The continually growing human population creates a concomitantly increasing demand for nutritious crops with high yields. Advances in high throughput sequencing technologies have revealed the genetic architecture of major crops. This includes extensive information enabling comprehensive genetic markers for breeding selection, new gene discoveries, and novel gene regulatory strategies for crop editing. RNA structure is an important type of genetic feature, essential for post-transcriptional regulation of gene expression. Here, we summarize recent advances in genome-wide RNA structure studies in crops and review the associated RNA structure-mediated regulation of gene expression. We also discuss the functional importance of those single nucleotide variations that induce large RNA structure disparities. Lastly, we discuss the potential role of RNA structure in crop molecular breeding. Crop breeding to achieve high productivity is hugely important to meet the growing demands of food sustainability and security , found that up to 39.5% of the homoeologs between A and B subgenomes showed significantly different RNA structure features   identified the global existence of RNA G-quadruplex structure (RG4), a specific RNA tertiary structure in rice  interacts with its sense RNA, the phosphate transporter PHOSPHATE1.2 (PHO1.2) at a high GC region across the PHO1.2 start codon  regulated photoperiod-sensitive male sterility  were previously identified for regulating vernalization in wheat   that is a single-stranded RNA-binding protein involved in the regulation of flowering  is a major cereal grain used for pasta and couscous production, evolved from domesticated emmer wheat (T. turgidum ssp. Dicoccum). Domesticated emmer wheat was derived from wild emmer wheat (T. turgidum ssp. Dicoccoides) in the Fertile Crescent about 10,000 years ago  was used to demonstrate its effect on molecular functions  and the National Key Research and Development Program of China (2021YFF1000900).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The results showed that 5% Cava lees promoted the growth of both lactic acid bacteria (LAB) and yeast in short fermentations (bread) but did not improve microbial growth in long fermentations (sourdough). Regarding volatile compounds, the addition of Cava lees increased the concentration of volatiles typically found in those products. Also, some compounds reported in sparkling wines were also identified in samples with Cava lees adsorbed on their surface. To sum up, the addition of Cava lees to sourdough and, especially, bread formulation may be a new strategy to revalorize such by-product.Wine lees are a by-product that represents a 25% of the total winery waste. Although lees are rich in antioxidant compounds and dietary fiber, they have no added value and are considered a residue. The aim of this study was to evaluate the effect of Cava lees (0 and 5%  In the EU, around 129 Mt of food waste is generated annually in the food supply chain . The curIt is particularly concerning to the winemaking industry, which includes the production of Cava. Cava is sparkling wine with Protected Denomination of Origin (PDO) that requires a second fermentation in the bottle with a biological ageing process in contact with lees for a minimum of 9 months . Wine elSaccharomcyces cerevisiae, tartaric acid and other adsorbed compounds [S. cerevisiae [Lees are the residue formed during wine fermentation and consist, mainly, of naturally plasmolyzed cells of ompounds ,7. It isompounds . Those lrevisiae ,5,6,7. Irevisiae ,10,11.Despite their composition, lees are an undervalued by-product mostly used for the recovery of tartaric acid and distillation to obtain alcohol . NeverthSalmonella spp. and L. monocytogenes), improving the microbiological safety of fermented sausages [In fact, Hernández-Macias et al. (2021)  reportedsausages . Hence, sausages .Indeed, flavor and especially odor are of great importance for consumer acceptance. It must be taken into account that the addition of by- and co-products to food formulation may change its sensory properties (from texture to aroma or color). For instance, Lafarga et al. (2018)  added brIn that regard, lees are able to adsorb volatile compounds during the biological ageing of sparkling wine . Consequw/w): carbohydrates 69.0, fat 1.4, fiber 4.2, protein 11.7 and moisture 15.0, was used.A commercial wheat flour  with the following composition (% w/w) and compared to a control without lees, based on previous in vitro studies [A parallel study was designed in order to compare breads with and without sourdough. Sourdoughs were prepared by mixing 100 g of flour and 100 mL of sterile distilled water , without studies . Sourdouw/w)), and salt (10 g). Cava lees were also added as a percentage of flour weight. The dough was manually mixed and kneaded for 10 min. The dough temperature at the end of kneading was between 22 and 24 °C. Once formed, dough was rested for 40 min, after which the dough was knocked back and rested for another 40 min. All bread was given a final proof of 20 min at 30 °C and 80% relative humidity. Following the processing of the dough, breads were baked in a convection-steam oven  at 220 °C for 30 min.Breads were made with the sourdoughs produced . Breads Microbial populations were monitored following the method described by Martín-Garcia et al. (2022) . BrieflyThe extraction of volatile compounds was performed using Headspace Solid Phase Microextraction (HS-SPME) as reported by Paraskevopoulou et al. (2012)  using a Chromatographic analysis was carried out in a 6890N Network GC system coupled to MS 5973 Network selective detector . Helium was used as a carrier gas. Separations were accomplished in a DB Wax USN 125-7031 column (30 m × 0.25 mm × 0.25 µm) . A splitless injector suitable for SPME was used. After extraction, the fiber was removed from the headspace vial and manually inserted directly into the injection port of the GC. The SPME fiber was thermally desorbed for 4 min at 260 °C.The initial temperature was held at 40 °C for 5 min and increased at from 40 °C to 190 °C at 3 °C/min and from 190 °C to 220 °C at 10 °C/min which was held for 5 min using splitless injection mode. GC-MS detection was performed in complete scanning mode (SCAN) in the 40–350 amu mass range with two scans per second. Electron impact mass spectra were recorded at an ionization voltage of 70 eV and ion source of 280 °C. Volatile concentrations reported were calculated by dividing the peak area of the compounds of interest by the peak area of the internal standard . The relative response factor was considered to be 1. Tentative Identification was performed by comparison of their mass spectra with those of the mass spectra library database Wiley 6.0., and their retention times with those of pure standards when they were available.p-value of ≤0.05. Principal component analysis (PCA) was also performed to determine differences between breads.The statistical analysis was performed using Prism 9 version 9.1.2 (225)  statistical package. The results are reported as the means ± standard error (SE) of triplicates for parametric data. A one-way ANOVA and comparison of the means were conducted using Tukey’s test, with a confidence interval of 95% and significant results with a w/w) Cava lees on LAB growth has been reported in vitro [w/w) Cava lees in wheat and rye sourdoughs [A control and a fortified (5% Cava lees) sourdough were prepared to assess the effect of Cava lees on the fermenting microbiota of sourdough. in vitro  and up turdoughs , it can w/w) was significantly lower (p < 0.05). In particular, SDC started the sourdough fermentation with a pH of 5.80 ± 0.01, while SD+L begun with a pH of 5.06 ± 0.07 due to the inherent acidity of Cava lees [The initial pH of sourdoughs with 5% Cava lees , especially in acids (660.46 ± 135.75 mg/kg), esters (1804.88 ± 274.08 mg/kg) and terpenes (215.74 ± 27.95 mg/kg). Oppositely, controls (SDC and BC) were richer in alcohols .In general, B+L had the highest concentration and number of different volatile compounds . Furthermore, SBC and BC breads had the highest concentration of acetic acid . In fact, acetic acid is one of the main organic acids responsible form microbiological shelf-life extension since it also possesses antiripeness and antimold activity [Control breads with and without sourdough (SBC and BC) presented no significant differences in organic acid concentration . The dominant alcohols in all bread samples were phenethyl alcohol and isoamyl alcohol. Phenethyl alcohol is derived from the fermentation of phenylalanine by yeast, and it has been reported that prolonged fermentations increase its concentration [Lactiplantibacillus plantarum (formerly Lactobacillus plantarum) [Alcohols are mainly produced during fermentation from flour amino acids via the Ehrlich pathway in yeast cells but may be also a product of lipid oxidation . SBC shontration ,31. SBC ntration ,24,31 anantarum) .Aldehydes are formed during lipid oxidation and decarboxylation of unsaturated fatty acids as well as from amino acid degradation by the Ehrlich pathway ,33. The L. plantarum and L. helveticus via amino acid  conversion [The addition of Cava lees increased the concentration of benzaldehyde, especially in yeast leavened bread . This compound is the result of both fermentative reactions and lipid oxidation and has been found in bread produced with and without sourdough ,24,34. Bnversion . Moreovenversion ,37,38, wnversion .Nonanal, another compound derived from fermentation and lipid oxidation  has alsoRegarding ketones, BC samples presented a greater variety of those compounds. Those volatile compounds are influenced by LAB in dough fermentation, and only certain homofermentative and facultatively heterofermentative bacteria are able to produce them . AcetoinEsters are characterized by a fruity odor resulting from a direct esterification between ethanol and acetyl co-A derivatives of fatty acids during fermentation mainly due to heterofermentative LAB ,35,39. ITerpenes are generally characterized by a floral odor and commonly found in sparkling wine ,38. FurtTDN  is a C13-norisoprenoid usually found in sparkling wine ,37,38. TLastly, the results obtained were subjected to a PCA to determine the differences between the breads produced with and without sourdough and Cava lees. It can be observed that both controls (SBC and BC) were placed in the same quadrant. In fact, both controls and SB+L were positioned opposite bread with Cava lees (B+L). SBC and BC were characterized by alcohols and ketones, while B+L was described by esters and terpenes. Moreover, a greater quantity of different volatiles was identified in B+L samples.w/w) improved microbial growth (both LAB and yeast) in short fermentations, although there were no significant differences in prolonged fermentations (sourdough). Actually, LAB and yeast release aroma compounds as well as aroma precursors (including carbohydrates and amino acids) that can be transformed into the corresponding volatiles. Thus, higher microbial populations obtained with Cava lees might produce a greater concentration of volatile compounds due to LAB and yeast fermentation in dough. In general, the addition of Cava lees to bread increased the concentration of volatiles typically found in bread and sourdough bread. Also, some compounds usually reported in sparkling wines were also identified in samples with Cava lees, supporting the fact that yeast lees adsorb volatile compounds during wine ageing.There are several factors involved in the development of bread flavor, from microbial activity to aroma precursors in the flour used. Therefore, it is important to determine the volatile fraction of the product to obtain consumers acceptance. Formulation of bread with 5% Cava lees (Therefore, it can be concluded that Cava lees promote the production of bread volatiles besides contributing with new odors from sparkling wine. Hence, the use of Cava lees as an ingredient in bread fermentation could be a new strategy to revalorize this winery by-product obtaining a new bread product. Also, further studies should focus on the effect of Cava lees on identified microorganisms of sourdough and bread, since the fermenting microbiota can influence bread aroma and flavor.


