This oligomer adaptation method improved 5′ end sequencing and ensured correct identification of the 5′ m7G capped ends. Second, among these 5′-capped nanopore reads, we screened for features consistent with a 3′ polyadenylation site. Combining these two steps, we identified 294,107 individual high-confidence full-length RNA scaffolds from human GM12878 cells, most of which  aligned to protein-coding genes. Of these, 4876 scaffolds indicated unannotated isoforms that were often internal to longer, previously identified RNA isoforms. Orthogonal data for m7G caps and open chromatin, such as CAGE and DNase-HS seq, confirmed the validity of these high-confidence RNA scaffolds.Nanopore sequencing devices read individual RNA strands directly. This facilitates identification of exon linkages and nucleotide modifications; however, using conventional direct RNA nanopore sequencing, the 5′ and 3′ ends of poly(A) RNA cannot be identified unambiguously. This is due in part to RNA degradation in vivo and in vitro that can obscure transcription start and end sites. In this study, we aimed to identify individual full-length human RNA isoforms among ∼4 million nanopore poly(A)-selected RNA reads. First, to identify RNA strands bearing 5′ m Most human genes encode multiple transcript isoforms. These isoforms are derived from alternative splicing, alternative transcription start sites (TSS), or alternative transcription termination sites (TTS). TSSs outnumber genes , and togN6-methyladenosine (m6A) (N7-methylguanosine (m7G)  , inosinene (m6A) , pseudoune (m6A) , and N7-ne (m7G)  have beene (m7G) . RNA strne (m7G) , further7G cap  tail through the 5′ cap, which we define as full-length RNA isoform scaffolds. NRCeq avoids common shortfalls of ligation methods, such as sequence bias (7G cap to a documented poly(A) site, including 4876 scaffolds of previously unannotated isoforms. We systematically correlated these scaffolds with orthogonal data, including 5′-RACE, CAGE, DNase-seq, and other markers of TSS validation, and showed that single reads could provide compelling evidence for bona fide RNA isoforms.In this study, we introduce a new chemo-enzymatic method, Nanopore ReCappable sequencing (NRCeq), that uniquely and specifically replaces mnce bias  and liga7G cap from poly(A)-enriched RNA, leaving 5′-diphosphate ends. Second, the 5′-diphosphate RNA strands were recapped with 3′-azido-ddGTP using Vaccinia capping enzyme  gene is shown in A typical cap-adapted ionic current trace for the human thymidine phosphorylase (S. cerevisiae poly(A) RNA. The yeast transcriptome is well-suited for this because the m7G cap is identical to the human m7G cap, and the transcriptome is small and relatively simple compared to other eukaryotes ; however, RNA degradation was unacceptable as measured by RNA integrity number (RIN) . Further, this change from copper-catalyzed to copper-free chemistry improved the percentage of yeast poly(A) RNA reads that were cap-adapted  and the read N50 , the latter being a measure where half of the total bases sequenced are contained in reads of that length or longer.We optimized the 5′ cap-adaptation strategy using an karyotes . InitialSupplemental Fig. 7a). We identified 574,091 (14.3%) of the treated reads as “cap-adapted” . As a control, we also performed standard native RNA nanopore sequencing using the same starting poly(A) RNA material (“untreated reads”) yielding approximately 3.8 million reads from two replicates .Having optimized 5′ cap-adaption chemistry and detection, we applied this strategy to poly(A) RNA isolated from GM12878 cells, a model human B-lymphocyte cell line. We acquired four million reads with quality scores greater than or equal to 7 that went through the cap-adaptation process . Given this difference, we were concerned that the cap-adaptation process biased the overall results either by inducing more RNA strand breaks or by removing longer RNAs from the sample. To test this, we compared the number of transcript copies per gene for the untreated and treated samples. The Spearman rank correlation score was very strong (0.95), indicating that the treatment protocol did not substantially alter the RNA composition of the sample , but that the overall impact on transcript detection was minor.The N50 value for the cap-adapted reads was 1301 nt, which was shorter than the N50 value for untreated reads (1614 nt) .Precise definition of TSS can be difficult. Therefore, we compared the start of the cap-adapted reads to those of other markers that are conventionally used for TSS determination. These included DNase-seq, Pol II ChIP-seq, SPI1 ChIP-seq, and CAGE, all performed on GM12878 cells. We found that a majority of the cap-adapted reads corresponded with these other markers of transcription initiation . These TSS were chosen because they started either at an internal exon or at an unannotated exon. Two highly expressed genes with documented TSS, ACTB, and TMSB10 were used as positive controls. To eliminate 5′ RACE products arising from uncapped phosphorylated ends, total RNA was first treated with a calf intestinal alkaline phosphatase (CIP). To identify RACE products originating from the position of capped ends, the RNA was subsequently treated with an RNA 5′ pyrophosphohydrolase (RppH) before library preparation, enabling the enzymatic ligation of RNA oligonucleotide adapters