Although it has been about 30 years since the discovery of circular RNAs (circRNAs) in mammalian cells, these subtypes of RNAs’ capabilities have come into focus in recent years. The unique structure and various functional roles of circRNAs in many cellular processes have aroused researchers’ interest and raised many questions about whether circRNAs can facilitate the diagnosis and treatment of diseases. To answer these questions, we will illustrate the main known functions and regulatory roles of circRNAs in the cell after presenting a brief history of the discovery of circRNAs and the main proposed theories of the biogenesis of circRNAs. Afterward, the practical application of circRNAs as biomarkers of different pathophysiological conditions will be discussed, mentioning some examples and challenges in this area. We also consider one of the main questions that human beings have always been faced, “the origin of life,” and its possible connection to circRNAs. Finally, focusing on the various capabilities of circRNAs, we discuss their potential therapeutic applications considering the immunity response toward exogenous circRNAs. However, there are still disputes about the exact immune system reaction, which we will discuss in detail. Our current knowledge of the role and importance of circRNAs in mammals, including humans, has come a long way. Since the first circular form of RNA was found in plant-infected viroids (uncoated RNA molecules that infect certain higher plants), scientist’s views on circRNAs have undergone tremendous changes, thanks to the development of new high-throughput sequencing techniques. The fluctuations in the circRNA discovery area continued until changes in the conventional algorithms and library preparation methods for RNA-seq in 2012, revealing the global expression of circRNAs in the mouse brain and different cell types . The tisHerein we summarize the known and unknown issues about circRNAs. The possible biogenesis models, roles in different pathologies, and the biological functions of circRNAs have been reviewed earlier , so we oThe precise mechanism of circRNAs biogenesis is still a point of discussion. Although spliceosome appears to be the main actor of circRNA biogenesis, this process’s details remained unclear. The two highlighted mechanisms recommended by the researchers are direct backsplicing and exon skipping (lariat model). In the direct backsplicing model, there are inverted repeats in the vicinity of the exons. The base pairing of inverted repeats leads to forming a stem-loop structure, which brings circRNA-forming exons into proximity, thus enhancing the interaction between donor splice-site and upstream acceptor site  . BacksplOne of the most prominent RBPs contributing to the biogenesis of circRNAs is the splicing factor, namely Quaking (QKI). QKI, which is a member of the STAR family , binds to conserved binding sites in the introns adjacent to the circularization location and seems to catalysis the circularization process by dimer formation .Muscleblind (MBL) -examined in Drosophila- is another splicing factor involved in circRNA’s biogenesis. MBL acts in a cis manner and binds to its conserved binding sites, and promotes the synthesis of circMbl, the most abundant circRNA in the Drosophila’s head. The feedback loop occurring in this process is remarkable. The MBL protein -synthesized from a linear Mbl transcript- helps create circular Mbl and prevents linear counterpart formation by competition with the canonical splicing .the ILF3 gene, also contribute to the backsplicing process. Studies indicated that circRNA’s overall expression decreases during viral infection, partly due to these antiviral operators’ nuclear export. Experiments such as the biotin-labeled RNA pull-down assays have shown that these proteins strongly bind to dsRNA and probably facilitate circRNA biogenesis by stabilizing RNA base pairing  and serine-arginine (SR). Previous studies have examined the role of hnRNPs in processes such as pre-mRNA maturation, cellular transport, and stabilizing of mRNA . SR protlaccase2 .ALUs repeats and unwind RNA base pairing and, as a result, inhibit circularization  and an RNA helicase called DHX9 can interact with inverted rization .As mentioned earlier, backsplicing is not the only possible mechanism of circRNA biogenesis. In the exon skipping (lariat) model, following canonical splicing, the intermediate lariat includes skipping exons endured backsplicing . In thisTwo other probable mechanisms of circRNA’s biogenesis are failure to debranch intron lariat  and tranDiscovering facts such as circRNAs tissue-specific expression and specific expression patterns during various development stages has led researchers to investigate the functions of circRNAs.Herein, we will give a brief overview of some of the main functions that have been described for circRNAs.MicroRNAs (miRNAs), RNA molecules with an approximate length of 22 nucleotides, are involved in post transcription regulation of genes by preventing their target mRNAs’ translation . HoweverCircRNAs are involved in several gene expression regulation stages, such as transcription initiation and elongation, splicing, and even translation. The three major known subtypes of the circRNAs, exonic circRNAs (EcircRNA), intronic circRNAs (CIRNA), and exon-intron circRNAs (EIciRNA), play roles in this area.The existence of 2’–5’ Phosphodiester-linked in CIRNAs supports the hypothesis that the formation of this particular subgroup of circRNAs happened because of lariat debranching failure . CIRNAs EIciRNAs, which contain both intron and exon in their structure, are also accumulated in the nucleus and indirectly regulate the RNA pol II by RNA-RNA interaction with the U1 snRNA subunit of spliceosome machinery and aid initiation of transcription . Both CICircRNAs are also involved in post-transcription regulation. Except for the sponge role of circRNAs described earlier, circular transcripts of a gene can prevent their linear counterparts’ efficient expression in other main ways. One way is the competition between the linear and the circular form of a gene in binding to translation-facilitating proteins. CircPABPN1, for example, appears to reduce the binding of HuR protein -which is known as translation promoter of PABPN1 mRNA- to its linear counterpart and thus reduces the efficiency of translation . There iCircRNAs interact with proteins, the important cellular directors, in a variety of ways . In addiAlthough bioinformatic studies were done based on nucleotide sequences, the RBP chances of binding to circRNAs are no more than linear forms, but the interaction seems stronger .Transfected cells with circAmotl1 showed increased AKT levels in the nucleus, supporting the fact that circAmotl1 may be involved in the nuclear translocation of AKT protein . PreviouEvaluation of the role of circANRIL in atherosclerosis and ribosomal RNA (rRNA) maturation process using mass spectrometric and RNA immunoprecipitation (RIP) assays showed two proteins; PES1 and NOP14 interacted mostly with circANRIL, respectively . Since tCells perform their vital functions through proteins. Proteins interact with circRNAs for a variety of purposes. As can be deduced from the above examples, some of the most important proteins are transported in the cell with the help of circRNAs. Besides, proteins can interact effectively with other proteins and molecules by binding to circRNAs as a platform. That is one of the many reasons why the cell faces a crisis if mutations affect the expression levels or RNA binding sites of circRNAs.CircRNAs are classified in the non-coding RNAs category. However, evidence from mass spectrometry, ribosomal footprinting, and CRISPR Cas9 based experiments indicated this subtype of RNAs’ potential to produce proteins . Two genSince there is no 5′ cap structure in circRNAs, the probable mechanism for circRNA translation is the cap-independent , and it M6A modifications involvement in processes such as mRNA stability, splicing, and translation were approved, and now it seems that m6A is also effective at the beginning of circRNAs translation . CircRNAArabidopsis thaliana  . siRNA’sc cancer . Similarc cancer .ATG7) overexpression by sponging miR-3657. Inhibition of circRACGAP1 leads to cell apoptosis instead of autophagy, which sometimes causes cancer cells to survive under environmental stress (Fusion circRNAs (f-circRNAs), primarily generated in chromosomal translocations during tumorigenesis, are potential cancer treatment targets. The formation of circRNAs can lead to drug resistance, cellular transformation, and other tumor-related hallmarks . F-circRl stress .in vivo research result demonstrated that a small dose of unmodified circRNA could sponge two miRNAs involved in cardiac hypertrophy, thereby preventing heart failure . However, there are still challenges that need to be addressed to expedite and facilitate such studies. RNA-seq and microarray are among the high-throughput technologies that are used to screen differentially expressed circRNAs in different conditions. RNA-seq can evaluate the expression levels of unknown circRNAs. However, different analysis algorithms and even different library preparation methods lead to different results. To get the most out of this method, developing a gold standard for library preparation and bioinformatics analysis pipelines is highly recommended (This review summarized the current and potential applications of circRNAs based on their various functions and features. CircRNAs have multiple roles in biological processes, like gene expression regulation, interacting with proteins for different purposes, and coding proteins. Most of the published papers focused on the post transcription regulation of circRNAs, miR sponging. However, researchers should consider items such as sufficient circRNA amount, co-expression with the target miR, and the ratio between circRNA and target miRs in cases where the “sponging” capacity has not been investigated by experimental methods . In addiommended . Althougommended .In addition to the role that circRNAs can play in the diagnosis and management of diseases, various features of circRNAs can also be used in the treatment of diseases. One possible treatment approach is to reduce the expression of circRNAs such as f-circRNAs and onco-circRNAs using siRNA designed for backspliced junction . The altAnother potential revolutionary circRNA-based treatment is achieving stable and efficient protein production in cells, regarding the resistance of circRNAs toward many ribonucleases. Nevertheless, further research needs to be done to establish the immune system’s precise mechanism of action toward exogenous circRNAs. If the immune system is not stimulated, stable protein expression is achieved. But if the activation of the immune system occurs, as happened in It is possible that by eliminating technical shortcomings and expanding knowledge in this area, circRNAs, once considered a by-product of splicing, shed new light on diagnosing and treating diseases.LY performed the literature search and drafted the manuscript. MA and MM critically revised the work. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Endothelial cell inflammation is a common pathophysiological process in many cardiovascular and cerebrovascular diseases. Small RNA is a kind of short nonprotein coding RNA molecule. Changes in the small RNA expression in endothelial cells have been linked to the development of cardiovascular and cerebrovascular diseases. We investigated and verified differentially expressed small RNAs in endothelial cells in response to inflammatory stimulation. α for 24 hours. Small RNA sequencing was used to generate extensive small RNA data. Significantly differentially expressed small RNAs identified in the analysis were further confirmed by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Then, we investigated the tissue-specific small RNA expression after RNA extraction from different tissues.  