Pseudouridine is the most abundant modification found in RNA. Today, thanks to next-generation sequencing techniques used in the detection of RNA modifications, pseudouridylation sites have been described in most eukaryotic RNA classes. In the present review, we will first consider the available information on the functional roles of pseudouridine(s) in different RNA species. We will then focus on how alterations in the pseudouridylation process may be connected with a series of human pathologies, including inherited disorders, cancer, diabetes, and viral infections. Finally, we will discuss how the availability of novel technical approaches are likely to increase the knowledge in this field.  A considerable number >100) of different RNA modifications have been described 00 of dif. For theThe presence of Ψ was first described in ribosomal RNA (rRNA) and transfer RNA (tRNA) , and subA series of studies, conducted mainly in the past two decades, has provided important evidence concerning the functional consequence of changes in RNA pseudouridylation for both physiological and pathological status. This review focuses on these aspects of pseudouridylation, with particular attention to what has been reported in mammalian or human models, or both.Ψ in RNA derives from the C-C glycosidic isomerization of uridine, and the incorporation of C5 into the glycosidic bond  and RluA, and one (PUS10 family) is not found in E. coli. In eukaryotes, pseudouridine synthases can be either stand-alone enzymes  which, although distributed quite uniformly over the lengths of 28S, 18S, 5.8S, and 5S, end up lying close to one another in the rRNA three-dimensional structure, once they are assembled with ribosomal proteins. In rRNA, uridine conversion to Ψ is carried out by enzymatic RNP complexes called boc H/ACA RNPs, each consisting of one H/ACA snoRNA and four core proteins . H/ACA snoRNAs contain a conserved sequence called ´box H´ (standing for hinge) and an ACA box, so called because of the three nucleotides present at their 3′ end. H/ACA snoRNA are structurally characterized by the presence of a double hairpin, each harboring a pseudouridylation pocket, which is specific to a particular target sequence, based on base pairing. After the occurrence of the interaction between the guide RNA and the substrate, this is further stabilized by multiple interactions with small nucleolar ribonucleoproteins (snoRNPs) and dyskerin, which carries out the isomerization of the target uridine .As previously mentioned, pseudouridylation also occurs in snRNA. In eukaryotic snRNA, a number of Ψs are present, and most of them, formation depends on RNA-guided RNP complexes . http://modomics.genesilico.pl) [As reported in the Modomics database (lico.pl) , pseudouAlthough pseudouridylation is more abundant in rRNAs, tRNAs, and other ncRNAs, its presence has recently also been described in mRNA ,20,21. TAlthough a clear functional role of each pseudouridine in RNA is far from being demonstrated, it is known that Ψ, because of the presence of an extra hydrogen bond donor at its non-Watson-Crick edge , is charCurrently, the functional importance of Ψ is well-accepted by the field-specific scientific community. This is due to its highly conserved but widespread localization within important functional RNA domains, and its abundance, and also due to increasingly plentiful evidence originating from different model organisms, ranging from bacteria through yeast to humans. Indeed, for most of the reported modifications, a functional role has been proposed.The main role of pseudouridylation in tRNAs is to increase their stability, thanks to the described chemical properties of Ψ . Ψs are As for rRNA, Ψs are distributed in the rRNA sequence in such a way that, within the mature ribosome three dimensional (3D) structure, they concentrate mainly in the decoding site, mRNA channel, peptidyl transferase center, tRNA binding site, and ribosomal subunit interface (reviewed in ). In theThere is indirect evidence that rRNA pseudouridylation may be important for the physiological maintenance of stemness: embryonic stem cells express high levels of rRNA pseudouridine synthase dyskerin, which has been found to regulate the expression of several stemness factors ,34. In aAlthough most investigations into the functional role of snRNA have been conducted in yeast, a few studies have been performed on human cells. U2 snRNA is the most extensively modified snRNA in humans: it contains more than 10 Ψ residues introduced by sequence-specific guide RNAs . In humaIn yeast, another known site of pseudouridylation is within U6 snRNA, located in the spliceosome core and pseudouridylated by Pus1p during a particular growth phase known as the filamentous growth. Indeed, the absence of the Ψ in U6 snRNA leads to an arrest in filamentous growth . MoreoveAmong ncRNAs, the telomerase RNA component (TERC), which has an H/ACA element, is also pseudouridylated by the pseudouridine synthase dyskerin. Thanks to the new methodology of pseudouridine sequencing (Ψ-seq), it has been demonstrated that TERC has two functionally important Ψ residues , one of Xenopus oocytes [Pseudouridines were found in each mRNA portion, including the 3′ untranslated region (3′ UTR), coding sequence (CDS), and 5′ UTR regions, without specific preferences. Little is known about the functional role of mRNAs pseudouridylation. The presence of the modification seems to affect splicing and translation during in vitro assays. In fact, the presence of Ψs may increase mRNA half-life during heat shock stress , and is  oocytes . Altogether, these findings suggest that Ψ may have different effects on the stability of mRNA and translational control, thus adding another level of post-transcriptional regulation to gene expression. The importance of Ψs in physiological processes may be better appreciated by taking a look at the other side of the same coin: pathology. The first association ever reported between uridine modification and human disease was found in studies focusing on urinary metabolites in cancer patients ,49. Ψ, lTwo of the best examples of a disorder associated with a pseudouridylation defect are X-linked Dyskeratosis Congenita (X-DC) and its more severe form, Hoyeraal-Hreidarsson syndrome; these are two rare inherited syndromes caused by mutations in DKC1, the gene encoding for the pseudouridine synthase dyskerin. X-DC was originally described by dermatologists by virtue of a peculiar muco-cutaneous triad: abnormal skin pigmentation, dystrophy of the nails, and leukoplakia of the oral mucosa . HoweverIn parallel to what has been found for dyskerin and pseudouridylation defects, we and other authors previously observed that, in human cancer, dyskerin expression and rRNA pseudouridylation levels may frequently be higher. In breast cancer, dyskerin expression levels and functions have been correlated with tumor progression and poor patient prognosis, with lower disease-free survival . This scAs described above, altered dyskerin pseudouridine synthase activity has been recognized as a potential trigger for cancer onset, in the case of both inherited syndrome-associated tumors and sporadic malignancies. It is not yet clear, however whether the altered dyskerin expression may be due to genetic mutations or to altered gene expression control. As far as the first hypothesis is concerned, a systematic analysis of the DKC1 gene sequence in mutational hotspots, in human sporadic cancers of different types, showed that DKC1 mutations are not a frequent event, and cannot be considered the cause of reduced dyskerin expression , thus suRecently, pseudouridylation has been implicated in other diseases, in addition to X-DC and cancer. Among these, Ψs have been recognized as regulators of viral latency processes in human immunodeficiency virus (HIV) infections . In partPseudouridylation has also been associated with the pathogenesis of maternally inherited diabetes and deafness (MIDD). In particular, a point mutation in a mitochondrial tRNA seems to prevent the pseudouridylation of one nucleotide, thus altering the tRNA tertiary structure. This may lead to higher tRNA instability, causing deficiencies in mitochondrial translation and respiration. Perturbations in respiratory patterns increase the production of reactive oxygen species that seem to affect more preferentially pancreatic beta cells, neurons, and hair cells in the cochlea, thus causing MIDD .PUS genes may be correlated to diseases, paralleling what occur in the case of DKC1. A known missense mutation in the PUS1 gene has been described to cause a mitochondrial myopathy and sideroblastic anemia (MLASA) [Mutations in  (MLASA) ,81. This (MLASA) . These m (MLASA) . Also, dPUS3 gene [PUS3 was demonstrated to cause an intellectual disorder. Notably, this phenotype was linked to the reduction of pseudouridylation in specific positions on tRNAs [In addition, a form of autosomal recessive mental retardation  is caused by a homozygous mutation in the US3 gene . Shaheenon tRNAs .PUS10 as a shared-risk locus in Chron’s disease ulcerative colitis and celiac diseases [Finally, three different meta-analysis genome-wide association studies identified diseases ,86,87,88In sum, different pseudouridine synthases are causative or participant in the onset of numerous diseases, underlying the importance of their function in the maintenance of cellular homeostasis.As can be seen from this review, the role of pseudouridylation in biological control processes is far from being defined, and should become the subject of many more studies. In fact, it is likely to play a very important role in various physiological and pathological contexts that may involve novel biological concepts such as ribosome diversity, tRNA mediated translational control, and dynamic RNA modification. In these contexts, pseudouridylation may represent an additional level of complexity in the regulation of gene expression, which has been only partially explored up to now.To tackle these issues, the contribution of innovative technical approaches is needed. In this regard, various techniques appropriate for the study of the role of pseudouridylation in the control of gene expression, have become available over the past few years; these are summarized for the reader’s convenience in During the 1980s, different techniques, spanning from liquid chromatography/mass spectrophotometry (LC/MS) to high performance liquid chromatography (HPLC) and capillary electrophoresis, were developed and optimized for Ψ identification See .N-cyclohexyl-N′-beta-(4-methylmorpholinium) ethylcarbodimide (CMC) [In 1993, Bakin and Ofengand set up the first Ψ chemical detection method based on the use of the de (CMC) . CMC hasde (CMC) , Pseudoude (CMC) , and CeUde (CMC) . These tde (CMC)  with theOf notice, the CeU-seq method by Li et al.  is basedIn addition to these next generation sequencing (NGS)-based techniques, the CMC property of binding tightly to pseudouridines was also taken advantage of in mass spectrometry detection. In recent decades, the Limbach group developed and optimized different methods for solving the problem of “silent” psi identification in mass spectrometry . The ligThe CMC is also used in in vitro assays, with the aim of registering pseudouridylation activity on synthetic rRNA substrates ,103. With regard to the application of CMC in Ψ identification, some caution should in any case be exerted, taking into account the reported inconstant efficiency of the CMC binding and cleavage to/from U and G residues . This woThe reported methods that have been recently made available are likely to increase the understanding of the role of RNA modifications, and their connections to the structure and the function of the modified RNA. In this sense, relevant information on the pathogenic mechanisms involved in most of the cited disorders has been obtained. This could hopefully pave the way to the identification of novel therapeutic approaches.


