Animals are thought to gain significant fitness benefits from choosing high-quality or compatible mates. However, there is large within-species variation in how choosy individuals are during mating. This may be because the costs and benefits of being choosy vary according to an individual’s state. To test this, I systematically searched for published data relating the strength of animal mate choice in both sexes to individual age, attractiveness, body size, physical condition, mating status, and parasite load. I performed a meta-analysis of 108 studies and 78 animal species to quantify how the strength of mate choice varies according to individual state. In line with the predictions of sexual selection theory, I find that females are significantly choosier when they are large and have a low parasite load, thus supporting the premise that the expression of female mate choice is dependent on the costs and benefits of being choosy. However, female choice was not influenced by female age, attractiveness, physical condition, or mating status. Attractive males were significantly choosier than unattractive males, but male mate choice was not influenced by male age, body size, physical condition, mating status, or parasite load. However, this dataset was limited by a small sample size, and the overall correlation between individual state and the strength of mate choice was similar for both sexes. Nevertheless, in both males and females individual state explained only a small amount of variation in the strength of mate choice. Does being choosy about mates vary with an individual animal’s state? I performed a meta-analysis of 108 studies and 78 animal species testing how an individual’s state  influenced the strength of mate choice. Females show stronger mating preferences when they are large, and have few parasites, and attractive males are choosier. Future mate choice experiments should take into account differences in individual state or risk obtaining invalid results. Across the animal kingdom, individuals of both sexes are often selective in who they choose to mate with . Mate chThe other key factor affecting the costs and benefits of being choosy is an individual’s own phenotype , which iSexual selection theory predicts that individuals should be choosier during mate choice when: 1) they have more resources to invest into mate assessment, so that they are more able to make accurate choices , 2) theyThe empirical data on state-dependent mate choice have been summarized in several narrative reviews . HoweverUnderstanding how individual state influences mate choice is important for several reasons. First, it provides insights into how animals balance the trade-off between survival and reproduction. Second, it will improve our understanding of the ecological context of mate choice, because the environment has the potential to influence state factors such as attractiveness, body size, condition, and parasite load. Identifying these links will improve our understanding of the conditions favoring the evolution of mate choice, their expression in natural populations, and the population-level consequences of environmental change on sexual selection and population fitness . Third, I searched for relevant papers in two ways. First, I obtained all papers cited by two reviews of state-dependent mate choice: (mate OR mating) AND (choice OR preference* OR choosiness OR rejection)courtship OR courting OR “sexual signalling”mate AND (sampl* OR search*)“species recognition” OR “mate recognition” OR “reproductive isolation” OR (conspecific* AND discriminat*) OR ((mate OR mating) AND (hybridization OR reinforcement)) OR (mating AND (heterospecific* OR interspecific*))The search and study selection processes are outlined in In order to be eligible for inclusion, a study had to report within-species variation in mate choice in relation to one or more of the six state factors listed above. I included data on any animal species, with the exception of humans and species without fixed reproductive roles (hermaphrodites). Studies also had to provide enough information to allow me to calculate an appropriate effect size. I considered only precopulatory mate choice, because of the difficulty in attributing postmating outcomes to either sex. I therefore excluded studies examining peri- and postcopulatory behaviors such as ejaculate allocation, copulation duration, mate guarding, and parental investment. I included both experimental and correlative data, from both lab and field studies, but excluded studies in which multiple state factors were confounded e.g., . I incluIn order to facilitate the calculation of an effect size, I used a broad definition of the strength of mate choice, incorporating three types of mating behavior. First, I included studies that recorded how chooser behavior was targeted at some courter traits or sexual signals over others. Common behaviors included in this category were association time, approach or approach latency, and number of courtship or receptive displays. For these studies I used the measure of “choosiness” formulated by Differences in some aspects of individual state may be detectable by courters, leading to changes in courter behavior. For example, males may increase their courtship effort toward large females compared with small females. This could result in an apparent increase in female choice that is entirely driven by changes in male behavior. I attempted to reduce this problem by excluding studies in which courters could physically interact with choosers, and so potentially coerce them into mating . This meant that I excluded most studies that used mating outcomes to infer mate choice. The exception to this were those species in which active participation in mating by choosers can be inferred. For example, I included two examples of male choice in species where males mount females during mating , as hereI included age-dependent effects as long as all individuals were mature (not juveniles). Importantly, age is often confounded with mating status in natural populations. To exclude this possibility, I only considered studies testing unmated individuals. I included both studies in which age could be measured precisely (as in lab studies) and studies in which age was estimated . I had no clear prediction for the relationship between choosiness and age, because of the expected variability in age-dependent resource levels and residual reproductive value .I define attractiveness as any aspect of chooser phenotype or behavior that could signal mate quality, and does not fall into any of the other categories listed. There were four main traits in this category. For females, I included inbreeding level and social rank. For males, I included social rank, ornament color, and nest quality. Studies of social rank had to test mate choice in the absence of rivals, as dominant individuals could potentially control subordinate mating behaviors. I predicted that attractive individuals would be choosier than unattractive individuals , as the This category included measures of body weight, body length, and other proxy measures of body size . I predicted that large individuals would be choosier than small individuals , becauseI define individual condition following This category included studies comparing mate choice between unmated and once-mated individuals. I assume that for once-mated individuals the mating resulted in successful reproduction. I did not include studies comparing mate choice in relation to the number of matings above one. I predicted that mated individuals would be choosier than unmated individuals , becauseThis category included any measurement of the number of internal or external parasites. I excluded vertically transmitted endosymbionts  because these associations can be complex, ranging from mutualistic to pathogenetic, and fitness costs to hosts are rarely measured . I also r as the effect size in this analysis. Here, r represents the extent to which the strength of choice varies between individuals in relation to one of the six state factors listed above. For all analyses, I used Fisher’s Z transform of the correlation coefficient (Zr) as the response variable, as this adheres better to a Gaussian distribution at high magnitudes  (I used the correlation coefficient gnitudes . The ass/(n − 3) , with n /(n − 3) . This coIn several cases, studies often reported nonsignificant results without describing the direction of the effect. These data points are traditionally excluded from meta-analyses. However, this systematically biases the dataset against the inclusion of nonsignificant results . In orded: r. For studies comparing mating behavior targeted at different courters or sexual signals, I calculated effect sizes in two ways. For frequency data, such as the numbers of choices for one option over another, an effect size could be calculated from a 2 × 2 frequency table, tallying the number of choices in each context. For continuous data, such as the time spent in association with a courter, preferences had to be presented as a single value, such as a difference score (the difference in preference between the preferred and the nonpreferred option) or some other metric, to facilitate comparison across contexts. In summary, I calculated effect sizes using one of four types of data: correlations , statistical tests comparing two groups , means and variances for two groups , and frequency data . I therefore also performed a sensitivity analysis to check for any systematic differences in the mean effect size between these four types of data, and found none for males or females (see I obtained effect sizes by: 1) obtaining correlations reported in the text or tables, 2) using presented data or statistical tests to calculate a correlation myself, or 3) re-analyzing raw data presented in the paper or the For each correlation, I additionally recorded information relating to four potential moderators of state-dependent mate choice:Taxonomic group. I predicted that mate choice would be most state dependent for members of relatively long-lived groups  compared with members of short-lived groups . This is because life-history theory predicts that short-lived species will benefit more by investing maximally into reproduction irrespective of individual state.State factor. I compared whether the extent of state-dependent mate choice depended on which of the six state factors was examined. As discussed below, I predicted that choosy individuals were more likely to be young, attractive, large, mated, in good condition, and have a low parasite load. I also predicted that body size, physical condition, and parasite load should have the strongest influence on mate choice, because these factors have the potential to greatly influence resource levels, competitiveness, and expected future mating opportunities.State variation. I recorded whether individual state varied naturally or was experimentally manipulated. I predicted that choice would be most state dependent when a state factor was experimentally manipulated, as this should increase the ability to statistically detect between-group differences.Courter interaction. I recorded the extent to which males and females could interact during mating trials. This resulted in three categories: 1) males and females could physically interact, 2) males and females could interact behaviorally but could not physically touch each other, or 3) live mates were not present during mate choice tests. If courters behave differently toward choosers of different states, then I predict that mate choice will be more state dependent in categories 1) and 2), as interaction between courters and choosers is not possible in category 3).Gryllus I used  database . Trees ws I used . For thee I used . For thes I used . Given ts I used . For anaZ transform of the correlation coefficient (Zr) as the response variable, weighted by study sampling variance, and included phylogeny, species, study ID, and observation ID as random factors. Phylogeny was incorporated into the model using a correlation matrix, assuming that traits evolve via Brownian motion. An observation-level random factor (observation ID) is required to correctly estimate residual heterogeneity  variance , differences between species (species-level I2), differences between studies (study-level I2), and differences between data points . I2 values of 25%, 50%, and 75% are considered low, moderate, and high, respectively  using the orchaRd R package  fixed effect. I ran a separate model for each moderator, and did not test for interactions between moderators. I determined significance using the  package . FinallyZr) and the standard error. To do this I ran a minus-intercept meta-regression model with phylogeny, species, study ID, and observation ID as random factors, and mean-centered publication year and standard error as fixed effects, following I searched for two signs of publication bias for males and females. First, I tested for a change in effect size over time, in relation to study publication year. Second, I searched for potential small-study effects by testing for a relationship between effect size (k = 69) and fish (k = 58). Of the six factors examined, the majority of correlations related to body size (k = 50) and condition (k = 67). Only four correlations considered female mate choice with respect to species identity. Overall, the mean correlation for females was significantly positive, suggesting that females exhibit stronger mate choice when they have more resources to invest into reproduction, a higher residual reproductive value, or the cost of rejecting potential mates is reduced . The full dataset showed “high” total heterogeneity , which is normal for multispecies ecological datasets (Zr (<0.1% each), with the remaining 41.5% attributable to observation-level differences.I obtained 179 correlations for females, from 82 studies and 62 species. I obtained data from 8 taxonomic groups, but most correlations were for insects .I found evidence for publication bias in the female choice dataset. The mean effect size decreased significantly over time  and fish (k = 29). Of the six factors examined, the majority of correlations related to body size (k = 29), condition (k = 14), and mating status (k = 17). Only five correlations considered male mate choice with respect to species identity. Overall, individual state did not significantly influence the strength of male mate choice . The full dataset showed high total heterogeneity , with 34.4%, 24.1%, and 18.1% attributable to phylogenetic history, species-level differences, and observational-level differences, respectively. Study-level differences explained a negligible amount of heterogeneity in Zr (<0.1%).I obtained 71 correlations for males, from 33 studies and 29 species. I obtained data from 6 taxonomic groups, but most correlations were for insects .There was no significant relationship between the degree to which male mate choice was state dependent and the year of publication  mate choice should consider controlling for differences in female health and body size, or run the risk of obtaining spurious results . Howeverarac100_suppl_Supplementary_MaterialClick here for additional data file.