to the originally capped 5′ end of transcripts. Finally, to confirm the occurrence of the cap, we demonstrated the dependence of 5′ RACE products on decapping with RppH.We used 5′ RACE  to test Supplemental Table 4). In addition to the RACE results, CAGE or TSS chromatin marks corresponded with the 5′ end of 64 of the 93 presumptive TSS . Additionally, it was possible that the 3′ end of a given read was not the mature 3′ end of the RNA. For example, we observed a small fraction of reads with 3′ ends in the middle of an internal exon. This could be the result of improper signal processing . The number of transcripts per protein-coding gene ranged from 1-to-7987 . Among these, we identified 4876  full-length RNA scaffolds with unannotated TSS that mapped to protein-coding genes .To address the possibility that there was an improper ligation event or improper signal processing, we screened the high-confidence 294,107  nanopore 5′ capped RNA reads for the presence of poly(A) tails using nanopolish-polya , which r−6 probability that the alignment was in the wrong position). Among the 209,093 RNA scaffolds, 151,182 (72.3%) had mapping quality scores of 60 . There were 14,282 (6.8%) full-length RNA scaffolds with mapping quality scores of zero. Among the 4876 full-length RNA scaffolds for unannotated isoforms, 4568 (93.7%) had mapping quality scores of 60 . There were 49 (1.0%) full-length RNA scaffolds with mapping quality scores of zero . By comparison, the untreated reads had 71.2% of the reads with a mapping quality score of 60.We then performed a statistical measure of confidence for each full-length RNA scaffold using mapping quality scores . These mComputational tools are often used to predict RNA isoforms using short-read cDNA data. Recently they have also been applied to nanopore direct RNA data . We reasSupplemental Table 6). Four results stood out: (i) overlap with the TSS markers was similar for StringTie2 (61.5%), FLAIR (74.7%), and cap-adapted reads (61.5%); (ii) overlap with the TSS markers was also similar for untreated (38%) and NRCeq treated (36%) reads; (iii) overlap with the TSS markers was strong for high-confidence scaffolds (92.2%); and (iv) overlap with Pol II alone was the highest among the three TSS markers for each data set. A possible explanation is that Pol II ChIP-seq covers more of the GM12878 genome .As a test, we compared three orthogonal TSS markers  with 5′ ends predicted by StringTie2, FLAIR, untreated reads, and NRCeq. For each data set, we merged all 5′ ends within a 25 nt window into individual unique 5′ ends. We then compared these unique 5′ ends with the orthogonal TSS markers .Next, we compared the predicted TSS from StringTie2 and FLAIR with TSS for the NRCeq high-confidence scaffolds. The overlap was 26.8% (3591 TSS) for StringTie2 and was 53.9% (7210 TSS) for FLAIR. We found that 21.6% (2894 TSS) unique 5′ ends were shared between all three data sets . The following two examples illustrate a pipeline we used to characterize two unannotated candidate mRNA isoforms.We proposed that high-confidence RNA scaffolds could help identify previously unannotated isoforms at sufficient precision to warrant further detailed biological experimentation, as well as detect RNA modifications on individual molecules (Diacylglycerol O-acyltransferase 1 (DGAT1) encodes a multipass transmembrane protein that catalyzes the conversion of diacylglycerol and fatty acyl CoA to triacylglycerol. There are six annotated isoforms in GENCODE  tail. GENCODE  and two  GENCODE  nor by R GENCODE . In neitAdhesion G protein-coupled receptor E1 (ADGRE1). ADGRE1 is a class II adhesion GPCR that is expressed in differentiated cells in the human myeloid lineage . The resulting cDNA reads confirmed the 5′ end and revealed amplicons with identical exon composition as the RNA nanopore scaffolds .The expected median identity is 87% for nanopore RNA sequencing reads . ConsequADGRE1 were identical to the unannotated isoform identified by the nanopore scaffolds  . Seven ocaffolds H. This i7G cap, the 3′ polyadenylation site, and the intervening sequence at 87% identity. A majority of these scaffolds had a mapping quality score of Q60. Most of these scaffolds (95%) confirmed isoforms previously annotated in GENCODE v32  RNA scaffolds. These scaffolds include the 5′ mCODE v32 . There wCODE v32 , a majorThis strategy includes a new chemo-enzymatic method to specifically adapt 5′ capped RNA strands. The RNA oligonucleotide component of the cap-adapter permitted both identification of the biological 5′ end and sequencing of approximately six additional nucleotides that were systematically missed using the conventional ONT RNA sequencing protocol. Due to the nature of the cap-adapter linkage used by NRCeq, approximately five nucleotides are still missing at the 5′ end of each strand, including the N1 and N2 positions which are often modified . For thiSupplemental Figs. 2, 3), suggesting that other factors may contribute to the lower percentage of cap-adapted reads. NRCeq does not eliminate RNA strand breaks that separate the adapted 3′ end from the native 5′ end. Therefore, the cap-adapter on several of the RNA 5′ ends does not get detected because of these truncations. This results in a lower observed cap-adaptation percentage than might otherwise be expected based on the efficiency of the chemo-enzymatic