DNA and RNA fractions from whole blood, serum and plasma are increasingly popular analytes that are currently under investigation for their utility in the diagnosis and staging of disease. Small non-coding ribonucleic acids (sRNAs), specifically microRNAs (miRNAs) and their variant isoforms (isomiRs), and transfer RNA (tRNA)-derived small RNAs (tDRs) comprise a repertoire of molecules particularly promising in this regard.In this designed study, we compared the performance of various methods and kits for isolating circulating extracellular sRNAs (ex-sRNAs). ex-sRNAs from one healthy individual were isolated using five different isolation kits: Qiagen Circulating Nucleic Acid Kit, ThermoFisher Scientific Ambion TRIzol LS Reagent, Qiagen miRNEasy, QiaSymphony RNA extraction kit and the Exiqon MiRCURY RNA Isolation Kit. Each isolation method was repeated four times. A total of 20 small RNA sequencing (sRNAseq) libraries were constructed, sequenced and compared using a rigorous bioinformatics approach. The Circulating Nucleic Acid Kit had the greatest miRNA isolation variability, but had the lowest isolation variability for other RNA classes . However, the Circulating Nucleic Acid Kit consistently generated the fewest number of reads mapped to the genome, as compared to the best-performing method, Ambion TRIzol, which mapped 10% of the miRNAs, 7.2% of the tDRs and 23.1% of the osRNAs. The other methods performed intermediary, with QiaSymphony mapping 14% of the osRNAs, and miRNEasy mapping 4.6% of the tDRs and 2.9% of the miRNAs, achieving the second best kit performance rating overall.In summary, each isolation kit displayed different performance characteristics that could be construed as biased or advantageous, depending upon the downstream application and number of samples that require processing.The online version of this article (doi:10.1186/s12864-016-3470-z) contains supplementary material, which is available to authorized users. Biomarkers come in many forms—proteins, nucleic acids, metabolites, small molecules—and can be evaluated as indicators of specific metabolic, physiologic, or pathologic states or conditions. Biomarkers have been used in numerous clinical assays to detect the presence or risk of developing disease. Biomarker assays should be conducted from an easily accessible source with minimally invasive medical procedures. One class of molecules, e.g., small RNAs (sRNAs), with great potential for biomarker utility is micro RNAs (miRNAs). miRNAs are 20-25nts in length and are one class of sRNAs that play vital roles in multiple cellular and developmental processes, primarily via post-transcriptional regulation of gene expression . CurrentmiRNAs are becoming increasingly recognized for their potential to diagnose and stage disease, with cancer being a great example –13. The Methods for RNA characterization can be classified into two major categories: hybridization-based microarray or synthesis/base-extension-based. Earlier sRNA studies mostly consisted of real-time quantitative polymerase chain reaction (RT-PCR) or hybridization-based assays. However, with the advancement of high-throughput sequencing technology, high-throughput sRNA screening has shifted from hybridization-based microarray technology to sRNAseq technology. One of the most considerable advantages that sRNAseq offers over microarrays is that it does not limit the detection of sRNA to a set of previously known targets. sRNAseq begins by constructing a cDNA sequence library reversely transcribed from short sRNA selected via different methods, e.g., size-selected gel electrophoresis. The prepared, indexed and pooled cDNA library can then be sequenced on different massive parallel sequencing platforms. Subsequent bioinformatic analysis of sRNAseq data provides the identification, quantification and differential expression of sRNAs. Since size-selection is agnostic to sRNA class , it has the potential to capture many species of sRNAs short in length, including miRNAs, miRNA isoforms (isomiRs) , 18, traAlthough sRNA isolation and sRNAseq have primarily been used to quantify miRNAs, it is not completely understood as to what extent RNA isolation and sRNA library kits capture other types of sRNAs. Furthermore, there are many commercially available sRNA extraction kits, and the field would greatly benefit from a carefully designed study to evaluate and compare kit efficiency and reliability. Motivated by these reasons, we benchmarked five popular extraction kits for performance in aiding ex-sRNA analysis.Five miRNA extraction kits were obtained: Qiagen Circulating Nucleic Acid Kit (NAK), ThermoFisher Scientific Ambion TRIzol LS Reagent, Qiagen miRNEasy, QiaSymphony RNA extraction kit and the Exiqon MiRCURY RNA Isolation Kit. Highly pure diethyl pyrocarbonate (DEPC)-free and nuclease-free water were purchased from Qiagen.After obtaining informed consent, blood from a single subject was collected in pre-chilled tubes containing ethylenediamine-tetra-acetate and placed on ice. Samples were centrifuged at 3,000 x g for 10 min, aliquoted and stored at -80 °C until further analyses were performed. sRNAs from the serum of a single subject were extracted four times per kit. For each replicate, 200 μl of serum was used. RNA was extracted with the miRNEasy Serum/Plasma Kit, QIAamp Circulating NAK, miRCURY RNA Isolation—BioFluids Kit, and with Ambion TRIzol LS reagent according to manufacturer’s instructions. RNA was extracted on the QIAsymphony SP  using the QIAsymphony RNA Kit  and protocol RNA_CT_400_V7, which incorporates DNase treatment. The resulting RNA was eluted with RNase free water and stored at −80 °C until use. Samples were initially quantified using a Qubit fluorometric RNA assay . Additional analyses of purity and total RNA quantification were performed using a NanoDrop spectrophotometer (Thermo Scientific) and Agilent RNA 6000 Pico chip (Agilent) according to the manufacturer’s protocol using the reagents, chips, and ladder provided in the kit. The RNA concentration were measured using Qubit. However, since the targets are extracellular miRNA, the concentration were often below the detection threshold. Additionally, we verified the RNA concentration using Agilent Bioanalyzer. The concentrations are reported in Additional file RNAseq was performed by the Vanderbilt Technologies for Advanced Genomics core (VANTAGE). Libraries were prepared using the TruSeq Small RNA sample preparation kit . The sRNA protocol specifically ligates RNA adapters to mature miRNAs harboring a 5’-phosphate and 3’-hydroxyl group as a result of enzymatic cleavage by RNase III processing enzymes, e.g., Dicer. In the first step, RNA adapters were ligated onto each end of the sRNA, and reverse transcription was used to create single-stranded cDNA. This cDNA was then PCR amplified for 18 cycles with a universal primer and a second primer containing one of 20 uniquely indexed tags to allow multiplexing. Size-selection of the cDNA constructs was performed using a 3% gel cassette on the Pippin Prep  to include only mature miRNAs and other sRNAs in the 5–40 bp size range and to remove adapter-adapter products. The resulting cDNA libraries then underwent a quality check on the Agilent Bioanalyzer HS DNA assay  to confirm the final library size and on the Agilent Mx3005P quantitative PCR machine using the KAPA library quantification kit  to determine concentration. A 2 nM stock was created, and samples were pooled by molarity for equimolar multiplexing. From the pool, 10 pM of the pool was loaded into each well of the flow cell on the Illumina cBot for cluster generation. The flow cell was then loaded and sequenced on the Illumina NextSeq500 to obtain at least 15 million single end (1x50 bp) reads per sample. The raw sequencing reads in BCL format were processed through CASAVA-1.8.2 for FASTQ conversion and de-multiplexed. The RTA chastity filter was applied, and only PF (pass filter) reads were retained for further analysis.We implemented a custom in-house data analysis pipeline  for sRNAThe map and cluster analysis were performed using Heatmap3  to evaluThe yields of small RNA were measured by Agilent Bioanalyzer and varied greatly (by at least one order of magnitude) (Table S1). We computed the coefficients of variance (CV) of yields within each kit as assessments of the repeatability of yields. MiRCURY kit achieved the lowest CV of 0.04, followed by Ambio TRIzol (CV = 0.29), QiaSymphony (CV = 0.31), Circulating NAK (CV = 1.05) and miRNAEasy had the highest CV of 1.31.The performance of each kit was first evaluated for sequencing quality and aligned reads. The factors we considered included: total number of reads sequenced, number of reads aligned to each of the sRNA classes, variation in number of reads, multiple alignment issues (reads perfectly mapped to multiple) genomic locations, and unmapped reads  to measure the repeatability of the extraction kits. For miRNAs, QiaSymphony achieved the highest ICC of 0.74; for isomiRs, tDRs, and osRNA, Circulating NAK achieved the highest ICCs of 0.79, 0.97 and 0.96, respectively. We also computed the ICC across all 20 samples to capture the overall homogeneity. It is worth noting that miRNAs had the worst overall ICC of 0.28, followed by isomiRs and osRNA. The mean ICC for tDRs across all isolated methods was 0.9  the number of reads sequenced and aligned to each species of sRNA; 2) the repeatability of the replicates within each kit; 3) the number of sRNAs detected. While each kit was used per manufacturer’s instructions, and the amount of nucleic acid input into each replicate for each kit was the same, there were notable kit-specific differences in RNA yield. Each kit designated a different volume of water for elution which is reflected in the concentration and total amount of RNA yielded, and may impart a certain level of bias in our results.All five kits had poor mapping rate to human genome (<50%), suggesting all kits captured a large amount of miscellaneous material. The phenomenon of high unmapped rates is quite common for high throughput sequencing experiments, however the scale of the unmapped rate depends on the sample quality, type of sequencing, and the abundance of the source RNA. In exome sequencing, it had been shown that the unmapped rate is anywhere from 5 to 19% . For smaAmbion TRIzol had the most reads annotated as sRNAs; however, the observed increase in genomic alignments did not translate into a higher number of detected sRNAs, suggesting either there was selection bias for Ambion TRIzol or the reads were dominated by miRNAs with high expression. Reproducibility was evaluated using cluster analysis and ICC, and Circulating NAK had the highest repeatability overall. In terms of the number of ex-sRNAs detected, Circulating NAK detected the most ex-osRNAs, ex-isomiRs and also performed well for ex-tDRs. miRCURY detected the highest number of ex-osRNAs. However, currently, ex-osRNAs are the least studied sRNAs and their biological functions remain largely unknown.In sRNA sequencing, especially extracellular sequencing, the detection threshold of miRNA can significantly affect the number of sRNAs detected. In our study, for demonstration purposes, we have used detection threshold of read counts >1, >5, >10, >15, >20. In reality, sRNA detected with less than 10 read counts are difficult to replicate by RT-PCR. For reliable sRNA detection, it is recommended to set a detection threshold with read count > 10 .In conclusion, our data suggest that each isolation kit displays inherent performance characteristics that may be construed as biased, or advantageous, depending upon the downstream application and number of samples that require processing. The Circulating NAK consistently generated the fewest number of reads mapped to the genome, in comparison to the best performing method, Ambion TRIzol, where 10% of the detected miRNAs, 7.2% of the tDRs and 23.1% of osRNAs were mapped. The performance of the other methods was intermediary, with QiaSymphony mapping 14% of osRNAs and miRNEasy mapping 4.6% of tDRs and 2.9% of miRNAs, making it the second best performing kit in terms of sRNA extraction efficiency. However, the Circulating NAK kit detected the highest number of miRNAs. These data suggest that the choice of sRNA isolation kits for ex-sRNA analysis is not trivial and may introduce significant bias that must be addressed when interpreting outcomes.