RAB13 selectively enriches RNA in sEVs and serves as an RNA signal for loading into sEVs. Our results demonstrate that RAB13 is the most enriched RNA in sEVs, and this enrichment is primarily driven by its 3′UTR sequence. These findings highlight the potential of the RAB13 3′UTR sequence as an RNA signal that enables the loading of target RNA into sEVs. This technology has the potential to improve EV-based drug delivery and other applications.Extracellular vesicles (EVs) play a critical role in the transport of functional RNAs to target recipient cells in numerous physiological processes. The RNA profiles present in EVs differed significantly from those in the originating cells, suggesting selective and active loading of specific RNAs into EVs. Small EVs (sEVs) obtained by stepwise ultracentrifugation have been reported to contain non-sEV components. Analysis of sEVs separated from non-sEVs components revealed that microRNAs may not be released by sEVs. This has raised interest in other RNA types, such as mRNA, which may be functional molecules released by sEVs. However, the molecular mechanisms underlying selective loading of mRNA into sEVs remain unclear. Here, we show that the part of 3′ untranslated region (UTR) sequence of  EVs transfer various molecular cargoes, including RNAs, proteins, and metabolites that perform specific functions in the recipient cells3. The RNA profiles in EVs often differ significantly from those in their corresponding cells of origin4, suggesting an active and selective mechanism for RNA sorting into EVs.Extracellular vesicles (EVs), which are released from diverse cell types, are critical for intercellular communication and play essential roles in many physiological processes5. High-resolution density gradient fractionation of sEVs and NVs has shown that microRNAs (miRNAs) are predominantly associated with the NV fractions rather than with the purified sEVs fractions, and proteins related to miRNA biogenesis, such as Argonaute 1–4, are absent from the sEVs fractions, suggesting that extracellular miRNAs may not be released by sEVs. Thus, other types of RNA, such as mRNA, among cargo RNAs, are of greater interest in terms of the functional molecules released by sEVs. However, the molecular mechanisms by which selective mRNAs are loaded into sEVs have not been fully elucidated. Some RNA motif sequences have been proposed to facilitate loading into sEVs; however, not all mRNAs enriched in sEVs possess these sequences.Small EVs (sEVs) constitute a subclass of EVs that includes the types of vesicles classically known as exosomes, which are derived from endosome, are 50–150 nm in diameter, and are enriched with tetraspanin proteins such as CD9 and CD63. Stepwise ultracentrifugation is often performed to purify sEVs, and ultracentrifugal fractions contain other non-sEVs components called non-vesicular (NV) compartments6. Transcriptome analysis revealed the enrichment of multiple mRNA species within EVs. Subsequent investigations identified the RNA region responsible for selective mRNA enrichment within EVs.The objective of this study was to elucidate the RNA signal sequences responsible for their sorting and packaging into sEVs. We employed a previously established immunoaffinity purification method to isolate CD9 positive EVsg, were further fractionated into low-density sEVs and high-density non-vesicular components (NV) using sucrose density gradient ultracentrifugation  fused to the RAB13 (783–1164) sequence in its 3′ end, in HEK293 cells. Analysis of the transcripts expressed in these cells and in the CD9 positive EVs showed 30-fold enrichment of the fusion gene in CD9 positive EVs  sequence. This transcript did not exhibited enrichment in CD9 positive EVs.To investigate the possibility of artificially sorting a transgene fused with RAB13, ANP32B, and NET1 in EVs. Notably, we found that the part of 3′UTR of RAB13 acted as a loading signal for CD9 positive EVs, with an EV enrichment ratio of over 10, while non-enriched RNA, such as ACTG1, had an EV/cell ratio of around 1. The presence of non-enriched RNA in EVs may be attributed to passive loading or contamination with cellular debris that cannot be removed during EVs purification.In the present study, we investigated the cargo RNA of CD9 positive EVs to identify the RNA sequences responsible for their loading into these EVs. We used an established immunoaffinity purification method to highly purify CD9 positive EVs from HEK293 cells. Transcriptome analysis revealed a high enrichment of RNA such as RAB13 was highly enriched in low-density sEVs isolated using sucrose density gradient fractionation as well as in CD9 positive EVs obtained through immunoprecipitation with anti-CD9 antibodies. Our results indicated the presence of heterogeneity within the sEVs population obtained through sucrose density gradient fractionation. Specifically, 73% of RAB13 mRNA was preferentially localized in low-density fractions and 54% of RAB13 mRNA was detected in fraction 9. This implies that distinct subpopulations of sEVs exist and that each subpopulation may have unique characteristics that dictate its cargo. Importantly, our study highlights the critical role of EVs purification methods, such as high-resolution density gradient fractionation and immunoaffinity purification, in understanding the cargo of sEVs. To further investigate the biological functions and underlying mechanisms of sEVs, it is crucial to develop reliable methods for isolating and purifying specific sEV subpopulations. Establishing such methods would greatly enhance our understanding of the biological diversity of sEVs and their potential clinical applications.We found that RAB13 in CD9 positive EVs from multiple cell lines, including HEK293, H1277, and MCF7  were present in RAB13. However, none of these motifs were found in the RAB13 sequence, suggesting that RAB13 transport is not mediated by a recognition system that utilizes these motifs. It has been suggested that the motif-based loading system is cell type-specific4, and the cell lines employed in this study may possess a mechanism that recognizes and transports different sequence motifs. Furthermore, we were unable to detect any other human gene sequences homologous to the RAB13 3'UTR sequence, indicating its specificity to RAB13. Therefore, it is conceivable that RAB13 and other enriched RNA could be loaded into EVs via distinct recognition mechanisms.It has been proposed that RNA transported to EVs possesses specific sequence motifs13. The eGFP::RAB13 3′UTR achieved an enrichment ratio of over 30 in HEK293 cells. Bolukbasi et al. provided a comprehensive analysis that indicated the EGFP-fused motif to have an enrichment ratio of approximately 2 in HEK239T cells. In addition, Szostak et al. investigated luciferase-fused motifs, specifically Cst6 and Net1 3′ UTR, in MLP29 cells. An enrichment ratio of around 20 was calculated using the EV/Cell ratio (log2(− 1.8) × log2(2.5)). Although it is challenging to make a direct comparison between different cell lines, the eGFP::RAB13 3′UTR exhibited a higher enrichment ratio compared to the others.We have compared loading efficiency of fusion gene with other reported RNA loading motifs14, would result in more of it being enriched in sEVs. We investigated the association between RNA stability and EV enrichment levels in endogenous RNAs globally. To evaluate RNA stability, direct RNA sequencing was performed to measure the poly(A) tail length of the poly(A) RNA extracted from HEK293 cells expressing the eGFP fusion gene containing the sense or scrambled RAB13 (783–1164) sequence. The results showed no correlation between the poly(A) tail length and CD9 positive EVs enrichment for endogenous RNAs  transcripts was 144.1 or 78.0 for the sense or scrambled sequences, respectively. The read count was 37 for the scrambled sequence, which was insufficient for a comprehensive comparative analysis. The poly(A) tail of the fusion gene was not particularly long among the endogenous genes, although it was highly enriched in sEVs. Despite a low read count of 15, the median poly(A) tail length of the endogenous RAB13 transcripts was recorded as 37.3. This does not constitute an exceptionally long poly(A) tail length among the endogenous genes. Collectively, these findings suggest that the poly(A) tail length is not a ubiquitous determining factor in the sorting of endogenous RNAs into sEVs, or specifically for RAB13.If RNA is passively loaded into sEVs, stable RNA would be preserved and appear enriched in sEVs. We expected that having more RNA with a long tail, which typically indicates it is stableRAB13 3′UTR sequence (783–1037) showed similar enrichment level to the untruncated 3′ end RAB13 3′UTR sequence (783–1164), whereas the shorter subsequences of the RAB13 3′UTR (783–953 or 869–1037) were not enriched in sEVs by more than twofold  exhibited higher enrichment in CD9 positive EVs compared to the 3'UTR subsequence (783–1163), while the intermediate sequence (575–1164) showed lower enrichment efficiency. These findings suggest that combining multiple sequences, such as the 5' UTR sequence and the 3'UTR sequence, may potentially increase the loading efficiency.Combining multiple regions of RAB13 3'UTR subsequence can be transported to sEVs. The RAB13 3'UTR subsequence has the potential to serve as an RNA loading technology for sEVs. Although several methods have been reported for transporting RNAs to EVs17, the use of RAB13 sequences could complement these approaches. Currently, we do not observe eGFP fluorescence in recipient cells when HEK293 cells stably expressing an eGFP gene fused with the RAB13 3'UTR subsequent are co-cultured with wild-type HEK293 cells. Therefore, we could not confirm whether the RNA loaded into the sEVs migrated to and functioned in the recipient cells, at least when the recipient cells were HEK293 cells. However, full-length RNA of endogenous RAB13 and other enriched genes, ANP32B and NET1, were present in sEVs, indicating that these RNA may retain their function as transcripts even after transfer to sEVs and subsequent release into recipient cells  were maintained in Minimum Essential Medium alpha (MEMα) supplemented with 10% fetal bovine serum (FBS) and penicillin–streptomycin. The cells were cultured in a 5% COg for 5 min at 4 °C, and then centrifuged at 10,000×g for 15 min at 4 °C. The obtained supernatant was layered onto 30 w/v% sucrose solution in ultracentrifugation tubes and subjected to ultracentrifugation at 100,000×g for 3 h at 4 °C using SW32Ti rotor (Beckman courter). The collected sucrose solutions were mixed with PBS and washed by ultracentrifugation at 100,000×g for 1 h. The resulting pellets were used as crude sEVs and referred to as P100 in this study.The conditioned medium from the cells was centrifuged at 2000×g for 20 h at 4 °C using P55ST2 rotor (Himac). Twenty-four fractions of 200 μL were collected from the top of the gradient. To determine the fraction densities, identical gradients with 1X PBS instead of the samples were generated using the same method. The fraction densities