process. Minor changes to RNA handling or samples with higher population of shorter average transcript lengths are likely to show an improved cap-adaptation efficiency. Additionally, despite these truncations, non-cap-adapted reads are not lost using NRceq, which can still be used to inform predictions about RNA isoforms and expression.Although only 14.3% of reads were identified as cap-adapted, the actual adaptation efficiency is likely much higher. The chemo-enzymatic process was demonstrated to be quantitative with synthetic RNAs  and Voltage Dependent Anion Channel 1 (VDAC1). We recommend comparing proposed unannotated isoforms against both of these gene model sets.In this study, we used GENCODE v32  to identn RefSeq  that matn RefSeq . Two exaADGRE1 isoform with an internal TSS and an in-frame ORF with the annotated isoforms. We substantiated this proposed ADGRE1 isoform using four criteria that could be broadly applied to any target scaffold: (i) establish that the alignments appear real  using alignment visualization software; (ii) confirm the TSS and TTS correspond with orthogonal markers of these genomic features; (iii) identify the isoform in a follow-up orthogonal experiment; and (iv) determine whether the isoform is a cell line artifact by comparing to long-read RNA-seq of primary tissue. These criteria can be completed in a few hours to a few days, increasing the viability of rapidly annotating new isoforms. However, unambiguous proof that this and other proposed mRNA isoforms are translated by the ribosome will require protein evidence.We identified an unannotated In summary, NRCeq enabled identification of individual, high-confidence, RNA scaffolds representing annotated, and unannotated full-length human RNA isoforms.2 RNA oligomer with a DBCO-sulfo-NHS ester . The 3′-NH2 RNA synthesis was performed on an ABI 394 DNA synthesizer (Applied Biosystems) starting with 3′-PT-amino-modifier C3 CPG  and UltraFast RNA TBDMS RNA amidites . The oligonucleotide was deprotected according to the manufacturer's protocol using ammonium hydroxide/methylamine and purified using a Glen-Pak RNA purification cartridge  followed by PAGE. The purified 3′-NH2 RNA was dissolved in 5 mL of 0.1 M sodium borate (pH 8.3). Then, 2.5 mL of a 20 mM solution of DBCO-sulfo-NHS ester in DMSO was added and stirred for 1.5 h at room temperature. The reaction was then dissolved in 0.1 M TEAB (up to 35 mL) and purified by C8 RP-HPLC  using 0.1 M TEAB and acetonitrile as mobile phase. The 3′-DBCO RNA oligonucleotide was concentrated and repurified by PAGE and desalted using a Clarity-RP desalting cartridge .The 45-nt 3′-DBCO RNA oligomer (CUCUUCCGAUCUACACUCUUUCCCUACACGACGCUCUUCCGAUCU) was synthesized by coupling the 3′-NHSupplemental Table 5 for target genes and primer sequences). The PCR reactions were performed using the forward primer AATGATACGGCGACCACCGAGATCTACACGTTCAGAGTTCTACAGTCCGA, and LongAmpTaq  with the following program: 94°C 1 min, followed by five cycles of 94°C 10 sec, 60°C 15 sec, 65°C 15 sec, followed by 32 cycles of 94°C 10 sec, 55°C 15 sec, 65°C 15 sec, followed by 65°C 5 min. An aliquot from each reaction (2 µL) was evaluated for quality and to estimate product concentration using the Agilent 2200 TapeStation system. A fraction of each reaction (varying from 1 to 10 µL depending on concentration) was used for pooling to obtain a mixture of all 96 PCR products. The DNA mixture was purified using 200 µL AMPure XP beads per 125 µL DNA, eluted in 40 µL water. Illumina adapters were added by amplifying the sample for four cycles using the SR primer and index primer from the kit . The resulting product was purified with AMPure XP beads at 1:1 ratio, and the eluted material was sequenced in an Illumina MiSeq sequencer using paired end 2 × 150 nt. The FASTQ files were processed to identify the 5′ end terminal sequence of each targeted transcript. RppH treated and untreated reads were distinguished using Cutadapt (version 2.10) with the following parameters: cutadapt -O 5 –action lowercase –trimmed-only –pair-filter first and -g ^GATTA (for reads from the RppH treated sample) and -g ^ATCAG (for reads from the untreated sample). The resulting paired end reads were trimmed using Cutadapt wrapped with trim galore (version 0.3.3)  was treated with QuickCIP  at 0.5 U/µL in the provided buffer at 37°C for 20 min and then purified using RNA Clean & Concentrator  with the standard protocol. The RNA was divided into two aliquots (1 and 2). Aliquot 1 was treated with RppH  in 1× Thermopol Buffer  at 0.5 U/µL at 37°C for 1 h, whereas aliquot 2 was incubated under the same conditions in the absence of RppH to serve as a non-decapped control. The two samples were purified as above and eluted in 20 µL of RNase-free water. Each sample (10 µL) was ligated to 5 pmol of a single-stranded RNA adapter (see below) using 1.5 µL T4 RNA Ligase I  in 1× T4 RNA Ligase Reaction Buffer in 20 µL total volume for 1 h at 25°C. Sample 1 (RppH treated) was ligated to the adapter SRGAUUA and sample 2 (mock treated) to adapter SRAUCAG, wherein SR denotes the sequence: GUUCAGAGUUCUACACUCCGACGAUC. The 3′ terminal five nucleotides of each adapter served as an identification index for the provenance of the sequence products . After ligation the two samples were pooled and mixed with 80 µL AMPure XP magnetic beads (Beckman Coulter) and processed