In the last decade, although studies on exosomal microRNAs (miRNAs) derived from serum and other body fluids have increased dramatically; the contents and biological significance of serum exosomes under normal conditions remain unclear. In the present study, we profiled the small RNA content of mouse serum exosomes (mSEs) using small RNAseq and found that fragments of transfer RNAs (tRNAs) and miRNAs were the two predominant exosomal RNA species, accounting for approximately 60% and 10% of mapped reads, respectively. Moreover, 466 known and 5 novel miRNAs were identified from two independent experiments, among which the five most abundant miRNAs  accounted for approximately 60% of all the aligned miRNA sequences. As inferred from the identities of the well known cell- or tissue-specific miRNAs, mSEs were primarily released by RBCs, liver and intestinal cells. Bioinformatics analysis revealed over half of the top 20 miRNAs by abundance were involved in inflammatory responses and further in vitro experiments demonstrated that mSEs potently primed macrophages towards the M2 phenotype. To the best of our knowledge, this is the first study to profile small RNAs from mSEs. In addition to providing a reference for future biomarker studies and extrapolating their origins, our data also suggest the roles of mSEs in maintaining internal homeostasis under normal conditions. Exosomes are 30–150 nm membrane-bound vesicles that are formed in endosomal multivesicular compartments and secreted into the external milieu when these compartments fuse with the plasma membrane . Almost In the last decade, numerous studies have been attempt to identify biomarkers from circulating nucleic acids, especially miRNAs . ExosomeOther than their miRNA content, an even more active area of exosomes research is their roles in intercellular communication. Since the finding that exosomes carry genetic materials, mainly mRNAs and miRNAs, and mediate genetic exchange between cells , accumulin vitro experiments. These data will not only contribute to biomarker studies on serum exosomal miRNAs, but also will deepen our understanding of their biological significance.Next-generation sequencing (NGS) is the best way to comprehensively and quantitatively analysis exosomal RNA content and has been successfully applied to profile exosomal RNA from multiple cultured cells  and huma8 particles/ml.Considering the limited volume of mouse serum and low yield of repeated ultracentrifugation, ExoQuick, a method based on precipitation, was selected to isolate mSEs. Consistent with the fundamental characteristics of exosomes , TEM anaWestern blot was performed to determine the expression of two exosomal markers TSG101 and CD63. As shown in Figure The size distribution and concentration of RNA extracted from the isolated particles were determined using the Bioanalyzer. Total RNA chip analysis showed that the lengths of the major mSEs RNAs were less than 200 nt, with a small portion of RNAs greater than 500 nt in length Figure . Small RSmall RNA sequencing was performed to profile the small RNA content of the mSEs. To evaluate the biological replication, two small RNA sequencing libraries were prepared individually from two batches of total exosomal RNA (7–9 mice per batch). The data produced during library construction are shown in From 2 libraries, 13,239,168 and 12,332,524 raw single-end reads were generated, respectively . This threshold excluded those miRNAs with only one read in both samples. Hence, 427 and 448 known miRNAs were identified, among which 409 were common between samples A and B, and 18 and 39 were unique to samples A and B, respectively Figure . In totaTo determine the reproducibility of biological replicates, linear regression analyses were performed on the log2-transformed read counts derived from two independent RNAseq experiments. The Pearson correlation coefficient between samples A and B was 0.9682 Figure . Three mRecent studies have analyzed the expression patterns of miRNAs in blood cells , 17–19 aIn the over 25 million reads obtained from small RNAseq, only five novel miRNAs were identified . CompareTo analyze the potential biological functions of mSEs, the targets of the top 20 miRNAs, which accounted for 86% of total mapped reads, were predicted using the microRNA target filter in the IPA software. With the selection of ingenuity expert findings and experimentally observed, 118 targets of the top 20 miRNAs were identified . Core anMacrophages are essential components of innate and acquired immunity and play central roles in inflammation and host defense , 32. PlaWhole serum, exosomes-depleted serum, or serum exosomes suspended in an equal volume of media were then added to RAW264.7 cells. After 12 hours, a panel of cytokines, including 4 M1 markers and 3 M2 markers, was detected by qPCR. Whole serum treatment significantly increased the expression of all the three M2 markers, Arg1, IL-10 and TGF-β1, whereas expression of the M1 markers, iNOS, IL 1β and TNF-α, but not IL-6, were significantly suppressed. Notably, levels of Arg1 and iNOS, two representative markers of the M1 and M2 phenotypes , were 2,in vitro experiments demonstrated that mSEs could prime macrophages towards a M2 phenotype. To the best of our knowledge, this is the first systemic and functional study of mSEs. In addition to providing a reference for future functional and biomarker studies on serum exosomes, our data also suggested their potential roles in maintaining homeostasis of the internal environment under normal physiological conditions.In the present study, we found that tRNA fragments and miRNAs were the two predominant small RNA species of mSEs and accounted for approximately 60% and 10% of mapped reads, respectively. Moreover, based on existing knowledge of cell- and tissue-specific miRNAs, our data suggested that the mSEs were mainly derived from RBCs, liver and intestines. Bioinformatics analysis revealed that more than half of the top 20 mouse serum exosomal miRNAs were involved in inflammatory responses, and further The existing RNAseq studies on the small RNA contents of serum exosomes are currently confined to humans , 15. As Several explanations for this discrepancy could exist, such as different methods for exosomes preparation and RNA extraction, different small RNAseq library building methods, and different sequencing platforms and data processing approaches, but we found that the different length distributions of the exosomal RNA could be the main reason. The lengths of the mouse serum exosomal RNAs ranged from 15 to 200 nt, and the small RNAs (15–40 nt) were approximately 30% of the total exosomal RNA Figure . HoweverAnother point worth noting is these detected tRNAs were tRNA fragments. The length that can be detected by the small RNAseq is less than 40 nt, whereas the lengths of mature tRNAs are between 70 and 90 nt. Many experiments have shown that the exosomal RNA component is very stable ; therefoTissue- and/or cell-specific expression of miRNAs is fundamental to biomarker studies, but to date, the contributions of different tissues or cell types to serum exosomes remains unclear. The only such efforts to understand these questions were implemented by Hunter et al., who used flow cytometry and cell-specific surface markers. These authors found that the majority of the peripheral blood microvesicles (MVs) are platelet-derived . Our datFurthermore, for the first time, our data demonstrated that exosomes are released into the body fluid by almost all types of tissues and blood cells, such as liver, muscle, intestine, heart and myeloid cells Table . ElevatiThe most interesting finding of this study was that serum exosomes from normal mice primed the macrophages towards the M2 phenotype Figure . The conMiRNAs are considered to be the main functional components of exosomes , and accAn unresolved puzzle for exosomes studies is whether the miRNA content or a specific miRNA in the exosomes is sufficient to exert effective biological functions, even though the accumulating studies have reported that the biological functions of exosomes could be mediated by even a single type of miRNA , 48. As Taken together, this study established a reference for biomarker studies of mouse serum exosomes, proposed the likelihood of their origin, and provided the first evidence that serum exosomes could prime the macrophage towards an M2 phenotype under normal conditions. Therefore, besides hormones and cytokines, serum exosomes can contribute to the homeostasis via their roles in the intercellular communications. To comprehensively understand the molecular mechanism underlying their multiple biological functions, further studies are needed to profile the other contents in the serum exosomes, especially other RNA species and proteins.The experimental design and procedures involving animals and their care were conducted according to the National Institutes of Health Guide for Care and Use of Laboratory Animals and were approved by the Animal Experimental committee of Jinan University.Male C57BL/6 mice (10 weeks old) were purchased from Medical Experimental Animal Center of Guangdong province. Anesthesia was induced with 1% chloral hydrate. After collecting by left ventricular puncture, blood was placed at room temperature (RT) or 37°C for 1 h to allow it to clot. Afterwards, samples were centrifuged at 500 g at 4°C for 10 min, the clear serum was amber and those with obvious hemolysis (pink or faint red) were discarded at this stage. Serum from 5 to 8 mice was then pooled and centrifuged at 500 g for 10 min and followed by another 10,000 g for 20 min at 4°C, the supernatant was immediately aliquoted and stored at −80°C.RAW264.7 cells, a murine macrophage cell line, were obtained from the American Type Culture Collection  and cultured in high glucose DMEM medium containing 10% heat-inactivated fetal bovine serum and antibiotics (100 U/mL penicillin and 100 U/mL streptomycin) . Cells were incubated at 37°C in a humidified atmosphere of 5% CO2 in air and the medium was changed every 2–3 days. The cultures were passaged when they reached 80% confluence.An ExoQuick kit  was used to isolate mSEs per the manufacturer's instructions with minor modifications. Briefly, frozen mouse serum was thawed on ice and centrifuged at 21,000 g for 15 minutes at 4°C to remove debris. The supernatant was then transferred to a new tube, and 1/4 volume EXOQ5A-1 solution was added and mixed gently. After incubation at 4°C for 2 h, the mixture was centrifugation at 1,500 g for 30 minutes, and the supernatant was removed by aspiration. After another 5 min centrifugation to remove the residual supernatant, pellet-containing exosomes was re-suspended in nuclease-free water or corresponding buffer for RNA or protein extraction.Size distribution and concentration of the isolated mSEs were measured by NTA as described previously . For calFor morphology observation, electron microscopy samples of mSEs were prepared as described by Thery et al. . ExosomeExosomal protein was extracted with RIPA lysis buffer . The protein concentration was determined by BCA assay . Forty-microgram protein was separated by 12% sodium dodecyl sulfate-polyacrylamide gel electrophoresis and electroblotted onto a PVDF membrane . The membrane was blocked with 5% bovine serum albumin for 1 h at RT, and then incubated overnight with antibodies against CD63  and TSG101 . After 3 times wash and incubation with HRP-conjugated secondary antibodies, the proteins were detected by enhanced chemiluminescence .Total RNA was extracted from the serum exosomes using SeraMir kit  according to the manufacturer's protocol. Purity and quantity of the isolated exosomal RNA were determined using Nano Drop . Integrity and size distribution of exosomal RNA were assessed by using Agilent Bioanalyzer 2100 system combined with RNA 6000 pico kit and small RNA kit .