Recent studies have shown that ubiquitin‐mediated cell apoptosis can modulate protein interaction and involve in the progress of oocyte maturation and spermatogenesis. As one of the key regulators involved in ubiquitin signal, ubiquitin C‐terminal hydrolase L1 (UCHL1) is considered a molecular marker associated with spermatogonia stem cells. However, the function of UCHL1 was wildly reported to regulate various bioecological processes, such as Parkinson's disease, lung cancer, breast cancer and colon cancer, how UCHL1 affects the mammalian reproductive system remains an open question.We identified papers through electronic searches of PubMed database from inception to July 2022.Here, we summarize the important function of UCHL1 in controlling mammalian oocyte development, regulating spermatogenesis and inhibiting polyspermy, and we posit the balance of UCHL1 was essential to maintaining reproductive cellular and tissue homeostasis.This study considers the ‘double‐edged sword’ role of UCHL1 during gametogenesis and presents new insights into UCHL1 in germ cells. UCHL1 and mammalian gametogenesis. UCHL1 affects apoptosis‐related factors in mammalian gametogenesis and controls mammalian oocyte development, regulates spermatogenesis and inhibits polyspermy. It may also have other biological functions in the testis or ovary, such as anti‐inflammatory or antiviral. The functional integrity of proteins in the process of oocyte maturation and spermatogenesis is an important guarantee for sperm‐oocyte fusion. Amounts of studies have shown that ubiquitin proteasome system (UPS) can not only degrade misfolded or damaged proteins but also regulate protein expression levels to maintain a relatively stable state for mammalian gametogenesis.2The ubiquitin proteasome system (UPS) is primarily driven by ubiquitin (Ub) as a degradation tag, a process controlled by various reversible enzymatic reactions. It includes ubiquitin, ubiquitin‐activating enzyme E1, ubiquitin‐conjugating enzymes E2, ubiquitin‐ligase enzymes E3 and proteasome.Deubiquitination (DUB) is the reverse process of ubiquitination and is accomplished by a series of DUB enzymes. DUB enzymes hydrolyze ubiquitin molecules on target proteins and dissociate ubiquitin chains into individual ubiquitin molecules, playing a significant role in maintaining a dynamic balance between free ubiquitin molecules and those bound to target proteins Figure . DUB enzUbiquitin C‐terminal hydrolase (UCH) is an important member of the DUB enzyme family.3Mammalian gametogenesis includes the development of sperm and oocyte, which are derived from primordial germ cells (PGCs). The formation of sperm and oocyte is a prerequisite for the formation of individual mammals.Spermatogenesis has a crucial role in controlling male fertility, and with the current advancements in various high‐throughput sequencing technologies, the integrity of the spermatogenesis process in different species has been further explained.Mammalian gametogenesis is regulated not only by gene transcription and translation but also affected by the degradation of various target proteins.3.1Kipl during oocytes formation in mice, suggesting that UCHL1 functions together with Jab1 and p27Kipl, and is synergistically involved in the selective elimination of mouse oocytes before sexual maturity.Protein degradation is critical for oocyte maturation, and UCHL1 is one of the most abundant proteins in mammalian oocytes, and it is mainly expressed in the cytoplasm of primordial follicles and developing oocytes in human ovaries.Oocyte maturation is considered as a physiological inflammatory response in a certain sense, accompanied by the participation of a variety of inflammatory factors. Studies have shown that supplementation with interleukin‐7 (IL‐7) enhances the meiotic maturation of porcine oocytes by improving both nuclear maturation and cytoplasmic maturation.3.2UCHL1, as a member of the DUB enzyme family in the UPS, is involved in the whole process of spermatogenesis. Therefore, disruption of UCHL1 homeostasis will lead to impaired spermatogenesis. For instance, the deletion of UCHL1 in mice shows a series of hindrances to spermatogenesis, such as abnormal morphology of seminiferous tubules, proliferation reduction of SSCs and spermatogenic dysfunction,As research progresses further, UCHL1 plays other roles in the testis in addition to controlling apoptosis. Such as resisting viral infection and maintaining cell homeostasis. Gao et al. and other scientists showed that giant salamander iridovirus (CGSIV) infection can be reduced by UCHL1 in giant salamander testis, but its detailed mechanism remains to be further studied.3.3Fertilization is an orderly and continuous process in which a fully mature oocyte is penetrated by viable sperm to ensure the rapid co‐development of male and female prokaryotes. Monosperm fertilization is mainly mediated by zona pellucida proteins consisting of ZP1, ZP2, and ZP3 and is critical for the initiation of growth and development.In mammals, the efficiency of in vitro fertilization (IVF) is relatively low compared to in vivo fertilization. In recent studies, it has been found that multiple sperm entry into eggs is one of the main causes of abnormal in vitro fertilization, and therefore low pregnancy rates, high abortion rates, high rates of malformed foetuses, or genetic disorders of the foetus can occur.4The ubiquitin‐proteasome is a large family and the balance between ubiquitination and DUBs precisely controls the dynamic levels of various proteins in spermatogenesis and oocyte development. The UCH family, as one of the key members of the UPS, plays an essential role in mammalian gametogenesis and fertilization. UCHL1 is closely associated with oocyte maturation and spermatogenesis, sperm‐oocyte union, and other processes. The roles of UCHL1 in oocyte maturation and spermatogenesis are distinct, and the expression levels of UCHL1 are positively and negatively determined by cell status and development, and we have supplemented the functions of UCHL1 in oocyte maturation and spermatogenesis (Table Donghui Yang and Jinlian Hua designed the study and wrote the manuscript. Qizhong Lu and Sha Peng made instructions and corrections to the language and content of the article.The authors declare that they have no competing interests.


Vascular diseases account for a significant number of deaths worldwide, with cardiovascular diseases remaining the leading cause of mortality. This ongoing, ever-increasing burden has made the need for an effective treatment strategy a global priority. Recent advances in regenerative medicine, largely the derivation and use of induced pluripotent stem cell (iPSC) technologies as disease models, have provided powerful tools to study the different cell types that comprise the vascular system, allowing for a greater understanding of the molecular mechanisms behind vascular health. iPSC disease models consequently offer an exciting strategy to deepen our understanding of disease as well as develop new therapeutic avenues with clinical translation. Both transcriptional and post-transcriptional mechanisms are widely accepted to have fundamental roles in orchestrating responses to vascular damage. Recently, iPSC technologies have increased our understanding of RNA-binding proteins (RBPs) in controlling gene expression and cellular functions, providing an insight into the onset and progression of vascular dysfunction. Revelations of such roles within vascular disease states have therefore allowed for a greater clarification of disease mechanisms, aiding the development of novel therapeutic interventions. Here, we discuss newly discovered roles of RBPs within the cardio-vasculature aided by iPSC technologies, as well as examine their therapeutic potential, with a particular focus on the Quaking family of isoforms. Vascular diseases encompass a range of pathological conditions that affect blood vessels, many of which can lead to the onset of a range of fatal complications. Accordingly, cardiovascular diseases (CVDs) are one of the leading causes of death worldwide . AlthougAlthough there are various treatment options that have led to a reduction in general mortality rates, there are currently no gene therapies or efficacious drugs able to restore blood vessel damage. The repair and regeneration of healthy blood vessels is consequently an important focus in regenerative medicine to improve vascular health . SamplinSuch models have demonstrated the ability of RNA-binding proteins (RBPs) to exhibit control over cellular phenotypes, as well as play a vital role in health and disease states. RBPs are proteins with the ability to bind to RNA and form ribonucleoproteins, which are then subsequently involved in a wide range of processes, including alternative splicing, localisation, stability and translation. As a result, RBPs can significantly alter RNA fate through a number of post-transcriptional mechanisms . In receRemarkably, research has revealed that reversing the effects of dysregulated RBPs can be beneficial in alleviating their associated complications, highlighting the potential of RBPs to become effective therapeutic strategies. The RBP Human Antigen R (HuR), for instance, has previously and repeatedly been implicated in the development of vascular diseases, with research displaying HuR expression to positively correlate with the presence and severity of cardiac and vascular dysfunction . TypicalIn this review, we explore the roles of RBPs within the vascular system with emphasis on their therapeutic potential. In particular, we review the recent findings aided by stem cell technologies that highlight the involvement of the QKI RBP isoforms within vascular health.The human genome codes for a total of ~20,000 genes. To produce the spectrum of proteins responsible for the higher degree of complexity observed in humans, 95% of these coded genes will undergo post-transcriptional gene regulation (PTGR). Moreover, recent evidence suggests that RNA level alone is not a correct index of determining gene expression level but post-transcriptional RNA modifications, so-called the epitranscriptomic code or epitranscriptome, determine the actual gene expression profile at protein level . PTGR enUnsurprisingly, proper functioning of these intricate post-transcriptional manipulations of RNA have shown to be essential for cell physiology and defects in either RBPs or RBP-regulated RNA network to be heavily associated with the onset and progression of pathological disorders. Due to the clear importance of RBPs in deciphering between health and disease states research to dissect the complex PTGR networks in various cellular systems is of great interest.Despite their centrality in cell function, much is still largely unknown of RBPs evidenced by at least a third of known RBPs having either unknown or inferred biological functions. This was further emphasised in a recent study which investigated 1217 RBPs encoded by the human genome and found only 73% of the RBPs to have assigned RNA biological functions or a known structural RNA-binding motifs . ConsequAlternatively, another approach to uncover native protein–RNA interactions known as interactome capture (RIC) has emerged and led to both the identification and insight into the functions of hundreds of RBPs, further developing our understanding of RBP-RNA interactions. This method uses ultraviolet light irradiation to covalently link proteins to RNA positioned in direct proximity followed by incubation of denatured cell lysis with poly(A) RNA on oligo(dT) beads and the identification of all bound proteins by quantitative mass spectrometry . RecentlAs stated, both ECs and SMCs are integral to vascular health. The examination of living cardiac tissues