Primary rat endothelial cells were obtained from Sprague-Dawley rats and treated with 10 ng/ml TNF- Small RNA sequencing demonstrated that 17 miRNAs, 1 piRNA, 10 snoRNAs, and 7 snRNAs were significantly differentially expressed. qRT-PCR identified 3 miRNAs, 2 snoRNAs, and 2 snRNAs with significantly different expression. Analysis of the tissue-specific expression showed that rno-miR-126a-5p was predominantly expressed in the lung, rno-miR-146a-5p in the intestines, and rno-novel-178 in the heart. Rno-piR-017330 was mainly expressed in the muscle. snoR-8966.1 was predominantly expressed in the bone. snoR-6253.1 was mostly expressed in the vessels and bone. snR-29469.1 was mainly expressed in the bone, and snR-85806.1 was predominantly expressed in the vessels and bone. α can regulate the expression of small RNAs in endothelial cells, and their expression is tissue-specific. We report for the first time the expression of small RNAs in endothelial cells under inflammatory conditions. TNF- Small RNAs are key regulators of biological activities and play an important role in the regulation of the gene expression, biological ontogenesis, metabolism, and the occurrence of diseases and other physiological processes. Generally, small RNAs include microRNAs (miRNAs), small noncoding RNAs (ncRNAs), small interfering RNAs (siRNAs), small nucleolar RNAs (snoRNAs), P-element-induced wimpy testis- (PIWI-) interacting RNAs (piRNAs), and repeat-associated siRNAs (rasiRNAs). Small RNAs regulate the growth and development of organisms and the occurrence of disease through a variety of pathways, including mRNA degradation, translational inhibition, heterochromatin formation, and DNA removal.Endothelial cell inflammation is a common process that is closely related to the pathophysiological mechanisms of the cardiovascular and cerebrovascular systems. Although several specific changes in miRNAs and other small RNAs in inflamed endothelial cells have been elucidated, a comprehensive analysis of small RNA changes has not been performed.In this study, primary rat endothelial cells were isolated and subjected to inflammatory stimuli. To gain insights into small RNA changes in endothelial cells in response to inflammation, we used small RNA sequencing  to generate extensive small RNA data, along with RNA-seq data. We identified differentially expressed small RNAs and performed tissue-specific analysis. These findings might provide novel insight into disease pathogenesis and early diagnosis.2 incubator. The medium was changed every 3 days. TNF-α stimulation was used to mimic an inflammatory environment. The cells in the TNF-α group were treated with 10 ng/ml TNF-α for 24 hours.The animal protocol was approved by the Institutional Animal Care and Use Committee (IACUC), and the experimental protocol was reviewed and approved by the Ethics Committee of Fudan University, Shanghai, China. Adult male Sprague-Dawley rats  weighing 200 to 250 g were used. The animals were anesthetized with Forane  using a vaporizer . Vessel segments were obtained from a healthy rat abdominal aorta. Samples were turned inside out to expose the endothelium. The ends of the blood vessels were ligated, and the samples were collected in DMEM containing 5% penicillin/streptomycin. Tissue segments were washed 3 times with phosphate-buffered saline (PBS) supplemented with 1% penicillin/streptomycin. A pancreatin-trypsin solution was used to digest the tissue for 3 minutes. DMEM  containing 10% FBS (Gibco) and 1% penicillin/streptomycin was used to terminate the digestion. Cells were cultured on fibronectin-coated dishes with endothelial cell medium  at 37°C in a 5% COThe quality of the original sequencing data was evaluated by FastQC using cutadapt to remove the adaptor and trimmomatic to remove low-quality bases at both ends and read filtering. BLASTn was used to compare reads with the small RNA, transfer RNA, small nuclear RNA (snRNA), and snoRNA sequences in the Rfam database, and the number and percentage of mapped reads were calculated. Bowtie was used to compare the read sequences with the exon and intron sequences of the species, and the number and percentage of mapped reads were calculated. Reads that mapped to an exon in the alignment but did not map to an intron were filtered out, after which bowtie was used to align the reads with the reference genome sequence, the number and percentage of mapped reads were calculated, and the reads that did not align were filtered out.ΔΔCt−) method and expressed as the fold change relative to the respective control. Each sample was analyzed in triplicate. The primer sequences used in this study are shown in Total RNA was isolated from cells with TRIzol Reagent (Invitrogen) according to the manufacturer's instructions. After the reverse transcription reaction, RT-PCR was performed with the ABI 7900HT system using SYBR Premix  according to the manufacturer's instructions. The RT-PCR conditions were as follows: denaturation at 95°C for 10 s, followed by 40 cycles at 95°C for 10 s, and 60°C for 30 s. A dissociation step was added at the end of the amplification procedure. No nonspecific amplification was observed based on the dissociation curve. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was used as an internal control. The data were analyzed using the comparative Ct  of its target gene. Full complementarity with the 3′-UTR of the target gene leads to accelerated degradation of the target mRNA, while partial complementarity inhibits target protein synthesis. As a negative regulator of the gene expression, miRNA can affect many important human physiological and pathological processes, including differentiation, proliferation, apoptosis, migration, homeostasis, and various disease processes. A number of miRNAs can regulate the physiological functions of vascular endothelial cells, and miRNAs related to vascular endothelial cell inflammation can become new targets for the treatment of cardiovascular and cerebrovascular diseases. In our current study, we found that the expression of rno-miR-126a-5p and rno-miR-146a-5p decreased under TNF-ADAMTS-4 . Many st pathway –13. Rno-α, and the role of piRNAs in endothelial cells is unknown. Previous studies reported that piRNAs exist only in germ cells; however, we found that piRNAs are also present in endothelial cells, although their specific functions need to be further elucidated.PiRNA is a type of small RNA with a length of approximately 30 nt and expressed in mammalian germ cells that plays its regulatory role only when associated with members of the PIWI protein family. At present, an increasing number of studies have shown that the regulation of the growth and development of germ cells by piRNAs is related to gene silencing caused by the Piwi-piRNA complex, but because research on piRNAs is still nascent, some of the specific functions of this complex are still under research. There are no reports on the relationship between piRNAs and TNF-α in endothelial cells. A number of snoRNAs were observed to be expressed in endothelial cells. In addition, we found that the expression of ENSMAUT00000006253.1 in tissues is vascular-specific; so, the role of snoRNAs in endothelial cells needs to be further explored.snoRNA is a type of small noncoding RNA widely distributed in the nucleolus of eukaryotic cells. It has conserved structural elements and is divided into three categories: box C/D snoRNA, box H/ACA snoRNA, and MRP RNA. Among them, box C/D and box H/ACA are the main types of known snoRNAs, which guide the methylation and pseudouridylation modification of ribosomal RNA by base pairing, respectively. Studies have found that in addition to playing a role in ribosomal RNA biosynthesis, snoRNAs can guide the posttranscriptional modification of snRNA, tRNA, and mRNA. In addition, there are a considerable number of snoRNAs with unknown functions, which are called orphan snoRNAs. Among mammalian orphan snoRNAs, imprinted snoRNA (imprinted snoRNA) is the most unique group, which is encoded by the genomic imprinted region and has obvious tissue expression specificity. The identification of snoRNA-like members in prokaryotic archaea indicates the ancient origin of these noncoding RNA family members, and the existence of a large number of snoRNA transposons in mammals provides a new way for people to explore the amplification and functional evolution of snoRNAs in the genome. We first studied the relationship between snoRNAs and TNF-α signaling pathway have not yet been reported. Similarly, we found that the expression of snRNA ENSRNOT00000085806.1 has vascular tissue specificity. The function and potential target of ENSRNOT00000085806.1 in endothelial cells will be explored and verified in our subsequent studies.snRNA is the main component of the RNA spliceosome in the posttranscriptional processing of eukaryotes and participates in the processing of mRNA precursors. In mammals, its length is approximately 100-215 nucleotides, and it is divided into 7 categories, numbered U1 ~ U7 based on the number of U residues. SnRNA exists only in the cell nucleus. Usually, snRNA does not exist freely but interacts with protein to form a complex called small nuclear ribonucleoprotein particle (snRNP). snRNA does not participate in protein synthesis activities but plays an important role in RNA processing. U3 snRNA is related to the maturation of 28S rRNA in the nucleolus, while U1 is related to the splicing and processing of precursor mRNA in the nucleus. The protein component of snRNA has nuclease and ligase activity, which can cut the transcript at the intron-exon junction and connect the two free ends. However, the role of snRNA in endothelial cells and its relationship with the TNF-Oxidative stress plays an important role in stroke, and endothelium inflammation is inevitable. Oxidative stress is often a trigger for inflammation. Increasing evidence demonstrates that oxidative stress responses participate in the pathophysiological processes of secondary brain injury following intracerebral haemorrhage, cerebral ischemia, and subarachnoid hemorrhage –17. Smalα stimulation. We found that TNF-α can regulate the expression of small RNAs in endothelial cells, and our study adds new information to better understand this phenomenon. This finding may shed light on the diagnosis and treatment of endothelial cell inflammation-related diseases. However, the function of small RNAs in endothelial cells needs further study.To our knowledge, no studies have reported the expression of small RNA in endothelial cells under TNF-