® Small RNA Library Prep Set for Illumina®  was used to generate exosomal small RNA libraries for next-generation sequencing on the Illumina platform. Per manufacturer's recommendations, total exosomal RNA was subjected to sequential 3′ and 5′ adapter ligations followed by reverse-transcription into a cDNA library. After PCR enrichment, the miRNA library was size (140 bp) selected from an 8% PAGE-gel and the library quality was assessed on the Agilent Bioanalyzer 2100 system . The qualified cDNA libraries were used for cluster generation and sequenced on the Illumina HiSeq 2500 platform  to obtain 125 bp single-end reads.The NEB NextA flow chart describing the sequencing data analysis is shown in Figure ® Premix Ex Taq™ II, Perfect Real Time kit  on a CFX96 PCR system . The relative expression of the detected miRNAs was expressed as ΔCT = 35- mean CTmiRNA.The exosomal miRNAs were detected by qRT-PCR as described previously . MiDETEC−ΔΔCt.Total RNA was extracted from RAW264.7 cells using Trizol . RNA quantity and quality were determined by a NanoDrop 1000 spectrophotometer . One microgram of total RNA was used to synthesize the cDNA via the Superscript First-Strand Synthesis System for RT-PCR . Primer sequences used for subsequent QPCR analysis were provided in The prediction of targets for exosomal miRNAs and following molecular and cellular functional analyses of these predicted targets were carried out by using the Ingenuity Pathway Analysis (IPA) software .Isolated mSEs were labeled with 10 μM of nucleic acids specific dye SYTO  for 20 min at 37°C, and washed three times with 100 kDa MWCO UltraCentrifugal Filters  to remove the unincorporated dye. The labeled exosomes were suspended in 200 μl PBS and added immediately to RAW264.7 cells; meanwhile, the flow through from the last wash was added as a control. After 2 h of incubation at 37°C, the cellular nuclei were stained with Hoechst 33258 (8 μg/ml) for 5 min, and exosomes uptake was visualized by fluorescence microscopy .t test or one-way analysis of variance using SPSS10.0 software. P values of less than 0.05 or 0.01 were regarded as statistically significant. Unless otherwise specified, all assays were performed in triplicate.The data are presented as the mean ± SEM. Comparisons between means were assessed by unpaired Student's


Fragile X premutation disorder is caused by CGG triplet repeat expansions in the 5′ untranslated region of FMR1 mRNA. The question of how expanded CGG repeats cause disease is a subject of continuing debate. Our work indicates that CGG-repeat structures compete with regulatory BC1 RNA for access to RNA transport factor hnRNP A2. As a result, BC1 RNA is mislocalized in vivo, as its synapto-dendritic presence is severely diminished in brains of CGG-repeat knock-in animals (a premutation mouse model). Lack of BC1 RNA is known to cause seizure activity and cognitive dysfunction. Our working hypothesis thus predicted that absence, or significantly reduced presence, of BC1 RNA in synapto-dendritic domains of premutation animal neurons would engender cognate phenotypic alterations. Testing this prediction, we established epileptogenic susceptibility and cognitive impairments as major phenotypic abnormalities of CGG premutation mice. In CA3 hippocampal neurons of such animals, synaptic release of glutamate elicits neuronal hyperexcitability in the form of group I metabotropic glutamate receptor–dependent prolonged epileptiform discharges. CGG-repeat knock-in animals are susceptible to sound-induced seizures and are cognitively impaired as revealed in the Attentional Set Shift Task. These phenotypic disturbances occur in young-adult premutation animals, indicating that a neurodevelopmental deficit is an early-initial manifestation of the disorder. The data are consistent with the notion that RNA mislocalization can contribute to pathogenesis. The fragile X premutation finds expression in two distinct disease manifestations. Young premutation carriers may present with seizure activity and cognitive disturbances, whereas advanced-age patients may experience intention tremor and gait ataxia, a condition known as FXTAS. In contrast to FXTAS, the early-onset phase of fragile X premutation disorder remains poorly understood. We discovered that in brains of CGG-repeat knock-in animals (a premutation mouse model), regulatory BC1 RNA is mislocalized, as its presence at synapto-dendritic domains is severely diminished. Lack of BC1 RNA is known to cause epileptogenesis and cognitive dysfunction, and we report that such phenotypic alterations are hallmarks of young premutation animals. The data are congruous with a potential role of RNA localization impairments in pathogenesis.In mammalian neurons, translational control mechanisms play important roles in the input-dependent regulation of local protein synthesis in postsynaptic dendritic microdomains . A key rRegulatory BC RNAs are small cytoplasmic RNAs (scRNAs) that operate in the translational regulation of local protein repertoires in neurons . DendritFragile X premutation disorder, a common inherited disability, is caused by CGG trinucleotide repeat expansions, in the range of 55–200 repeat units, in the 5′ untranslated region (UTR) of FMR1 mRNA . The FMRHow does CGG-repeat RNA cause cellular dysfunction and disease? FMR1 mRNA premutation CGG repeats can sequester RNA binding proteins (RBPs), as a consequence making them unavailable to perform their normal cellular functions . CGG repWe hypothesized that premutation FMR1 mRNA is pathogenic because its 5′ CGG-repeat stem-loop structures feature noncanonical nucleotide interactions that are similar to those in 5′ BC RNA dendritic targeting elements (DTEs). Accordingly, our working hypothesis made two specific predictions: (1) CGG RNA competition for hnRNP A2, which is required for BC1 RNA transport , will co105 was used to generate (CGG)105 RNA (We used plasmid pBCX607 to generate full-length rat BC1 RNA  and plas)105 RNA .m Tris-borate, pH 8.3, in the presence of 15 mm MgCl2 at room temperature for 12 h at 15 V  were heated for 10 min at 70°C, cooled for 5 min at room temperature, and incubated together with proteins in binding buffer  for 20 min at room temperature.Native PAGE gels were run on 8% polyacrylamide gels (ratio acrylamide/bisacrylamide 19:1) in 90 m at 15 V . EMSA an at 15 V . 32P-labm) was pre-incubated with BC200 RNA or BC1 RNA at 37°C for 15 min, at which point (CGG)105 repeat RNA was added at a 1:3 molar ratio to the respective BC RNA. Aliquot samples were collected at the time points indicated in For EMSA competition experiments, recombinant hnRNP A2  in murine and human premutation cells, levels of Fmr1/FMR1 mRNA are significantly increased, up to 8-fold in human carriers with CGG expansions in the 180-repeat range ; and (2)eviously .n knock-in (KI) animals were initially generated by exchanging the murine (CGG)8 repeat with a human (CGG)98 repeat 8 repeat . Subseque colony . CGG andm of each primer, 250 μm of dNTPs, 2.5 m betaine (Sigma-Aldrich), and 1.25 U KAPA2G Hotstart. The forward primer sequence was 5′-CGGGCAGTGAAGCAAACG-3′, and the reverse primer sequence was 5′-CCAGCTCCTCCATCTTCTCG-3′. PCR steps were 5-min denaturation at 95°C, followed by 35 cycles of denaturation for 15 s at 95°C, annealing for 15 s at 55°C, and elongation for 1.5 min at 72°C. Final extension was performed for 5 min at 72°C. DNA samples were resolved on 1.5% agarose gels.CGG animals were genotyped for CGG-repeat lengths as follows . Genomicpost hoc, i.e., after completion of experiments). A validated (CGG)180 animal colony was maintained through CGG repeat length monitoring 18Such exclusion was also mandated by FMRP expression level considerations. FMRP levels may be reduced, depending on CGG repeat length, in premutation human subjects and animal models . A reduc180 animals were ∼25% lower than respective average levels in age-, sex-, and strain-matched WT animals. Also in agreement with the earlier data 180 animals were within the range of WT animal levels. (CGG)180 animals whose brain FMRP levels were outside the WT range were not admitted into analysis. Brain FMRP levels were established post hoc for experimental animals. FMRP levels were determined by Western blot (see below) using antibody 4317  or antibody ab17722  as described  to 250 units . In agreier data , we obseescribed .m): 124 NaCl, 5 KCl, 1.6 MgCl2, 2 CaCl2, 26 NaHCO3, and 10 d-glucose. Slices were continuously perfused with ACSF bubbled with 95% O2/5% CO2 to maintain the pH near 7.4. The temperature was maintained at 33–35°C.Transverse hippocampal slices (400 μm) were prepared as described . They weAB_532292, 1:1000 dilution). Membranes were washed and incubated for 1 h with horseradish peroxidase–conjugated anti-rabbit and anti-mouse antibodies (Kindle Biosciences). Chemiluminescence levels were established using a Kwik Quant Imager (Kindle Biosciences). Bands were quantified using ImageJ software. FMRP levels were normalized to levels of γ-tubulin, which was used as a loading control.Brains were collected and rinsed three times in PBS. They were homogenized in RIPA lysis buffer (Thermo Fisher Scientific) containing protease inhibitors (Roche) with a Dounce tissue homogenizer. Supernatants were collected after centrifugation , and protein concentrations were determined using the Bradford Protein Assay (Bio-Rad). Brain extracts (30 μg per well) were resolved by SDS-PAGE on 10% gradient polyacrylamide precast gels (Bio-Rad) and transferred to nitrocellulose membranes. Membranes were blocked for 1 h at room temperature with 5% nonfat dry milk (Bio-Rad) in Tris-buffered saline (TBS) with 0.01% Tween 20. Membranes were incubated overnight at 4°C with primary antibodies in blocking buffer. Primary antibodies were rabbit anti-FMRP  and mouse anti–γ-tubulin  in 10 mm Tris/HCl, 500 mm NaCl, pH 7.5, for 45 min at 37°C), a second wash in 4 liters of 2× SSC at 45°C for 1 h, and a high-stringency wash in 4 liters of 0.1× SSC, 0.05% sodium pyrophosphate, and 14 mm 2-mercaptoethanol for 3 h at 50°C, followed by an overnight wash in the same buffer at room temperature. Chemicals were from Sigma-Aldrich.CGG and WT animals were perfusion-fixed with 4% formaldehyde  in PBS, brains were sectioned coronally at 10–12 μm, and specimens were postfixed by UV illumination . Tissue For data acquisition, we used a Microphot-FXA microscope , in a water bath at 95°C for 30 min, after which sections were allowed to cool in the bath (by turning bath off) for 30 min. Sections were permeabilized using 0.2% Triton X-100 in PBS for 10 min at room temperature. Sections were subsequently incubated in superblock .Immunocytochemistry was performed with coronal brain sections prepared as described above (In situ hybridization). Antigen retrieval was performed by heating sections in citrate buffer , anti-MAP2, monoclonal, raised in mouse .Primary antibodies (incubation in superblock overnight at 4°C) were as follows: anti-synaptophysin, monoclonal, raised in rabbit  and incubated with species-specific secondary antibodies in 0.01 m PBS with 0.05% Tween 20, 1% BSA, 1.5% normal goat serum, and 1% sodium azide for 2 h at room temperature. Secondary antibodies were used as follows: anti-rabbit labeled with Alexa Fluor 488  for synaptophysin-labeled sections, and anti-mouse labeled with Alexa Fluor 594  for MAP2-labeled sections. Background labeling was ascertained by performing experiments in the absence of primary antibodies.The next morning, sections were washed four times in 0.01 Microscopy was performed on a Microphot-FXA microscope Nikon; . The folm). Voltage signals were displayed on an oscilloscope  and digitized and stored on an Intel Pentium-based computer using a Digidata 1322A converter controlled by pClamp 8 software (Molecular Devices).Hippocampal slices were allowed to recover from the isolation procedure for at least 1.5 h. Intracellular recordings were performed in CA3 pyramidal cells using an Axoclamp 2A amplifier (Molecular Devices). Electrodes were pulled with thin-walled glass tubing (World Precision Instruments) and had resistances of 30–50 MΩ when filled with potassium acetate (2 A antagonist bicuculline (50 μm). The NMDAR antagonist (RS)-CPP (20 μm) was added to the perfusate of those hippocampal slice preparations that did not transition from short bursts to prolonged bursts within 60 min of bicuculline application. The group I metabotropic glutamate receptor 1 (mGluR1)-selective antagonist (S)-(+)-α-amino-4-carboxy-2-methylbenzeneacetic acid  and the mGluR5-selective antagonist 2-methyl-6-(phenylethynyl)-pyridine hydrochloride  were used to test mGluR dependence of epileptiform activities (obtained from Tocris Bioscience).Pharmacological agents were used as follows. Baseline epileptiform activities for experiments in WT- and CGG-animal hippocampal slices were elicited by continuous bath perfusion of the GABAData analysis was performed as follows. Durations of individual synchronized discharges were measured from the beginning of the first action potential to the repolarization of the last action potential of the discharge. Membrane potentials were kept within a few millivolts throughout the experiment. Frequency histograms included the durations of all synchronized discharges that were recorded in 6-min periods for each slice under the respective experimental conditions. Based on the distribution of the synchronized discharge durations reported previously Dbi, “shoAudiogenic seizures were induced as follows . 