for magnetic purification of the RNA, which was eluted in 20 µL water. The RNA was used for first strand cDNA synthesis with random priming using the Protoscript II First Strand Synthesis Kit  in a total volume of 40 µL for 1 h at 42°C. The cDNA was diluted to 150 µL and aliquoted into 96 wells of a PCR plate (1.5 µL/well), each of which contained 20 pmol of a reverse primer specific for each target sequence (see n 0.3.3) . The trin 0.3.3)  using deg (4°C), washed in 1/10th volume of PBS (pH 7.4), and combined for homogeneity. The cells were then evenly split between 8 × 15 mL tubes and pelleted at 100g for 10 min at 4°C. The cell pellets were then snap frozen in liquid nitrogen and immediately stored at −80 °C.GM12878 cells were cultured the same as in 7 GM12878 cells and vortexed immediately. This sample was incubated at room temperature for 5 min. CHCl3  was added per mL of sample, vortexed, incubated at room temperature for 5 min, vortexed again, and centrifuged for 10 min at 12,000g (4°C). The aqueous phase was pooled in a LoBind Eppendorf tube and combined with an equal volume of isopropanol. The tube was mixed, incubated at room temperature for 15 min, and centrifuged for 15 min at 12,000g (4°C). The supernatant was removed, the RNA pellet was washed with 750 μL 80% ethanol and then centrifuged for 5 min at 12,000g (4°C). The supernatant was removed. The pellet was air-dried for 10 min, resuspended in nuclease-free water , quantified, and either stored at −80°C or processed further by Poly(A) purification.GM12878 RNA was isolated the same as in Poly(A) RNA was purified from GM12878 total RNA with NEXTflex poly(A) beads  using 50 µL of beads per 100 µg of total RNA. GM12878 poly(A) RNA was aliquoted and stored at −80°C.S. cerevisiae S288C. The S. cerevisiae was grown in 1 L YPD media  at 30°C. The cells were pelleted and resuspended in cold 10 mM EDTA. The cells were again pelleted and resuspended in 5 mL of 50 mM sodium acetate (pH 5.5), 10 mM EDTA, 1% SDS. An amount of 5 mL of Acid-Phenol:Chloroform:IAA  was added, and the mixture was vortexed. The mixture was incubated in a 65°C water bath with brief vortexing every 5 min for a total incubation time of 30 min. The mix was placed on ice for 10 min, and the phases separated by centrifugation. The upper phase was collected, and an equal volume of chloroform was added. The mixture was vortexed again, and the phases separated by centrifugation. The upper phase was collected, and 0.1 volume of 3 M sodium acetate pH 5.3 was added. An equal volume of isopropanol was added, mixed, and the RNA was precipitated at −20°C. The resulting RNA precipitate was dissolved in 5 mL of TE buffer. The RNA was reprecipitated by adding 0.25 volume of 1 M sodium acetate pH 5.5 and 2.5 volumes of ethanol and incubated for 60 min at −20°C. The total RNA pellet was redissolved in TE buffer.Total RNA was purified from S. cerevisiae RNA using the PolyA Spin mRNA Isolation Kit . After a single round of isolation, the RNA was precipitated by adding glycogen and 2.5 volumes of ethanol. The poly(A) RNA pellet was dried and resuspended in 1 mM Tris-HCl pH 7.5, 0.1 mM EDTA.Poly(A) RNA was isolated from 2 mg of total Vaccinia Capping Enzyme (VCE)  in 1× VCE reaction buffer , 6 µL Escherichia coli inorganic pyrophosphatase , 0.5 mM 3′-azido-ddGTP , 0.2 mM S-adenosylmethionine (SAM)  in 60 µL total volume for 30 min at 37°C. The recapped RNA was purified with RNA Clean & Concentrator as above.Poly(A) RNA was decapped and recapped according to methods previously described . In brieAzido-ddGTP recapped RNA (1–2 µg) was concentrated briefly on a SpeedVac vacuum concentrator (Savant) to reduce the volume to ∼5–10 µL. Copper-free Click Chemistry reactions were performed in a total volume of 50 μL, containing 25% v/v PEG 8000  and 20% v/v acetonitrile  in 0.1 M sodium acetate buffer, pH 4  and 10 mM EDTA . Azido-ddGTP recapped RNA and the 3′-DBCO RNA adapter  were added and shaken for 2 h at room temperature. Then, acetonitrile was removed by brief concentration on a SpeedVac, and the adapted RNA recovered using RNA Clean & Concentrator  following the protocol to separate large RNA (desired) from small RNA (excess adapter).CATTGCAAGCAGTGGTATCAACGCAGAGTACATrGrGrG) and a poly(dT) reverse transcription primer. ADGRE1 cDNA was PCR amplified using a forward primer (underlined sequence of the TSO) and a gene-specific reverse primer with Q5 Hot Start High-Fidelity 2× Master Mix . cDNA was prepared for sequencing using the barcoded NBD 104 expansion of the SQK-LSK109 protocol following the manufacturer's recommendations and sequenced using a Flongle flow cell. Ionic current traces were base called with MinKnow real-time base calling using the high-accuracy model.cDNA for 5′ RACE sequencing was made with the 5′ RACE protocol using the template-switching RT Enzyme Mix . ADGRE1 cDNA was reverse transcribed from total GM12878 RNA using a template-switching oligo (TSO) (GCTAATPoly(A) RNA samples were split and either processed for cap-adaptation (treated) or used as a matched negative control (untreated). Both treated and untreated poly(A) RNA (500–775 ng) were prepared for nanopore direct RNA sequencing generally following the ONT SQK-RNA002 Kit protocol, including the optional reverse transcription step recommended by ONT. Instead of using Superscript III, as in the ONT protocol, Superscript