Antiretroviral therapy (ART) suppresses HIV-1 replication but does not eradicate the virus. Persistence of HIV-1 latent reservoirs in ART-treated individuals is considered the main obstacle to achieving an HIV-1 cure. However, these HIV-1 reservoirs are not transcriptionally silent, and viral transcripts can be detected in most ART-treated individuals. HIV-1 latency is regulated at the transcriptional and at multiple post-transcriptional levels. Here, we review recent insights into the possible contribution of viral RNA processing to the persistence of HIV-1 reservoirs, and discuss the clinical implications of persistence of viral RNA species in ART-treated individuals. In infected individuals who adhere to combination antiretroviral therapy (ART), HIV-1 replication is potently and durably suppressed, which restores the immune function and prevents the development of AIDS ,3,4,5. Lenv open reading frame of these RNAs .,22.21,22ese RNAs ,28,29. Iese RNAs ,31,32.In the pre-ART era, the relative dynamics of US and MS RNA in untreated HIV-infected individuals was studied by a number of laboratories. Several groups reported an association between the US/MS RNA ratio and disease progression: higher US/MS RNA ratios were measured in typical/rapid progressors, while slow progressors and long-term nonprogressors were characterized by low ratios ,35,36,37Remarkably, we recently demonstrated that the same US/MS RNA ratio, measured at 12 weeks of ART, was negatively predictive of the immunological response to ART (absolute and relative CD4+ T-cell count) at 48 and 96 weeks of therapy, outperforming a number of immunological biomarkers of T-cell activation, exhaustion, and apoptosis . MoreoveHIV-1 splicing is relatively inefficient, even in a productive infection, due to the relative weakness of the viral splice sites, except D1 and D4 ,47. Thestat/rev region, the absence of Tat can impose an additional block to the production of spliced HIV-1 RNA species. In addition, splicing requires the presence of several intact genomic regions, such as splice sites of exonic splicing enhancers. Therefore, US RNA+ cells probably harbor defective proviruses more frequently than MS RNA+ cells, and the MS RNA transcription competence may be a more proximal surrogate of proviral replication competence compared to the US RNA transcription competence [However, even upon full stimulation, MS RNA levels remain much lower than US RNA levels, and MS RNA+ cells remain much rarer than US RNA+ cells ,60. In pmpetence ,56,66. Tmpetence . Neverthmpetence ,67,68,69mpetence . This sumpetence ,70,71.In addition to the genuine lower copy numbers of MS RNA as compared to US RNA, MS RNA is also more challenging to detect in infected individuals by qPCR-based or digital PCR-based approaches due to primer/probe-template mismatches that occur more frequently with MS RNA assays. The reason for this difference is that any MS RNA-specific amplicon  should span the D4-A7 exon-exon junction, which strongly constrains the primer and probe design to two very short regions just 5′ of D4 and 3′ of A7. These regions are quite heterogeneous in sequence, even within one HIV-1 subtype, thus resulting in primer/probe-template mismatches. In contrast, the US RNA-specific assay design is much less constrained in terms of the genomic region targeted, allowing one to select highly conserved regions with much lower sequence heterogeneity. This means that the chance of under-quantitation  due to primer/probe-template mismatches is higher for MS than for US RNA. It is important to realize that not all primer/probe-template mismatches, even if a small deletion at the target site is present, are due to defective proviruses and in many cases the provirus will still be intact despite harboring a sequence variation at the primer/probe binding site. However, the differences between US and MS RNA copy numbers have been measured even when patient-matched primers and probes were used, arguing that detection issues only partially contribute to the observed differences in abundance between the US and MS HIV-1 RNA species ,72.BACH2, MKL2, STAT5B, and others, in cells from ART-treated individuals [HIV-1 latent reservoirs are believed to persist primarily by cell longevity and proliferation . Studiesividuals ,75,76. Iividuals ,78. Indeividuals ,79,80 orividuals . These cividuals ,79 and iividuals . By CRISIf such aberrant splicing patterns are indeed beneficial to the persistence of both the provirus and the infected host cell, then one would expect them to be selected with time on ART, especially in expanded cell clones. Such in vivo selection has yet to be demonstrated, but Pinzone et al. observed that, with time, ART-treated individuals select for defective proviruses that retain the D4 splice donor site . The sampol, nef), but it is unclear whether this reflects a specific latency block to polyadenylation or simply a gradual decrease in the transcript copy number with increasing transcript size, due to premature termination of transcription during elongation [Not much is currently known about a possible role of other HIV-1 RNA processing steps such as capping, polyadenylation, and nuclear export in viral persistence. Yukl et al.  observedongation ,85. MS Rongation ,87. Thisongation . In addiongation ,89,90. Iongation . Effectsongation . Other hongation .Although HIV-1 latency is clearly regulated at both transcriptional and post-transcriptional steps of viral gene expression, it is still unclear whether RNA processing has a critical role in the persistence of the viral reservoirs. HIV-1 has evolved weak splice sites to allow export of US and incompletely spliced RNA species, but this may also lead to aberrant virus–host splicing events. If such aberrant splicing leads to changes in the expression of cellular genes involved in cellular proliferation, this can enhance HIV-1 persistence in expanded cellular clones. At the same time, these virus–host splicing events can inhibit the use of A7 splice acceptor site, and therefore suppress MS RNA production, potentially inducing a state of “deep latency”. In light of the recent efforts to develop new therapeutic interventions that could induce a “blocked and locked” state of HIV-1 proviruses ,93, HIV-


Extracellular vesicles (EVs) are 50–1000 nm vesicles secreted by virtually any cell type in the body. They are expected to transfer information from one cell or tissue to another in a short- or long-distance way. RNA-based transfer of information via EVs at long distances is an interesting well-worn hypothesis which is ~15 years old. We review from a quantitative point of view the different facets of this hypothesis, ranging from natural RNA loading in EVs, EV pharmacokinetic modeling, EV targeting, endosomal escape and RNA delivery efficiency. Despite the unique intracellular delivery properties endowed by EVs, we show that the transfer of RNA naturally present in EVs might be limited in a physiological context and discuss the lessons we can learn from this example to design efficient RNA-loaded engineered EVs for biotherapies. We also discuss other potential EV mediated information transfer mechanisms, among which are ligand–receptor mechanisms. Extracellular vesicles (EVs) are sub-cellular entities delineated by a lipid bilayer, containing biomolecules from parental cells, released either spontaneously or after induction. EVs are subcellular entities that partly reflect the composition of their parental cells, containing a portion of parental cell cytosol  encapsulated by a bilayer membrane with membrane proteins, lipids, etc. EVs contribute to intercellular communication by delivering a variety of bio-molecule cargo like nucleic acids, proteins, and lipids that modify the recipient cells. Their composition depends on the mother cells and on the environmental cues triggering EV secretion. Extracellular vesicles are usually described in three common subtypes: exosomes are 50–200 nm entities produced in multivesicular bodies and secreted after fusion with the plasma membrane, microvesicles are 100–1000 nm vesicles shed directly by the plasma membrane by budding and apoptotic bodies are 50–5000 nm objects secreted specifically during apoptotic cell death. All these objects are found mixed together in biofluids, and most of the time are called by the generic term extracellular vesicles, as our ability to specifically enrich or purify and characterize a specific subset is very limited, leading to complex mixtures in most cases. This terminology is strongly recommended by the International Society of Extracellular Vesicles , immunomThe hypothesis that EV’s main activity could be due to RNA transfer has been raised in the seminal publication from Valadi et al.  in 2007.Since then, a vast sum of articles has been published to describe how EVs may be a natural vector for information transfer through RNA  and how As in the study by Valadi et al. , the preThe field also benefited from the interest of researchers at the intersection between three emerging and attractive domains: RNA and si/miRNA biology, drug delivery and extracellular vesicles’ biology. EVs were subsequently discussed as potential interesting biogenic nanovectors for RNA as an alternative to synthetic vectors with specific advantages. Compared to synthetic lipid nanoparticles, EVs’ intrinsic “biological nature” might be responsible for their limited toxicological profile, low immunogenicity, extravasation in tissue, crossing biological barriers like the blood–brain barrier, the ability to target specific cell types, the ability to fuse with cell membrane with exceptional endosomal escape and cargo delivery. A substantial amount of papers later on described the use of EVs to deliver various drugs ranging from small molecules like chemotherapies to complex biomolecules including proteins, receptors or nucleic acids  ,12. Howe9 EVs in the mouse striatum, i.e., about 6 × 106 cells or 210 EVs/cell) is highly supra-physiologic compared to what was detected in the first experiments (0.3% of cells containing EVs) [In glioblastoma (GBM), Abels et al. describe short distance communication through EVs from tumor cells to microglia to induce microglia reprogramming. The presence of EVs was detected in 0.3% of microglial cells, and the presence of the miRNA of interest transferred by EVs was detected in these sorted 0.3% of cells. However, no clear target protein silencing was found . It may be possible that the miRNA detected would partly be coming from the retention of EVs (and its associated miRNA) in endosome of microglial cells, without intracytosolic delivery. Furthermore, to clarify that the mechanism was mediated by EVs, they injected EVs in mice brains, but the dose used (1.26 × 10ing EVs) .Lucero et al. demonstrate the short distance effect of glioblastoma-derived EVs to induce angiogenesis via miRNAs in vitro, and claim it to be also valid in humans only based on a correlation with a human glioblastoma transcriptomic “fingerprint” . HoweverShen et al. demonstrate the effect of EVs derived from tumors to induce stemness via miRNA in surrounding cells in vitro (at supra physiologic doses) and claim it to be also valid in vivo in tumor-bearing mice. However, they used Rab7 KO tumors as a control to inhibit EV production, a KO that also has a lot of other side effects . It is tYing et al. demonstrate a role for miR-155 transferred by EVs in vitro in glucose tolerance and use an elegant system of bone marrow transplantation to investigate the role of hematopoietic derived miR-155 in a KO mouse. They later claim that the partial rescue of physiologic glucose tolerance is mediated by EVs in vivo although it may also be mediated by other intercellular transfer mechanisms like tunneling nanotubes (TNT), especially to transfer at short distance miRNA from a very macrophage-rich organ like liver to surrounding hepatocytes . The samChen et al. claimed that miR-375 overexpressing EVs were able to promote bone regeneration but the effect in vivo is not significantly different from the EV control group [Thomou et al. help us to raise other non-trivial questions on vesicular versus non vesicular mediated RNA transfer. He proposed that EVs from adipose tissue would be able to transfer miRNA to liver cells and induce RNA silencing in vivo. The protein expression is reduced by up to ∼95% after injection of serum-derived EVs (from donor mice with brown adipose tissue expressing the miRNA of interest) to miR-KO mice . Strictlintra-cytosolic transfer of at least one CRE protein or RNA). More importantly, it may be biased by the transfer of a single CRE recombinase protein (instead of CRE-mRNA) from the donor EVs, although it was not detected in these articles. On the contrary other teams clearly reported the presence of CRE protein in EVs produced from CRE-producing cells [pre-existing phagocytosis phenotype even