and vessels is largely unattainable. Protocols to effectively derive iPSCs from patients as well as their subsequent differentiation into associated cell types to create effective disease models have therefore emerged; models that have since allowed for the elucidation of novel pathogenic mechanisms ,55. ConsBoth ECs and SMCs are known to have their gene expression profile regulated by both transcriptional and post- transcriptional mechanisms ,65,66. AThe QKI RBPs belong to the evolutionally conserved Signal Transduction and Activation of RNA (STAR) family. Analysis of the family has revealed QKI to contain a KH RNA-binding motif domain, N-terminal Qua1 and C-terminal Qua2 domains as well as multiple phosphorylation sites . Due to Whilst an initial role for QKI in vascular development was first suggested following Drosophila studies , it was Of the three major isoforms QKI-5 is the most abundantly expressed in ECs . Using iUnsurprisingly, QKI-5 downregulation has been associated with various vascular disease pathologies also. Analysis of diabetic hearts for instance found QKI-5 to be significantly downregulated and enhance ischemic intolerance through promoting Forkhead Box O1 (FOXO1) activation, mediating myocardial cell death and increasing the susceptibility of ischemic injury . IntroduAs stated, the differences between the isoforms resides within the C-terminal. Whilst the C-terminal of QKI-5 and QKI-6 predominantly regulate the localisation of their proteins, the unique 14 amino acids comprising the C-terminal of QKI-7 has shown to have key roles in increasing translation of mRNA by promoting the polyA tail extension of target mRNAs via the recruitment of poly(A) RNA polymerase GLD2 (PAPD4) . MoreoveQKI-7 has also recently been identified to have a significant negative impact on cell function, contrary to the other QKI isoforms. Analysis of iPSC derived diabetic ECs revealed CUG-BP and hnRNPM regulation of QKI-7 to be abnormal, resulting in its overexpression . SimilarAs discussed, RBPs typically fall into one of two categories: pathogenic or protective. Based on this, efforts of designing RNA-based therapeutics should be focused on mimicking protective RBPs or inhibiting pathogenic RBPs to restore health and prevent disease-causing function, respectively. Every RBP has unique domains to bind its target RNAs in sequence-dependent fashion with structural conformation . This prRBPs have continually been found to be involved in regulating the transcriptome during phenotype switching in CVDs. In addition, studies have shown many RBPs to be unique to various cellular components of the cardiovascular system. For example, Liao et al. using mRNA interactome capture and RBDmap identified 1,148 RBPs, 393 of which were unique to cardiomyocytes and many of these RBPs linked to heart diseases . MoreoveDespite only one RBP-based therapy currently being approved for use in patients, widescale clinical applications of RBPs are inevitable. Recent developments have highlighted a pivotal role for RBPs and RNA interactions in mediating between health and disease states. Therefore, the ability of RBPs to control cellular phenotypes has great potential in therapeutics through restoring or preventing disease phenotypes. Due to the large uncertainties surrounding RBP functions, it is essential we deepen our understanding of RBPs to unveil their full potential and unlock new therapeutic avenues for currently undruggable targets, as seen following the comprehensive studies of the QKI isoforms. Considering recent developments, it is also important to consider the therapeutic potential of RBPs with respect to both treatment and generation of effective models within the cardiovascular system. With this in mind, future work regarding disease pathogeneses should evaluate specific roles of RBPs within both health and disease states in order to reveal novel routes of pathogenesis and treatment strategies, the groundwork for which has already been laid through the development of databases such as RBPDB, RBPmap, AttRACT, hRBPome and oRNAment.


Recent studies of the barley yellow dwarf virus (BYDV) have revealed eukaryotic initiation factor 3 (eIF3) plays a significant role in facilitating communication between its 5′ and 3′ UTRs by binding both UTRs simultaneously. This report uses in vitro translation assays, fluorescence anisotropy binding assays, and selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) footprinting to identify secondary structures that are selectively interacting with eIF3. SHAPE data also show that eIF3 alters its interaction with BYDV structures when another factor crucial for BYDV translation, eIF4F, is introduced by the 3′ BYDV translational enhancer (BTE). The observed BTE and eIF4F-induced shift of eIF3 position on the 5’ UTR and the translational effects of altering eIF3-binding structures (SLC and SLII) support a new model for BYDV translation initiation that requires the reorientation of eIF3 on BYDV UTRs. This eIF3 function in BYDV translation initiation is both reminiscent of and distinct from eIF3–RNA interactions found in other non-canonically translating mRNAs (e.g. HCV). This characterization of a new role in translation initiation expands the known functionality of eIF3 and may be broadly applicable to other non-canonically translating mRNAs.Viral mRNAs that lack a 5′ m Most RNA viruses have unique translation mechanisms that utilize only a subset of the eukaryotic translation initiation factors (eIFs) required for canonical translation of cellular mRNAs. Many RNA viruses lack the post-transcriptional modifications of a 5′ 7-methylguanosine cap and a 3′ poly-adenosine (poly-A) tail. Because of this, two eIFs frequently bypassed in viral translation mechanisms are the cap-binding initiation factor 4E (eIF4E) and the poly-A binding protein (PABP) .Cellular mRNAs undergoing canonical translation use eIF4F (a complex of eIF4E and eIF4G in plants) and PABP to bridge the two ends of the mRNA. This eIF4F-PABP bridge facilitates communication between the UTRs and serves as the basis of the closed-loop model of canonical translation. Several important functions have been ascribed to this canonical 5′-3′ mRNA cyclization including promotion of ribosome recycling and prevention of mRNA decay which both ultimately enhance translation efficiency . This alRNA viruses that lack a 5′ cap and 3′ tail can garner similar degradation-preventing and translation-enhancing benefits by using structural elements in their untranslated regions (UTRs) that form direct RNA-RNA interactions between UTRs and that recruit eIF4F or other eIFs to facilitate bridging between UTRs. These UTR-bridging complexes may also serve functions unique to RNA viruses like toggling between translation and replication ,7. Many Luteovirus) contains a BTE (BYDV-like CITE) structure in its 3′ UTR. BTE structures are found in all members of the Luteovirus, Dianthovirus and Necrovirus genera and in some umbraviruses. All BTE structures contain a conserved 17 nucleotide sequence that binds eIF4F (via eIF4G) and forms SLI, the first of up to six stem–loops that all extend from a central hub. In most BTEs one stem–loop contains a hairpin sequence that is complementary to a hairpin in the 5′ UTR  footprinting of the BYDV UTRs in the presence of mixtures of initiation factors  assays. The sequences of SLC-U, SLC-rev, SLC-ILF and SLC-ILR mutants used in IVT and binding studies are shown in Figures Four mutants of the 5′ UTR stem–loop C (SLC) were designed for use in anisotropy binding studies and 105 is a reporter plasmid used for IVT assays that contains the firefly luciferase gene flanked by the BYDV genomic 5′ UTR and 3′ BTE (105 nt). This reporter is a truncated version of BlucB  (105 were made replacing SLC with each of the four SLC mutants explained previously (excluding the added GC pairs) and two deletion mutants that removed either SLA from the 5′ UTR (dSLA) or SLII from BTE (dSLII). These mutants were prepared using the Q5 Site-Directed Mutagenesis Kit (NEB). The BlucB105 Plasmid and its mutants were linearized using SmaI restriction enzyme (NEB), transcribed using the HiScribe T7 High Yield RNA Synthesis Kit (NEB), and purified using the ssDNA/RNA Clean & Concentrator kit (Zymo Research).BlucB LUC869)  which coTwo RNA oligomers were designed for use in SHAPE and SHAPE footprinting studies . To stud105 reporter RNAs, excluding plasmid linearization. All RNA concentrations were determined using a Thermo Scientific NanoDrop 1000 spectrometer and RNA integrity was verified by 1% agarose gel electrophoresis. Some of the purified FUS RNA was 3′-fluorescein labeled . ATP sodium salt was used for most experiments, however binding experiments using ATP-MgCleviously  except teviously ,23.105 reporter gene contains a firefly luciferase gene surrounded by the full BYDV 5′ UTR and the 105 bases of 3′ BTE (105 or mutant BlucB105 RNA to 25 μl in vitro reaction mixtures made using the wheat germ extract (WGE) in vitro translation system from Promega. The reaction mixtures contained 50% (v/v) WGE (Promega), 80 μM amino acid mixture (Promega), 60 mM potassium acetate (Promega), and 1.6 units/μl RiboLock RNase inhibitor (ThermoScientific). After addition of RNA to the mixtures, reactions were incubated at 25°C for 40 min and stopped by the addition of 60 μM puromycin. 3 μl of each reaction was added to 30 μl Bright-Glo luciferase assay reagent (Promega) and measured for luminescence using a SpectraMax iD5 microplate reader (Molecular Devices) with default endpoint luminescence settings. Each reaction was measured in triplicate and repeated at least 5 times. Relative translational efficiency of each RNA was determined in relation to wt BlucB105 reacting concurrently and using WGE from the same batch. Averages and standard deviations of all translation reactions were calculated and graphed in excel.The BlucBf 3′ BTE . IVT ass2, pH 7.5) by incubation for 5 min at 65°C followed by 1 min on ice. 10 pmol of refolded RNA were added to 25 μl reactions containing 1× SHAPE buffer, 1 μl RiboLock RNase inhibitor (ThermoScientific) and eIF mixtures . eIF mixtures without eIF4F, eIF3, or both were also prepared. RNA was incubated with the initiation factor mix for 30 min at 37°C, then reacted with 3 μl of 14 mM (25 mM for larger FUS RNA) 1-methy-7-nitroisatoic anhydride (1M7) (SHAPE footprinting reactions followed established protocols with some modifications . Brieflyde (1M7)  for 70 sde (1M7) . CE datade (1M7) . SHAPEfi2 was used, however Mg2+ will differ in experiments where ATP sodium salt was used depending on the Mg2+ partitioning between the RNA and ATP. Helicase factors eIF4A and eIF4B alone do not have strong affinity for either 5′ or 3′ UTR, and eIF4F only shows strong affinity for the 3′ BTE (n = 9). The mean differences between sets of reaction conditions were calculated and subject to a two-sided Student t-test to show which nucleotides had statistically significant changes in SHAPE reactivity caused by the presence of 3′ BTE, eIF4F and eIF3. These changes can be caused by shifting RNA–protein interactions, or changes to RNA structure caused by those interactions.To characterize eIF-RNA interactions on the 5′ and 3′ UTRs, SHAPE footprints ,30,31 wee 3′ BTE . To corrNearest-neighbor predictions of 5′ UTR and FUS oligomer secondary structure with SHAPE data constraints were calculated using RNAstructure  and modeTo better visualize the shift of eIF3–RNA interactions caused by eIF4F and BTE, predictions of 5′ UTR and FUS oligomer tertiary structures were generated using RNA composer ,35. Nucl105 reporter construct to determine the translation of these mRNA.Stem loop C (SLC) of the BYDV 5′ UTR was identified as a potential eIF3 binding site due to sequential (a U-rich hairpin) and