNA degradation . They arThe aim of this study was to evaluate whether micro-RNA profile from LREC women can be correlated with the occurrence of recurrences and used as a tool to adapt therapeutic management.Many of the methods related to our patient cohort, micro-RNA extraction, microarray hybridization and analysis, qRT-PCR, and minimal p-value approach have been previously published , 11.Approval for the present study was obtained from the local Medical Ethics Committee .The experimental design for profiling changes in micro-RNA according to the occurrence of recurrence in LREC FFPE primary tumor specimens is shown in Fig. According to the ESMO-ESGO-ESTRO classification, low-risk group included FIGO Stage I endometrioid carcinoma, histological grade 1–2, < 50% myometrial involvement, lympho vascular space involvement (LVSI) negative tumors. They all had disease free pelvic lymph nodes. Women underwent primary surgical treatment  and age with two women without any recurrence (R−), who were used as control subjects. The exclusion criteria were as follows: Lynch syndrome (the search for a loss of expression of one of the Mismatch Repair proteins by immunohistochemistry and for tumor instability (microsatellite instability replication error repeats phenotype) were performed when EC occurred before the age 50 years or when there was a suggestive family history) and refusal of consent.Data of women included in the study were retrospectively abstracted from prospectively maintained databases. They included age, parity, body mass index , comorbidities , 2009 FIGO stage, histologic type and grade, depth of myometrial involvement, LVSI status, and time between surgery and sample analysis.Their follow-up was conducted every 3 months for the first 2 years, every 6 months for the following 3 years, and once a year thereafter. It consisted of physical examinations, and the use of imaging techniques or histological biopsies according to the findings. Recurrences were defined according to previous reports : local rFor each woman, total RNA was extracted using the micro-RNAeasy FFPE Kit , according to the manufacturer’s instructions.Briefly, FFPE tissues were obtained from the hysterectomy specimens. FFPE tissue blocks were sectioned on a standard microtome (Leica-microsystems RM 2145) to generate successive 10 μm sections. A pathologist evaluated each slide, and regions of invasive carcinoma were confirmed and marked. For each sample, marked regions from several slides  were microdissected using a new sterile blade, and the dissected tissues were placed immediately into an RNase-free microcentrifuge tube. After a deparaffinization step, the tissues were digested with protease and treated with DNase. After washing, the RNA, including the small micro-RNA fraction, was eluted with 20 μl distilled water. The concentrations and quality of the RNA recovered were measured using the Nanodrop 1000A spectrophotometer . The median ratio of 260/280 was 1.85 (interquartile range (IQR): 1.70–1.91) and the median concentration was 143 ng/μl (IQR: 91–378).Microarray analysis was conducted on 21 distinct specimens: 7 specimens from R+ women, each one of them matched with 2 specimens from R− women. Microarray hybridization on Micro-RNA 4.0 chips (Affymetrix) was conducted at the genomic platform of the Institut Cochin, Paris. After validation of the RNA quality with Bioanalyzer 2100, 1 μg of total RNA was biotin labeled following the FlashTag Biotin HSR RNA labeling kit (Affymetrix).After overnight hybridization, the Micro-RNA 4.0 chips were washed in the Fluidic Station FS450 (Affymetrix) following a specific protocol and scanned using the GCS3000 7G. The scanned images were then analyzed with Expression Console software (Affymetrix) to obtain raw data (CEL files) and metrics for Quality Control. No apparent outliers were detected. Specific micro-RNA analysis was performed using Partek Flow software, version 3.0 Copyright, 2014 . CEL files were imported and normalized using robust multi-array averaging.Micro-RNAs detected with a variation of at least 2, and with a nominal p-value ≤ 0.05, between R+ and R− women were considered to be differentially expressed, and thus selected for further analysis.® and Mirtarbase®. Gene Ontology (GO) enrichment analysis was performed on the lists using Enrichr [To estimate the biologic effects of the differentially expressed micro-RNAs, lists of validated target genes were determined using currently available databases, including Tarbase Enrichr , 15.®RT Kit, using the 5× miScript HiSpec Buffer method, according to the manufacturer’s instructions (Qiagen) with a Thermo Hybaid PXE 0.2 Thermal Cycler. Complementary DNA samples were stored at − 20 °C for further use.One microgram of total RNA was used in all RT reactions that were performed with the miScriptII® 7300. The results are expressed as Ct values and normalized on the calculated median Ct of each sample (ΔCt). Micro-RNA primers were from Qiagen. Relative expression was calculated using the comparative Ct method (2−ΔΔCt). SNORD68, SNORD61, and RNU6 were used as endogenous controls for data normalization [Micro-RNA expression was analyzed by real-time PCR using miScript SYBR Green PCR Kit (Qiagen) according to the manufacturer’s instructions, with an initial activation step at 95 °C for 15 