were measured using a PAL-BX/RI refractometer (Atago). Each collected fraction was mixed with 1X PBS and ultracentrifuged at 100,000×g for 1 h. The pellets were resuspended in PBS.To prepare the sucrose gradient solutions, 1.4 mL of 2 M sucrose in 1X PBS, 1 mL of 1.5 M sucrose in 1X PBS, and 1.4 mL of 1 M sucrose in 1X PBS were layered into an ultracentrifugation tube. A 1 mL sample of crude EVs collected by ultracentrifugation was layered on top of the sucrose gradient solutions. The solutions were ultracentrifuged at 150,000×The culture medium was mixed with in-house anti-CD9 antibody-conjugated magnetic beads and EV-binding buffer . The mixture was rotated overnight at 4 °C. To decrease viscosity, cellulase was added to the samples for degrading carboxymethyl cellulose, followed by incubation at 37 °C for 10 min. After incubation, the samples were washed once with PBS. The samples were processed for RNA extraction and western blotting.Samples were mixed with 4 × Laemmli sample buffer (Bio-Rad) without a reducing agent and heated at 95 °C for 5 min. Subsequently, the samples were loaded onto 4–20% gels (Bio-Rad), and electrophoresis was performed at 200 V for 20 min. The proteins were then transferred onto polyvinylidene fluoride (PVDF) membranes using a wet transfer method. Membranes were blocked for 1 h at room temperature using Blocking One . Primary antibodies were added to the samples in Can Get Signal Solution 1 (Toyobo) and incubated overnight at 4 °C. After washing three times with 1X PBST, the samples were incubated with secondary antibodies in Can Get Signal Solution 2 (Toyobo) for 1 h at room temperature and then washed three times in 1X PBST. Finally, imaging was performed using Amersham ECL Select (Cytiva) and ImageQuant LAS500 (Cytiva) software. The antibodies used in this study are listed in Supplementary Table PMMoV) partial sequence) was added to the crude sEVs sample at a final concentration of 5 pM. The sample was then treated with either 0.5 μg/μL RNase A (Nippon gene) or PBS for 20 min at 37 °C. Total RNA was extracted using TRIzol LS Reagent (Thermo Fisher Scientific) following the manufacturer's instructions. Reverse transcription qPCR (RT-qPCR) was performed using primers specific for RAB13 and a spike-in sequence.A spike-in control of the synthesized RNA (Pepper Mild Mottle Virus (GAPDH) and the EV/cell ratio were calculated. Primers used in this study are listed in Supplementary Table Total RNA was extracted using TRIzol Reagent (Thermo Fisher Scientific) following the manufacturer's instructions. The extracted total RNA was subjected to reverse transcription using ReverTraAce qPCR RT Mix/gDNA remover (Toyobo). RT-qPCR was performed using KOD SYBR qPCR Mix (Toyobo) on a QuantStudio 5 Real-Time PCR System (Thermo Fisher Scientific) following the manufacturer’s instructions. The abundance of the transcripts of interest (relative to 18 and mapped to the GRCh38.p12 reference genome using hisat2 (2.1.0)19. The FPKM was calculated and differential abundance genes between EVs and cells were analyzed using cufflinks (2.2.1)20. Genes enriched in sEVs that met the following criteria were extracted: FPKM in the EV sample > 100, log2 fold change (EV/cell) > 5, and p < 0.01.Total RNA was extracted from both cells and CD9 positive EVs using the TRIzol reagent (Thermo Fisher Scientific). Poly(A) RNA was isolated with the NEBNext Poly(A) mRNA Magnetic Isolation Module (New England Biolab) following the manufacturer’s protocol. The library was prepared using the GenNext RamDA-seq Single Cell Kit (Toyobo), according to the manufacturer’s instructions. Paired-end sequencing was performed with 150 bp reads using NextSeq (Illumina). Raw reads were filtered using fastp (0.19.4)RAB13 were chemically synthesized using an artificial gene synthesis service (GenScript) and inserted into either the pcDNA3.1 + or pcDNA3.1_N-eGFP vectors using the In-Fusion HD Cloning Kit (Takara). Following the manufacturer's instructions, the Lipofectamine 3000 Transfection Reagent (Thermo Fisher Scientific) was used to transfect the plasmids into the cell lines. Subsequently, stably expressing cells were selected using Geneticin Selective Antibiotic (Thermo Fisher Scientific).Complete or partial sequences of eGFP::RAB13 (783–1164) sense or scrambled strands. Poly(A) RNA was isolated using a NEBNext Poly(A) mRNA Magnetic Isolation Module (New England Biolabs). Direct RNA sequencing libraries were prepared using a Direct RNA Sequencing Kit (Oxford Nanopore Technologies) and sequenced on MinION Mk1C with flow cells R9.4.1 (Oxford Nanopore Technologies). The sequenced data were base-called using guppy (5.1.12) with an HAC model. The resulting reads were mapped to the reference human transcript sequences from GRCh38.p12, to which the eGFP sequence was manually added, using minimap2 (2.24)21. Reads with a mapping quality of less than three were filtered out. The poly(A) tail lengths were calculated using tailfindr (1.3)22 in R (4.2.1)23. To analyze the relationship between poly(A) tail length and the EV/cell ratio, RNA-seq data obtained using a short-read sequencer were combined. The average poly(A) tail length of each gene was calculated and genes with zero FPKM values or zero poly(A) tail lengths in the cells were excluded from the analysis. Finally, the Pearson correlation coefficient between the poly(A) tail length and EV/cell ratio was calculated using R (4.2.1).Total RNA was extracted from HEK293 cells expressing Statistical analyses were performed using R (4.2.1). All data were presented as mean ± standard deviation.During the preparation of this work, the authors used DeepL and ChatGPT to improve the readability and proofreading of the manuscript. After using these tools, the authors reviewed and edited the content as required and took full responsibility for the content of the publication.Supplementary Information.


An extremely small proportion of the X-ray crystal structures deposited in the Protein Data Bank are of RNA or RNA–protein complexes. This is due to three main obstacles to the successful determination of RNA structure: (1) low yields of pure, properly folded RNA; (2) difficulty creating crystal contacts due to low sequence diversity; and (3) limited methods for phasing. Various approaches have been developed to address these obstacles, such as native RNA purification, engineered crystallization modules, and incorporation of proteins to assist in phasing. In this review, we will discuss these strategies and provide examples of how they are used in practice. Of the over 190,000 macromolecular structures present in the Protein Data Bank, as of 2022, only 3678 represent X-ray structures of RNA or RNA–protein complexes. Of these, the majority are of small RNAs (≤50 nucleotides in size), making large RNA crystal structures even more of a minority. RNA crystallography is challenging for several reasons: (1) traditional RNA purification methods resulting in low yields of pure, properly folded RNA; (2) nucleic acids having less sequence diversity than proteins, making crystal contacts harder to come by; and (3) difficulties in solving the phase problem in RNA-only structures.RNA crystallization is complicated by inherent difficulties, such as vulnerability to degradation by RNases and susceptibility to misfolding. A major bottleneck in RNA crystallography is often the production of sufficient amounts of high-quality, homogeneously folded RNA. This review is meant to be used as a reference for the crystallization of large-structured RNAs such as ribozymes and riboswitches. We will discuss the advantages and disadvantages of both traditional and native RNA purification methods. Furthermore, examples of crystallization strategies using both RNA-dependent and protein-driven modules will be discussed. Finally, strategies for phasing—including molecular replacement and general tools for heavy metal soaking to obtain anomalous diffraction data—will be presented.T7 RNA polymerase is commonly used to produce large quantities of RNA by in vitro transcription . DespiteglmS, can be engineered into the sequence to produce homogenous ends.In vitro transcription produces a continuum of transcript lengths due to premature polymerase termination. In some cases, this heterogeneity can be problematic for crystallization. To overcome this, self-cleaving ribozymes, such as the hammerhead and 2+ at pH 7.5 ,152+ at  termini B 10]. U. U2+ at glmS ribozyme is a self-cleaving sequence found in the 5′ UTR of the glmS gene, encoding the protein glucosamine-6-phosphate aminotransferase. In vivo, this ribozyme serves as a riboswitch, regulating glucosamine-6-phosphate (GlcN6P) production by inactivating glmS mRNA through self-cleavage only when GlcN6P is bound in the active site [glmS ribozyme consists of a nested double-pseudoknot fold at its core, and another pseudoknot-containing domain peripheral to the core  were added, allowing buffer exchange and concentration of the RNA [Native folding of RNA during transcription can eliminate almost all of the aforementioned issues. Larger RNAs, such as group II self-splicing introns in both pre- and post-catalytic states ,32, demo the RNA . This prPseudomonas phage 7 (PP7) [RNAs natively fold when transcribed in cells and can possess unique post-transcriptional modifications that are important for understanding their structure and function. Modifications can include methylation , acetyla 7 (PP7) , can als 7 (PP7) ,40.The crystallization of large RNAs can be a difficult task, as nucleic acids do not tend to crystallize as readily as proteins. The diversity of protein side chains offers more opportunities for crystal contacts due to properties such as differential charge, polarity, and hydrophobicity ,41. In cPlacement of engineered crystallization modules requires careful consideration to ensure solvent accessibility and to prevent the disruption of the native core RNA structure. A useful tool to help guide the modification of the RNA crystallization target is the use of phylogenetic sequence analysis to identify non-conserved variable regions within the RNA periphery. These variable regions are most likely non-essential to the function of the RNA and, thus, can be altered in order to promote crystallization. It is important to have an experimental method to assess the activity and proper folding of the modified RNA sequence following the addition of such modules. Information on phylogenetic variants can be obtained from RNA family databases such as Rfam . AdditioCrystallization of large RNAs often requires the insertion of a “crystallization module”, which promotes nucleation and enhances crystal growth. This method takes advantage of common RNA tertiary structural elements such as tetraloop (four-nucleotide loop sequence)  interactTetrahymena thermophila ribozyme’s P4-P6 domain [The interaction between a GAAA tetraloop and a specific engineered tetraloop receptor can also be used as a crystallization module. The GAAA tetraloop, in conjunction with the 11-nucleotide tetraloop receptor motif B, has be6 domain . Costa a6 domain . Finally6 