19–21-dTo examine whether audiogenic seizure induction required de novo protein synthesis, CGG mice were injected i.p. with 75 mg/kg anisomycin 1 h before auditory stimulation. To examine dependence of group I mGluR signaling, CGG mice were injected i.p. with 40 mg/kg mGluR5 antagonist MPEP (Bio-Techne) 30 min before auditory stimulation. Doses of anisomycin and MPEP used here were the same as previously reported with BC1 knockout (KO) mice .Animals were scored for self-grooming behavior as described . For habThe Attentional Set Shift Task (ASST) protocol was carried out as recently described for BC1 KO animals . It has A Plexiglas apparatus was used in which a crosswise moveable divider gate separated the holding area from the testing area, the latter equipped with an immovable central divider that split the area lengthwise into two compartments. In initial shaping sessions, animals were trained to retrieve a food reward  from two clay pots in the testing area, one on either side of the central divider. Animals were allowed to retrieve rewards from bowls that were initially free of scent, digging medium, or excess texture. In the following shaping session, the bowls were filled with shredded tissue as a digging medium, and the rewards were placed on top of the medium. In the final shaping session, the rewards were buried in the digging medium.In discrimination training sessions, animals had to learn to use up to three classes of discriminanda (dimensions) in their reward retrieval strategies: the textures of the bowls’ outer surfaces, the digging media in which the rewards were buried, and the scents associated with the digging media and thus the bowls. In four phases over a 3-d period, animals engaged in a total of nine learning sessions that included Simple Discrimination (SD) Learning, Compound Discrimination (CD) Learning, and Conflict Learning sessions . Phase 1Animal performance was scored as follows. For a successful completion of a training session, i.e., to reach criterion, an animal had to make a minimum of 8 correct choices in 10 consecutive trials. Scored were (a) the number of incorrect choices  and (b) the number of trials needed to complete the session . An animal that did not reach criterion was excluded from analysis.If significant differences between animal groups were detected in Conflict Learning sessions, the experimental results were analyzed for the types of error committed . We diffSpontaneous alternation was analyzed in T-maze experiments . An animSPSS Statistics (IBM), SAS , and Statistica (StatSoft) software were used for statistical analyses. Adobe Illustrator (Adobe Systems) and Prism (GraphPad Software) was used to generate graphs.The Cox proportional hazards regression model was used to analyze ASST data . The prepost hoc analysis) was used to examine RNA localization data. One-way ANOVA (with post hoc Tukey HSD test) was used to analyze intracellular recordings data 180 repeats  repeats . These mpairment , are recThe group of regulatory BC RNAs includes primate BC200 RNA and rodent BC1 RNA . Both RN105 repeat RNA with hnRNP A2 resulted in an analogous but non-identical shift to lower mobility. We next pre-incubated hnRNP A2 with BC200 RNA for 15 min. After addition of (CGG)105 repeat RNA (molar ratio to BC200 RNA 1:3), we observed a complete displacement of BC200 RNA from hnRNP A2 in less than 10 min 105 repeat RNA decreased.We used electrophoretic mobility shift assay (EMSA) analysis  to ascern 10 min A. Over t105 repeat RNA was added at a 1:3 molar ratio to BC1 RNA. BC1 RNA was completely displaced from hnRNP A2 within 30 min 105 repeat RNA decreased, a result analogous to the one obtained with BC200 RNA We performed in situ hybridization  with bra180 Fmr1 mRNA, the dendritic localization of BC1 RNA is severely reduced, while somatic retention is significantly increased . The data indicate that BC1 RNA expression levels are unaltered in CGG animal brains.Phenotypic alterations in BC1 KO animals  are caus180 Fmr1 mRNA is a feature specific to regulatory BC1 RNA or is rather a general feature that can also be observed with dendritic mRNAs. To address this question, we performed in situ hybridization directed at dendritic MAP2 mRNA AP2 mRNA . We obseAP2 mRNA . In CA1,AP2 mRNA A, B: in AP2 mRNA , MAP2 mR180 Fmr1 mRNA, the subcellular localization of endogenous BC1 RNA is severely compromised, as it is barely detectable in synapto-dendritic regions where it is abundant in WT neurons. Lack of BC1 RNA causes neuronal hyperexcitability and cognitive impairment  30 min after application of bicuculline (Addition of the respective mGluR1 and mGluR5 blockers LY367385 and MPEP resulted in a complete reversion of prolonged to short discharges (average duration: 0.640 ± 0.011 s) within 60 min .n = 6). After application of bicuculline, average burst durations remained stable at <1.5 s, and application of group I mGluR blockers had no effect on the duration of synchronized discharges  seizure activity. In the absence of BC1 RNA, epileptic activity following auditory stimulation takes the form of generalized, tonic-clonic seizures . We now Upon auditory stimulation (120 dB), CGG animals within seconds initiated wild running and jumping, activity that was followed (within 1 min) by clonic-tonic convulsions. 83% of CGG animals underwent such seizures, in comparison with 84% of BC1 KO animals . The ageImpaired cognitive competence has recently been reported for animals lacking regulatory BC1 RNA . Our wor180 animals for such alterations. In a self-grooming test, the cumulative time an animal spent in all-body grooming activity was recorded over a 10-min time window. We found that CGG mice spent significantly more time self-grooming than WT mice  and the CD Learning session . Initial discrimination learning was also found to be impaired in BC1 KO animals  Learning session was sage (reward-predictive) and cinnamon. Additional stimulus pairings . The results reveal impaired cognition in CGG animals: when presented with changed external contingencies, they continued to apply a previously adopted strategy that had become inappropriate in the new situational context.In the subsequent Conflict Learning session, stimulus pairings remained unchanged except that cinnamon rather than sage was now the reward-predictive odor. The performance of CGG animals was significantly impaired in this session, as indicated by the higher number of ETC and TTC in comparison with WT mice (p = 0.85). Also, in the following session Conflict Learning 2 (rosemary reward-predictive), CGG animal performance was not significantly different from that of WT animals (p = 0.46). It appears that additional training in Phase 2, performed on the same day as the preceding CD Learning 1 and Conflict Learning 1 sessions, has improved CD and conflict learning performance of CGG animals to a degree that it was now not significantly different from WT animal performance.All stimuli were changed in Phase 2 of the ASST analysis, with odor remaining the reward-relevant dimension . However, in the following session Conflict Leaning 3 (nutmeg reward-predictive), CGG animals performed significantly worse than WT animals (p = 0.032). The results indicate persistently impaired cognitive control as CGG animals adhere to outdated and therefore inappropriate response strategies.The following day, Phase 3 presented animals with another all-change scenario of novel stimuli. Odor continued to be the reward-relevant dimension in an oregano/nutmeg pairing. In session CD Learning 3 (oregano reward-predictive), CGG and WT animals performed similarly in terms of ETC and TTC (p = 0.24). In the following session Conflict Leaning 4 (sand reward-predictive), we also did not observe significant differences between CGG and WT animal performance (p = 0.11). The results indicate that in Phase 4, conflict learning performance has improved as a result of extended training. The ASST assesses prefrontal cortical function , the digging medium became the reward-relevant dimension with a perlite/sand pairing. In session CD Learning 4 (perlite reward-predictive), CGG and WT animal performance was similar  or p = 0.0548 (Mann–Whitney test). Since the distribution of regressive errors committed by CGG animals was skewed, the Mann–Whitney test is preferred, and we consider the CGG–WT difference in regressive errors borderline significant. Regressive cognitive impairment has also been observed in BC1 KO animals . The level of significance was  animals .In summary, we conclude that CGG animals are impaired when confronted with a novel situational context that conflicts with previously acquired memories, and that such impairment is alleviated by extended training.Spontaneous alternation is an expression of innate spatial curiosity in rodents . In a T-p = 0.202, 8 CGG animals and 10 WT animals; data not illustrated). We conclude that spontaneous alternation, a form of hippocampal spatial cognition, is intact in BC1 KO animals as well as in CGG animals.In previous work, BC1 KO animals did not significantly differ from WT animals in their spontaneous alternation performance . Here, wIn the work presented above, three behavioral approaches were applied to test a prediction resulting from our working hypothesis. The prediction was that behavioral abnormalities resulting from severely diminished presence of regulatory BC1 RNA in synapto-dendritic domains would recapitulate those resulting from global absence of the RNA in animal brains. The prediction was corroborated by the experimental results.180 KI animals, dendritic delivery of endogenous BC1 RNA is severely impaired, as a result of CGG-repeat competition, and the RNA remains largely restricted to perikaryal somatic areas. Absence of regulatory BC1 RNA causes early-onset phenotypic abnormalities including epileptogenic susceptibility and cognitive dysfunction (Regulatory BC RNAs, which reversibly repress translation of neuronal target mRNAs , are locfunction . We therBC RNA DTEs reside within 5′ stem-loop structures . A 5′ DTThe above considerations prompt the question whether dendritic RNAs in addition to regulatory BC RNAs may similarly engage with hnRNP A2 and may thus be subject to CGG-repeat competition as well. This does not seem to be the case for dendritic MAP2 mRNA as it localizes normally in CGG animal brains . AlthougAs discussed above, the near-complete lack of BC1 RNA in CGG-brain synapto-dendritic layers prompted the prediction, to be considered in the following, of BC1 KO–like early-onset abnormalities including hyperexcitability and cognitive impairment.For BC1 KO animals, epileptogenic susceptibility has been reported at an age of 18–21 d  and cognIn hippocampal slice preparations from 18–21-d-old CGG animals, but not in matched WT animal preparations, prolonged synchronized discharges were recorded in CA3 principal neurons after synaptic activation of group I mGluRs. Blockade of group I mGluRs converted such epileptiform discharges back to short synchronized bursts. We conclude that neuronal hyperexcitability triggered by synapto-dendritic reduction of BC1 RNA manifests in the same type of prolonged epileptiform discharges as does hyperexcitability triggered by global lack of BC1 RNA .In vivo, susceptibility to audiogenic seizures was observed in 18–21-d-old CGG animals at a rate and in a manner similar to those in young BC1 KO animals . In bothCognitive-behavioral deficits of young-adult CGG animals also recapitulate those of BC1 KO animals : excessiSimilar cognitive inflexibility has been observed in human ASD . Our datA key goal of this paper was the test of the prediction that absence, or significantly reduced presence, of regulatory BC1 RNA in synapto-dendritic domains would engender phenotypic abnormalities identical or similar to those resulting from global absence of the RNA. This prediction was corroborated experimentally. We further established that such abnormalities, hyperexcitability and ASD-like cognitive dysfunction, manifest early-onset in young-adult CGG animals. Subsets of human premutation carriers also exhibit early-onset neurodevelopmental dysfunction that may include epilepsy, ASD, and related cognitive impairment . Early-oThe fragile X premutation disorder may be of multifactorial causality , as prevWe conclude by emphasizing that the work presented here does not, and cannot, constitute proof of the hypothesis that BC RNA mislocalization is the cause of fragile X premutation phenotypic manifestations. Inasmuch as definitive proof of any hypothesis is impossible on theoretical grounds , the cur