structural  features that SLC shares with other eIF3-binding structures . These S105 luciferase reporter construct in place of wt SLC  were inserted into the BlucBt Figure  and tran105 indirectly highlight the significance of an eIF3-SLC interaction. The 5′ UTR stem loop A (SLA) lacks the potential eIF3-binding features of SLC, and SLA deletion has no effect on WGE translation (dSLA: 96 ± 6%). Because eIF3 simultaneously interacts with 5′ and 3′ UTRs deletion of the 3′ BTE stem loop II (SLII) allows us to look at the translational effect of a potential eIF3 binding site in the 3′ BTE without significantly disrupting known translational requirements of eIF4F-SLI binding and the SLD-SLIII kissing loop (dSLII: 67 ± 11%). Earlier studies have shown that disruption of the “kissing loop” interaction abolishes translational efficiency  from SHAPE reactivity. In the presence of eIF4F, the eIF3 complex maintains some protection of SLB, loses protection of the 5′ SLC-IL, but gains protection on the 3′ SLC-IL. This ability of eIF4F to direct eIF3 interactions with 5′ UTR nucleotides is highlighted by red and blue boxes in Figure Figure The ability of eIF3 to bind both the 5′ and 3′ UTR of BYDV specifically, simultaneously, non-competitively and in a helicase-dependent manner was identified by analyzing BYDV UTRs both individually and together in trans . To bettFigure In order to try to gain further insight into the orientation of eIF3 on the FUS oligomer, additional binding and SHAPE footprinting studies were carried out with the eIF3a N-terminal domain and eIF3b subunits . The N-tThe translation of barley yellow dwarf virus relies on its 3’ translational enhancer, BTE, to recruit translational machinery to the 3’ BTE, communicate with its 5’ UTR, and transfer said machinery onto its 5’ UTR. The mechanism of this complex process, however is not well understood. In this study, we explore the 3’ BTE and 5’ UTR binding sites of eIF3 and how this binding changes with addition of key eIFs. The results of this study build on previous work to establish an expanded model of BYDV translation in which eIF4F promotes the rearrangement of eIF3 binding on the 5’ UTR suggesting new roles for both factors.BTE contains a conserved 17 nucleotide sequence  measured when both eIF3 and eIF4F are present  in the conserved sequence as a potential binding site for the 40S ribosomal subunit. Active helicase factors eIF4A, eIF4B and eIF4F expose bases in that complementary sequence that are otherwise involved in secondary structure which helps recruit 40S ribosomes to BTE with  present . This ennslation  and suggnslation ,43,44. Tnslation .105 containing SLC-rev, SLC-ILF and SLC-ILR suggests that the formation of a proper SLC-SLI bridge or the ability of eIF3 to shift away from it was effectively disrupted by these mutants. This supports the existence and physiological relevance of a pre-shifted eIF3 bridge. BYDV translation efficiency assays performed in W. Allen Miller's lab that removed SLC have also shown SLC can affect translation especially when the assays are carried out in protoplasts . The hepatitis C virus and classical swine fever virus both contain 3’ cis-acting structures that significantly increase translation by interacting with both eIF3 and the 40S ribosome ,52,53. OIn conclusion, this study supports an expansion of a translation initiation model for genomic BYDV mRNA and introduces a novel role for eIF4F in reconfiguring the initiation complex of eIF3 to help shuttle factors from 3’ BTE to the 5’ UTR .RNAstructure is available from the Matthews lab webpage (https://sourceforge.net/projects/rna2drawer/).RNA2drawer is available in the SourceForge repository (http://rnacomposer.cs.put.poznan.pl/).RNA composer is available at (https://ribosnitch.bio.unc.edu/software/).CEQ programs are available on the Laederach Lab webpage (https://github.com/drsuuzzz/ShapeFinder).SHAPEfinder software is available in the GitHub repository (https://pymol.org/2/).PyMOL software is available at (gkac284_Supplemental_FileClick here for additional data file.


Extracellular vesicles (EVs) are g7aining increased attention as carriers of cancer-derived molecules for liquid biopsies. Here, we studied the dynamics of EV levels in the plasma of breast cancer (BC) patients undergoing neoadjuvant chemotherapy (NAC) and explored the relevance of their RNA cargo for the prediction of patients’ response to the therapy. EVs were isolated from serial blood samples collected at the time of diagnosis, at the end of NAC, and 7 days, 6, and 12 months after the surgery from 32 patients with locally advanced BC, and 30 cancer-free healthy controls (HCs) and quantified by nanoparticle tracking analysis. The pre-treatment levels of EVs in BC patients were higher than in HCs, significantly increased during the NAC and surgery, and decreased to the levels found in HCs 6 months after surgery, thus showing that a substantial fraction of plasma EVs in BC patients are produced due to the disease processes and treatment. RNA sequencing analysis revealed that the changes in the EV levels were associated with the alterations in the proportions of various RNA biotypes in EVs. To search for RNA biomarkers that predict response to the NAC, patients were dichotomized as responders and non-responders based on Miller-Payne grades and differential expression analyses were carried out between responders and non-responders, and HCs. This resulted in the identification of 6 miRNAs, 4 lncRNAs, and 1 snoRNA that had significantly higher levels in EVs from non-responders than responders at the time of diagnosis and throughout the NAC, and significantly lower levels in HCs, thus representing biomarkers for the prediction of response to NAC at the time of diagnosis. In addition, we found 14 RNAs representing piRNA, miRNA, lncRNA, snoRNA, and snRNA biotypes that were induced by NAC in non-responders and 2 snoRNAs and 1 piRNA that were induced by NAC in patients with early disease progression, thus warranting further functional studies on their role in chemoresistance and metastasis. Neoadjuvant chemotherapy (NAC) is increasingly used in the management of patients with locally advanced breast cancer (BC) to render inoperable tumors operable, reduce the extent of surgery, and prevent the spreading of metastatic cells , 2. MoreLiquid biopsies are samples of body fluids that are used for the analysis of circulating tumor cells (CTCs) or cancer-derived molecules such as cell-free tumor DNA or RNA , 9. TheyThis study aimed to evaluate the relevance of EV RNA cargo for the management of patients with locally advanced BC. We performed EV RNA sequencing analysis in serial blood samples collected at various time points from 32 BC patients undergooing NAC and 30 cancer-free females. In addition, full transcriptome libraries were prepared from BC and normal breast tissues from 10 patients. Patients were dichotomized as responders and non-responders based on Miller-Payne grades. To identify cancer-derived EV-RNA biomarkers that predict the response to NAC at the time of diagnosis, we searched for EV-RNAs that were differentially expressed between responders and non-responders and had significantly higher levels in BC patients compared to cancer-free controls. In addition, we searched for EV-RNAs that were induced by NAC, absent or low in controls and higher in non-responders than responders, thus suggesting that the induction of their expression may functionally contribute to drug resistance and cancer cell survival.g for 10 min, aliquoted, and stored at -80°C. BC and adjacent normal tissue specimens were macroscopically dissected by a histopathologist during surgery and stored in RNALater  at -20°C till processing. Plasma samples from 30 cancer-free age-matched women were obtained from the Latvian Genome Database.BC patients were recruited between June 2019 and October 2020 at Riga East University Hospital and followed-up 18 months after the surgery. Inclusion criteria: previously untreated invasive primary BC diagnosed by core needle biopsy, stage II to III at diagnosis, age 18-78 years, prescribed NAC. Exclusion criteria: blood transfusion in the last six months, another oncological disease. The blood samples were collected at specified time points – at the time of diagnosis, at the end of NAC, 7 days after the surgery, and 6, 12, and 18 months after the surgery. Blood samples were collected in EDTA-coated tubes and processed at room temperature within 2 hours. Plasma samples were centrifuged twice at 3000The NAC regimens contained Doxorubicin, Docetaxel, Cyclophosphamide, Paclitaxel, 5FU, and Epirubicin. The patients were dichotomized based on the Miller-Payne grades: patients with grades 1 to 3 were classified as non-responders and patients with grades 4-5 as responders. The characteristics of the study population are provided in The study was conducted according to the Declaration of Helsinki. The specimens were collected after the patients’ informed written consent was obtained. The samples were stored in the Latvian Genome Database. The biobanking procedures have been approved by the Latvian Central Medical Ethics Committee  and the use of clinical samples for this study was approved by the Committee of Biomedical Ethics of Riga East University Hospital and the Latvian Central Medical Ethics Committee .EVs were isolated from 1 ml of plasma, using size exclusion chromatography (SEC). SEC columns were prepared from 10 ml of Sepharose CL2B  in TELOS SPE columns . Plasma samples were loaded on the columns and gravity-eluted with PBS-DEPC, and the eluate was collected in 15 sequential 500 μl fractions. Fractions were measured with Zetasizer Nano ZS  and fractions containing particles larger than 35 nm, were collected and concentrated to 100 μl using Amicon Ultra 3 kDa centrifugal filters . To check the purity and quality, EVs from 4 patients and controls were analyzed by transmission electron microscopy (TEM) as described before . All samEVs were heated for 5 min at 95°C with reducing Laemmli buffer and amounts corresponding to 100 μL plasma were loaded per lane of a 10% SDS-PAGE gel. After separation, the proteins were transferred to nitrocellulose membranes, which were subsequently blocked using 10% (w/v) fat-free milk. Membranes were incubated with primary antibodies against TSG101 , Calnexin  and PDCD6IP/ALIX  overnight at +4˚C. After washing in TBST, membranes were incubated for 1h at room temperature with anti-rabbit IgG, F(ab’)2-HRP , goat anti-mouse m-IgG BP-HRP , or HRP-conjugated antibody against CD63 . After washing in TBST, immunoreactive bands were visualized using Amersham™ ECL Select™ Western Blotting Detection Reagent kit  and pictures were taken using a Nikon d610 dSLR body (Nikon) with Sigma 35mm f/1.4 DG HSM Art lens (Sigma).g at +4˚C. The aqueous phase was transferred to a fresh tube and 1.5 vol of 100% ethanol was added, and the mixture was loaded onto the MinElute Spin column. Columns were centrifuged and washed according to the manufacturer’s protocol and the RNA was eluted using 12 μl of RNAse-free water. To determine the concentration and assess the quality, the RNA was measured using Agilent 2100 Bioanalyzer and RNA 6000 Pico Kit .Before RNA extraction, the EV samples were treated with 1 mg/ml proteinase K  for 30 min at + 37˚C. Proteinase was inactivated by heating the sample at + 65˚C for 10 minutes, and then the samples were treated with 10 ng/μl RNAse A  for 15 minutes at +37˚C. Immediately after that, EV samples were lysed by adding 5 volumes of QIAzol Lysis reagent and EV-RNA was extracted using miRNeasy Micro Kit  according to the manufacturer’s protocol. Briefly, 1 ml of chloroform was added to the EV lysate, incubated at room temperature for 10 minutes, and centrifuged for 15 minutes at 12000® DNA-free™ kit .For RNA extraction from tissues, 45-50 mg samples were cut from tissue specimens preserved in RNAlater , overlaid with 700 μL QIAzol Lysis Reagent (QIAGEN) in Lysing Matrix A tubes , and homogenized twice for 40 seconds at 6m/s using FastPrep-24™ device . Differential extraction of long- and short- RNA enriched fractions was carried out using miRNeasy mini and micro kits (QIAGEN) according to the manufacturer’s protocol. Long RNA fractions were subjected to on-column treatment with RNase-Free DNase Set (QIAGEN), while short RNA fractions were treated using Ambion® Small RNA Library Prep Kit  according to the manufacturer’s protocol. Briefly, one-fifth of the total RNA obtained from EVs and 100 ng of tissue RNA was used for library construction. 