domain . This in6 domain ,62. GAAA6 domain ,65,66,676 domain .As mentioned above, loop–loop or “kissing” loop interactions are another RNA tertiary motif that can be utilized as a module for crystallizing large RNAs ,68. KissThe most notable example of a kissing loop interaction being used as an RNA crystallization module is in the human spliceosomal U1 snRNP structure . The NagCrystallization modules can also be composed of RNA-binding proteins or individual protein domains. Protein crystallization modules introduce surfaces that are chemically different from the negatively charged RNA surface, which help to position molecules in a repeating pattern and facilitate the growth of well-ordered crystals .glmS ribozyme–riboswitch [Azoarcus group I intron [The most widely used protein crystallization module has been the U1A protein—one of the components of the spliceosomal U1 small nuclear RNP (snRNP), which specifically recognizes a 10-nucleotide sequence in stem-loop II of the U1 snRNA . This seboswitch , the AzoI intron , and theI intron . This moI intron ,88,89,90Haloarcula marismortui, is ~15 nucleotides, containing two helices interrupted by a 3-nucleotide bulge [Another protein crystallization module used to create crystal contacts is the kink-turn (k-turn) motif. This RNA motif, originally discovered in 2001 by Klein et al. in the 50S ribosomal subunit of de bulge . The asyde bulge ,92. Flande bulge ,92.The interactions between k-turns and the L7Ae family of proteins are similar to those of DNA-binding proteins ,93. The Antibody fragments have been used as crystallization modules or chaperones for many proteins that have proven difficult to crystallize under traditional methods ,99,100. Formation of electron density maps of crystallized macromolecules requires the amplitude and phase of each diffracted wave . X-ray dMolecular replacement (MR) is one commonly used method for solving the phase problem, especially for protein crystallography ,106,107.If no structures are available, a search model can also be designed by homology modeling of the target molecule, or by de novo structure predictions ,111. ComMolecular replacement can be a particularly convenient method for phasing RNA–protein complex crystals, especially when the protein in the complex has already been solved. For example, protein crystallization modules such as U1A , L7Ae faIn addition to serving as a search module in molecular replacement methods, methionine residues in protein crystallization modules can also be replaced with selenomethionine derivatives. Selenomethionine substitution of Met sites has shown to make the U1A module suitable for phase determination by multiwavelength anomalous dispersion  ,118. Thi2+ or Mn2+ that support both their structural integrity and catalytic activities [Most large RNAs bind metal ions such as Mgtivities ,121. In tivities ,106. Thitivities ,106. Thetivities ,106.Isomorphous replacement can be performed as a single method but has often been combined with anomalous scattering ,106. Her2+’s binding to RNA [2+ is crucial to the structure and folding of RNA and is frequently found coordinated to the negatively charged phosphate backbone or in the major groove at the base edge of tandem guanines [2+ ion prefers octahedral geometry of coordination and can adopt a fully hydrated coordination sphere, Mg(H2O)62+, or a partially hydrated shell in which inner-sphere contacts are provided by the RNA [2+ have been used extensively for phasing RNA crystals, as they tend not to disrupt the structure [2O)62+ mimics, including those of Co(III), Os(III), and Ir(III) [2+ and water [3 group is unable to accept a hydrogen bond, unlike H2O, which means that the amine coordination shell will gravitate to negatively charged environments [3)6(III) also resist exchange relative to the rapid exchange observed for water in the coordination sphere of Mg(H2O)62+ [Heavy-atom derivatives are typically produced using metals that mimic Mgg to RNA . Mg2+ isguanines . The Mg2 the RNA . Heavy mtructure ,123,124.nd water . Hexaminronments . Amine g(H2O)62+ . Iridium(H2O)62+ , the P4-(H2O)62+  and, mor(H2O)62+ .2+ mimics typically include heavy metals in the lanthanide series, such as Yb3+, Sm3+, Ln3+, and Eu3+. Diffraction experiments on the P4-P6 group I ribozyme domain led to the observation that the unit-cell dimensions changed as a function of increasing ionic radius for lanthanides in the series from Lu3+ to Sm3+, and that the mosaic spread of the diffraction pattern increased as a function of increasing ionic radius for all lanthanides except Sm3+ [Azoarcus group I ribozyme [Inner-shell Mgept Sm3+ . Inner-sept Sm3+ ,129, a hept Sm3+ , group Iept Sm3+ ,50, and ribozyme .Heavy metal derivatives have historically been produced by a method affectionately referred to as “soak and pray”, where the crystal is soaked in a heavy metal atom solution with the hope that the heavy metal atoms will bind to one or more specific locations within the RNA . AlthougX-ray crystallography of proteins has been greatly impacted by the utilization of selenium derivatization as a phasing module . SeleniuMAD and SAD methods combined with halogen soaking have also been used as techniques for phasing, with limited success . Both brX-ray crystallographic methods have proven to be an invaluable tool in the study of large RNAs. The purification, crystallization, and phasing strategies presented here have helped counter the inherent challenges of RNA crystallography, enabling the determination of structures of many difficult-to-crystallize RNAs. The development of new tools and techniques is continuing to improve the convenience and resolution of these methods, allowing us to understand the functions and potential applications of structured RNA in research and therapeutics. In addition, cryo-electron microscopy (cryo-EM) is gaining traction as a method of RNA structural biology. It is likely that combined approaches using X-ray crystallography, cryo-EM, and even small-angle X-ray scattering (SAXS) will become increasingly popular to fully appreciate the dynamics of structured RNA molecules and to validate structural observations.


O-methylation and CoV RNA capping. We describe the attenuation phenotypes of NSP16-mutant CoVs, the roles of MDA5 and IFITs in sensing and antagonizing viral RNA lacking 2′O methylation, and the dependence on 2′-O-methylation in other virus families. We also detail the growing body of research into targeting 2′-O-methylation for therapeutics or as a platform for live attenuated vaccines. Beyond its role in RNA capping, NSP16 may have yet uncharacterized importance to CoV replication, highlighting the need for continued studies into NSP16 functions. Understanding the full contribution of NSP16 to the replicative fitness of CoVs will better inform the development of treatments against future CoV outbreaks.Coronaviruses (CoVs) have been the source of multiple epidemics and a global pandemic since the start of century, and there is an urgent need to understand CoV biology and develop better therapeutics. Here, we review the role of NSP16 in CoV replication, specifically its importance to 2′- The cur in bats . The pro in bats . While m in bats , and con in bats . Thus, t2Nidovirales (nidoviruses), and are so named because of the 3′ co-terminal, or nested (Latin: “nido”), set of sub-genomic RNA species that are generated during transcription of the viral genome. In addition to CoVs, nidoviruses include the newly distinguished Tobaniviridae family (which include the toroviruses), the genetically smaller viruses of the Arteriviridae family (arteriviruses), invertebrate viruses of the Roniviridae and Mesoniviridae families, and novel families recently delineated by the International Committee on Taxonomy of Viruses   . The viraxonomy) .As positive-sense RNA viruses, nidoviruses, including CoVs, initiate translation of the viral genome upon release into the cytoplasm . ImportaO-MTase (NSP16), the latter two important in viral RNA capping , an encoded ribosomal frameshift mechanism that permits the extension of the second of these two ORFs from the first, a conserved nucleotidyltransferase domain adjacent to the RNA-dependent RNA polymerase (RdRp), and a conserved helicase-containing domain . Notably capping .3O-MTase, NSP16, catalyzes the transfer of a methyl group to the viral RNA cap O-MTase, n-viable , suggest4O-MTase, requiring each residue for activity   MHV-A59 administered intrahepatically, compared to a control peptide with scrambled sequence  . Like NSne (SAM) . In the ne (SAM) . Targetisequence . Togethe5O-methylation on viral replication and pathogenesis are well established, including in mouse hepatitis virus (MHV)  , HCoV-22us (MHV) , SARS-Cous (MHV) , MERS-Cous (MHV) , and SARus (MHV) . These sus (MHV) . Confirmus (MHV) , infectiiral RNA . Mutatiniral RNA . Additioiral RNA . Togethein vitro, CoV NSP16 mutants caused less severe disease in vivo, varying across different animal models -infected mice. Finally, SARS-CoV-2 NSP16-D130A mutants were attenuated in both a hamster model  4F for binding of RNA cap, impeding 48S ribosomal complex formation and thereby inhibiting translation of cap0 RNA . Moreovevice versa. Yet, these results indicate that IFIT proteins directly interact with cap0 viral RNA and may also amplify the IFN-I response leading to more antiviral activity.Interestingly, IFIT proteins have been shown to also interact with components of the IFN-I induction pathway. IFIT1 binds stimulator of interferon genes (STING) to modulate its interactions with other IFN-I pathway components . IFIT3 iin vitro of NSP16 mutants of MHV -I-like sensor helicase, is a primary sensor of CoVs . It is ghylation . MDA5 suhylation . Using ahylation . However in vivo , 2014b; T levels . Notablypertoire  Fig. 2)O-methyla8O-MTase function and the availability of viral MTase structural data  hydrolase and acts as a competitive inhibitor of MTases  platform. NSP16 2′-ol virus , 2014b. ol virus . Despiteol virus . TherefoWhile single targeting of NSP16 carries significant risks, properties of the NSP16 mutation allow its use in combination with other attenuating mechanisms. Importantly, NSP16 mutants have no major replication attenuation in permissive cell lines such as Vero , allowin10O-MTase function of NSP16, NSP16 may still have other roles that warrant additional exploration. Supporting this concept, CoV mutants engineered without NSP16 could not be recovered  and by a STARs Award provided by the University of Texas System. CS was supported by NIAID of the NIH (T32-AI007526) and by a grant from the Institute of Human Infections and Immunity at UTMB COVID-19 Research Fund.Craig Schindewolf: Conceptualization, Formal analysis, Funding acquisition, Investigation, Project administration, Supervision. Vineet D. Menachery: Conceptualization, Formal analysis, Funding acquisition, Project administration, Supervision.The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: VDM has filed a patent on the reverse genetic system and reporter SARS-CoV-2. Other author declare no competing interests.