min, followed by 50 cycles of 94 °C for 15 s, 55 °C for 34 s, and 70 °C for 30 s. A final melting curve analysis was performed to verify that a single product was amplified. All steps were performed in duplicate using an Applied Biosystemslization , 11.For the results from the qRT–PCR on micro-RNA expression, data are expressed as mean ± sem. Means between two groups were compared using the Mann–Whitney test. p < 0.05 was considered to be statistically significant. GraphPad Prism version 7 was used for analysis of tissue samples .For qualitative analysis, we calculated optimal cut-offs for each micro-RNA to correlate semi-quantitative expression and the occurrence of recurrence. The optimal fold-change cut-off was determined by a minimum p-value approach.Unless otherwise specified, data were managed with an Excel database and analyzed using R 3.1.3 software, available online.The clinical and pathologic variables of the women are shown in Table The median age was 66 years (interquartile range (IQR): 57–76) in the R+ group and 60 years (IQR: 45–82) in the R− group (p = 0.45). The two groups were comparable for histologic grade and tumor size. Furthermore, the comorbidities (diabetes and dyslipidemia), parity and body mass index did not differ between R+ and R− women. Neither did the treatment modalities. The median time between surgery and sample RNA extraction was 165 months (IQR: 94–204) in the R− group and 137 months (IQR: 59–202) in the R+ group (p = 0.52).We focused our study on the 2578 probes containing sequences for mature micro-RNAs. As illustrated by the volcano plot representation Fig. .Fig. 2VoWe extracted from the micro-RNA databases the known validated targets of the micro-RNAs exhibiting at least a 2-FC, with significant value (p < 0.05), in the LREC tumors from R+ women vs. R− women: it displays 813 genes, with the majority associated with micro-RNA-497 and micro-RNA-195. Genes involved in PI3 kinase signaling pathway, cancer and cell cycle checkpoint were specifically enriched for these two micro-RNAs. In addition, micro-RNA-7162 and micro-RNA-184 were associated with telomerase maintenance and DNA replication, micro-RNA-6080 with RNA processing, and micro-RNA-196b with endometrial cancer and MAPK/cdk5  were significantly lower in the R+ group compared to those in the R− group. The expression levels of the 6 micro-RNAs of interest are shown in Fig. Optimal cut-offs relevant to the strongest correlation between quantitative expression of the micro-RNAs that had been selected from the previous step and the occurrence of recurrence in LREC women are summarized in Fig. Our results show that in LREC, R+ women have different micro-RNA profiles from R− women. Moreover, we found that recurrence status can be accurately assessed using micro-RNA expression levels on primary FFPE tumor.The crucial issue in managing patients with early-stage EC is to determine the risk of recurrence to better adapt the therapeutic strategy. Currently, higher risk of recurrence has been described when early stage EC was associated with histological settings, including myometrial involvement > 50% , histoloRecently, Levine et al. described four molecular EC categories based on integrated genomic, transcriptomic, and proteomic analysis: p53-mutant, microsatellite instability (MSI), POLE-mutant, and no specific molecular profile . ConfirmAfter validation by RT-qPCR, we found that micro-RNA-184, -497-5p, and -196b-3p emerged as being particularly relevant to determine the occurrence of recurrence in women with LREC.Micro-RNA-184 has been described as a tumor suppressor in various cancers (lymphoma , renal cSimilarly, a decreased expression of micro-RNA-497-5p is associated to multiple myeloma , thyroidFinally, micro-RNA-196 is a known emerging biomarker for digestive tract cancers, where it plays the role of an onco-miR, as it is consistently found over expressed in digestive tract cancer tissues –73. It hBoth micro-RNA-184 and -196 have been described as tumor genesis marker. Indeed, MicroRNA 184 was also described as significantly downregulated in tissues from patients with colorectal cancer, ovarian cancer or prostate carcinoma, compared with matched normal adjacent tissues , 81, 82.In our study, we focused on post-operative FFPE EC specimen, since it is known that FFPE tumors can be used for RT-PCR-based quantitative micro-RNA profiling . This enSome limitations of the study should be underlined. Because of the use of FFPE specimens with insufficient quality and RNA integrity, we could not assess the expression of exact targets for the micro-RNAs validated in this study. Further studies are thus needed to precisely determine the different pathways affected by the selected micro-RNAs. Second, we included patients with both local and distance recurrence that have different long-term prognosis. Indeed, recently, Ouldamer et al. demonstrated that patients recurring under the form of distant metastasis or carcinomatosis had poorer prognosis . MoreoveThis is, to our knowledge, the first study investigating a correlation between micro-RNA signatures in tumor tissues from LREC women and the occurrence of recurrences.Our results may provide a basis for further study of the micro-RNA function EC, and be used as a valuable tool for better diagnostic and therapeutic management.