3’ and 5’ adapters were ligated to the RNA and then the tagged RNA library was reverse transcribed. After that index primers were added, and the RT product was amplified by PCR using 15 cycles for tissue RNA and 18 cycles for EV RNA. The obtained libraries were analyzed with Agilent 2100 Bioanalyzer and Agilent High Sensitivity DNA Chip . The libraries were cleaned using Blue Pippin DNA Size Selection with 3% gel Blue Pippin Cassette , setting a tight target length to 140 bp, thus selecting a size range from 126 – 154 bp. Then the library concertation was measured using Qubit and the libraries were diluted as required and sequenced on Illumina NextSeq500 instrument using NextSeq 500/550 Mid Output Kit v2.5 (150 cycles) .Small RNA libraries were constructed using CleanTag® fluorometer . The libraries were then pooled according to the index sets, as required by the manufacturer for circularization, circularized, digested, and then sequenced with the MGI DNBSEQ-G400 sequencer .Transcriptome libraries were constructed using MGIEasy RNA Directional Library Prep Kit  according to the manufacturer’s protocol. Briefly, 200 ng of total RNA was subjected to rRNA removal with the MGIEasy rRNA depletion kit . Next, the RNA was fragmented into 250 bp pieces, reverse transcribed and the second strand was synthesized. Then adapters were ligated to the product, and it was amplified by PCR. The length of the inserts was measured using Agilent High Sensitivity DNA Chip on Agilent 2100 Bioanalyzer . The concentration was measured using a Qubitad-hoc R script pipeline, which included the trimming of adapters using Cutadapt  curve was constructed from the normalized read counts and the area under the curve (AUC) was calculated to evaluate the predictive value of the selected RNA biomarkers. Cutoff points on the ROC curves for determining sensitivity and specificity were defined using Youden Index .9 to 7.92 x 1011 EVs per ml of plasma , at the end of NAC (NAC-BC) and 7 days (PostOp-7d), 6 (PostOp-6m), 12 (PostOp-12m), and 18 months (PostOp-18m) after the surgery from 32 patients with locally advanced BC and 30 age-matched cancer-free healthy controls (HC) using SEC , whereasOn average a total of 4.2 million raw reads were obtained per EV sample, and an average of 2.4 million reads remained after quality control, adaptor trimming, and filtering out fragments smaller than 15 nt, and an average of 61% of reads were mapped to the human genome version GRCh38.The most abundant RNA biotype in EVs was lncRNA (26%), followed by mRNA (25%), piRNA (18%), miRNA (17%), and tRFs (4%) Figure 2p<0.05 were considered to be differentially expressed. Although NAC is expected to elicit EV release from various tissues, we reasoned that the RNA biomarkers that are associated with the presence of a drug-resistant tumor should remain detectable throughout the NAC, therefore only those RNAs that were differentially expressed in both time points were considered as biomarker candidates. Next, the levels of selected candidate biomarkers were compared to HCs and between responders vs non-responders at all subsequent time points as well as between tumor and adjacent normal breast tissues. Small RNAs  were analyzed in small RNA libraries constructed from the tumor and normal breast tissues, whereas mRNAs and lncRNAs were analyzed in full transcriptome libraries.To identify EV-enclosed RNA biomarkers that can predict patients’ response to the NAC at the time of diagnosis, differential expression analysis between responders and non-responders was carried out for each of the major biotypes at two time points: diagnosis and the end of NAC. RNAs with Log2FC>1 and adj. vs non-responders at the time of diagnosis revealed 48 differentially expressed genes (DEGs), including 42 that had higher levels in non-responders  at the time of diagnosis and 49 DEGs after the NAC (47 of them higher in non-responders), but only 4 of them - lnc-ALX1-2, lnc-KLF17-1, lnc-DPH7-1, and lnc-PARP8-6, coincided in both time points Table 2.p = 0.02) in non-responder EVs. However, GKAP1 mRNA was also present in 20% of HCs and increased in responders during the NAC which limits its value as a predictive biomarker.A comparison of mRNA profiles in responders and non-responders at the time of diagnosis revealed only one differentially expressed gene – G kinase anchoring protein 1 (GKAP1) that had a significantly higher level  for predicting response to NAC Table 2.vs Dg-BC to identify NAC-induced RNAs (2); NAC-BC vs HCs to identify RNAs that are specific to BC and (3) non-responders vs responders (or patients with or without progression) at the end of NAC to identify RNAs that are associated with poor response to the NAC or clinical progression within 18 months after the surgery. Five piRNAs: piR-28104, piR-22021, piR-25412, piR-33202, and piR-19110, 4 miRNAs: miR-651-5p, miR-370-5p, miR-4326, and miR-539-5p, 2 lncRNAs: lnc-CCR6-1 and lnc-JHY-2, snoRNAs: SNORA71E and SNORD115-6 and 1 snRNA: RNU6-677P were found to be elicited by the NAC, present in BC EVs at the end of NAC at significantly higher levels than in HCs and have higher levels in non-responders than responders : NAC-BC sponders Table 3.EVs have gained increasing attention as a source of cancer-derived biomarkers in liquid biopsies. Potentially, they may have several advantages over other sources like CTCs and cell-free DNA or RNA: they are highly abundant in circulation as compared to CTCs, they protect their molecular cargo against degradation, they carry molecular signatures associated with specific phenotypes of parental cells, and transfer phenotypic traits to their recipient cells , 32. In Elevated levels of plasma EVs have been found in patients with various solid tumors –35, inclNext, we studied whether the RNA cargo of EVs could inform about patient’s response to the NAC. We found that the changes in the EV levels were associated with the alterations in the proportions of various RNA biotypes with tRFs, lncRNAs and snoRNAs being the most significantly shifted. Analysis of EV RNA content revealed 6 miRNAs, 4 lncRNAs, and 1 snoRNA that had significantly higher levels in EVs from non-responders than responders at the time of diagnosis and throughout the NAC. Importantly, they were not detectable or had significantly lower levels in EVs from cancer-free controls thus making them attractive biomarker candidates for the prediction of a patient’s response to chemotherapy at the time of diagnosis. All of the identified biomarker candidates had specificity of 1 for discriminating between responders and non-responders, but the sensitivity was ranging from 0.18 to 0.31, suggesting that individually they have moderate clinical value, whereas combining them in a biomarker panel would increase their translatablity. However, in the current cohort of patients, 6 out of the 20 non-responders were negative for all of the biomarker candidates thus showing that this biomarker panel would not be suitable for predicting response to the NAC in a subgroup of patients. Noteworthly, this subgroup was not associated with the hormone receptor expression or HER2 status. Hence, larger cohort of patients would be needed to discover biomarkers in various subgroups of BC.Increasing evidence suggests that in some patients chemotherapeutic drugs used in the NAC setting can induce metastatic progression of the disease , 44. SevAmong the resistance-associated miRNAs was miR-190b-5p, which previously has been found to be strongly overexpressed in ER-positive vs ER-negative BCs . A high Among the NAC-induced miRNAs in non-responders is miR-539-5p, which has been shown to act as a tumor suppressor in BC by targeting EGFR, LAMA4, and SP1 thus leading to the inhibition of proliferation and migration of BC cells –49. ThusA study by Yang et al. had previously demonstrated that NAC elicits secretion of miR-378a-3p and miR-378d enriched EVs from BC cells that in turn activate WNT and NOTCH stem cell pathways in drug-naïve BC cells leading to the acquisition of drug resistance . We alsolncRNAs are non-coding transcripts that range from 200 nucleotides up to ~100 kilobases in length and affect many biological and pathological processes by regulating gene expression at transcriptional and post-transcriptional levels . An incrsnoRNAs are 60-300 nucleotides long non-coding RNAs that primarily accumulate in the nucleoli and are responsible for the posttranscriptional modification and maturation of ribosomal RNAs. Recent evidence suggests that snoRNAs also regulate alternative splicing and editing of mRNAs . An incrTaken together, this study demonstrated that a substantial fraction of plasma EVs in BC patients are produced due to the disease processes or treatment. The EV RNA cargo consists of various RNA biotypes whose diagnostic and prognostic significance has not been explored before. The EV RNA cargo in BC patients is altered as compared to cancer-free healthy controls and dynamically reflects the clinical events. A set of BC-specific RNA biomarkers that can predict patients’ response to the NAC at the time of diagnosis was identified. Individually, they had very high specificity yet low sensitivity. Another set of RNAs that are induced by NAC in EVs from non-responders or patients with early disease progression was identified and warrants further functional studies.https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-12014/, E-MTAB-12014.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: All the experimental protocols involving human data were in accordance with the Declaration of Helsinki. The patients were enrolled in the study after the patients’ informed written consent was obtained.The study protocol was approved by the Latvian Central Medical Ethics Committee (decision No. 1839).AL, ML, and JE designed research, LS, JA, LK, EE, KE, and IR-S performed research and participated in the analysis and interpretation of the results, and PZ performed the RNAseq data analysis and statistical analyses. KE, EA, and IL-K collected the clinical samples and information. AL and LS wrote the manuscript. EE, ML, and JE revised the manuscript. All authors have read and approved the manuscript.This work was funded by the ERDF project No. 1.1.1.1/18/A/084.We are thankful to our colleagues from the Genome Database of Latvian Population for providing the plasma samples used in this study.Author IR-S is employed by Genera Ltd.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The publisher apologises that upon publication of this article the authors affiliations were not correctly assigned to the authors.The correct listing is as below:1, Min Zhao1,3, Qianying Wu1, Chenyi Ma1, Xiangdong Du2, Xinchuan Lu2, Qiufang Jia2, Chuanwei Li2*Jing Lu1.Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China2.Suzhou Guangji Hospital, Affiliated Guangji Hospital of Soochow University, Suzhou, Jiangsu, China3.Shanghai Key Laboratory of Psychotic Disorders, Shanghai, ChinaThe online version of this article has been updated.