before EV absorption. Other authors proposed other potential biases in this kind of experiment, like the change of phenotype due to liposome/EV absorption by cells [Other teams claimed the demonstration of an efficient transfer of CRE-mRNA via EVs ,28,29 inng cells . Neverthby cells ,33.Many teams reported an (mi)RNA-based mechanism of action for EVs. Most proofs of concept are however in vitro, and in vivo data are relatively scarce or not designed to claim an RNA based mechanism of action delivered via EVs at long distances and/or limited to short-distance transfer : In glioAlthough the in vivo relevance of RNA-mediated information transfer via EVs lacks clear demonstration in physio-pathological settings, plenty of proofs-of-concept are described in vitro with efficient silencing efficacies . This maOf note, apart from the classical “EV travel to the other organ” mechanism of information transfer to distant locations, this transfer may also happen via EV transfer at short distance to a circulating cell , that can later on reach other organs to exert an effect.4), enrichment may largely be due to the nonspecific size selection biased to the smaller sizes such as tRNAs. As an example, 16 S RNA , a typical medium-size RNA has a hydrodynamic diameter of ∼30 nm [Sverdlov claimed that it is very unlikely that naturally circulating EVs transfer a significant part of information through RNA in vivo at long distances in physiological states . He arguf ∼30 nm , whereasf ∼30 nm . Before f ∼30 nm , most off ∼30 nm , the pref ∼30 nm  on the 1f ∼30 nm ,46, a fif ∼30 nm , may par10 EVs dosed by bulk representing ∼6500 RNA molecules per EV [Sverdlov proposed a rough approximation of the maximal amount of RNA per EV if they are densely packed in EV of 100 nm diameter: ∼1600 RNA/EV for 1000-nt RNA and ∼6700 RNAs/EVs for 200 nts RNA. However, when measured by total RNA quantification , the nums per EV , but thes per EV ,50,51,52s per EV . Knowings per EV  use a dis per EV , one canAlthough miRNA levels in EVs seem too low and not packaged in order to be effective, one can expect that other coding mRNA may explain the therapeutic effects mediated by EVs, especially if they are protected by EVs from serum RNAse. Once again, quantitative analysis gives us insights into this hypothesis. Even more than for miRNA, RNA amount in EVs is very low and biased toward small RNAs (<300 nts) that are mostly fragments of degraded mRNA . Altogether, it indicates that mRNA can hardly be a good candidate for EV mechanism of action. This data review gives a glimpse of the natural amount of RNA molecules  in EV cargo, an unexpectedly limited one. Hence, in the therapeutic use of EVs relying on RNA transfer, one should not count on naturally present RNA molecules in EVs. Instead, efficient engineering method are needed to optimize the loading of RNA in EVs. Once loaded with RNA, in order to exert an effect at a significant distance, EVs have to get inside the bloodstream to reach other tissues. If not produced directly inside the bloodstream, they are secreted in interstitial fluid to later on get to the bloodstream. The main physiological barrier to the long-distance travel of EVs from organs is probably their limited ability to get to the bloodstream. There is a probably significant recapture by surrounding cells in the interstitial fluid (ISF) before getting to the bloodstream. Indeed, the amount of interstitial fluid is expected to represent about 16% of the body weight (11 L in a 70 kg adult), and the concentration of EVs in ISF is on overall ∼12 times more concentrated than in plasma . The ISFCtot(EV) is the total concentration of EVs in the blood (in EV/L), Vol(plasma) is the volume of plasma in the organism (3 L for humans), f(EV subtype) is the fraction of a particular EV subtype of interest,t is the number of minutes per day (1440), f(target tissue) is the fraction of EVs that target a particular tissue of interest and Nb Cell(tissue) is the number of cell in the tissue of interest. Of note, this model does not take into account the excretion of EVs in urine and other biofluids as it is considered to be negligible in biodistribution studies [A simple model taking into consideration key EV pharmacokinetic parameters helps to get an idea of what happens in physiological conditions for EVs navigating in the bloodstream. Indeed, the amount of EVs received per cell from blood per 24 h can be estimated by the following Equation (1):  studies . FurtherIt has to be noted that the limited half-life calculated in mice is usually longer in humans  for simi14/day, the mean number of EVs received per cell is ∼11.5 although it largely varies from an organ to another, e.g., is ∼1069/day per liver cell and 0.7/day per brain cell. On another side, significant variations occur depending on the subtype of interest considered: contrary to a quite common vision, most (99.8%) of EVs in the blood come from hematopoietic cells whereas only 0.2% of them come from non-hematopoietic tissues, most of them (81%) coming from adipose tissue. Therefore, the distant effect of a particular organ , a relatively small mass compared to the number of cells that die every day in our body, i.e., ∼1011 per day [13 cells, although a fraction may rather be lost by desquamation and not transferred to surrounding cells) that would then be transferred by apoptotic bodies to surrounding cells at a short distance. Most cells in the body in physiologic conditions die and transfer their content to the surrounding cells via apoptosis and subsequent apoptotic bodies, one of the subtypes of EVs. Interestingly, this comparison gives an insight on the relative importance of both long distance communication via EV (∼0.39 g of tissue transferred from one cell to another) versus short distance communication inside tissues (or lymph) that do not usually reach the bloodstream (representing ∼100 g of tissue transferred from one cell to another). It also shows the relative importance of relatively small EVs found in the bloodstream (mostly exosomes and microvesicles) versus the amount of small and large EVs  exchanged inside tissues. More details on the quantitative data on RNA transfer at short distances are discussed in Using this equation, the mean total amount of EVs received from blood from all kinds of parental cells by all cells of the organism is estimated to be ∼4.3 × 10 per day , represeSpecial attention should be paid to the short estimated EV half-life compared to most hormones, proteins and drug delivery systems. Indeed, apart from being an interesting parameter for modelization, it is also an important driver to control the potential specific targeting of a tissue/cell type by EVs. Indeed, the whole blood volume circulates ∼3 times/min , and theBox 18 EV/mL range , whereas the concentration in plasma is ∼109 EV/mL and concentration in ISF is ∼12 fold higher, i.e., probably ∼1.2 × 109 EV/mL [Haimovich et al.  recently09 EV/mL . Of noteAltogether, these assumptions give a quantitative insight on how difficult the long-distance trip of a particular EV may be to a particular cell of interest in a distant organ. Once in the bloodstream, long-distance travel of EVs is mostly limited by their non specific capture and subsequent elimination by RES, and this has proved true for all kinds of EVs tested from hematopoietic origin or not ,65. HoweOnce EVs reach an acceptor cell, the way they are internalized and the mechanisms by which they deliver their cargo are still not fully clarified. De Jong et al. reported with an interesting CRISPR-based system that delivery was partly dependent on micropinocytosis  or endocytosis (in particular Cav1 and RhoA) . It is n6 RNA molecule per cell). In that case, one may see that the amount of RNA delivered in these conditions by EVs is small . For comparison, it is estimated that the number of proteins per EV is ∼500–2000 proteins [Preceding discussions show us that once a vesicle of interest, on average containing ∼1 miRNA (among about 100 different ones) and ∼100 small RNAs (∼200 nts) reaches a cell, it has about 20% chance to liberate its cargo. Taking into consideration the average number of natural EVs received per day in physiological conditions that vary from ∼0.7 (brain) to 1000 (liver) per cell (with a mean at ∼10), the amount of a specific miRNA received ranges from ∼0.014 to 2 per day . This number has to be considered relative to the reported number of miRNA/cell that varies from 115,330 copies in mice liver cells  and 11,587 in hematopoietic stem cells . Differeproteins . 10 to 1012 EV/mouse depending on EV preparation and dosage methods. If we consider ∼1 miRNA per EV, this dose represents ∼1010 to 1012 miRNA per dose, corresponding to about ∼0.2–20 ng of miRNA/mouse or ∼0.016–1.6 pmol/mouse. siRNA doses reported to be efficient in vivo in systemic injections are rather in the microgram range  for effects mediated by non-modified native EVs in therapeutic conditions has previously been challenged by comparing it to data obtained from siRNA experiments. In most preclinical studies, EV doses usually range from ∼1 to 200 µg per mouse , correspµg/mouse ,83). Oneefficacy ,80. Howe4) were reported in the delivery efficacy in favor of EVs in vitro [Indeed, recently reported delivery efficacy of EVs obtained in vivo show a ∼10–300 fold improvement in favor of EVs  comparedin vitro . Importain vitro . Box 2Quantitative data on EVs present in pathological conditions  are relatively limited. Here, we propose working hypotheses based on little literature data. Quantitative hypotheses are used here as a way to estimate a maximal limit of RNA transfer in pathological conditions and to discuss whether this upper limit may be sufficient to achieve a sufficient amount of RNA transfer at a long distance to achieve an effect. Loading of RNA in pathological conditions  may be different from physiological ones, but it should still recapitulate parental cell RNA with a bias toward small RNAs with a total amount of miRNA per EV of about 1 as the total concentration of RNA does not substantially change in most pathological conditions. Nonetheless, it may largely increase the specific amount of an inflammation-induced (mi)RNA of interest up to 15 fold . We hypoIn total, when taking into account most favorable estimations of each parameter, we can calculate the following estimations: (i) In the inflammatory context, ∼2000 platelet-derived EVs/cell/day would reach liver cells (∼4 fold increase), and lead to the delivery of a maximal ∼80 miRNA molecules of interest in the cell cytosol (40 fold increase compared to non-inflammatory state) compared to an estimated >2000 copies of a specific miRNA needed to exert an effect ,78, (ii)3–104) in terms of the dose delivered to obtain an effect in vivo between siRNA delivered by synthetic vectors and miRNA in EVs may therefore be at least partly explained by the reported very good delivery efficacy of EVs (∼10–300 fold improvement), although other mechanisms of action might also contribute. Altogether, these quantitative estimates show . Whether it is sufficient to explain the therapeutic effect of MSC-EVs seems once again unlikely in systemic administrations, apart from a very efficient RNA “cocktail effect”. Local administrations  of similar doses (1012–1013) would lead to the delivery of ∼18–180 EVs and ∼0.35–3.5 intra-cytosolic copies of an miRNA of interest to each cell. It seems also difficult therefore that unmodified EVs’ therapeutic effect may be mostly mediated by RNAs.RNA delivery has been reported to be the mechanism of action of natural EVs administrated for therapeutic purposes like MSC-EVs . The maiAlthough unmodified EVs may difficultly have an effect via their RNA cargo, EVs may be very interesting vectors for synthetic delivery of exogenously loaded RNA (mi/si/mRNA). Exogenously EV-loaded synthetic RNA delivery for therapeutic purposes have been reported to treat various pathological conditions ,102. The5 copies per EV [intra-vesicular) encapsulation, as an example, Reshke et al. reported the loading of 1 siRNA per EV [intra- versus extra-vesicular loading has demonstrated to be of a real importance. Proof of concept studies of siRNA delivery in vivo using EVs classified in these two categories lead to very different results: (i) siRNA loaded with electroporation (aggregated to EV) or EV coated with siRNA (lipid conjugated siRNA) need relatively high doses of siRNA to induce target knockdown  only need doses of about 10–300 ng/kg [intra-vesicularly loaded EVs, an estimated human dose would then be ∼3 × 1013–9 ×1014 if calculated using a linear relation from mice to human and ∼2 × 1012–7 × 1013 if calculated taking using FDA guidelines [The major difference of engineered EVs compared to the above described example of MSC-EVs is the high enrichment in the (mi/si)RNA of interest, in theory up to ∼60,000 for densely packed siRNA (21 nt) in 100 nm EV , although some papers report unexpected loading of up to 1.8 × 10s per EV   is usually not reported, and various team advocated for a common data reporting frame for EV loading [In terms of future improvement, achieving intra-vesicular loading  of ∼10% of the EV cytosol, a similar fraction of what is achieved with lipid nanoparticle (LNP) would already be very interesting for EV based therapeutics as it would already represent ∼6000 siRNA per EV. Of note, as it was reviewed and discuss recently ,112, intThe search for EV mechanism of action, as expected by regulatory agencies for drug approval, is still a partially answered question. The complexity of EVs assessed by omics tools like proteomics shows us that EV content is nearly as diverse as cellular ones. Therefore, the analogy with cell therapy’s mechanism of action may be of interest. As an example, mesenchymal stem cell (MSC) based cell therapies  can hardly be explained by one specific pathway, and is now considered to be the result of a multifactorial effect involving both IDO-1 , PGE2, NSimilarly, dendritic cell-derived EVs (Dexosomes) were expected to mediate an effect via MHC class II peptide presentation , whereaAs discussed above, an interesting observation is that EV exerts a large part of their effect in <60 min after their administration . ThiFinally, one may keep in mind that EV purification strategies may still largely be improved, as depicted by the very large range of the particle/microgram ratio (a widely reported purity marker) used in the published papers. EV preparations are therefore probably most of the time contaminated by protein aggregates and cytokines that may explain a large fraction of the EV effect, as was demonstrated by Whittaker et al. in a very interesting article . Of noteQuantitative estimates from simplified pharmacokinetic and pharmacodynamic models based on quantitative data reported in the field suggest that information transfer through RNA by naturally circulating EVs may be limited in terms of efficacy at long distances. We suggest that if it exists, it may rather be at a short distance and/or in the reticulo-endothelial-system ,84. We c


Extracellular vesicles (EVs) are important for intercellular communication and act as vehicles for biological material, such as various classes of coding and non-coding RNAs, a few of which were shown to selectively target into vesicles. However, protein factors, mechanisms, and sequence elements contributing to this specificity remain largely elusive. Here, we use a reporter system that results in different types of modified transcripts to decipher the specificity determinants of RNAs released into EVs. First, we found that small RNAs are more efficiently packaged into EVs than large ones, and second, we determined absolute quantities for several endogenous RNA transcripts in EVs . We show that RNA polymerase III (pol III) transcripts are more efficiently secreted into EVs compared to pol II-derived transcripts. Surprisingly, our quantitative analysis revealed no RNA accumulation in the vesicles relative to the total cellular levels, based on both overexpressed reporter transcripts and endogenous RNAs. RNA appears to be EV-associated only at low copy numbers, ranging between 0.02 and 1 molecule per EV. This RNA association may reflect internal EV encapsulation or a less tightly bound state at the vesicle surface. Extracellular vesicles (EVs) are omnipresent lipid particles released by nearly every cell and are involved in cell–cell communication ,2,3. As Apart from the identification of distinct RNA classes, the question of a specific and direct loading into EVs has remained unanswered. Some mechanisms have been proposed, such as protein-mediated secretion of RNAs, in which RNA-binding proteins assist with selective release of RNAs to EVs, some through recognition of RNA-binding motifs, in particular for miRNAs ,25,26,27Here, we used different reporter expression constructs to determine which RNA characteristics mediate the preferential release of RNAs into vesicles. In addition to a length dependence, we were able to show that RNA polymerase III transcripts are preferentially associated with EVs. In addition, we did absolute quantification of various endogenous RNAs in EVs versus total cells. In sum, EV-mediated RNA secretion appears to be inefficient and only a few RNA molecules are present within each vesicle. Our results and EV-RNA quantitation should contribute to our mechanistic understanding of EV-mediated transfer and functional range of RNAs.2 in Dulbecco’s Modified Eagle Medium  supplemented with 10% fetal bovine serum . For EV production, between 8 × 106 and 2 × 107 cells were seeded per T175 flask , depending on the cell line. After 24 h, the medium was changed to CD293 medium  supplemented with 1% penicillin-streptomycin and 1x GlutaMAX™ , followed by cell culture for 48 h. To generate EV-depleted FBS, FBS-derived EVs were pelleted by ultracentrifugation at 110,000× g at 4 °C for 18 h. The EV-depleted supernatant was mixed with DMEM to obtain a final concentration of 5%.All cell lines A549, ES-2, HEK293, HepG2, and U373 were maintained at 37 °C in 5% CO6 cells per 15-cm dish (Greiner Bio-One GmbH) were seeded in DMEM supplemented with 10% FBS one day before transfection. Transfection was performed by TurboFect , using 20 µg plasmid per plate. After 4 h, the medium was changed to DMEM supplemented with 5% EV-depleted FBS, and cells were cultured for 24 h.For transfection, 5–6 × 10g for 10 min, and second, at 5000× g for 5 min at 4 °C. Supernatants (200 mL) were filtered through a 0.22 µm pore filter , followed by ultrafiltration through centrifugal filters  to a final volume of 500 µL. Subsequently, the concentrated EV samples were subjected to size exclusion chromatography using qEVoriginal 70-nm columns . The EV-containing fractions were pooled and subjected to a final ultracentrifugation step at 110,000× g at 4 °C for two h . Finally, the EV pellet was resuspended in filtered 1 × PBS.EVs were isolated from the cell culture medium by differential centrifugation, first at 2000× 9 EVs were lysed with 2 × SDS sample buffer  and run on an SDS-12% polyacrylamide protein gel. Proteins were transferred to a nitrocellulose membrane  and membranes were blocked with 5% (w/v) milk powder (Carl Roth) in phosphate-buffered saline supplemented with 0.05% Tween (PBS-T) for one hour. Subsequently, membranes were probed using the following antibodies: mouse anti-human CD63 , rabbit anti-human Alix , and rat anti-human Flotillin-1  overnight at 4 °C in 5% milk in PBS-T. The blot was washed three times with PBS-T and then incubated with HRP-conjugated anti-mouse, anti-rabbit, and anti-rat secondary antibodies , respectively, for one hour at room temperature. After washing three times with PBS-T, the blots were incubated for five minutes at room temperature with Lumi-Light Western Blotting Substrate  and visualized with Amersham HyperfilmTM ECL .The 1 × 10EVs were prepared for electron microscopy as described previously  with few8 particles/mL, which were also used as a reference for particle concentration. In addition, monodisperse silica beads (NanoFCM Co. Ltd.) of four different sizes served as size reference standards to calibrate the size of EVs. Freshly filtered (0.22 µm) 1 × PBS was analyzed as background signal and subtracted from the other measurements. Each distribution histogram or dot plot was derived from data collected for one minute with a sample pressure of 1.0 kPa. The EV samples were diluted with filtered (0.1 µm) 1 × PBS, resulting in a particle count in the optimal range of 2500–12,000 events. Particle concentration and size distribution were calculated using the NanoFCM software .For NanoFCM, a Nano Analyzer  equipped with a 488 nm laser, was calibrated using 200 nm polystyrene beads (NanoFCM Co.) with a defined concentration of 2.08 × 10g for 10 min, the supernatant was added to 2 × 108 purified EVs, followed by incubation overnight at 4 °C under constant shaking and washing with 1 mL 1x PBS by ultracentrifugation at 110,000× g for 60 min at 4 °C . The pellet was resuspended in 50 μL 1 × PBS for NanoFCM analysis.For immunofluorescence staining, the following antibodies were used (BioLegend): FITC-conjugated mouse anti-human CD9 antibody (clone HI9a), FITC-conjugated mouse anti-human CD81 antibody (clone TAPA-1), and PE-conjugated mouse anti-human CD63 antibody (clone H5C6); as isotype controls, FITC-conjugated mouse IgG1, κ (clone MOCP-21), and PE-conjugated mouse IgG2a, κ (MOPC-173), 2 ng/µL of each antibody in 100 µL 1 × PBS. After removing antibody aggregates by centrifugation at 12,000× https://primer3.ut.ee/; accessed on 30 September 2021). All oligonucleotides can be found in ® Universal qPCR Master MIX; New England Biolabs, Ipswich, MA USA). Relative expression levels of transcripts in cells and EVs were calculated by the ΔΔCt-method, with each target normalized to Y1 and vault RNA [−ΔΔCt) and normalized to the geometric mean of two housekeeping genes. For absolute quantification, RNA standards were generated by in vitro transcription , based on templates carrying the T7 promoter and the sequences of U6 snRNA, Y1 RNA and U1 snRNA, respectively. Moreover, 50 ng of each standard was reverse-transcribed  and diluted in six 10-fold steps (50 ng to 5 fg). Based on this, a standard curve was calculated with three replicates for standard deviations. Absolute concentrations of the samples were determined using the following equation:RNA was isolated using the Total RNA Purification Kit , followed by analysis of RNA concentration and integrity . For RT-PCR, 1 ng of total EV-RNA and 1000 ng of total cellular RNA were used for cDNA synthesis , with primers specific for the constant region of the reporter constructs. All primers were designed using primer3 version 4.0  and PCR, using primers against GAPDH, U6 snRNA and Y1 RNA. For standard agarose gel electrophoresis of RT-PCR products, DNA marker was used GeneRuler Ladder Mix with 500, 400, 300, 200, and 100 bp (Thermo Fisher Scientific).For Xrn1 degradation assays, 200 ng total RNA from EVs and cells were digested with 2 units of Xrn1 (New England Biolabs) for one hour at 37 °C. RNA was purified using MonarchHindIII at the 5′- and Xba1 at the 3′ end. For the Pol III/U6-term construct, first, the CMV promoter in pcDNA3.1 was deleted and replaced by a synthetic insert  consisting of the U6 promoter, a multiple cloning site, and the U6 termination signal (pcDNA3_U6). Then a synthetic fragment consisting of the random-generated 40-bp sequence and the unique flanking constant regions was inserted (see above). For sequences, see Pol II/poly(A), pol II/U1-3′ box, und pol III/U6-term: based