Cryogenic electron microscopy (cryo-EM) is a promising method for characterizing the structure of larger RNA structures and complexes. However, the structure of individual aptamers is difficult to solve by cryo-EM due to their low molecular weight and a high signal-to-noise ratio. By placing RNA aptamers on larger RNA scaffolds, the contrast for cryo-EM can be increased to allow the determination of the tertiary structure of the aptamer. Here we use the RNA origami method to scaffold two fluorescent aptamers (Broccoli and Pepper) in close proximity and show that their cognate fluorophores serve as donor and acceptor for FRET. Next, we use cryo-EM to characterize the structure of the RNA origami with the two aptamers to a resolution of 4.4 Å. By characterizing the aptamers with and without ligand, we identify two distinct modes of ligand binding, which are further supported by selective chemical probing. 3D variability analysis of the cryo-EM data show that the relative position between the two bound fluorophores on the origami fluctuate by only 3.5 Å. Our results demonstrate a general approach for using RNA origami scaffolds for characterizing small RNA motifs by cryo-EM and for positioning functional RNA motifs with high spatial precision. RNA molecules have the ability to fold into complex structures with binding and catalytic properties such as small-molecule binding RNAs , riboswiTo alleviate this problem, scaffolding has been used to facilitate data acquisition in cryo-EM. A strategy based on homo-oligomerization of the target RNA structure using kissing loops has recently been developed to increase molecular weight and mitigate flexibility . FollowiEnsemble and single-molecule measurements of Förster resonance energy transfer (FRET) can be used to characterize distances in scaffolds, conformational changes in dynamic devices and colocalization of molecules . By ratiHere, we use RNA origami to scaffold RNA aptamers to investigate their structural properties by cryo-EM. As an example of structural characterization, we develop a new FRET pair using FAs, where the Broccoli/DFHBI-1T complex acts as a donor and the Pepper/HBC620 complex acts as an acceptor, and by tuning their relative positioning we obtain high FRET. Using cryo-EM single particle averaging methods, we reconstruct the apo and fluorophore-bound states of our RNA origami FRET pair to 4.5 Å resolution. Supported by SHAPE probing experiments, we find that the Broccoli aptamer does not change shape upon ligand binding, while Pepper is rigidified upon ligand binding. Finally, we use 3D variability analysis of the particles isolated from the cryo-EM data to model the positional variance of the two fluorophores in the RNA scaffold and find that the Förster radius varies by only 3.5 Å and is dominated by translational/non-rotational modes of movement. Our results demonstrate that scaffolding small RNA aptamers with RNA origami can facilitate structure solving tasks with cryo-EM and that apta-FRET provides complementary information of high spatial precision.et al.  of double stranded gene fragments (gBlocks) synthetized by Integrated DNA Technologies (IDT). Amplifications were performed in 100 μl reactions containing 1X Phusion HF buffer (NEB), 1 μM of each primer (ordered from IDT), 200 μM dNTPs (Invitrogen), 4 ng of gBlock template and 1 Unit of Phusion DNA polymerase. The reaction was subjected to a 2-min initial denaturation at 98°C, followed by 30 cycles of: 98°C for 10 s, 68°C for 15 s and 72°C for 10 s, followed by a final extension step at 72°C of 2 min and cooling down to 10°C. The amplicons were purified using NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel) following the manufacturer's instructions.in vitro transcription. In a volume of 500 μl, 5–10 μg purified DNA template was mixed with transcription buffer , 10 mM NTPs (2.5 mM each), 10 mM DTT, 0.4 U/μl RiboLock Inhibitor (Thermo Scientific) and in-house produced T7 RNA polymerase. The reaction was incubated at 37°C overnight and stopped by adding 2 units of DNase I (NEB) and incubating at 37°C for 15 min. The reactions were centrifuged at  17000 RCF for 10 min to pellet the precipitated pyrophosphate. The supernatant was loaded onto a Superose 6 size exclusion column  equilibrated with 40 mM HEPES pH 7.5, 50 mM KCl and 5 mM MgCl2.RNA was produced by 2. Excitation of DFHBI-1T was performed at 460 nm and emission was recorded at 505 nm. Excitation of HBC620 was performed at 580 nm and emission was recorded at 620 nm. Fluorescence coming from FRET was measured by exciting at 460 nm and collecting at 620 nm.Excitation and emission spectra of the aptamer-fluorophore complexes were identified with spectral scan measurements on a CLARIOstar Plus multi-mode microplate reader (BMG LABTECH). All fluorescence measurements were performed at room temperature on sample volumes of 50 μl containing 100 nM RNA, 500 nM DFHBI-1T (Lucerna Technologies), 500 nM HBC620 (FR Biotechnology), 40 mM HEPES, 50 mM KCl and 5 mM MgClFRET was calculated using the following formula :D (460 nm) and exA (585 nm), respectively. The emissions measured at DFHBI-1T and HBC620 wavelength are denoted with emD (505 nm) and emA (620 nm), respectively. ID, IA and IDA refer to intensities measured in the presence of DFHBI-1, HBC620 and both fluorophores, respectively. Adir and DLeak were measured and can be found in The excitations at DFHBI-1T and HBC620 wavelength are denoted with exPurified RNA  was spin concentrated to ∼2.5 mg/ml in Amicon centrifugal filters with molecular cuttoff weights of 10 kDa at 21°C. Protochips AU-Flat 1.2/1.3 300 mesh grids were purchased from Jena Bioscience. Immediately prior to use the grids were glow discharged for 45 s with a current of 15 mA in a Pelca EasiGlow. A Leica GP2 was used for plunge-freezing, the sample chamber was kept at 15°C and 100% humidity. 3 μl of sample was applied to the gold foil and, after a delay of 4 s, blotted onto a double layer of Whatman number 1 filter paper for 6 s of total blot time followed by immediate plunging into liquid ethane (∼−184°C).−/Å2. Automated data collection was performed with EPU and the data saved as gain normalized compressed tiff files with a pixel size of 0.645 Å/px.All data were acquired at 300 keV on a Titan Krios G3i (Thermo Fisher Scientific) equipped with a K3 camera (Gatan/Ametek) and energy filter operated in EFTEM mode using a slit width of 20 eV. The data were collected over a defocus range of −0.5 to −2 micrometers with a targeted dose of 60 eab initio reconstruction resulted in 729630 particles, which were extracted with box size of 592 and Fourier cropped to 174 pixels. Three-class ab initio reconstruction using 30000 particles resulted in two junk and one good class. Heterogeneous refinement was used to sort the particle stack into one good (reaching Nyquist) and two junk classes . The transcription mix contain 200 mM Tris−HCl (pH 8), 40 mM DTT, 16 mM NTPs (4 mM each), 20 mM MgCl2, 2 mM spermidine, 10 μl of T7 polymerase and 20000 units of RNase inhibitor (ThermoFischer). Transcription was run overnight at 37°C and RNA was purified on a 6% denaturing PAGE.For the investigation of the secondary structure of the Pepper aptamer, the construct with the F30 scaffold  fused to2 and incubated at 65°C for 5 min, followed by a 5 min incubation at room temperature. 4 μl of dye at 50 μM was mixed in their corresponding tubes  and 4 μl of DMSO anhydrous was added to the mock and neg samples. Samples were incubated 5 min at room temperature before each being transferred into an Eppendorf tube containing either DMSO (mock) or 5 μl of 2 M benzoyl cyanide. As the chemical modification is done after 1 s . For each reaction, 10 pmol of RNA was used. In total, 5 different reactions were setup. A control reaction (mock) that did not undergo any SHAPE treatment, a sample without HBC dye (neg) and three samples containing one of the fluorophores , respectively. All RNAs were mixed in a buffer containing 40 mM HEPES pH 7, 50 mM KCl, 5 mM MgClFor the primer extension, a 2X master mix was prepared containing 2.5× SSII buffer, 500 μM dNTPs, 1.5 μM Alex647 modified reverse primer (Rev_Shape_Alexa647) and 20 mM DTT. 10 μl of this mix was added to the 9 μl of RNA. For the sequencing lanes of the gel, four samples containing 10 pmol of RNA were prepared, containing each a different ddNTP at 500 μM. All the samples were heated up at 65°C for 5 min, then 5 min at 35°C followed by 5 min at 25°C. 1 μl of SSII (ThermoFischer) was added to each tube. The samples were incubated 1 min at 45°C, 20 min at 52°C and 5 min at 65°C before being kept at 4°C. Once the cDNA synthesis was over, the remaining RNA was degraded by adding 1 μl of 4 M NaOH and incubated 5 min at 95°C. All samples were ethanol precipitated and resuspended in 15 μl of loading buffer (97% formamide and 20 mM EDTA).For the observation of the result, a 12% PAGE (40 cm × 20 cm × 0.07 cm) was cast. Prior loading, all samples were heated up at 95°C for 3 min before being snapped cool on ice for 3 min. After 15 min of running at 1000 V, 3 μl of the samples  and 1.5 μl of the sequencing samples were loaded on the gel. After 5 h of migration, the gel was scanned on a Typhoon FLA-9500.Reactivity was calculated by measuring the peak intensity of each band using the ImageJ software. Each sample was normalized based on their signal intensity of the C28. This nucleotide is located in the P1 apical tetraloop, far from the binding region and is assumed to have no reactivity variation upon target binding due to the inherent stability of a UUGC tetraloop.in vivo  places the aptamers at ∼16 nm distance, the latter being outside FRET distance. The secondary structures and sequences for each design can be found in To obtain FRET, we incorporated the Broccoli and Pepper aptamers onto a 3-helix RNA origami scaffold Figure , similaret al. . We desiet al.  and keepThe highest FRET output of 37 ± 0.4% was observed for the 1,2-B12-P12 design, since moving the Pepper aptamer to either the left  or right  of this position resulted in reduced FRET efficiency of 34 ± 0.04% and 29 ± 0.1%, respectively Figure . This deTo better understand the relative positioning of the fluorescent aptamers on our RNA origami scaffold, we used cryo-EM single particle averaging methods to determine the structure of the 1,2-B12P12 scaffold with and without fluorophores bound. Our cryo-EM data set for the Bound scaffold contained 5354 movies, resulting in a refined particle stack containing 150704 particles that produced a reconstruction with a GSFSC (0.143) resolution estimate of 4.43 Å . The ApoThe Apo- and Bound-scaffold overlap very well, with only slight positional variability in the aptamer regions Figure . An unexIn contrast, the Pepper aptamer is missing or has weak signal from key ligand-binding regions in the apo state that are clearly present in the bound state Figure . SpecifiComparing the per-residue cross-correlation (CC) values from our models and their respective EM reconstructions shows that we can model each residue with similar confidence in both the apo and bound state, except for the Pepper aptamer, which has lower CC values in the Apo model . AlthougTo study the ligand binding further, we performed SHAPE analysis on the Pepper aptamer in the absence and presence of 3 Pepper ligands: HBC485, HBC497 and HBC620 Figure . The junLow SHAPE reactivity was observed for the first nucleotide (C4) from J3/2 both in the absence and presence of ligands Figure . From thB-factor. The mixed-base tetrad is stabilized by stacking on the G19–C37 base pair. These nucleotides are both unreactive in the unbound and bound state, indicating that the base pairing observed in the crystal structure is maintained in the absence of ligand. A36 from J1/2 is reactive in the apo form and stabilized in the bound state, confirming its role in stabilizing the binding pocket by stacking on top of G16   under the PDB ID 7ZJ4 and 7ZJ5, respectively. The volumes from the final refinements of our cryo-EM SPA datasets have been deposited to the ePDB under accession codes EMDB-14740 and EMDB-17471. Other data are available from the corresponding author upon request.The atomic coordinates for the 1,2-B12P12 RNA origami scaffolds in the bound and apo states have been deposited in the PDB (gkad224_Supplemental_FileClick here for additional data file.