Escherichia coli. Using in vivo gene expression assays, we show that a combination of transcriptional termination and ribosome binding site sequestration increases repression from 85% to 98%, or activation from 10-fold to over 900-fold, in response to cognate antisense RNAs. We also show that orthogonal repressive versions of this mechanism can be created through engineering minimal antisense RNAs. Finally, to demonstrate the utility of this mechanism, we use it to reduce network leak in an RNA-only cascade. We anticipate these regulators will find broad use as synthetic biology moves beyond parts engineering to the design and construction of more sophisticated regulatory networks.RNA transcriptional regulators are emerging as versatile components for genetic network construction. However, these regulators suffer from incomplete repression in their OFF state, making their dynamic range less than that of their protein counterparts. This incomplete repression causes expression leak, which impedes the construction of larger synthetic regulatory networks as leak propagation can interfere with desired network function. To address this, we demonstrate how naturally derived antisense RNA-mediated transcriptional regulators can be configured to regulate both transcription and translation in a single compact RNA mechanism that functions in  RNAs are now understood to play broad regulatory roles across the cell . As suchDespite these advantages, RNA transcriptional regulators still suffer from low dynamic range, the ratio of maximum (ON) to minimum (OFF) signal, in comparison to protein-based regulators. This can be caused by excess signal in the OFF state, causing networks that contain these regulators to be disrupted by low, transient amounts of gene expression signal called network leak. Leak can propagate through the network causing it to function incorrectly, for example by causing a network to express a gene when repression is desired. Previous research has focused on reducing leak to diminish undesired effects, for example by using a recombinase to control gene availability in a multigene network to construct a digital switch biosensor . Howeveret al. recently combined transcriptional and translational control with two distinct mechanisms—inducible promoters and orthogonal translational riboswitches—to achieve tight control of fluorescent proteins  of a small transcription activating RNA (STAR) system based on the pT181 hairpin from 10-fold (±3.7) to 923-fold (±213). Our next goal was to create a library of orthogonal dual control repressors that can function independently in the same cell as components of larger genetic networks. To do this, we converted previously published orthogonal pT181 variants that functioned at the transcriptional level  of a fluorescent reporter protein from 85% (±3.4%) to 98% (±0.4%) in al level  into duaet al.  collected over three separate days. Plasmid combinations were transformed into chemically competent E. coli TG1 cells, plated on Difco LB + Agar plates containing 100 μg/ml carbenicillin and 34 μg/ml chloramphenicol and incubated overnight at 37°C. Plates were taken out of the incubator and left at room temperature for ∼9 h. Three colonies were picked and used to inoculate 300 μl of LB containing carbenicillin and chloramphenicol at the concentrations above in a 2 ml 96-well block (Costar 3960), and grown ∼17 h overnight at 37°C at 1000 rpm in a Labnet Vortemp 56 benchtop shaker. Six microliters of each overnight culture was then added to separate wells on a new block containing 294 μl (1:50 dilution) of supplemented M9 minimal media  containing the selective antibiotics and grown for 4 h at the same conditions as the overnight culture. Cultures (6–12 μl) were then transferred into a FACS round-bottom 96-well plate with 244 μl of PBS containing 2 mg/ml Kanamycin to stop translation. The plate was then read on a BD LSR II using the high throughput setting with the high throughput sampler (HTS). The samples for Figure All experiments were performed in Data for the following parameters were collected on the BD LSR II: forward scatter (FSC), side scatter (SSC) and SFGFP  fluorescet al. (E. coli TG1 cells that do not produce SFGFP (transformed with control plasmids JBL001 and JBL002). The mean MEFL or MEPE value of TG1 cells without SFGFP or mRFP expression, respectively was subtracted from each colony's MEFL or MEPE value. Mean MEFL or MEPE values were calculated over replicates and error bars represent the standard deviation. For repressors, the OFF level is the MEFL or MEPE of cells containing the sense plasmid and the antisense plasmid and the ON level is the MEFL or MEPE of cells containing the sense plasmid and a no-antisense control plasmid. The percent repression for each antisense RNA/attenuator plasmid combination was calculated by subtracting the OFF level divided by the ON level from 1 (1 – OFF/ON). For activators the ON level is the MEFL or MEPE of cells containing the sense plasmid and the antisense plasmid and the OFF level is the MEFL or MEPE of cells containing the sense plasmid and a no-antisense control plasmid. The fold activation was calculated by dividing the ON level by the OFF level (ON/OFF).Data analysis and FACS calibration was performed according to the supplementary info of Lucks et al. . SpherotE. coli TG1 cells transformed with three cascade plasmids . The block contained three replicates of each control. OD and FL values for each colony at each time point were first corrected by subtracting the corresponding values of the media blank at that same time point. The ratio of FL to OD (FL/OD) was then calculated for each well (grown from a single colony), and the mean FL/OD of TG1 cells without SFGFP at the same time point was subtracted from each colony's FL/OD value to correct for cellular autofluorescence. Experiments were performed for nine biological replicates collected over three separate days. One day is shown in Figure On each 96-well block, there were two sets of controls; a media blank  and and translation of the downstream gene. Specifically, in the presence of antisense RNA, the formation of the terminator hairpin should both repress transcription of the downstream gene, as well as occlude the initiation of translation of any mRNA transcripts that were extended due to imperfect termination efficiency. Thus, we expected the dual control translational fusions to exhibit lower OFF levels than the transcription-only regulators.We first sought to evaluate the performance of the dual control repressor by configuring it as a translational fusion with a downstream reporter gene Figure . BecauselacZ gene exhibited 62% repression in the presence of an antisense RNA as measured by Miller assays  repression to 98% (±0.7%) repression Figure . However reduced , possiblTo further investigate the mechanism of the attenuator, we performed qRT-PCR experiments on a transcriptional and a translational fusion construct . The traWe next designed a construct to compare dual transcription/translation control to transcription-only control using a dual reporter protein operon Figure . In thisWe next sought to determine if the dual control strategy could be applied to an RNA-based transcriptional activator mechanism derived from the pT181 system. Small transcription activating RNAs (STARs) were recently engineered to activate, rather than repress, transcription in the presence of designed antisense RNAs . In the E. coli TG1 cells along with either its cognate STAR antisense or a no-antisense control plasmid  repressor, the mutant repressors Mut 1,2) ( (2) and We next sought to test the orthogonality of the dual control repressors. In addition to requiring multiple dual control regulators to build genetic networks, these regulators must also be orthogonal, or only interact with their cognate target. Previous work showed that the original transcription-only chimeric fusions exhibited limited crosstalk between non-cognate antisense/sense target pairs, making them highly orthogonal . To testDespite starting from a set of highly orthogonal transcriptional repressors, we observed significant crosstalk between the dual control regulators. Earlier work on elucidating the mechanism of antisense-mediated translation repression suggested that flanking sequences in the antisense RNA can form extended interactions with the sense target RNAs . We thusFinally we sought to test the dual control regulators in an RNA-only network context that would demonstrate how reduced leak improves network performance and confirm that orthogonal versions can function correctly in the same cell. RNA repressor cascades were the first RNA-only network built  and haveE. coli cultures that contained the cascade plasmids with either the transcriptional or dual control repressor cascade plasmids for the bottom level of the cascade. After incubating overnight in LB media, the cultures were diluted into M9 supplemented media and incubated for four hours. The cultures were then diluted again into fresh M9 media to a consistent OD and incubated for four more hours. From here, we sampled cultures every 30 min to measure SFGFP fluorescence and culture OD over time. Theophylline was added to some cell cultures at the beginning of sampling to measure the cascade response  for the transcriptional-only case to 98% (±0.4%) in the dual control case. In addition to decreasing OFF levels in the presence of antisense RNA, this configuration increased the ON level in the absence of antisense RNA.In this work, we have demonstrated the utility of an RNA structure that regulates both transcription and translation in a single, compact mechanism by showing that it improves dynamic range of antisense RNA-mediated control of gene expression and reduces leak when used in RNA genetic networks. Specifically, translational fusions between the pT181 attenuator and downstream reporter genes allowed the transcription of these genes to be regulated by the pT181 terminator hairpin and the translation of these genes by the B. cereuscrcB fluoride riboswitch in which the anti-terminated form shows increased RBS exposure (To further investigate the mechanistic details of the dual control repressors we performed qRT-PCR experiments . The traexposure . In addiexposure  allowingInterestingly, our results are different from those observed by previous studies of the pT181 attenuator . ThroughOverall the dual control mechanism significantly improved the dynamic range of RNA regulators over RNA transcriptional repressors and is better than the ∼90% repression seen for the best RNA translational repressors  and the In order to build robust genetic networks in which the parts act independently and predictably the parts must be orthogonal or act independently of other regulators in the system. However, the initial dual control riboregulators exhibited