on pCDNA3 as vector, RNA expression constructs were designed and synthesized , each containing the same random-generated sequence of 40 bp and unique flanking constant regions (20 bp each). The insert for Pol II/U1-3′ box additionally contained the sequence of the U1-3′-box at the 3′ end. Both inserts were flanked by restriction sites for HindIII and XbaI sites and generating length constructs 80, 120, 200, 360, 680.Length constructs : Based on the pcDNA3_U6 vector (see above), a series of constructs with inserts of different lengths was derived. The synthetic inserts  consist of two 40-bp sequences of multiple copies thereof, inserted between the 6 cells/mL and 350 mL supernatant) was mixed with glyoxal loading dye (Ambion) and incubated at 50 °C for 30 min. RNA was separated by agarose gel electrophoresis (1.2% in 1 × MOPS buffer). The gel was transferred onto a nylon membrane by semidry blotting followed by RNA crosslinking using UV-light  and probed with a single-stranded RNA probe , directed against GAPDH mRNA. Hybridization was performed in NorthernMax buffer (Thermo Fisher Scientific), and washes of the blot and probe detection with alkaline phosphatase-conjugated anti-DIG-Fab fragments were done as described in the Roche manual (Roche).For glyoxal Northern blot analysis, 300 ng of both total RNA from cells and the corresponding EV-RNA  2018 guidelines [To obtain a more detailed view of our isolated samples, we also conducted single-particle analysis by NanoFCM e. The th5, for pol II/U1-3′ box 1.8 × 105, and for pol II/poly(A) 1.8 × 105 molecules per cell, whereas the number of RNA molecules inside EVs was quite low. The most abundant detectable RNA inside EVs was the pol III/U6-term with 0.1 molecules per EV. Both overexpressed pol II/U1-3′ box and pol II/poly(A) RNAs were even less abundant with 0.02 and 0.003 molecules per EV, respectively , Y RNA, vault RNA, and U6 snRNA. To determine more systematically whether RNAs are EV-loaded in an RNA polymerase-dependent manner, we used reporter constructs and compared three different expression platforms, based on three constructs a: all thectively b.Since several studies indicate that smaller RNAs are preferentially associated with EVs, we designed five pol III promoter-based reporter constructs, expressing RNAs of different lengths with a random sequence , U373 (glioblastoma), HepG2 (hepatocyte carcinoma), A549 (lung adenocarcinoma), in addition to HEK293 cells. Purified EVs of all these cell lines were measured by NanoFCM , followe6) were used to evaluate the presence of a poly(A) tail. In the dN6 approach, U6 snRNA and Y1 RNA were detectable in cells as well as in EVs, whereas ß-actin (ACTB), could not be detected. However, if cDNA synthesis was performed with oligo(dT) instead, the Y1 RNA signal was strongly reduced, as expected for a non-polyadenylated RNA. In contrast, the signals for GAPDH and ACTB mRNA remained similar in cellular RNA, comparing oligo(dT) and dN6 approaches, but the GAPDH signal strongly decreased for the EV-RNA in the oligo(dT) assay. This suggests that GAPDH mRNA exists predominantly in a non-polyadenylated form when EV-associated. To further confirm this quantitatively, we also performed RT-qPCR  tail for EV-associated GAPDH mRNA.The specific characteristics of the individual RNAs within EVs have not yet been fully clarified. In particular, whether mRNAs that are detectable in EVs exist in full-length copies and carry of a functional poly(A) tail, could not yet be verified in sufficient detail. To initially analyze this, we isolated RNA from ES-2 cells and their corresponding EVs, followed by semi-quantitative RT-PCR assays a. For cD RT-qPCR b, which 7G cap remain protected from degradation. After Xrn1 treatment, the tested GAPDH mRNA was no longer detectable in the EV fraction, while the signal in the cellular fraction remained. In contrast, U6 snRNA with its γ-monomethyl cap was not a substrate for Xrn1 and, thus, remained stable. The same applied to the Y1 RNA signal  was used. The GAPDH signal detected in cells corresponds to the expected size of the GAPDH mRNA  tail). However, the signal in EVs appears to be weaker and, furthermore, the band runs slightly below the cellular GAPDH mRNA, which indicates a truncated form . To assaA signal d. In sumResearch activities on EV-associated RNAs have recently focused on miRNAs, as they represent suitable candidates for biomarkers and, thus, harbor great potential for the diagnosis and early detection of diseases . In addiThe initial assumption that RNA represents one of the major constituents of EVs has recently been challenged by several quantitative studies. In particular, miRNAs were found as stable extracellular RNAs in non-vesicular fractions . TherefoWhy are small RNAs preferentially secreted? On the one hand, it may represent a disposal pathway whereby RNA fragments are packaged into EVs, or on the other hand, it may be explained by the fact that there is simply not enough space within the vesicle for larger RNA molecules. Assuming that an RNA of approximately 1000 nts can be organized in an ellipsoidal structure of approximately 40 nm in diameter  and a siAt the same time, we were able to clearly show that the RNA-loading capacity of EVs appears to be limited to a surprisingly low number of molecules, based on measuring absolute copy numbers of specific RNA transcripts, both after overexpression of transcripts and for endogenous RNAs , as well as by comparing EVs from five different cell lines. Since this limits the application of EVs as effective cargo vehicles, it would be useful to evaluate also expression platforms other than CMV- or U6 promoter-based ones, or other cell lines, as alternatives.Complementary to artificial transcripts generated by different expression platforms, we quantitatively compared EV packaging of a set of natural RNAs that represent different biogenesis pathways resulting in different terminal modifications: Y1 RNA, U1, U6 snRNA, and GAPDH mRNA. Y1 RNA and U6 snRNA are Pol III-derived, both exhibiting different 5′ modifications  tail. Because of this limitation to two mRNAs, we cannot exclude that there may indeed be a transfer of a set of translatable mRNAs into EVs, as shown in several studies ,44,45.


Analysis of PIP-seq data from a model of erythropoiesis reveals dynamics of RBP–RNA interaction and changes in RNA secondary structure throughout red blood cell development. Two features of eukaryotic RNA molecules that regulate their post-transcriptional fates are RNA secondary structure and RNA-binding protein (RBP) interaction sites. However, a comprehensive global overview of the dynamic nature of these sequence features during erythropoiesis has never been obtained. Here, we use our ribonuclease-mediated structure and RBP-binding site mapping approach to reveal the global landscape of RNA secondary structure and RBP–RNA interaction sites and the dynamics of these features during this important developmental process. We identify dynamic patterns of RNA secondary structure and RBP binding throughout the process and determine a set of corresponding protein-bound sequence motifs along with their dynamic structural and RBP-binding contexts. Finally, using these dynamically bound sequences, we identify a number of RBPs that have known and putative key functions in post-transcriptional regulation during mammalian erythropoiesis. In total, this global analysis reveals new post-transcriptional regulators of mammalian blood cell development. In eukaryotic systems, RNA-binding proteins (RBPs) interact with RNAs from synthesis to decay, thereby adding complexity to the transcriptome. A recent whole mRNA RBPome study identified several hundred RBPs in HeLa cells that have the capacity to play critical roles in determining RNA functions . These fRBPs have been shown to interact with their binding targets in a sequence and RNA secondary structure specific manner . SeveralTo date, the known repertoire of RBP–RNA interaction sites has been built on a protein-by-protein basis, with studies identifying the targets of a single protein of interest, often through the use of techniques such as crosslinking and immunoprecipitation sequencing (CLIP-seq) . In CLIPStudies on mammalian erythropoiesis have revealed that this process involves a series of complex and stage-specific changes in gene expression. Mature erythrocytes are derived from hematopoietic stem and progenitor cells, which undergo a series of increasingly restrictive lineage commitment events. Importantly, this process includes a significant dependence on post-transcriptional regulatory processes, especially during its terminal steps . TerminaHere, by performing PIP-seq on mouse erythroid leukemia (MEL) cells, we identify RBP-binding sites, provide a transcriptome-wide look at RNA secondary structure, and profile these RNA features and their interactions throughout the terminal stages of red blood cell development. Our results produce an unbiased view of RBP-binding events and RNA secondary structure changes that occur during the terminal stages in mammalian red blood cell development. In addition, the datasets provide a resource for future investigations of the functional importance of bound RNA regions and RNA secondary structures to this critical process of cellular differentiation.Murine erythroleukemia (MEL) cell lines are arrested at the proerythroblast stage of development, show very low level of spontaneous differentiation, can be maintained indefinitely in tissue culture, and can be induced to differentiate along the erythroid lineage through treatment with a variety of chemical compounds such as DMSO. Studies show that by day 3 of growth on DMSO, there is a 20-fold increase in heme uptake, and a 12-fold increase in hemoglobin synthesis compared to unstimulated cells and by day 4 the cells have mostly matured to normoblasts that stain positive for benzidine reflecting high levels of hemoglobin content .NM_001278161 (Hbb-b1), NM_016956 (Hbb-b2), NM_008218 (Hba-a1), NM_001083955 (Hba-a2), NM_001201391 (Hbb-bs), and NM_010405 (Hba-x) are among the genes whose abundance continuously increases throughout the three time points of differentiation used in this study . Previous work has also characterized a set of genes whose expression is repressed as MEL cells progressively commit to the erythroid lineage. We find that our data recapitulates several of these repressed marker genes . On the other hand, continuously decreasing transcripts showed an enrichment for those associated with nucleosome assembly  and other terms associated with DNA binding and gene regulation  enrichment analysis of the differentially abundant transcripts showed an enrichment for erythrocyte developmental genes in transcripts that are continuously increasing  . PIP-seqThe 24 resulting libraries  produced between 36 and 58 million raw reads per library. We first assessed the reproducibility of our technology by using a 1,000-nt sliding window approach to correlate the read abundance between the biological replicates of the footprinting or structure-only samples at each time point. Overall, we observed high Pearson correlation values between 0.985 and 0.989 , indicatTo identify PPSs in the three different MEL developmental stages, we used a Poisson distribution model to identify regions enriched in the footprinting samples as compared to the structure-only libraries for the three different time points at a FDR of 5% as previously described , 20, 21.P-value < 2.2 × 10−16; asymptotic t approximation)  for all detectable protein-coding transcripts across the three developmental time points as previously described , 23, 24.imation)  between P-value < 2.2 × 10−16; asymptotic t approximation)  surrounding the start and stop codons of mRNAs   between P-value < 2.2 × 10−16; χ2 test) more