Central dogma is the most fundamental hypothesis in the field of molecular biology and explains the genetic information flow from DNA to protein. Beyond residue‐by‐residue transmission of sequential information, chemical modifications of DNA, RNA, and protein are also relayed in the course of gene expression. Here, this work presents recent evidence supporting bidirectional interplay between chromatin modifications and RNA modifications. Furthermore, several RNA modifications likely affect chemical modifications of proteins. The relay of chemical modifications occurs co‐transcriptionally or co‐translationally, ensuring crosstalk among chemical modifications at the DNA, RNA, and protein levels. Overall, this work proposes a hypothetical framework that represents transmission of chemical modification information among chromatin, RNA, and proteins. Chemical modifications of DNA, RNA, and proteins are interdependent, establishing chemical modification information flow. Epigenetic information can be relayed to mRNA modifications and then to protein modifications. Additionally, RNA modifications may reciprocally affect the structures and epigenetic states of chromatin. Intimate associations among DNA, RNA, and protein modifications increase the complexity of biological regulation. The information encoded in DNA is transcribed into messenger RNA (mRNA), which in turn directs the synthesis of proteins. Central dogma, which has been established as the most solid framework in the field of molecular biology, explains the flow of genetic information with a particular focus on the sequence information of DNA, RNA, and protein.Figure Accumulating recent evidence indicates that chemical modifications of DNA, RNA, and proteins define additional information, beyond the sequence‐based genetic information. Interestingly, these modifications of information‐carrying biopolymers are sometimes interdependent, facilitating chemical modification information flow. Some chromatin modifications can be co‐transcriptionally passed to RNA modifications (‘epitranscription’), and several RNA modifications also reciprocally influence the epigenetic features of the genome, such as chromatin modifications and 3D folding (‘reverse epitranscription’). Furthermore, several RNA modifications probably affect chemical modifications of proteins co‐translationally (‘epitranslation’) Figure 1. Consi6–methyladenosine (m6A) modification in both animals and plants. Mechanistically, H3K36me writer and/or reader proteins interact with m6A writers during RNA transcription, relaying chromatin modifications to RNA modifications. Additionally, the H3K27me3 demethylase KDM6B co‐transcriptionally recruits the m6A methyltransferase complex, resulting in m6A deposition in mRNAs, where corresponding chromatin H3K27me3 is depleted. Notably, the epigenetic features of promoter regions affect m6A modification of mRNAs. In human fetal tissues, the METTL3 m6A writer preferentially binds to CpG‐rich promoters that usually undergo cytosine methylation; therefore, RNAs produced from genes with CpG‐rich promoters are enriched with m6A modification.Epitranscription is reflected by the genome‐wide association of specific chromatin and mRNA modifications. For instance, a genomic landscape of methylation of histone H3 lysine 36 (H3K36me) is highly correlated with the profile of mRNA Nd plants.1, 26A reader protein YTHDC1 interacts with the histone demethylase KDM3B and enables it to bind to m6A‐associated chromatin regions, promoting H3K9me2 demethylation for robust transcriptional activation. Moreover, YTHDC1 also binds to m6A‐marked endogenous retroviral RNA sequences, recruiting METTL3 and the H3K9 tri‐methyltransferase SETDB1 to ensure the integrity of heterochromatin. In addition to covalent chemical modifications of chromatin, the architectures of chromatin/DNA can be reciprocally altered by RNA modifications. R‐loops consisting of a DNA:RNA hybrid and a single DNA strand influence various biochemical processes including transcription. In human pluripotent stem cells, m6A modification is frequently enriched in RNAs of RNA:DNA hybrids and destabilizes R‐loops. Indeed, the m6A reader protein YTHDF2 co‐localizes with RNA:DNA hybrids to promote degradation of m6A‐marked mRNAs. Depletion of YTHDF2 increases accretion of m6A on RNA:DNA hybrids and accumulation of R‐loops. On the other hand, it has also been reported that METTL3‐mediated m6A RNA modification can promote the formation of R‐loops around transcription end sites in HeLa cells. Overall, the variable impacts of RNA modifications on chromatin modifications and architectures reflect the versatile repertoires of m6A‐dependent biochemical processes in a context‐dependent manner. Although the detailed mechanism by which m6A affects chromatin states remains to be explored, liquid‐liquid phase separation‐dependent condensate formation, which spatially confines m6A‐modified RNAs and their corresponding chromatin regions, is possibly involved.Bidirectional interplay between chromatin and RNA modifications has also been suggested. Similar to the reverse transcription process, mRNA modifications influence chromatin modifications and/or architectures. The mtivation.6 More6A methylation of chromosome‐associated regulatory RNAs (carRNAs) including promoter‐associated RNAs, enhancer RNAs (eRNAs), and repeat RNAs enhances their stability, increasing the active histone marks H3K4me3 and H3K27ac at corresponding genomic loci. The carRNA‐regulated open chromatin state is further maintained by repelling the transcriptional repressive complex as well as recruiting active transcription factors in a m6A‐dependent manner. Consistently, m6A RNA modification also likely affects the formation of liquid‐like condensates with transcriptional coactivator. YTHDC1 is recruited to m6A‐marked eRNAs, which facilitates the formation of BRD4 coactivator condensates at the corresponding genomic loci, promoting neighboring gene expression.The presence of non‐coding regulatory RNAs substantially expands genomic regions that are regulated by modified RNAs. Reducing mmic loci.12 The For instance, pseudouridine, an isomer of uridine, in mRNA coding sequences promotes incorporation of alternative amino acids by the ribosome, leading to amino acid substitution under stress conditions, especially when aminoacyl‐tRNAs are limited. Notably, m6A recognition proteins interact with translation cofactors, as shown by the functional interplay between the m6A reader protein YTHDF1 and translation initiation factors in the control of translation. The translation initiation factor ABCF1 is also involved in translation of mRNAs bearing 5′‐UTR methylation. Moreover, the YTHDC2 protein resolves the secondary structures of m6A‐marked mRNAs and increases translation efficiency. The impact of mRNA modifications on translation may not be limited to translation efficiency, but may also affect a variety of co‐translational biochemical processes. Considering that several N‐terminal protein modifications such as N‐acetylation, N‐glycosylation, and N‐myristoylation often occur co‐translationally, these modifications might be dependent on mRNA modifications. However, it remains to be determined whether, and if so how, mRNA modifications affect these protein modifications.Transmission of chemical modification information from mRNA to protein has been rarely proposed. However, some chemical modifications of mRNAs affect translation.16 For2Epigenetic information can be relayed to mRNA modifications and then to protein modifications. Additionally, RNA modifications may reciprocally affect the structures and epigenetic states of DNA and chromatin. It is also noteworthy that carRNAs can transmit epitranscriptomic information to genome‐wide corresponding targeted chromatin regions, extending the repertoires of epitranscriptomic and epigenetic interactions. Given that positional overlaps between chromatin and RNA modifications have been frequently observed, we cannot rule out the notion that the specificity of epigenetic information flow is dependent in part on sequence‐based genetic information. However, transmission of chemical modification information essentially requires physical interactions between chromatin‐, RNA‐, and protein‐modifying enzymes. Thus, chemical modification information flow is enabled cotranscriptionally or cotranslationally, ensuring another layer of the information flow. Taken together, in a potential linkage with sequence‐based information, intimate associations between chemical modifications of DNA, RNA, and protein further increase the complexity of biological regulation. Increased understanding of functional interplay between epigenetic features will shed new light on chemical modification information flow.The authors declare no conflict of interest.