significant crosstalk. We hypothesized that this was due to additional interactions between the antisense RNAs and the sense target RNAs that caused translation to be repressed even after the transcriptional decision had been made. For a transcriptional decision to be made, the antisense RNAs must interact cotranscriptionally. However, the antisense can still bind the dual control sense target after transcription and affect RBS availability. This would indicate that the modifications between mutants, fusions, and the original pT181 are enough to inhibit crosstalk during transcription but the increased time for antisense-sense target interactions before translation initiation allows shared sequences in non-cognate pairs more opportunity to interact. We therefore decided to reduce the redundant pT181 sequence to reduce the affinity of non-cognate antisense RNAs for sense target regions. While not all non-cognate antisense and sense target pairs were improved by the truncations, on average the worst performing pairs improved by 20% repression. By truncating redundant pT181 sequence we greatly improved orthogonality, making it possible to use these dual control repressors in RNA networks.Finally, we used dual control RNA repressors to address a current problem with RNA only networks, which is leak that results from parts that do not allow complete repression of their targets. Specifically, we used the dual control repressor in a repressor cascade and found that it reduced network leak and background fluorescence. Additionally, the repressor cascade demonstrates that the orthogonal attenuators can function together in the same cell.This work demonstrates a novel RNA motif that regulates multiple aspects of gene expression in a single compact mechanism, and that displays a dynamic range of gene regulation comparable to protein-based mechanisms. As such, this is another example of how RNAs may be optimized to function as well as proteins. We anticipate that as synthetic biology moves beyond the creation of regulator parts libraries and into building more sophisticated networks, RNA regulatory mechanisms such as dual control repressors will find increased use in designing RNA genetic networks with predictable function.Supplementary DataClick here for additional data file.


Alternative splicing, polyadenylation, and chemical modifications of RNA generate astonishing complexity within eukaryotic transcriptomes. The last decade has brought numerous advances in sequencing technologies that allow biologists to investigate these phenomena with greater depth and accuracy while reducing time and cost. A commensurate development in biochemical techniques for the enrichment and analysis of different RNA variants has accompanied the advancement of global sequencing analysis platforms. Here, we present a detailed overview of the latest biochemical methods, along with bioinformatics pipelines that have aided in identifying different RNA variants. We also highlight the ongoing developments and challenges associated with RNA variant detection and quantification, including sample heterogeneity and isolation, as well as ‘Omics’ big data handling. The RNA world hypothesis, a widely prevailing idea among molecular biologists, describes self-replicating RNA as the precursor to all modern life forms. Besides recognizing its primordial origin, we have come to realize that RNA is a highly complex and diverse macromolecule, which plays central roles in protein synthesis by serving as the encoder (mRNA), the decoder (tRNA), and the catalyst (rRNA). But our understanding of RNA diversity, form, and function continues to evolve as we keep discovering new and exciting classes of RNAs that carry out unexpected functions. Recent transcriptome studies, which capture nearly every transcript in a cell, have revealed an overwhelming number of RNA variants resulting from multiple transcription initiation sites, alternative pre-mRNA splicing and polyadenylation, post-transcriptional editing, and direct chemical modifications of RNA . These s1. The advent of microarrays in the early 2000s expedited the discovery of new RNA variants3, but the technology had several limitations, including (i) high background levels due to dependence on hybridization, (ii) reliance on existing sequence knowledge, and (iii) a limited range of detection. In the last decade, the field has taken a significant leap forward with the development of next-generation sequencing. The timeline of major milestones that facilitated the discovery of novel RNA variants is provided inThe systematic study of RNA variants began in the 1990s when cloning of expressed sequence tags revealed the diversity of mRNA populations expressed in different cells and tissues of metazoans5. Next-generation sequencing, however, uses advanced optics and flow cells to identify nucleotide sequences rapidly, making the analysis and sequence determination quicker and more accurate8. A number of next-generation sequencing platforms have been developed, and the most popular ones are Illumina Solexa9 , Roche 454 sequencing10 , Ion Torrent11 , and SOLiD sequencing12 . Of the above methods, the first three are classified as sequencing by synthesis methods whereas SOLiD is under the sequencing-by-ligation category. The field of sequencing by ligation has made rapid strides with the development of complete genomics technology that uses combinatorial synthesis methods, involving the use of multiple probe-anchor combinations to determine sequence13. Additionally, Pacific Biosciences has recently introduced single-molecule real-time sequencing14 of full-length transcripts, and the average read length is 2.5 kb .First-generation sequencing required separate reactions for individual nucleotides and was limited by a gel electrophoresis step, which was further complicated by the need to use radioactive reagents15. It involves sequencing of cDNA fragments generated from total RNA after oligo-dT or random priming. For most transcriptome studies, mRNA is enriched from the total RNA pool by using either a poly(A) pull-down or selective depletion of rRNA. Following sequencing, the reads are aligned to a pre-annotated reference genome or assembledde novo to provide both the qualitative and the quantitative information of entire transcriptomes. This technique now can be used to identify new transcripts without havinga priori knowledge of all gene models16. Recent mapping tools can also identify splice junctions by using reads spanning across two exons, bolstering identification of alternative splice isoforms. Initially, RNA sequencing (RNA-seq) studies examined only the steady-state levels of RNA populations; however, the relative contribution of variable transcription (synthesis) and RNA degradation (stability) in those studies was unclear. New techniques such as Net-Seq18 and BRU-seq19 address this problem. Net-Seq employs DNA-RNA-RNA polymerase complex stability to isolate 3′ ends of nascent transcripts. Bru-Seq uses pulse-chase labeling of newly transcribed RNA with bromouridine to determine rates of RNA synthesis and stability.Perhaps the most significant breakthrough in the field was the development of massively parallel cDNA sequencing, commonly referred to as RNA-seq22. The increasing sensitivity of detection, wherein extremely minute quantities (picograms) of RNAs can be amplified and sequenced, has also spurred development of single-cell RNA-seq (scRNAseq)23 for identifying heterogeneity within cell populations and uncovering transcriptome differences that may be hidden in bulk RNA-seq24. Some of the commonly used sequencing technologies and bioinformatics packages for the detection of different RNA variants are provided inThe past five years have witnessed a surge of new techniques employing RNA-seq to identify many new RNA variants, and isolation/enrichment methods are based on each variant type. In parallel, significant advances in computing power have expanded our ability to annotate and quantify these variants with high confidence from a single cell to complex tissues, along with the advent of consortium projects that have given rise to databases for their comparative and meta-analysesIn this review, we discuss different types of mRNA variants commonly found in metazoans, consider the latest advances in techniques to identify them, and provide a brief overview of the recent developments in bioinformatics pipelines for their quantitative analyses.25 and to generate variability in untranslated regions (UTRs) which modulate mRNA translation efficiency, stability, and localization27. Given the direct and widespread impact of alternative splicing on eukaryotic gene expression, cataloging the full repertoire of splice variants within individual cell types, developmental stages, and different human diseases has been a focus of many research laboratories.Pre-mRNA splicing is an essential step for the expression of most genes in metazoans. Splice sites within a pre-mRNA facilitate the splicing and ligation of introns and exons, respectively, producing a mature mRNA transcript. Alternative splicing involves differential usage of splice sites to yield multiple mRNAs from a single gene. The functional outcomes are increased proteome diversity and the introduction of premature termination codons to degrade mRNAs by nonsense-mediated decay30. The results transformed our view of the extent and complexity of eukaryotic transcriptomes, as more than 95% of human multi-exon genes were found to have alternatively spliced isoforms31. Importantly, calculating the number of mapped reads against individual exons plus each splice junction provided a direct measure of splicing efficiency that allowed precise quantification of isoform ratios.The initial RNA-seq studies for detecting splicing patterns suffered from short read lengths, limiting the reliable genomic alignment of the alternative exon-exon junctions. Clever computational approaches, however, overcame these limitations through the addition of all potential splice junctions to the reference genome and accounting for them within the alignment mix33, and many novel splice variants in vertebrate species35. Emerging technologies are geared toward sequencing the full-length transcripts  such that variably spliced regions from different areas of a single gene can be accurately connected to provide information about the entire mRNA isoforms . There has also been a strong push to improve the existing algorithms to capture the full complexity of splicing patterns within existing RNA-seq data. For instance, Vaquero-Garciaet al. have recently developed a new computational pipeline to accurately detect, visualize, and quantify complex local splice variants that are tissue-specific or change dynamically across different experimental conditions37.The development of strand-specific and paired-end sequencing led to improved mapping, the discovery of pervasive anti-sense transcription39. Recursive splicing uses cryptic sites within the intron, known as recursive splice (RS) site/ratchet points, which are surprisingly more conserved than the regular 5′ and 3′ splice sites. However, the identification of these RS sites required extensive deep sequencing with over a billion paired-end reads, without poly(A) selection in an attempt to capture pre-mRNA and nascent RNA transcripts41. Likewise, RNA-seq has renewed interest in studying circular RNAs (circRNAs). The discovery of circRNAs, though known for over two decades, remained mostly serendipitous43. Typical RNA-seq experiments fail to conserve RNA circularity because fragmentation and the absence of a poly(A) tail or free 5′ or 3′ end cause de-enrichment of circRNAs during most library preparations. However, the lack of exposed 5′ or 3′ ends also stabilizes the circRNAs, making them resistant to exonuclease-mediated degradation. The recent development of biochemical methods to enrich for circRNAs  along with new computational strategies (CIRCexplorer44) to mine the existing Encyclopedia of DNA Elements (ENCODE) Ribozero RNA-seq data has aided theirde novo detection in humans, mice, andCaenorhabditis elegans. The results have revealed that most circRNAs are non-coding splice variants containing multiple exons located in the middle of genes that are often expressed in a tissue- and developmental stage-specific manner and that these variants may act as sponges to antagonize microRNA activity46.In addition to the discovery of variably spliced regions, RNA-seq has been an incredible asset for the investigation of splicing intermediates and novel splice products. For instance, it led to the identification of recursive splicing where long introns require specific exon contexts and are spliced out in phases rather than the usual two-step process47. A substantial number of these alternative TSSs alter the 5′ UTRs, in turn modulating the translational output of mRNAs48. 