enriched for CLIP-detected PABPC1 and PABPC4-binding sites than a set of randomly selected background control set of sites . Specifically, 9.53% of day 0 PABPC1 CLIP sites overlapped with the day 0 PPSs by at least one nucleotide compared to 0.24% of the background control set. Similarly, 7.41% of day 2 PABPC1 CLIP sites overlapped a high confidence PPS site, while only 0.29% of the same sites overlapped the background sites  across the three developmental time points . On avernd sites . The samnd sites , 27; PPSnd sites .As outlined above, PIP-seq identified a total of 245,466 PPSs across the three developmental time points. These PPSs could be further classified into “control PPSs,” representing the PPSs that were found in undifferentiated cells , or “developmental PPSs,” which are the PPSs found only in differentiated cells . The rest of the PPSs were distributed among the possible combinations of time points, with 36,510 PPSs being shared between all three measured timepoints . The PPSP-values < 0.001, Kolmogorov-Smirnov test) higher PhastCon scores than their equally sized flanking regions that occur directly up- and downstream of the PPS   , indicatsection) .The current understanding of RBP–RNA interactions is that of a dynamic relationship where RBPs bind to specific locations along an RNA molecule depending on the RBP’s role in the RNA’s lifecycle. Thus, specific RBP–RNA interactions can occur in a cell and developmental specific context to affect a specific aspect of the target RNA molecule. In support of this, we witnessed the dynamics of these relationships in our PIP-seq data, as there were PPSs that are only detected after MEL cells have been induced to differentiate , and we When we measured the percentage of a transcript that was covered by a PPS at a given timepoint, we observed that some transcripts become increasingly bound throughout development, just as there were transcripts who become less covered as cells develop . After i−35, hypergeometric test), mRNA transport , and positive regulation of translation . Furthermore, of the 58 RNA-binding proteins that have established roles in the erythropoietic pathway according to their GO annotations, 13 are differentially abundant in our MEL model, with the majority of them showing decreased abundance during the course of this developmental process   . In the P-values < 0.001; Wilcoxon test) higher than those at day 0  transcripts, we wondered how much, if any, Hbb contributed to the overall patterns of RNA secondary structure and PPS density changes observed in our PIP-seq analysis. To answer that question, we re-analyzed our data after removing Hbb transcripts and found that neither the RNA secondary structure  nor the P-values < 3.97 × 10−4; asymptotic t approximation) in all three developmental time points  as compared with the 200 nt surrounding the stop codon, which demonstrated less, but still significant, anti-correlation in the two developmental time points  in RNA secondary structure as controls. When we looked at the change in differentiated cells from their undifferentiated state, we found that most of the enriched GO terms are involved in general biological pathways, including primary metabolic and nucleic acid metabolic process (−5)  . OverallNext, we used the change in the average RNA secondary structure score in the 200-nt window around the start codon to partition the transcripts into six distinct clusters. Each of the six clusters showed different dynamics in the pattern of changes in RNA secondary structure around the start codon. However, the changes in RNA secondary structure did not appear to correlate with changes in mRNA abundance or in PPS coverage. In fact, when comparing the mRNA abundance patterns for all six clusters, we noticed that they all exhibited a similar pattern despite how their RNA secondary structure changed throughout development . In addi−33) and cellular macromolecule metabolic process (FDR: 2.82 × 10−26). The next cluster with the most significant terms was cluster 3, which contained transcripts that increase and then decrease in RNA secondary structure in the developmental window that we probed. Cluster 3 showed an enrichment in the same terms as cluster 2, however to a lesser degree. Cluster 4, which has transcripts going from unpaired to a more paired state, and cluster 5, its converse, have no significant terms to note, potentially because of the small number of transcripts that fall within either cluster (69 for cluster 4 and 208 for cluster 5). Transcripts in cluster 6 started off being paired, changed to a more unpaired state upon differentiation, and then somewhat increased their overall RNA secondary structure score in the latest time point. These transcripts showed a very minor enrichment for the same terms that are also found in clusters 2 and 3   or the more specific abnormal erythropoiesis (FDR: 0.037) and abnormal blood cell morphology/development (FDR: 1.88 × 10−5) . This ob−5) , 21.As we’ve confirmed the presence of known RBP-binding sequences in our PPSs, we next sought to leverage our collection of PPSs to discover additional overrepresented sequences, which could be new binding sequences for RBPs that are key regulators of terminal erythropoiesis in our model. To do so, we first isolated protein interaction sites that overlapped the 50–100-nt window immediately downstream of annotated stop codons. We decided on this region because the large majority (∼90%) of annotated 3′ UTRs are at least 50 nts long and that window encompassed a region of change in RNA secondary structure where structure increases in induced cells when compared with uninduced cells . We thenP-value associated with the motif. We also took into account the information content of each nucleotide in the motif and tried to maximize the number of positions with high information content in each motif. Both selected motifs showed an increase in RNA secondary structure around the beginning and throughout the motif site at day 2, followed by a decrease in structure in day 4, although the trend was more obvious in the Motif 2 (day 4 3′ UTR motif) than Motif 1 (day 2 3′ UTR motif)  in differentiated cells and the PPS density decreases at day 2 and then increases to a level higher than day 0 by day 4 . These cR motif) . An addiR motif)  and has R motif) . To idenR motif)  by at leR motif) . From thR motif) . InteresR motif) ; TRIM10,R motif) ; and othR motif) . AlthougR motif) . NotablyAn additional approach to identify RBP-binding motifs that are functionally important for the post-transcriptional regulation of erythropoiesis is to start with PPSs found specifically in transcripts identified relevant to red blood cell development. Using the Ensembl database and the R biomaRt tool (version 2.40.3), we identified 248 mRNAs encoding proteins that make up the categories labeled erythropoiesis or hematopoiesis in the current mouse GO annotation. To test the accuracy of the annotation, we analyzed the GO annotation of these identified transcripts using DAVID  and veriAfter identifying these four motifs of interest , we then used reverse scanning on our entire collection of PPSs because we could not reliably restrict downstream RNA affinity tests to look at a specific genic region, to identify the number of total PPSs that contained each motif. This analysis revealed that motif 1 was found in a subset of 53,649 PPSs, motif 2 in 77,740 PPSs, motif 3 in 22,252 PPSs, and motif 4 in 2,296 PPSs. Although most of the PPSs were found within the intron region of transcripts, once the frequency was normalized to the overall distribution in the genome, PPSs were found to be positively enriched for these protein-bound motifs within the CDSs and both UTR regions of their respective transcripts , similarWe selected a small subset of motifs (Motifs 1–4) for downstream identification of their corresponding RBPs. These motifs were selected on the basis of being novel, being located in RNA regions with dynamic RNA secondary structure(s), and being enriched in hematopoiesis relevant transcripts. RNA affinity chromatography was followed by mass spectrometry analysis. In this technique, we covalently attached a synthetic RNA motif to agarose beads. We then incubated these RNA baits, as well as a bead-only control, with whole MEL cell protein lysates from the three developmental time points. The beads were then stringently washed and tightly bound proteins were pulled down and then identified via mass spectrometry (MS). Using this approach, we detected the presence of ∼647 proteins (Table S3) that potentially interacted with at least one of the four different motifs.2 (FC) values of double-stranded RBPs.LogTable S1 HOMER results.Table S2 iBAQ values of proteins pulled down using motifs 1–4 at 0-, 2-, or 4-d post induction.Table S3 To identify proteins that showed a stronger binding to a certain probe, we performed a fold change analysis on data normalized to the sample mean. To do this, we calculated the fold change in the normalized values for the protein of interest from that particular motif compared to the other three motifs and selected only proteins that were >10-fold enriched in at least one motif as compared with other probes. This analysis identified a smaller subset of proteins (n = 315) as being >10-fold enriched in at least one motif as compared with other probes   and 95 have the more general description of being nucleotide-binding . Although many terms relevant to RNA binding were significantly enriched with Bonferroni values < 0.05, the most significant terms were RNA binding and poly(A) RNA binding  or translation . Specifically, proteins pulled down by motifs 3 and 4 were highly enriched for those that are part of the mouse spliceosome, whereas those enriched in motifs 1 and 2 are involved to a smaller degree with RNA transport  . A prote < 0.05) . GO anal binding . GO analransport . This enTo further narrow the list of interacting proteins to those that are relevant to red blood cell development, we focused on RBPs that were enriched only after differentiation  but not in day 0. Motif 1 failed to pull down any proteins matching this criterion while DKC1 was pulled down using motif 2 in both day 2 and day 4 samples. Motif 3, which is a potential binding motif for an RBM38 interacting protein, had five proteins  that matched the requirement and motif 4, the poly A motif discovered in erythropoiesis relevant transcripts, had another five such proteins  . A KyotoPura deletion is detected in patients with acute myelogenous leukemia, linking this protein to a role in mammalian hematopoiesis  motif 4, showed no identified abnormal hematopoietic phenotype in mouse models, it is a RBP recently noted to be lost in the 5qn humans . Furtherhenotype .Hnrnph1 have phenotypic features such as decreased mean corpuscular hemoglobin concentration and increased mean corpuscular volume according to the Mouse Genome Database  in mice and reticulocytopenia (Z-score: 3.9), abnormality of cells of the erythroid lineage (Z-score: 3.497), and abnormal number of erythroid precursors (Z-score: 3.359) , 175.2 TPM (HNRNPH1), and 70.1 TPM (HNRNPH2), respectively, were also correspondingly high. Mice heterozygous for Database . Using Z: 3.359) , 58. TheWe identified DKC1 as a protein of interest based upon its enrichment in the RNA affinity pulldown experiment using motif 2  that is P-values < 0.05; Wilcoxon rank sum test). We did not observe the same level of enrichment in the interaction between DKC1 and the negative control transcripts Aff1 and Adar, validating our predictions.Based on these phenotypes and our RNA-binding observations, we selected this protein for further study. Specifically, we sought to validate some of the transcripts predicted to interact with this RBP based on sequence similarity to the motif