IGH) region can be challenging due to its hyper variability. Our aim was to develop a workflow containing the algorithms FusionCatcher, Arriba, and STAR-Fusion, to achieve best-case sensitivity for IGH gene fusion detection. We analysed 35 patient samples harbouring IGH gene fusions and assessed the detection rates for each caller, before optimising the parameters to enhance sensitivity for IGH fusions. FusionCatcher and Arriba outperformed STAR-Fusion; however, by adjusting specific filtering parameters, we were able to improve STAR-Fusion’s performance. This analysis highlights the importance of filtering optimization for IGH gene fusion events, offering alternative workflows for difficult-to-detect high-risk B-ALL alterations.B-cell acute lymphoblastic leukaemia (B-ALL) is a haematological malignancy driven by diverse genomic alterations, the most common being gene fusions, which can be detected via transcriptomic analysis. However, detecting gene fusions involving the Immunoglobulin Heavy Chain (IGH) locus can be difficult to detect with standard gene fusion calling algorithms and significant computational resources and analysis times are required. We aimed to optimize a gene fusion calling workflow to achieve best-case sensitivity for IGH gene fusion detection. Using Nextflow, we developed a simplified workflow containing the algorithms FusionCatcher, Arriba, and STAR-Fusion. We analysed samples from 35 patients harbouring IGH fusions  and assessed the detection rates for each caller, before optimizing the parameters to enhance sensitivity for IGH fusions. Initial results showed that FusionCatcher and Arriba outperformed STAR-Fusion (85–89% vs. 29% of IGH fusions reported). We found that extensive filtering in STAR-Fusion hindered IGH reporting. By adjusting specific filtering steps , we achieved a 94% reporting rate for IGH fusions with STAR-Fusion. This analysis highlights the importance of filtering optimization for IGH gene fusion events, offering alternative workflows for difficult-to-detect high-risk B-ALL subtypes.B-cell acute lymphoblastic leukaemia (B-ALL) is characterised by diverse genomic alterations, the most frequent being gene fusions detected via transcriptomic analysis (mRNA-seq). Due to its hypervariable nature, gene fusions involving the Immunoglobulin Heavy Chain ( B-cell acute lymphoblastic leukaemia (B-ALL) is a haematological malignancy that results in the over-proliferation of lymphoblasts . While iPrior to the introduction of Next-Generation Sequencing (NGS) and transcriptomic analysis, the detection of prognostically significant gene fusions with well-characterised breakpoints was performed using diagnostic analysis, such as fluorescence in situ hybridisation (FISH), reverse transcriptase polymerase chain reaction (RT-PCR) and G-banded karyotyping ,8,9; howIGH) chain, remains a challenge for these algorithms [Gene fusion detection via mRNA-seq involves identifying instances in which sequences from two distinct genes or genomic regions are combined, typically through structural variations, such as chromosomal rearrangements or translocations A. When sgorithms ,15.IGH fusions. Located in the sub-telomeric region of chromosome 14q, IGH is responsible for producing an individual’s highly variable repertoire of antibodies and comprises multiple gene regions [IGH gene rearrangement [IGH can result in a gene or gene segment from another chromosomal location being spliced upstream of an IGH enhancer, resulting in the upregulation of the partner gene, which acts as an oncogenic driver. A consequence of this aberrant recombination is the insertion of non-template nucleotides at fusion breakpoints [IGH fusion breakpoints, leading to the under-reporting of these fusions by standard diagnostic and transcriptomic analysis methods.While relying on spanning reads and junction reads is adequate for confirming most gene fusion events that occur at exon–exon boundaries, this is not the case for  regions . These gangement ,18. The akpoints . These aakpoints ,16,19,20IGH fusions also have a diverse range of partner genes [IGH fusions are indicative of a poor prognosis. In 2014, Russel et al. [IGH partner genes, including CRLF2 and EPOR, across 75 B-ALL patients, all of which were associated with high-risk subtypes. However, the partner gene remained unknown for a further 84 patients who tested positive for an IGH translocation via FISH probe. As a result of the increased detection sensitivity through the implementation of mRNA-seq analysis, the number of successfully identified IGH partner genes has risen in the intervening years to also include the cytogenetically cryptic DUX4 [er genes ,22,23. Wtic DUX4 .IGH fusions. By assessing these difficult-to-detect fusions through mRNA-seq analyses, we aim to identify potential areas for improvement in sensitivity and accuracy.The identification of gene fusions in patient samples in a time-efficient manner is crucial for effective risk stratification and personalised treatment planning. Current mRNA-seq analyses are time-consuming, at times exceeding 20 h to analyse one sample , and reqRIGHT (Recovering IGH fusion Transcripts) workflow   for genomic testing were selected . These 3 gene fusions were chosen as they are the most common IGH fusions seen at SAHMRI. IGH::CRLF2 fusions were confirmed via fluorescent in situ hybridization (FISH) using two separate break-apart probes, a Vysis IGH dual colour probe (Abbott) and a CRLF2 dual colour probe (Cytocell). Gene expression profiling was used to confirm IGH::DUX4 fusions, and the visual inspection of BAM files in the Integrative Genomics Viewer (IGV) was needed to confirm the presence of IGH::EPOR  fusions, while the cytogenetically cryptic IGH::DUX4 fusions (n = 15), which frequently go undetected, were reported in 93% (n = 14) of samples by Arriba and in 80% (n = 12) by FusionCatcher. In contrast, STAR-Fusion failed to detect any of the IGH::EPOR fusions and only reported 33% (n = 5) of the IGH::DUX4 fusions. Of the 17 samples harbouring IGH::CRLF2 fusions, FusionCatcher and Arriba reported 94% (n = 16) and 70% (n = 12), respectively, while STAR-Fusion again underperformed with only 29% (n = 5) of the fusions being detected.Initially, we processed the 35 patient samples through the lgorithm . Betweendetected , with 74A trade-off exists between high sensitivity and an increased number of overall predicted fusions, many of which are not clinically relevant or potential false positives. While STAR-Fusion demonstrated lower sensitivity, this was balanced with fewer total predictions, an average of 12 fusions per sample, in contrast to FusionCatcher which reported an average of 259 fusions per sample .IGH fusions were initially predicted, but most were subsequently excluded as they did not meet one or more of the default filtering parameters. This included insufficient junction read support and/or novel junction read support (nine samples for each criterion). For example, one sample had a read support depth of 1298 spanning fragments, but no junction reads, leading to exclusion from further filtering  in reported fusions when both the junction read and novel junction support read values were altered. The number of IGH::CRLF2 fusions reported increased as well (n = 6), while IGH::EPOR fusions remained undetected. The overall reporting rate increased from 29% using default values to 60%, still lower than that of Arriba and FusionCatcher. Additionally, despite increasing sensitivity, the total number of fusion events detected decreased compared to the default settings, averaging 10 fusions per sample (The number of reported r sample . It is uIGH::DUX4 fusions reported stayed the same (n = 14) while the IGH::CRLF2 fusions increased (n = 12), with STAR-Fusion also reporting all IGH::EPOR (n = 3), for an overall reporting rate of 82.9% (n = 29) . By default, a candidate fusion breakpoint must have an FFPM value greater than 0.1, equating to one supporting read per 10 million mRNA-seq reads. We found that 10 candidate fusions were subsequently rejected because of an FFPM value lower than 0.1. By reducing the minimum FFPM in combination with the lowered minimum read support, the number of (n = 29) . We also(n = 29) . Despite(n = 29) .IGH fusion reporting, we conducted two additional tests using these undocumented parameter bundles  Xeon(R) Silver 4309Y CPU (@2.80 GHz) with 32 threads and 128 GB of memory, with Ubuntu 20.04.5 LTS as its operating system. RIGHT offers a runtime that is solely dependent on the slowest algorithm, by allowing all tools to be executed in parallel and independently of each other. Among the algorithms used, Arriba demonstrated the shortest runtime, averaging 15 min per sample  of IGH gene fusion events being reported by two or more callers, but this decreased to 28.6% (n = 10) when detected by all three callers , surpassing both Arriba and FusionCatcher . This remarkable improvement can be attributed to the insufficiency of standard minimum filtering parameters, which fail to exhibit the necessary sensitivity for detecting many of these events due to the absence of distinct exon-exon breakpoints in IGH fusions. However, the trade-off between sensitivity and specificity must be considered, as higher sensitivity can also lead to an increase in prospective fusions, which would be unfeasible to manually curate. Moreover, despite the potential value of identifying all gene fusion events in a given patient sample, the number of clinically actionable fusions remains low [The default parameters employed by standard gene fusion detection algorithms generally demonstrate adequacy in identifying gene fusion events in B-ALL, as is shown here when analysing patient samples that harbour translocation events involving distinct exon-exon breakpoints . Howeverains low , making IGH gene fusion events detected by at least two algorithms compared to the default pipeline (85.3% vs. 74.3%)  , while dIGH fusions, is critical for B-ALL treatment and research, as they are often therapeutically targetable and frequently associated with the development and progression of various subtypes of the disease. Therefore, selecting the best-case parameters for a given algorithm, in order to achieve the most clinically significant results, is of paramount importance. However, while manually testing combinations of parameters, as seen here, can be time-consuming, machine learning and deep learning algorithms are rapidly evolving and have the potential to provide a more efficient means of identifying biomarkers in multi-omics datasets for personalised medicine [The identification of gene fusion events, particularly high-risk medicine ,37. UltiIGH gene fusions through bioinformatic analysis, it is imperative to address some limitations that have potential implications for the interpretation of our results. The use of short-read sequencing technology presents an acknowledged constraint. Short reads have been shown to exhibit lower accuracy in comparison to long reads [IGH gene fusion detection. Additionally, the rapidly evolving landscape of bioinformatics introduces the consideration of newer gene fusion calling algorithms that might offer enhanced accuracy and sensitivity. The evaluation and incorporation of such algorithms, specifically those that have been tailored for the detection of difficult-to-identify gene fusions, warrant exploration to comprehensively assess their performance in our context [In the present study, while our findings show promising insights into the detection of ng reads ,39, pote context ,41,42.IGH locus can be improved when compared to default values. However, balancing sensitivity and specificity is crucial to avoid a disproportionate number of fusions that are not clinically relevant. By using an ensemble detection approach and Nextflow, the workflow manager, we increased the detection rate of IGH fusions and significantly reduced the processing time. With the use of optimised algorithms determined through machine learning, the future holds promise for the efficient identification of biomarkers and parameter selection in personalised medicine for B-ALL.The accurate and rapid detection of gene fusions is vital for assessing prognosis and guiding treatment in B-ALL. However, the lack of a universally accepted detection tool necessitates using multiple algorithms, leading to a time-consuming process in clinical practice. Our study showed that through curated parameter values for specific algorithms, detecting gene fusions involving the hypervariable


Genes & Development by Shimazu et al. that describes the role of CARMT1, a histidine methyltransferase, in RNA processing, commenting on CARMT1's substrate specificity and developmental essentiality.In this Outlook, Boutz discusses a study in this issue of  Genes & Development, Shimazu and colleagues (pp. 724–742) make the remarkable discovery that CARNMT1 acts as a dual-specificity histidine methyltransferase, modifying both the small-molecule dipeptide carnosine and a set of proteins, predominantly within RNA-binding C3H zinc finger (C3H ZF) motifs. As a result, CARNMT1 modulates the activity of its protein targets to affect RNA processing and metabolism, ultimately contributing an essential function during mammalian development.Specialized enzymes add methyl groups to the nitrogens of the amino acid histidine, altering the chemical properties of its imidazole ring and, in turn, the function of the modified (poly)peptide. In this issue of  Histidine methylation was discovered in 1967, but in comparison with other post-translational modifications such as arginine methylation, its biological functions have remained obscure until very recently. The two nitrogens in the imidazole ring of histidine (N1 and N3) are involved in hydrophobic interactions, confer affinity for divalent metal ions including zinc, and can switch from neutral to positively charged depending on their protonation state. Both can undergo methylation, which affects their biochemical properties  reduced cellular N1-methylated histidine amounts by ∼50%, while the double knockout resulted in undetectable amounts of the modification, indicating that CARNMT1 and METTL9 likely share between them most of the histidine N1-methyltransferase activity in the cells tested.In order to identify the protein substrates of CARNMT1, CARNMT1 knockout cells were incubated with or without recombinant CARNMT1 protein, and modified proteins were enriched and subjected to mass spectrometry. Using this methodology, 22 proteins were identified as CARNMT1 targets and several contained C3H zinc finger (C3H ZF) motifs, a type of RNA-binding domain that is found on many proteins involved in RNA metabolism and regulation (for review, see To gain insight into the physiological functions of C3H ZF methylation, Given the almost complete methylation of the first C3H ZF of the core splicing factor U2AF1 in the presence of CARNMT1, it is reasonable to assume that mRNA splicing patterns might be affected by U2AF1 methylation status. Indeed, almost 300 alternative splicing differences were detected by RNA-seq in CARNMT1 KO versus wild-type cells. Many of the affected events were cassette exons possessing weak 3′ splice sites with a preference for U versus C at the −3 position, reminiscent of exons differentially affected by U2AF1 S34F/Y mutations found in numerous cancers . With adIn contrast to METTL9, whose N1 methylation of its HxH consensus site alters the affinity for metal ions , synthetIntriguingly, carnosine but not anserine inhibited CARNMT1-dependent protein methylation, suggesting competition between substrates for limiting methyltransferase activity. Carnosine and anserine have been proposed to have exocrine functions when released from muscle during exercise . CARNMT1Finally,