RNA sequencing has become the gold standard for transcriptome analysis but has an inherent limitation of challenging quantification of low-abundant transcripts. In contrast to microarray technology, RNA sequencing reads are proportionally divided in function of transcript abundance. Therefore, low-abundant RNAs compete against highly abundant - and sometimes non-informative - RNA species.We developed an easy-to-use strategy based on high-affinity RNA-binding oligonucleotides to block reverse transcription and PCR amplification of specific RNA transcripts, thereby substantially reducing their abundance in the final sequencing library. To demonstrate the broad application potential of our method, we applied it to different transcripts and library preparation strategies, including YRNAs in small RNA sequencing of human blood plasma, mitochondrial rRNAs in both 3′ end sequencing and long-read sequencing, and MALAT1 in single-cell 3′ end sequencing. We demonstrate that the blocking strategy is highly efficient, reproducible, specific, and generally results in better transcriptome coverage and complexity.Our method does not require modifications of the library preparation procedure apart from simply adding blocking oligonucleotides to the RT reaction and can thus be easily integrated into virtually any RNA sequencing library preparation protocol.The online version contains supplementary material available at 10.1186/s12575-023-00193-3. RNA sequencing has become the gold standard for transcriptome characterization. Numerous RNA sequencing library preparation procedures have been developed to quantify various RNA biotypes, including amongst others polyA+ RNA sequencing, total RNA sequencing, 3′ end RNA sequencing and small RNA sequencing. Regardless of the library preparation method, RNA sequencing reads are distributed across RNA transcripts proportionally to their abundance. Consequently, highly abundant RNA species, often deemed non-informative, can dominate the RNA sequencing library and hamper the detection of lower abundant transcripts. A well-known example is ribosomal RNA (rRNA), which typically accounts for more than 80% of all RNA transcripts  in celluRemoving sequence fragments derived from these excessively abundant transcripts from a sequencing library is instrumental in obtaining sufficient coverage of the informative fraction of the transcriptome without having to sequence libraries to extreme depth with diminishing returns. Several workarounds have been proposed to tackle this problem. The concentration of rRNA is reduced using many different strategies: subtractive pull-down , 12–14  using the BD Vacutainer Push blood collection set (21G needle). We collected the blood samples according to the Ethical Committee of Ghent University Hospital approval EC/2017/1207, following the ICH Good Clinical Practice rules, and obtained written informed consents from all donors. We inverted the tubes 5 times and centrifuged within 15 minutes after blood draw . Per donor, we pipetted the upper plasma fraction (leaving approximately 0.5 cm plasma above the buffy coat) and pooled in a 15 ml tube. After gently inverting, five aliquots of 220 μl platelet-rich plasma (PRP) were snap-frozen in 1.5 ml LoBind tubes (Eppendorf Protein LoBind microcentrifuge tubes Z666548 - DNA/RNA) in liquid nitrogen and stored at − 80 °C. We centrifuged the remaining plasma  and transferred to a new 15 ml tube, leaving approximately 0.5 cm plasma above the separation. Next, we centrifuged this plasma a 3rd time , and transferred it to a 15 ml tube, leaving approximately 0.5 cm above the separation. The resulting platelet-free plasma (PFP) was gently inverted, snap-frozen in five aliquots of 220 μl and stored at − 80 °C. The entire plasma preparation protocol took less than 2 h. We isolated RNA from 200 μl PRP or PFP. For the spike-in RNA titration experiment, the protocol was identical, except for the fact 4 EDTA tubes of 10 ml were used and that the second centrifugation step was different .For the cancer patient experiment, plasma samples are acquired from ProteoGenex  under EC/2017/1515 from Ghent University Hospital. Blood was collected in EDTA vacutainer tubes. After inversion (10 times), we centrifuged the vacutainer tubes at 4 °C for 10 minutes at 1500 g without brakes. The plasma is then transferred into a 15 mL centrifuge tube and centrifuged for a second time for 10 minutes at 1500 g. Finally, the plasma was transferred into cryovials and stored at − 80 °C until shipment. The cancer types included are colorectal cancer (CRC), lung adenocarcinoma (LUAD), and prostate cancer (PRAD).Total RNA was isolated from platelet-free (PFP) and platelet-rich plasma (PRP) using the miRNeasy Serum/Plasma Kit . We used 200 μl of plasma as input. For the cancer patient experiment, 2 μl of 1x RC PFP spikes were added to the plasma during isolation. The elution volume was 14 μl, and we added 2 μl of 1x LP PFP spikes (Thermo Fisher). Detailed descriptions of the spike-in controls can be found in the exRNAQC study . From thThe YNRA4 fragment (32 nucleotides) was tiled with 16 bp long complementary nucleotides resulting in 17 possible designs. We mapped the full set of antisense oligonucleotides to the human transcriptome (Ensembl v84) and miRBase. Oligonucleotides with no off-targets when 3 mismatches are allowed were retained. Of the retained LNAs, we chose the oligonucleotide with the highest melting temperature (Tm). The resulting fully modified LNA  has a Tm of 89.9 °C. In addition to the fully LNA-modified oligo, for the same sequence we ordered 2′-O-methyl and 2′-methoxy-ethoxy modified nucleotides and half modified  oligos at Integrated DNA Technologies. Sequences are available in Supplemental Table We used the TruSeq small RNA library prep sequencing kit  for library preparation according to manufacturing instructions, except for the changes listed below. After adaptor ligation and before the reverse transcription step, 2 μl LNA with a concentration of 0.25 μM (LNA1x) or 2.5 μM (LNA10x) was added to 14 μl of the adaptor-ligated RNA. In the experiments with the cancer patient samples and alternative modifications, only the 0.25 μM (LNA1x) concentration was analyzed as we showed that the 10-fold higher concentration had no added value. As a negative control for LNA blocking (LNA0x), 2 μl of water was added to 14 μl of RNA. Next, we used 6 μl of each sample to start the reverse transcription and continue the library prep. Since the input amounts are low, the number of PCR cycles was set at 16 (the manufacturer recommends 11) during the final PCR step.We performed a size selection for 125–163 bp on all libraries using 3% agarose dye-free marker H cassettes on a Pippin Prep . Next, the libraries were purified by precipitation using ethanol and resuspended with 10 mM Tris-HCl buffer (pH 8.0) with Tween 20. After dilution, the libraries were quantified using the KAPA Library Quantification Kit . Healthy donor samples were sequenced using a NextSeq 500 using the NextSeq 500 High Output Kit v2.5 (75 cycles) . We loaded the library at a concentration of 2.0 pM with 10% PhiX and obtained a total of 268 M reads. We loaded the cancer patient samples on one lane of a NovaSeq 6000  instrument at a concentration of 300 pM with 10% PhiX using the NovaSeq 6000 SP Reagent Kit v1.5 (100 cycles)  , resulting in 267 M reads. For the chemical modification comparison experiment, we used one lane of a NovaSeq 6000 SP Reagent Kit v1.5 (100 cycles)   (1 × 100 bp), loading 300 pM with 10% PhiX, resulting in a total of 548 M reads.We used a dedicated in-house small RNA-seq pipeline for the quantification of small RNAs. This pipeline starts with adaptor trimming using Cutadapt (v1.8.1) , which dWe used R (v3.6.0)  for furtWe used HEK293T cells that were grown in RPMI 1640 medium with GlutaMAX supplement  supplemented with 10% fetal calf serum  and were lysed with SingleShot lysis buffer .From previous experiments, we identified three transcripts without poly(A) tail that are abundant  in 3′ end sequencing data of HEK293T cells: MT-RNR1, MT-RNR2, and RNA45S. We visually inspected the RNA sequencing data using IGV_2.7.2  and confWe combined four different LNA mixes  to have a final solution containing each LNA at 25 μM (100x). We mixed 2 μl of LNA to 3 μl of RNA sample. From this solution, we used 2.5 μl as input for the library preparation.For the library preparation, we used the QuantSeq 3′ mRNA-Seq Library Prep Kit FWD for Illumina . We performed the ‘low input’ version of the protocol.We sequenced the libraries using the NovaSeq 6000 SP Reagent Kit v1.5 (100 cycles)  on a NovaSeq 6000  instrument at a concentration of 300 pM with 10% PhiX.We used BBMap (v38.26) to trim off the poly(A) tails and adapter sequences and to perform quality trimming. Next, all FASTQ files were subsampled to 2,000,000 reads with Seqtk (v1.3) and mapped to the hg38 genome using STAR (v2.6.0). We used SAMtools (v1.9) to count the reads mapping to the LNA-targeted genomic regions. We used htseq-count (v0.11.0)  to generWe used R (v4.1.0)  and tidyWe cultured HEK293T cells in RPMI medium supplemented with 10% fetal calf serum to 80% confluence in a T75. The cells were washed with 2 ml versene and incubated with 2 ml of trypsin for 3 minutes at 37 °C. We neutralized the mixture with 8 ml fresh medium. We centrifuged for 5 minutes at 2000 rcf at 4 °C and removed the supernatants. We resuspended the cells in 1 ml of QIAzol and flash-froze the mixture in liquid nitrogen.We extracted RNA using the RNeasy Micro Kit  according to the manufacturer’s protocol. We checked the quality of the RNA (RQN = 10) using a Fragment Analyzer .We combined four different LNA mixes  to have a final solution containing each LNA at 25 μM. We then made a 10-fold dilution series to obtain three different LNA solutions: LNA1x (0.25 μM), LNA10x (2.5 μM) and LNA100x (25 μM). For each library preparation, 2 μg of total RNA was mixed with 2 μl of the corresponding LNA dilution. 