5′ UTRs are known to comprise many regulatory features—5′ cap structure, internal ribosome entry sites, G-quadruplexes, and so on—that can drastically affect the ability of ribosomes to assemble and initiate translation. Apart from forcing the gain or loss of such regulatory features, alternative TSSs generate RNA variants with different upstream open reading frames (uORFs). Ribosome footprinting studies have uncovered a significant fraction of such short translated uORFs within 5′ UTRs that potently repress the translation of downstream ORFs51.Whereas the gene body variants in mRNAs promote proteome diversity, variability at the terminal ends is known to serve more of a regulatory role in protein synthesis. This realization has led to a strong surge in the development of new methodologies to identify all the 5′ and 3′ end variants within eukaryotic transcriptomes. For instance, recent methods have revealed that alternative transcription start sites (TSSs) are prevalent in the human genome and that approximately 30 to 50% of human genes have more than one promoter52. Analysis of existing RNA-seq data with new computational pipelines (MISO and DaPars)54 or sequencing of the libraries enriched for 3′ ends of mRNAs  have documented that over 50% of human mRNAs express variable 3′ UTRs—generated by alternative cleavage, splicing, and polyadenylation. Importantly, these 3′ UTR variants are differentially expressed during differentiation, development, and disease58, and they often influence the accessibility of microRNAs and RNA-binding proteins (RBPs) to the 3′ UTRcis-regulatory elements affecting mRNA stability, localization, and translation efficiency59.Apart from providing proper signals for mRNA 3′ end formation and poly(A) site selection, the 3′ UTR is considered to be a regulatory hub for gene regulation. Proper 3′ end formation is essential for nuclear export and stability of mature mRNAs and their efficient translation60 and ePAT61) can accurately measure the poly(A) tail lengths of individual genes, precise measurements at a genome-wide scale are hampered by difficulties in sequencing homopolymeric sequences longer than 30 nucleotides. However, new advances in poly(A) tail length profiling methods (TAIL-seq and PAL-seq) use specialized 3′ adapters, RNAse T1 treatment (digests the mRNA body but not the poly[A] tail), and the spike in controls in library preparation. These biochemical improvements in conjunction with refined machine-learning algorithms to analyze the intensity of raw fluorescence signals allow the precise estimation of poly(A) tail lengths. These new methodologies have started to provide the first high-resolution, global views of poly(A) tail length variants and their relevance to mRNA stability and translational efficiency in cells and tissues from a variety of species, including yeast,Drosophila, zebrafish,Xenopus, mouse, and human64.Poly(A) tail length is another important 3′ UTR feature that impacts the post-transcriptional fate of mRNAs. Although some biochemical methods  plus editing (A–I or C–U) is now commonly referred to as “epitranscriptomics”68. Much like the epigenome, the epitranscriptome expands the information content of nucleic acids, rendering them additional structural and functional flexibility. Recent advances have led to the profiling of millions of modification sites for each of the known mRNA modifications, including methylation, as well as base editing (such as A–I or C–U)70. Whereas most of these have been observed across all genes, some are limited to specific classes of transcripts.Though originally discovered in highly abundant rRNAs, tRNAs, and snRNAs, newly tailored mRNA sequencing methods have revealed that base modifications and editing sites are also highly prevalent in mRNAs74. The impact of these post-transcriptional marks is now recognized in almost every step of gene regulation, including mRNA processing, stability, and translation75. An interesting example is the recent work on ADAR (adenosine deaminase acting on RNA) enzymes, showing their role in protecting double-stranded RNA (dsRNA) against recognition by dsRNA response pathways76. Importantly, the writers, erasers, and reader proteins for some of these modifications have been identified, and the phenotypic and molecular consequences of their gain or loss of function have been demonstrated77.Remarkably, the genome-wide mapping of modifications and editing sites has not only expanded the landscape of this new class of mRNA variants within eukaryotic transcriptomes but also unraveled their dynamics in abundance and site selection in response to intra- and extra-cellular stimuli78 or a modification-selective RNA chemistry approach (m5C and Ψ)80 to enrich for the target modification during library preparation. However, further optimization of these methods is needed to simultaneously obtain single-nucleotide resolution and stoichiometry of RNA modifications, as fractional modification represents another mechanism to generate functional diversity within individual mRNA pools. Current epitranscriptome profiling techniques accurately identify the modification sites within RNA transcripts but fail to provide information about the modified fraction for each site. Given that a modification may alter the mRNA secondary structure or binding site for RBPs, quantitative fractional information about each modified site in the transcriptome is highly desired.Today, the transcriptome-wide mapping of modified nucleosides relies on either antibody-based immunoprecipitation 81. Therefore, estimating average gene-level translation by ribosome profiling82—a technique that uses short, approximately 28 nucleotide ribosome footprints on mRNA as a means of quantifying protein output—may not provide an accurate measure of the translational potential for individual RNA variants or isoforms. This is especially true for splice variants, which can exhibit variable coding capacities because of (i) sequence differences, (ii) altered mRNA half-lives, or (iii) differential nucleo-cytoplasmic export. Indeed, several recent efforts to measure isoform-specific translation have revealed discrete effects of 5′ end83, 3′ end84, and coding sequence86 diversity.Early dogma predicted that the amount of mRNA for a given gene directly correlated with the amount of protein made. However, we now know that through sequence variations discussed above and along with other RNA control elements, the translation of specific transcripts can be fine-tuned for the purpose of responding to cellular needs in a variety of contexts83. Complementing this work, Spieset al. acquired decay and translation rates for proximal and distal 3′ UTR variants using poly(A)-primed sequencing (2P-seq) but found only a modest regulatory influence for alternative 3′ UTR sequences84. By comparing isoform ratios in cytoplasmic and polyribosomal extracts from human cells (Frac-seq), Sterne-Weileret al. observed that not all splice isoforms are similarly loaded into polyribosomes and that the presence of microRNA binding sites or premature termination codons can greatly affect the loading of individual isoforms into polyribosomes85. Similarly, a major impact of mRNA isoforms on protein output was observed by Floor and Doudna, who recently adapted the classic approach of polysome profiling to measure isoform-specific translation (TrIP-seq)87.Using translation-associated transcript leader sequencing (TATL-seq), Arribere and Gilbert successfully mapped transcript leader boundaries across the yeast genome and discovered hundreds of new alternative transcript leader variants, the majority having differential translation efficienciescis-acting RNA elements,trans-acting RBPs that bind to these elements to control the production of variants, and their expression and subcellular localization is paving the way to uncovering the underlying code for generating much of the RNA diversity89. If sequencing costs continue to decline as anticipated, clinical measurements of normal and aberrant RNA variants in body fluids or tissue biopsies in the not-so-distant future may become part of personalized medicine, making for more accurate diagnoses and offering better-informed treatment plans.Emerging technologies have given us a powerful toolbox to detect and quantify RNA variants from simple unicellular organisms to complex vertebrates. These new techniques allow for previously unimagined access to compare transcriptome patterns across species, tissues, developmental stages, and disease states. We now can isolate and sequence picogram amounts of RNA from fresh and formalin-fixed paraffin-embedded tissue specimens and even identify variants within single cells that are isolated from a wide range of biological samples. Moreover, the ENCODE project has generated unprecedented information about the many different RNA variants present in model organisms and human cells. Systematic discovery of90, developmental stage-specific91, or disease-specific92 manner, particular attention must be paid when selecting optimal biological specimens for sequencing. For instance, individual cell types from most tissues now can be isolated by serial dilution, fluorescence-activated cell sorting, or laser-capture micro-dissection. Therefore, developing compound protocols that combine cell type-specific purification strategies with sequencing may provide more reliable information on RNA variants within particular cell types under normal or disease states. Second, identifying RNA variants from single-cell RNA-seq often requires multiple amplification steps to produce a sufficient quantity of nucleic acid for sequencing. These amplification steps introduce stochastic bias for certain sequences, which may cause dropout of certain low-abundance variants while amplifying others, thereby reducing the accuracy, reproducibility, and quantification power of detecting variants within single cells93. Third, there has been a rapid surge in the deposition of transcriptome data in public repositories in recent years. This has resulted in new challenges about how to effectively integrate the RNA variant information with other “Omics” datasets and build streamlined platforms for metagene analyses. Finally, perhaps the biggest challenge facing the field is the storage, distribution, and analysis of massive amounts of sequencing data. Multiple consortium projects like ENCODE, TCGA (The Cancer Genome Atlas), ExAC (Exome Aggregation Consortium), and other individual centers generate sequencing data at an astounding pace, doubling their numbers every seven months94. As the world moves toward a future of personalized genomic diagnostics and medicine, Stephenset al. propose that real-time transcript expression level analysis and the development of genomic variant databases against reference genomes will help solve the data storage problem94. With growing data, there is a need for platform development for data distribution. Cloud computing-based analysis may serve as the most practical option. While sequencing-based variant analysis provides a big boost for the future of precision medicine, handling genomics data is one of the most pressing challenges for the next decade.However, as with most opportunities, certain challenges must be overcome to realize their full potential. What are the common challenges facing global profiling of RNA variants? First, biological specimens are usually heterogeneous, composed of multiple cell types. Since most RNA variants are expressed in a single cell type-specific