BackgroundPre-eclampsia remains a leading cause of maternal and foetal mortality with a poorly understood pathophysiology. It can lead to a range of clinical presentations, but proteinuria and hypertension are key components of the diagnosis. These signs arise due to disordered placental implantation due to poor trophoblastic invasion, resulting in placental oxidative stress due to hypoxia. Oxidative stress triggers the release of syncytiotrophoblast microvesicles (STMBs), of which placenta-derived exosomes may be a key component. The high specificity of exosomes for their cell of origin makes them ideal candidates as diagnostic biomarkers. We are particularly interested in the miRNAs (microRNAs) contained within these exosomes, as they may give us an insight into the genomic regulation within the pre-eclamptic placenta that leads to the disease state. The development of workflows for miRNA quantitation may enable us to identify novel biomarkers.MethodsWe extracted exosomes and purified total RNA from 23 serum samples using the Norgen Plasma/Serum Exosome Purification and RNA Isolation Midi Kit. We then used the bioanalyser to determine the concentration and quality of the RNA obtained. It uses rapid electrophoresis, requires minimal sample sizes, and can assess the quality of genetic material as small as 25 bases.ResultsWe have successfully isolated RNA from these samples; however, the concentration of the total RNA was too low for downstream molecular analysis. We did gain insight into how to optimise and develop the workflow so that, with each attempt, the yield increased. Our greatest concentrations were obtained by combining serum samples from multiple patients, demonstrating that we needed a higher volume to optimise the yield. Future studies should aim to obtain samples specifically for use in this research so that we can process a larger volume of serum.ConclusionsWe have also noted that there is a positive correlation between the overall concentration of total RNA and a high sFlt-1/PlGF ratio. Preliminary analysis from Illumina identified with a high degree of confidence the presence of three miRNAs, namely, mir-498(46), mir-122(1), and mir-134(41). Further work is necessary to validate these findings and should focus on the possible future role of these miRNAs as biomarkers for the early diagnosis of pre-eclampsia. Pre-eclampsia is a common disorder of pregnancy with a poorly understood pathophysiology . It is dThe multi-system nature of pre-eclampsia makes its aetiology difficult to identify. It is understood that the underlying pathogenesis is based on disordered placentation due to poor trophoblastic invasion, resulting in insufficient remodelling of the maternal spiral arteries ,3. This It is vital that we continue to gain an improved understanding of the molecular basis of pre-eclampsia, as it remains a leading cause of maternal mortality, causing 70,000 maternal deaths and 500,000 foetal deaths worldwide every year . The manSince pre-eclampsia is most common during the third trimester, routine antenatal care for this period incorporates screening for the disorder through blood pressure monitoring and urinalysis to screen for hypertension and proteinuria . In receThe sFlt-1/PlGF ratio has been evaluated as a test to predict the presence of pre-eclampsia, but Zeisler et al. found that a ratio above 38 had a positive predictive value of 36.7% and a sensitivity of 66.2% for a diagnosis of pre-eclampsia within four weeks . The tesEvidence that placenta-derived exosomes may be a key component of STBMs has identified them as a possible biomarker for pre-eclampsia. Exosomes are vesicles released from cells, containing cell-specific genetic material, proteins, and lipids. Their high specificity for their cell of origin makes them ideal candidates as diagnostic biomarkers . ParticuPrevious studies have identified specific miRNAs that may be of interest when investigating biomarkers of pre-eclampsia by carrying out microarrays to look at the differential expression of miRNAs between normal and pre-eclamptic pregnancies . A numbeThe aim of this study is, therefore, to develop a workflow through which exosomes and miRNA can be extracted from serum or plasma. This workflow will consist of the isolation of placental exosomes from the blood of pregnant people with and without pre-eclampsia. We will then extract the exosomal RNA, hopefully including the miRNAs of interest. We will verify the presence of RNA with the bioanalyser, which determines the quality and quantity of the RNA in our samples. Iterative refinements of the protocol will hopefully improve the concentration of these samples so that they are suitable for downstream molecular applications. We then hope to compare the exosomal miRNA profiles of the different samples through next-generation sequencing (NGS) to identify consistent biomarkers of pre-eclampsia.The main method of molecular analysis that we would like to use is NGS. This involves the development of RNA libraries through the generation of cDNA from our extracted RNA . This woThe concentrations of the RNA alone may also be able to give us information about a possible pre-eclampsia diagnosis. The use of RNA biomarkers is increasingly being incorporated into the diagnosis of certain cancers, just by looking at the levels of non-coding RNAs ,19. GiveThis work was previously presented as a poster presentation at the 2023 INSPIRE National Graduate Entry Medicine Research Conference in Coventry, UK, on September 2, 2023.Patients and sample collectionThis study was approved by the clinical research ethics committee of the Arden Tissue Bank . We used 23 anonymised samples, which were surplus to requirements from routine samples taken for the purpose of investigating the patients for pre-eclampsia or undergoing routine first-trimester antenatal testing. All tissue samples were provided by the Arden Tissue Bank. They were secured and stored on our behalf by the pathology department at University Hospital Coventry and Warwickshire.Exosome isolation and RNA purificationExosomal miRNAs were purified from the serum of patients under investigation for pre-eclampsia or undergoing routine first-trimester antenatal testing. The exosomes were isolated, and the exosomal RNA was extracted using a Norgen Plasma/Serum Exosome Purification and RNA Isolation Midi Kit  accordinBioanalyserThe Agilent 2100 Bioanalyser was used to perform an RNA pico assay with the Agilent RNA 6000 Pico Kit. This involves the separation of nucleic acid fragments by size using electrophoresis, the subsequent generation of a size profile, and an assessment of the integrity and concentration of the RNA. The quality and concentration of the RNA were determined according to the manufacturer’s protocols .Next-generation sequencingSequencing libraries were generated using the Illumina TruSeq® Small RNA Library Prep Kit, according to the manufacturer’s instructions . The iSeSample concentrationsTable Samples 1-6 were from the initial attempt, using only one patient’s serum per extraction, giving very low concentrations of RNA that were insufficient for downstream applications.For the next two, we decided to compare routine first-trimester samples with samples from patients specifically being investigated for pre-eclampsia. The RNA concentration of the serum under specific investigation is much higher. If our hypothesis is correct that increased hypoxia in pre-eclampsia would lead to increased STMBs and an increased RNA concentration, this could be reflected in this data. Unfortunately, we do not have the sFlt-1/PlGF ratios for these patients, so we cannot draw this conclusion.Then we compared fresher samples with those that had been frozen for a longer time (samples 9-13). Samples 9-10 were the fresh samples, and samples 11-13 were the frozen ones. There is no discernible pattern in the RNA concentrations between the fresh and frozen samples, a conclusion that is supported by the literature .Samples 14-22 were more combined samples of patients under investigation for pre-eclampsia but without a confirmed diagnosis. Sample 23 contained serum from only one patient who had a confirmed diagnosis of pre-eclampsia. Of particular interest are the samples that contain serum with an sFlt-1/PlGF ratio greater than 33, which is the cut-off for ruling out pre-eclampsia between weeks 20 and birth . SamplesAlso of note is sample 23, the only sample in this group that contained serum from only one patient with a confirmed pre-eclampsia diagnosis. This sample’s RNA concentration is comparable to that of sample 21, which contains the serum of three patients in whom pre-eclampsia can be ruled out. This further demonstrates that an increased overall RNA concentration may be caused by the release of STMBs in pre-eclampsia. Although further validation of this result will be required, we hope it may reflect a possible future role for miRNAs in the diagnosis of pre-eclampsia, as this correlation is quite striking.Bioanalyser resultsFigure Figure Figure Next-generation sequencing resultsNGS was run on sample 22, the most concentrated sample at 1131 pg/μl. Unfortunately, the bioinformatic analysis will not return in time for the sequence data to be included in this report, but we do have preliminary data available.The total yield of the sequenced output was 0.1 GB (gigabases). A Q  score is assigned to each base to indicate the quality of the reading. If a score of Q30 is assigned to the base, there is a 1 in 1000 probability of that base being called incorrectly . Sample The percent passing filter (%PF) indicates the percentage of reads that pass Illumina’s internal quality filtering procedure, called the 'chastity filter', which removes the least reliable clusters from the results. The %PF generated for sample 22 is 17.3%, so a very low percentage of reads have passed the chastity filter.Inadequate concentrations of the RNA library may have led to this discrepancy between the Q30% and %PF. The %PF may be low because of the low concentration of the RNAs, making the clusters less discernible, while the high Q30% reflects that those clusters that did pass the chastity filter are usable. Hopefully, in future research, protocol modifications will increase the yield of RNA so that the NGS reads are of superior quality. Nevertheless, preliminary analysis from Illumina identified with a high degree of confidence the presence of 3 miRNAs, namely, mir-498(46), mir-122(1), and mir-134(41).In our efforts to increase the yield of the RNA extraction, we tried using larger sample volumes by combining multiple samples for one extraction. We also tried processing fresher samples in case the stability of the RNA was being affected by long-term freezing or repeated thawing. However, this is harder to organise since the samples are not taken specifically for our study. A closer examination of the literature demonstrates that multiple freeze-thaw cycles do not negatively impact the stability of miRNAs . UltimatAnother option that was considered was to use the miRNeasy Serum/Plasma Advanced Kit from Qiagen  to isolaCombining multiple samples to increase the overall concentration was a successful approach. We did see an overall increase in concentrations with the combined samples and higher volumes. The limitations of this approach will come in the downstream analysis. Since we did not know the sFlt-1/PlGF ratios of the samples before they were combined, some contain miRNA from patients both with and without pre-eclampsia. Therefore, when we sequence the samples, it will be hard to distinguish which miRNA is specific to the pathology. In future work, we would want to obtain samples specifically for use in this research so that we could process a larger volume of serum, since using leftovers from the lab often meant that we received less than a millilitre to process. Unfortunately, the time limitation and ethical implications meant that this was beyond the scope of this project.Alternatively, we could use the Norgen mini kit instead of the midi kit that we used. This would mean fewer reagents being added to the serum, hopefully making it less dilute and increasing the yield at each stage of the purification.We have successfully tested various steps, identified limitations, and generated information useful for the development of a successful workflow for the purification of exosomal RNA from serum. We have successfully demonstrated the presence of miRNA in these samples through the bioanalyser traces. We were limited in our ability to carry out downstream molecular analysis due to the low concentrations obtained.Our findings indicate the need for higher volumes of serum in this workflow to optimise RNA concentration and the continued refinement of this protocol. We have also identified a positive correlation between total RNA concentration and raised sFlt-1/PlGF ratios. A future investigation of this finding may illustrate the role of regulatory RNAs in pre-eclampsia pathogenesis.