IV  was used for reverse transcription. RNA sequencing on the MinION was performed using ONT R9.4 flow cells and the standard MinKNOW protocol (48 h sequencing script) as recommended by ONT, with one exception. We collected bulk phase continuous data files for 2 h of sequencing and then restarted the sequencing runs after the 2 h of initial sequencing.https://github.com/rrwick/Porechop). We used barcode_diff 1 and barcode_threshold 70 or 74 for S288C or GM12878 reads, respectively . The adapters were untrimmed while optimizing parameters and trimmed for all other analysis. The barcode search sequence, TCCCTACACGACGCTCTTCCGA, was added to the end of the adapter list in the adapters.py. Reads were then aligned to the appropriate reference, sacCer3 or GRCh38, using minimap2 (We used the ONT Guppy workflow (version 3.0.3 + 7e7b7d0 configuration file rna_r9.4.1_70bps_hac.cfg) for base calling direct RNA. NanoFilt (version 2.5.0)  was usedminimap2  . Reads were mapped to the human genome (GRCh38.p3.genome.fa) using Minimap2 (version 2-2.9) with the following parameters: –secondary = no -ax splice -k14 -uf. Remaining secondary alignments were removed (using samflag -F 2048). Reads containing 15 or more soft or hard clipped bases at the 5′ end of the read were removed to avoid positioning TSS at the wrong locations (properly mapped reads). Finally, for adapted reads only, the untrimmed reads containing less than 15 soft or hard clipped bases were flagged and the equivalent trimmed reads removed. This filter removes the reads that were called adapted by Porechop but the adapter is matching the genomic sequence (true positive reads). Unless otherwise stated, subsequent analyses for the untreated sample or unadapted fraction of the treated sample were done using the properly mapped reads. For the adapted fraction of the treated sample, subsequent analysis was done on the properly mapped/true positive reads.TSS filtering pipeline is available on GitHub , ENCFF289XSX (SPI1 ChIP-seq), ENCFF093VXI (DNase-seq), and ENCFF580WIH (CAGE). For plotting purposes, all the data sets were down sampled to the total number of read defining novel TSS in the treated sample (9116 reads). To avoid repeats in the data set, all the reads within a 50 bp window were merged. The most 5′ end of the mapped reads were used to define the reference locations. deepTools 3.3.0  was usedThe genomic position of the 5′ end of mapped nanopore reads was compared to GENCODE annotation v32  using beStringTie2 (v2.1.5) was executed using the following command: stringtie -L -o untreated_reads_stringtie.gff untreated_reads.bam.First, python flair/bin/bam2Bed12.py -i untreated_reads.bam > untreated_reads.bed12.Second, python flair.py correct -q untreated_reads.bed12 -c GRCh38_with_decoys.fa.sizes -f gencode.v32.annotation.gtf.Third, python flair.py collapse -g GRCh38_with_decoys.fa -r untreated_reads.fq -q unttreated_reads.bed12.The basic FLAIR (commit 150c33e915aaf72ad3d39bca0a99d969850a284c) workflow was followed using these commands:Supplemental Methods and visualized in Supplemental Figure 10.The ionic current traces were visualized using a custom MATLAB script. Determining the ionic current of the cap-adapter is described in https://github.com/mitenjain/dRNA_capping_analysis. Additionally, the nanopore data can be found at ENA with the accession number PRJEB43374.Data and analysis scripts can be found at the following GitHub: Supplemental material is available for this article.M.G.W., I.S., G.T., J.B., I.R.C., and L.E. are employees of New England Biolabs Inc. New England Biolabs commercializes reagents for molecular biology applications. M.A. holds options in Oxford Nanopore Technologies (ONT). M.A. is a paid consultant to ONT. L.M., M.A., and M.J. received reimbursement for travel, accommodation, and conference fees to speak at events organized by ONT. M.A. is an inventor on 11 UC patents licensed to ONT . M.A. received research funding from ONT.


We are the first to apply the Quality by Design (QbD) framework to enhance rapid-response RNA vaccine manufacturing against known and future viral pathogens. This QbD framework aims to support the development and consistent production of safe and efficacious RNA vaccines, integrating a novel qualitative methodology and a quantitative bioprocess model. The qualitative methodology identifies and assesses the direction, magnitude and shape of the impact of critical process parameters (CPPs) on critical quality attributes (CQAs). The mechanistic bioprocess model quantifies and maps the effect of four CPPs on the CQA of effective yield of RNA drug substance. Consequently, the first design space of an RNA vaccine synthesis bioreactor is obtained. The cost-yield optimization together with the probabilistic design space contribute towards automation of rapid-response, high-quality RNA vaccine production.Rapid-response vaccine production platform technologies, including RNA vaccines, are being developed to combat viral epidemics and pandemics. A key enabler of rapid response is having quality-oriented disease-agnostic manufacturing protocols ready  Vaccines are highly effective for stopping epidemics and pandemics. However, the development of vaccines using conventional production methods is becoming too slow to effectively respond to new viral outbreaks in the 21st century4, the frequency of which