with which we found it interacts (motif 2). We selected two transcripts (Dido1 and Appl2) that contained at least one matching motif and were annotated to exhibit abnormal erythrocyte phenotypes when mutated in mouse models . We perfWhen we examined our mRNA-seq data, we found that Appl2 shows a continual and significant increase in RNA abundance throughout MEL development, whereas Dido1 mRNA steady-state levels remain relatively constant throughout this developmental time period . Thus, tIn this study, we have applied the high-throughput sequencing technology PIP-seq to a model of erythropoiesis and characterized the transcriptome-wide landscape and dynamics of RNA secondary structures and RBP occupancy. Our data suggest that PPSs, which are potential sites of RBP occupancy, are more conserved throughout evolution, possibly because of their biological function. This conservation also aligns well with the idea that RNA sequence is one of the factors that determines RBP binding . The impWe further note that throughout development, we observe an increase in the average RNA secondary structure  in the 4One application of PIP-seq data is using motif enrichment analysis followed by RNA affinity pulldown and mass spectrometry to identify potential post-transcriptional regulators of erythropoiesis. Using this process, we were able to identify several proteins that could potentially be regulators of erythropoiesis. Specifically, the KEGG pathway enrichment analysis revealed an enrichment for proteins involved in splicing and RNA transport, which tracts well with the idea that RNA splicing plays a large role in terminal erythropoiesis , and sugTaken together, these data establish a comprehensive database of in vivo RNA secondary structure and RBP–RNA interactions for the important process of mammalian erythropoiesis. In total, our findings reveal the power of using a global genomic screen of RNA secondary structure and RNA-protein interaction site dynamics using PIP-seq to identify potential new post-transcriptional regulators of an important developmental process. In addition, these data provide a resource for future studies that can focus on identifying corresponding functions and novel pathways of post-transcriptional control during terminal erythroid differentiation. In the future, these newly described proteins and corresponding collections of target RNAs will be further studied to better understand the mechanisms by which they regulate this important mammalian developmental process.5/ml were supplemented with 2% DMSO (Sigma-Aldrich) to induce differentiation, and cells were collected at various time points for further analysis.MEL cells were grown under standard conditions in minimal essential medium (MEM) and supplemented with 10% (vol/vol) FBS and 1× antibiotic antimycotic (Invitrogen). MEL cells in suspension culture at the log phase of growth at a density of 2 × 102O. Then each sample was split into four libraries: two for structure only libraries and two for footprinting libraries. Footprinting libraries were treated with either 100 U/ml of a ssRNAse (RNaseONE [Promega]) in 1× RNaseONE buffer for 1 h at room temperature, or 2.5 U/ml of a dsRNase (RNAse V1 [Ambion]) in 1× RNase buffer for 1 h at 37°C as previously described (PIP-sequencing libraries were constructed as outlined in Silverman and Gregory (2015) . Brieflyescribed . ProteinPIP-seq reads were trimmed using cutadapt to remove 3′ sequencing adapters (cutadapt version 1.2.1 with parameters –e 0.006 –O 6 –m 14). Resulting trimmed reads were then collapsed into unique reads and aligned to the mm10 mouse genome sequence using TopHat (version 2.0.10 with parameters–library-type fr-secondstrand–read-mismatches 2 –read-edit-dist 2 –max-multihits 10 –b2-very-sensitive–transcriptome-max-hits 10 –no-coverage-search–no-novel juncs). Any PCR duplicates were collapsed to single reads for all downstream analysis.mRNA-seq libraries were constructed in biological replicates, following standard library construction protocol. Briefly total RNA was extracted using Trizol Reagent as per the manufacturer’s protocol and then sequenced using TruSeq stranded mRNA-seq. Adapters were trimmed using cutadapt with the following parameters (-f fastq -a [ADAPTER] -e 0.06 -O 6 -m 14) and then aligned against the mm10 genome using TopHat (v2.1.0) with the following parameters (--library-type fr-secondstrand --read-mismatches 2 --read-edit-dist 2 --max-multihits 10 --b2-very-sensitive --no-coverage-search -p 15).2 fold changes of MEL cells after 0, 2, or 4 d of DMSO induction were calculated. Normalized read count values are displayed using TPM values.Unadjusted read counts from the TopHat alignment were inputted into the R package DESeq2  and log2Various lists of transcripts were analyzed using DAVID , 31, usiPPSs were identified using a modified version of the R package CSAR . Read coBackground mock PPSs were generated by taking the high confidence PPSs and then randomly shuffling them in the transcriptome  such that chromosome, feature size, and strandedness were preserved while averting any region that is called as a PPS in any sample. This was accomplished by using the bedtools shuffle feature with the following parameters (-incl -[annotated_gene_file] -excl [PPS_file_for_timepoint_anyrep] -chrom -noOverlapping) using the browser extensible data (BED) file of high-confidence PPSs for a particular time point as the input.PPS annotation was performed using the mm10 genome annotations in a “greedy” fashion such that all annotations overlapping a PPS by one nucleotide was counted equally . Conservation of PPSs was determined by comparing PhastCons scores and the number of SNPs within PPSs relative to equally sized flanking regions. PhastCon scores for PPSs compared with the same-sized flanking regions were calculated as previously described , 21.nds) and ssRNA-seq (nss) of a given base I, the structure score is calculated as follows:Si is the structure score, dsi and ssi are the normalized read coverages, and Lds and Lss are the total covered length by mapped dsRNA-seq and ssRNA-seq reads, respectively. In this analysis, we used the total coverage length because we believe it is a more reasonable assumption for the transcriptome to have comparable levels of paired/unpaired regions. We used a generalized log ratio (glog) instead of the normal log-odds because of its ability to tolerate values of 0 . In addition, the glog function is still asymptotically equivalent to the standard log ratio when coverage values are large. Because we are only interested in the intra-molecular interactions that contribute to the self-folding secondary structure, we used only sense-mapping reads. Near the CDS boundaries, we calculated the structure score for up/downstream of the CDS start and end sites, aligned those to detectable mRNAs, and then averaged the score to produce the profile.For every base in our set of detectable transcripts, we calculated the ratio of dsRNA-seq and ssRNA-seq coverages from the structure only samples as previously described . BrieflyIdentifying changes in RNA secondary structure was carried out by calculating the structure scores as outlined above and then filtering for the transcripts with a structure score at all time points. Hierarchical clustering was performed by first calculating the average RNA secondary structure score in the 200-nt window around the start codon for each transcript and then using the built-in hierarchical clustering function in R with default parameters to identify the six clusters. Transcripts from each of the cluster was then submitted to MouseMine for GO and MP analysis .HOMER  was usedThe collection of PPSs was scanned for each of the motifs in order to identify which PPSs contained those sequences, and subsequently which encompassing transcripts, contained binding sites for putative RBPs using HOMER’s scanMotifGenomeWide.pl command with the -bed option to format output into a BED file.The list of sites containing each motif was then mapped back to their corresponding transcripts using genomic coordinates. The transcripts were then subsequently analyzed for GO enrichment using DAVID’s default options against the mm10 background , 31.g for 10 min at 4°C. Samples were stored at −80°C. We then used motifs identified within PPSs of interest  with ∼ 50 μg of protein lysate for 30 min at RT. Beads were then washed four times with GFB-200  and once with 20 mM Tris–HCL (pH 7.4). The proteins were then directly trypsinized on the beads.MEL cells 0, 2, and 4 d post differentiation were collected and washed 2× with 1× PBS and lysed in 500 μL–1 ml of Lysis buffer with the addition of complete EDTA-free protease inhibitor . Cells were sheared with a 26-gauge needle and the supernatant was collected after spinning at 13,000interest  as “baitinterest . Briefly4HCO3 containing ∼6 ng/μl of MS-grade trypsin (Promega) and incubated at 37°C overnight for 12–18 h. The samples were then vacuum dried before being submitted to MS analysis.RNA-bound proteins were treated with 100 mM NHMass spectrometry analysis was performed similarly to previous reports , 67. Sam2 fold change of a test probe versus the average of all other probes tested in the same sample. The list of enriched proteins was then manually checked in the MGI database and proteins with a known hematopoietic phenotype was noted.Any proteins without an iBAQ value were assigned a default value of 0 before the analysis. Possible contaminants and confounding proteins such as keratin and ribosomal proteins were filtered out from the analysis. The iBAQ values were then log transformed and centered around the mean of each sample. Enriched proteins were identified by calculating the logMEL cells were induced with DMSO and collected after 4 d. These cells were then cross-linked by adding 37% formaldehyde drop-wise to confluent cell culture dishes for a final concentration of 1%. Then the cell cultures were incubated for 10 min at room temperature before being treated with 1 M glycine for a final concentration of 125 mM. Cell lysis and the subsequent RIP protocol was performed using the Magna RIP Kit  according to manufacturer instructions with a rabbit polyclonal antibody α−DKC1  and the rabbit polyclonal antibody against β-ACTIN  as control. The resulting RNA was reverse transcribed using the iScript Reverse Transcription Supermix for RT-qPCR  and then pre-amplified with SsoAdvanced PreAmp Supermix , following the manufacturer’s instructions. Standard qPCR was performed for Adar , Aff1 , Appl2 , Dido1  and GAPDH . The primers for Appl2, Dido1, Aff1, and Adar were identified using PrimerBank  and 69, Protein lysates were prepared from cross-linked 4-d post-DMSO induction cells using RIPA buffer and run on 4–12% Bis–Tris Gels  in MES at 100 V for 2 h. Then the gel was transferred to polyvinylidene fluoride (PVDF) membranes at 200 mA at 4°C for 2 h. After 2 h of blocking in 5% BSA at room temperature, the membrane was incubated in primary α−DKC1  or α-ACTIN  antibody overnight in 4°C. Membranes were then washed three times in tris-buffered saline (TBST) for 10 min each then incubated for an hour in goat anti-Rabbit secondary . After three 10-min washes with TBST, the membrane was then removed from the liquid and ECL Prime Western Blotting Detection Reagent  was applied to the membrane for 1 min. Images were then taken in 10 s increments until saturation.GSE142242 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE142242). The FASTQ files for accompanying mRNA-seq are available on GEO under the accession number GSE148421 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE148421).Raw FASTQ files for the PIP-seq libraries as well as processed BED files containing PPSs and calculated RNA secondary structure scores are available on GEO under the accession number