1 μl of RNase-free water was added to 2 μg of total RNA as a non-treated sample (LNA0x). The samples are placed on ice for 5 minutes.We prepared direct-cDNA libraries using the SQK-DCS109 Kit . The exact protocol was followed except for the following changes: the RNA-bead binding steps were performed for 5 minutes on a Hula Mixer and 5 minutes on the bench at room temperature; the RNA elution steps were performed for 5 minutes at 37 °C and 5 minutes on a Hula Mixer at room temperature; and 300 μl of 80% ethanol was used for the beads wash steps.We sequenced each library using two Flongle Flow Cells  with a MinION Sequencer . Sequencing was either stopped after 24 hours or when no more pores were available.We basecalled the raw fast5 files using Guppy (v3.5.2)  on a GPUWe used R (v4.1.0)  and tidyWe collected whole blood in EDTA tubes. The blood was transferred to Leucosep filtered tubes (Greiner Bio-One) containing 15 ml of Ficoll Paque Plus  and diluted (1:2) with the same volume of 1X DPBS . We centrifuged the samples at room temperature for 18 minutes at 800 rcf and extracted the PBMCs from the resulting buffy coat. The extracted PBMCs were centrifuged and washed twice with 1X DPBS . We took a sample for counting, and assessed the cell viability and concentration with a Neubauer chamber, counting at least two different squares. PBMCs were then resuspended in freezing mix (complete medium (RPMI + 1% pen/strep + 10% FCS) + 10% DMSO) in cryovials with no more than 10 million cells. The vials were stored first at − 80 °C inside a freezing container for 24 h and then at − 150 °C. We thawed the vials just before live-death sorting.After visually inspecting 3′ end sequencing data from PBMCs using IGV_2.7.2 , the optWe diluted the LNAs at 125 μM of which 2 μl was used. This concentration is higher than the YRNA experiment, as we expect the total RNA concentration to be higher for this experiment. For the pre-RT blocking, 2 μl of the oligonucleotide mix was added to the master mix . The master mix is then combined with the cell suspension to a total volume of 80 μl. For the pre-cDNA amplification blocking, we added 2 μl of the oligonucleotide mix to the cDNA amplification mix (including Amp Mix and cDNA primers).Sorted single-cell suspensions were resuspended in PBS + 0.04% BSA at an estimated final concentration of 1000 cells/μl and loaded on a Chromium GemCode Single Cell Instrument , Chip G  to generate single-cell gel beads-in-emulsion (GEM). We prepared the scRNA-seq libraries using the GemCode Single Cell 3′ Gel Bead and Library kit, version NextGEM 3.1  according to the manufacturer’s instructions.The Chromium libraries were equimolarly pooled and loaded on a NovaSeq 6000  instrument in standard mode with a final loading concentration of 340 pM and 2% PhiX. We obtained a total of 952 M reads with q30 of 91.32% with an SP100 cycles  kit. The number of (pre-filtered) cells per experiment were highly comparable, 13,841 cells for the noLNA sample, 13,279 cells pre-RT, and 13,893 cells post-RT. The FASTQ files were subsampled based on the number of cells to obtain a comparable number of reads/cell over all samples.Demultiplexing of the bcl files was performed with cellranger mkfastq (v6.0.1), after which gene counts per cell were obtained with cellranger count (v6.0.1).The count matrixes were loaded into R (v4.1.0)  and furt). This sample had a sequencing depth of 18,043,131 reads.We downloaded one of the whole blood 3′-end RNA sequencing (QuantSeq) samples generated by Uellendahl-Werth et al.  (SRR1102We used BBMap (v38.26) to trim off the poly(A) tails and adapter sequences and to perform quality trimming. Next, we mapped the trimmed reads to the hg38 genome using STAR (v2.6.0). We used htseq-count (v0.11.0)  to quantAll simulations were run using R (4.1.0). First, we generated the sampling distribution by first removing the ENSG00000244734 (HBB) reads and calculating the fraction of reads appointed to each gene relative to the total amount of reads. We used this distribution to guide the subsampling. We then subsampled the count tables for a varying total number of counts , initial HBB abundance , and percentage of depletion . Last, we calculated the number of genes with 10 counts or larger. Finally, we analyzed and visualized (v1.2.1) .1X DPBS .RPMI 1640 medium with GlutaMAX supplement Chromium GemCode Single Cell Instrument Direct cDNA sequencing kit Eppendorf Protein LoBind microcentrifuge tubes Ficoll Paque Plus Fragment Analyzer RNA Kit GemCode Chip G GemCode Single Cell 3′ Gel Bead and Library kit, version NextGEM 3.1 KAPA Library Quantification Kit MinION sequencer miRNeasy Serum/Plasma Kit .NextSeq 500 High Output Kit v2.5 (75 cycles) NextSeq 500 Sequencing System NovaSeq 6000 Sequencing System NovaSeq 6000 SP Reagent Kit v1.5 (100 cycles) Pippin Prep .QuantSeq 3′ mRNA-Seq Library Prep Kit FWD for Illumina RNeasy Micro Kit SingleShot lysis buffer TruSeq small RNA library prep sequencing kit Vacutainer Hemogard Closure Plastic K2-Edta Tube, 10 ml, Vacutainer Push blood collection set HEK293T To prevent the incorporation of unwanted (fragments of) transcripts in RNA sequencing libraries, we reasoned that LNA-modified oligonucleotides would block reverse transcription and PCR amplification when bound downstream of the priming site because of their extremely high affinity to RNA and cDNA. The approach we took to design blocking LNA oligonucleotides depends on the characteristics of the unwanted RNA sequence and the library prep procedure. We selected four library prep procedures and defined highly abundant and mostly unwanted target RNA sequences for LNA oligonucleotide design Fig. A. These We first focused on blocking RT and amplification of RNY4 fragments in human blood plasma small RNA-seq libraries. We tested the blocking efficiency on platelet-rich plasma (PRP) and platelet-free plasma (PFP) from healthy donors, with PRP having the highest fraction of RNY4 fragments . We then spiked two different concentrations of a blocking LNA oligonucleotide  in the RT reaction of the TruSeq small RNA library prep, and compared the results to the standard workflow. We only observed 0.09% RNY4 in PFP and up to 0.16% RNY4 in PRP  and Spearman (0.70–0.78 for PFP and 0.81–0.82 for PRP) correlation coefficients , prostate adenocarcinoma or PRAD (n = 4) and lung adenocarcinoma or LUAD (n = 4)) that were processed with the standard and LNA1x spike protocol . The 2’OME modification, however, is less efficient and resulted in a reduction of just 1.22-fold (p = 0.005) compared to the LNA modification. In addition, we investigated the potency of partially modified  oligonucleotides for both LNA, 2’OME, and 2’MOE. We observed that the partially modified LNA RNY4 oligonucleotide was as potent as a fully modified LNA RNY4 oligonucleotide  and still outperforms fully modified 2’OME RNY4 oligonucleotides (p = 0.0005)  and 2′-methoxy-ethoxy (2’MOE) in PRP. We observed that an LNA-modified RNY4 oligonucleotide is equally efficient (median reduction of 6.45-fold) compared to a 2’MOE modified oligonucleotide (median reduction of 6.11-fold)  primers can bind internal poly(A) sequences of mitochondrial and nuclear ribosomal RNA species, which eventually get incorporated in the RNA sequencing library . AltAdditionally, we explored whether the previously described rRNA  blocking strategy can also be applied to Oxford Nanopore Technologies (ONT) sequencing of poly(A)-primed cDNA libraries. More specifically, we performed direct-cDNA sequencing to investigate the blocking effect on just the reverse transcription step. We added three different concentrations  of the rRNA LNA oligonucleotides (as used in the 3′ end library preparation) to the reverse transcription reaction of four different samples. For all targeted fragments, we observed a substantial decrease in counts per million with increasing concentration of LNA oligonucleotides, except for 45S pre-ribosomal RNA in the LNA100x condition  .We finally evaluated if our method would also be applicable to single-cell RNA sequencing. More specifically, we designed two half-modified LNA oligonucleotides to block MALAT1 in single-cell 3′ end sequencing libraries of PBMCs. In PBMCs, MALAT1 can consume > 40% percent of reads through priming of internal poly(A) stretches  Fig. A. The hiAlthough our wet-lab experiments indicate that high-affinity binding oligonucleotide blocking can efficiently deplete transcripts of interest, it remains to be determined what the relationship is between initial abundance and level of depletion in order to offer substantial benefit (such as increased library complexity). Some of our applications had a more significant impact on the number of additionally detected genes and on coverage increase than others. We assume that this impact is highly dependent on the initial fraction of targeted reads, the depletion efficiency, and the sequencing saturation. In order to investigate in detail, we simulated different abundances and depletion efficiencies of beta-globin (HBB) using publicly available whole blood 3′-end sequencing data, in which HBB accounted for 20.8% of all reads ). FigureWe demonstrate that high-affinity binding oligonucleotides can be applied to block reverse transcription and PCR amplification of various RNA transcripts in different RNA-seq library preparation protocols. We present a flexible and robust method that can drastically increase the detection and coverage of (low abundant) genes in the library. While LNA oligonucleotides have been used before to block PCR amplification , we provOur method has several advantages compared to existing protocols. First, the method only requires a single additional step that can be implemented in any RNA-seq library preparation workflow. Second, no nucleic acid sample or library material is lost because of enrichment or washing steps, which we believe has a positive impact on detection sensitivity, especially for low-input samples.While we generally observe potent blocking of targeted transcripts, we also observe a few minor unwanted effects. First, in single-cell RNA sequencing, adding LNA oligonucleotides to the GEMs during 3′ end sequencing resulted in a