is predicted to increase3.The outbreak and spread of viral diseases, such as the COVID-19 pandemic caused by the SARS-CoV-2 virus, the 2015–2016 Zika virus epidemic in Brazil and American continents, the re-emerging Nipah outbreaks in South and Southeast Asia, and the 2013–2016 Ebola virus epidemic in West Africa, pose tremendous healthcare and economic challenges6. The RNA (both mRNA and saRNA) drug substance is purified using tangential flow filtration (TFF) and chromatography techniques, such as ion-exchange or multimodal chromatography7. Then the RNA drug substance is formulated into lipid nanoparticles and filled into vials or other containers7. A process diagram showing RNA vaccine drug substance and drug product manufacturing are shown in Supplementary Fig. To address this pressing need, rapid-response vaccine production platform technologies are being deployed, such as the messenger RNA (mRNA) and self-amplifying RNA (saRNA) platforms, herein collectively referred to as RNA vaccine platforms. The mRNA and saRNA vaccine production process involves cell-free DNA-templated RNA synthesis based on the in vitro transcription (IVT) reaction catalysed by the T7 RNA polymerase enzyme (T7RNAP)8. The only component in this production process that needs to be changed is the template DNA based on which the RNA is enzymatically synthesised. The rest of the materials, equipment, consumables, unit operations, formulation components, fill-to-finish processes as well as quality control and quality assurance methods remain unchanged when switching to the production of a new RNA sequence encoding for a new vaccine antigen. This is possible because the RNA vaccine manufacturing process produces only the genetic instructions for expressing an antigen in human cells, and not the actual antigen. Using this technology, candidate vaccines can be produced against any known or currently unknown future pathogens. For example, mRNA and saRNA vaccine candidates against COVID-19 have been recently produced with an unprecedented speed: in 2 weeks after obtaining the genetic sequence information of the antigen10. The mRNA vaccines developed by BioNTech and Moderna gained emergency use authorisation against Covid-19 at record speed, despite the RNA vaccine platform being a new technology that had not been approved by regulatory authorities in the past.RNA vaccines involve rapid development and production timelines because the production platform is agnostic to the disease target as RNA sequences translating into any vaccine protein antigen can be produced using the same production processprior to the production of a particular product, ideally in a disease- and product-agnostic manner to complement the flexible manufacturing platform of vaccine candidates against a wide range of pathogens. The quality by design (QbD) framework has been used to aid the regulatory approval and production of small molecule pharmaceuticals12 and monoclonal antibodies14 by establishing a design space (DS) in which the production process can be operated to consistently obtain the required quality target product profile. Regarding vaccines, some are currently in development based on QbD frameworks16. However, to the knowledge of the authors, there are currently no vaccines approved by regulatory authorities based on a full QbD filing. The QbD framework consists of two key steps: (1) a risk assessment based on the identification of product critical quality attributes (CQAs) and critical process parameters (CPPs) and (2) definition of the DS in the CPPs space which is obtained by defining mathematical relationships between CPPs and CQAs. For the first step, quality attributes are commonly ranked based on their impact and uncertainty scores for both product safety and efficacy, obtaining this way a severity score based on which the CQAs are identified15. Next, the CPPs are identified by assessing the impact of PP ranges on the identified CQAs, predominantly by using a binary, yes or no, approach based on expert knowledge and product-process understanding15. Alternatively, CQAs and CPPs can also be identified and ranked using fishbone diagrams, cause–effect matrices and failure mode effect analyses19. However, none of these existing methods is able to capture the direction, magnitude and shape of the impact of the CPPs on the CQAs. Therefore, more advanced approaches are needed to better describe the relationship between CPPs and CQAs in a data-poor environment, which is typical to the early phases of production process development.The development of process monitoring and quality assurance approaches remains a key challenge for quickly and cost-effectively ensuring that the drug substance is produced with consistently high quality. This should be explored and developed 20. Such mechanistic models tend to outperform statistical or data-driven (e.g. machine learning) models in data-poor environments, such as during the early stages of process development. This is the first bioprocess model of an RNA vaccine synthesis bioreactor in support of DS identification and optimisation. The proposed qualitative QbD methodology which maps the direction, magnitude and shape of the impact CPP–CQA relation together with the bioprocess model forms the QbD framework. Overall, the framework is to become universally applicable to mRNA and saRNA vaccine manufacturing using wild-type nucleotide triphosphate NTPs and is independent of the viral infectious disease indication, because both the RNA vaccine manufacturing process and the QbD framework can be applied to produce any antigen-encoding RNA sequence5. The QbD framework applied to the RNA platform further supports upstream process optimisation during both development and manufacturing and is anticipated to expedite the regulatory approval process by providing a form of “pre-qualification” by re-using and processing disease agnostic-prior knowledge4.To address this need, we developed and implemented a new qualitative QbD methodology that assesses the criticality of PPs considering the direction, magnitude and shape of the CPP–CQA relation. Furthermore, we developed a bioprocess model to map the multi-dimensional DS of RNA synthesis substantially faster and with fewer resources compared to the experimental design of experiments (DoE) protocols. This bioprocess model was built on previously published RNA synthesis kineticsThe mRNA and saRNA and their intrinsic quality features are created during the in vitro transcription (IVT) reaction, therefore the QbD framework, consisting of a qualitative methodology and a quantitative bioprocess model, has been applied to this unit operation. As shown in Fig. In the third step, the qualitative methodology is used to identify and rank the CQAs of the mRNA and saRNA vaccine, as shown in Fig. 25. The bioprocess model involves bi-substrate kinetic formulae for the transcription reaction, adapted from a previously published multiphysics kinetic model20, to compute the RNA transcription yield. Given the prior knowledge that RNA degrades in alkaline as well as acidic environments26, and that high Mg2+ concentration favours RNA degradation27, RNA degradation rate was modelled as a series of three power laws, each first order in RNA and first-order in either proton, hydroxy or Mg2+ concentration. Four CPPs identified in Table 2+ and Mg. Mg2+ is used to refer to free solution magnesium, while Mg is used when referring to total magnesium concentration in free solution together with magnesium in complexes, often in the context of initial experiment conditions. The remaining nine CPPs were not considered in this RNA synthesis bioreactor model because these CPPs can be well controlled in commercially available bioreactor setups implemented in facilities following cGMP guidelines.The four CQAs from Supplementary Table 28. This dataset includes NTP and T7RNAP screening experiments, and more thorough analysis of RNA yield surface response on Mg concentration. 33 samples correspond to the RNA yield at 0.04 M NTP and 1 × 10−8 M of T7RNAP vs. 11 concentrations of Mg ranging from 0.025 to 0.125 M after 2, 4 and 6 h , solution turbidity due to Mg2PPi precipitating after the formation of PPi as a byproduct and pH. Through such measurements, one can more easily discriminate the relative importance of the physical phenomena contributing to the degradation of RNA. Ultimately, these measurements would also help in determining tighter bounds on kapp as the most significant parameter.On top of the dataset being skewed towards the dependence on Mg and T7RNAP rather than NTP, many of the RNA yield values are clustered close to zero, c.f. Supplementary Table R2 value of 0.398 and an MAE of 0.570 g/L due to its inability to capture non-linearities. Only after including squared terms in both Mg and NTP and their interaction term in the regression (seven coefficients plus a constant), did the fit of the statistical model increase to an R2 value of 0.766 and an MAE of 0.167 g/L, which are comparable to that of the mechanistic model . The summary of the models’ prediction plots and descriptive statistics can be found in Supplementary Fig. Despite these current limitations, the mechanistic model performs well in comparison to conventional statistical modelling techniques. Multiple linear regression (MLR) using four linear explanatory variables (four coefficients plus a constant) gave an The current model performed well in the region of interest at medium-to-low Mg and NTP concentrations and hence this model was used to create the first DSs. In conjunction with cost and safety considerations, this leads to first recommendations about the desired operating region and subsequent experimental design. The limitations of the model at high Mg and NTP concentrations will be resolved in future iterations with the use of additional experimental data that will be obtained from optimal experiment designs.The deterministic DS and concentration-cost-yield plots produced by the model are shown in Fig. z-axis of Fig. −8 M T7RNAP concentration, at 40.8 mM NTP concentration and at an RNA yield of 4.34 g/L, as shown below in Table In addition to obtaining high values for the RNA effective yield, the product should also be produced at low cost. For this, Fig. The results indicate that the relatively high NTP concentration contributes more to the RNA cost per gram compared to the lower concentration of T7RNAP and that cost optimality is reached at the highest T7RNAP concentration. This holds true as long as RNA yield continues to grow linearly with T7RNAP, or as long as the solution is rich in NTP. The range of T7RNAP and NTP concentrations for which this is valid needs to be investigated through further