higher fraction of mtRNA reads. As living cells are incubated with LNA oligonucleotides for 18 min, the oligonucleotides may enter the cells and induce cell death. A large fraction of mtRNA co-occurred with few detected genes. Since adding the LNA oligonucleotides post-PCR also results in potent target blocking, we propose to use this approach instead. Second, the optimal concentration of LNA oligonucleotide may be application and target-dependent. A dedicated optimization step is warranted for optimal performance. This necessity is reflected in the single-cell RNA sequencing experiment, where the benefit (in terms of the number of detected genes) depends on the cell type. Factors to consider are the original fraction of the targeted RNA transcript and the input RNA concentration of the library preparation protocol. We advise to combine samples in one single library prep to exclude batch effects, as is generally advised for RNA-seq experiments. Third, we observed a limited number of off-target effects upon adding specific LNA oligonucleotides . We did not observe significant sequence complementarity between the LNA oligonucleotides and the presumed off-targets. Nevertheless, off-target effects are not entirely unexpected given the relatively short length of the LNAs, their high RNA-binding capacity, and the small design space. The latter lowers the number of possible oligonucleotides and thereby the chances of designing one without off-target effects. Increasing oligonucleotide length or reducing the number of LNA nucleotides to lower binding affinity may improve specificity. A fourth limitation of the method is that it may only be applicable to small RNA sequencing or RNA-sequencing library prep methods employing an oligo(T) or a gene-specific RT primer. When the priming is random, it is impossible to design a single LNA oligonucleotide to block reverse transcription of the whole fragment. One option would be to design multiple LNA oligonucleotides spanning the entire transcript, but this could become prohibitively expensive, depending on the length of the fragment. Fifth, LNA synthesis is costly. Nevertheless, the amount of oligo that is required for efficient blocking is limited. Even at low synthesis scale, several hundreds of reactions can be performed, resulting in a limited per-sample cost. As fully and partially modified LNA oligonucleotides are equally efficient for YRNA depletion, partially modified LNA oligonucleotides could be used to further reduce oligo synthesis cost . Notably, blocking unwanted transcripts may also help reduce the sequencing cost. Finally, the observed shortening in read length with increasing LNA concentration in the Oxford Nanopore Technologies experiment is problematic, as it suggests off-target binding of the LNAs. Although the LNA oligonucleotides are expected to preferentially bind sequences with a lower number of mismatches, the steady decline in coverage towards the 5′-end of RNA transcripts points towards close to non-specific binding of the LNA oligonucleotides when supplied at high concentration. The possibility of the LNA oligonucleotides inhibiting the sequencing by binding to the final library can be dismissed by investigating the adaptor-to-adaptor reads (which signify complete sequencing of the read).We believe the method presented here is versatile and can be used for other applications not investigated here, including hemoglobin mRNA blocking in whole blood samples  or trypIn conclusion, we present a novel and broadly applicable method to specifically block unwanted RNA transcripts during RNA sequencing library preparations by simply adding a target-specific high-affinity oligonucleotide to the RT or PCR reaction.Additional file 1: Supplemental Figure 1. Count distributions of noLNA, LNA1x, and LNA10x in PFP and PRP. Supplemental Figure 2. Read distribution for each sample and treatment. Supplemental Figure 3. Correlation plots comparing all biological replicates. Supplemental Figure 4. Length distribution of full-length transcripts after Oxford Nanopore direct-RNA sequencing. Supplemental Figure 5. Quality score distribution of full-length transcripts after Oxford Nanopore direct-RNA sequencing. Supplemental Figure 6. Illustration of problematic MALAT1 fragments and design space. Supplemental Figure 7. MALAT1 transcription in each cell type. Supplemental Figure 8. Correlation plot between prePCR and noLNA samples. Supplemental Table 1. Sequences of designed synthetic oligonucleotides. For each modified oligonucleotide the identification, gene target and sequence are provided. The sequences contain modification information in the generally accepted standard of notification.


RNA granules represent a broad variety of RNA and protein condensates. These granules can be constitutive or stress-induced, nuclear or cytoplasmic, static or dynamic. RNA granules have been functionally associated with a myriad of biological processes and disease states, and the depth of their physiological importance is ever growing. The aim of this Research Topic was to bring together the latest information about the form and function of a diversity of RNA granules. It was our pleasure as guest associate editors to collect and juxtapose these articles, which reveal the breadth of RNA granules and the exciting research frontiers yet to come.Rhine et al. describes the dynamics of RNA granules, particularly stress granules (SGs), in the development of age-related disease. The authors frame RNA granule formation as an “inherently risky maneuver for cells”, given the propensity for these granules to transition from functional liquid-like bodies to pathogenic gel-like or solid-like aggregates in the context of aging and neurodegeneration. The article highlights the current state of imaging, sequencing, and biochemical technologies for studying RNA granules. The application of these technologies to understand liquid-to-solid granule transitions will be key to understanding and treating the formation of pathological aggregates that cause neurodegeneration.A mini-review by Cabral et al. collates the existing knowledge on so-called canonical and non-canonical SG subtypes. The authors focus a discussion on an oft-cited but little-studied non-canonical SG subtype, ultraviolet radiation (UV). The authors present some preliminary new evidence that suggests UV SGs may not contain mRNA and may be cell-type specific, in that cell types that experience UV regularly, such as keratinocytes, may be resistant to UV-induced SGs. The authors also highlight evidence that UV SGs, unlike most other SGs known, appear to be specific to the G1 phase of the cell cycle. They conclude by proposing that understanding the mechanisms of resistance to UV SG formation could lead to insights about how to control undesirable SG formation in the context of disease.Also related to SGs, a Perspective article by Lacroix and Audas assessed the conservation of a variety of nuclear RNA granules from species across the eukaryotic domain. Here, the authors noted considerable diversity in the emergence and retention of these structures, as some biomolecular condensates were seen in a large cross-section of organisms, while others possessed a more limited organismal distribution. By mapping the molecular residents, regulators, and functions of these RNA granules, the authors generated a guide for examining these structures across the evolutionary landscape. As the importance of membrane-less organelles continues to emerge in disease etiology, the identification of additional species that possess these conserved structures will be fundamental for unlocking new tools and model systems for health research.A review by West et al. reported the roles of zinc in the formation of cytoplasmic TIA-1 condensates through liquid-liquid phase separation (LLPS). TIA-1 is a key protein in the formation of stress granules induced by various cellular stresses. TIA-1 could form fibrillar aggregates in vitro, but it is not present as fibrils in physiological intracellular conditions. This study used an in vitro phase separation assay and NMR and showed that zinc ions induce LLPS of TIA-1 together with nucleic acids in vitro through the interaction between RRM2 of TIA1 and zinc. This interaction also suppresses the fibrillar aggregate formation. Thus, this study provides important insights into the mechanism of how intracellular condensates are maintained as liquids without forming toxic aggregates.In many phase separation studies, the mechanism is explained by liquid-liquid phase separation (LLPS), a type of phase separation. The following two articles deal with a new mechanism of intracellular biomolecular condensate formation called micellization, distinct from LLPS.Yamamoto et al. reported a detailed theoretical model for the formation of nuclear bodies called paraspeckles. In this model, paraspeckles are treated as amphipathic block copolymer micelles. This micellization mechanism constructs biomolecular condensates (BMCs) with optimal size, shape, and internal structure, which are different from typical disordered spherical LLPS droplets. This model can explain recent and previous experimental observations (e.g., ns e.g., . Thus, tYamazaki et al. reviewed the roles of RNAs scaffolding BMCs and their recent finding that BMCs use micellization of block copolymers as the formation mechanism. This review also discusses the importance of this micellization mechanism in biological contexts and designer condensates with RNA scaffolds formed by micellization. Thus, this review provides an outlook for future studies on this newly uncovered mode of action of RNPs.Creating this article Research Topic has highlighted for us new considerations about relationships between RNA granule types and the mechanisms of their formation. RNA-mediated LLPS has recently dominated the intellectual landscape of RNA granule formation; however this Research Topic reveals that other factors, including physiological conditions, micellization, amyloidogenesis, and RNA-independent interactions also have important roles to play.