experiments.However, given that at the experimental optimums NTPs are relatively more expensive than the other two components, the operating point should be chosen at the minimum NTP concentration that ensures reaching the desired CQA with a certain probability. The cost values reported above correspond to the cost of the T7RNAP and NTPs expressed per g of RNA, but this is not the total production cost of the RNA drug substance. In fact, the major cost driver in RNA vaccine production is the 5′ cap analogue purchase price. The concentration of the 5′ cap analogue remains unchanged when RNA vaccines of different length are used due to the very high molar excess of the 5′ cap analogue used relative to the final molar RNA concentration; the calculations are available in Supplementary Table 7.Production cost components other than the cost of the T7RNAP and NTPs were not included in this model as they were considered fixed. For a detailed analysis of the RNA vaccine drug substance production cost see−8 M T7RNAP concentration in Fig. To address uncertainties and ascertain process operational flexibility, a probabilistic DS was created by adding 20% standard deviation to the fitted model parameters from Eq. . Monte C35 offering additional benefits through increased production volumes and speeds and reduced production costs7. The QbD framework presented herein aids the acceleration of the development and manufacturing of both mRNA and saRNA vaccines, here collectively referred to as RNA vaccines.The emergency use authorisation granted to the BioNTech/Pfizer and Moderna mRNA vaccines underlines the crucial importance of this new vaccine platform technology, which succeeded in developing and producing vaccines against a new coronavirus at record speeds even though it was a new technology that had never gained regulatory approval in the past. The mRNA platform is also well-positioned to rapidly deploy vaccines against new SARS-CoV-2 variants. saRNA vaccines are also being developed4. This “pre-qualification” is facilitated by the platform nature of both the RNA vaccine manufacturing process and of the QbD framework. The mRNA and saRNA vaccine production platforms facilitate this “pre-qualification” provision by re-using disease-agnostic prior knowledge, production process understanding, expert knowledge, experimental and clinical data from old RNA vaccines to produce new RNA vaccines and vaccine candidates. The QbD framework aids this “pre-qualification” by processing all the information from the mRNA and saRNA vaccine development and manufacturing processes to obtain the optimal outcomes in terms of product safety, efficacy and cost.The QbD framework is anticipated to expedite the regulatory approval process by providing a form of “pre-qualification”4. Within this, the criticality of product quality attributes and PPs is evaluated. Next, the impact of CPPs on CQAs is assessed using the qualitative methodology shown in Table The QbD framework incorporates disease-agnostic prior knowledge, production process understanding, expert knowledge, current experimental and clinical data. This framework can serve as a “pre-qualification” for speeding up pre-clinical and clinical development and regulatory approval processes for future outbreaks. Such a framework is especially beneficial when combined with a vaccine production platform technology because both the RNA platform and the QbD framework are disease-agnostic. The implementation of the QbD framework follows an iterative development cycle, as shown in Fig. 1The next step in the QbD framework would include defining the NOR within the DS as a safety margin against the process and material fluctuations, model uncertainties and other uncertainties. This allows for operational flexibility in a production process following current Good Manufacturing Practices (cGMP), offering substantial advantages compared to a conventional “frozen” cGMP process in which the operating parameters are fixed. The NOR can be defined from the probabilistic DS, shown in Fig. The RNA platform combined with the QbD framework is suitable for producing vaccines rapidly against new diseases or against new variants of the same pathogen in case the virus mutates. When switching to develop and mass-produce a new vaccine, the genetic sequence of the antigen or candidate antigen of the viral pathogen is a product-specific prerequisite. This genetic information is then transferred into the template DNA and once the template DNA is produced, the other components of the RNA vaccine production platform technology and the QbD framework can be re-used from the previous RNA vaccine or vaccine candidate production process, thus these are agnostic to the vaccine product. Therefore, raw materials—with the exception of the template DNA—consumables, equipment, upstream and downstream unit operations, formulation components, fill-to-finish processes and quality control and quality assurance approaches can all remain unchanged when starting the production of a new vaccine or vaccine candidate. The CQAs identified in Supplementary Table 25. In this study saRNA synthesis based on wild-type, NTPs was modelled but in future iterations, the model can also be adapted to described RNA synthesis using modified NTPs, such as N1-methylpseudouridine-5′-triphosphate41. Wild type NTPs are used for the production of Covid-19 vaccines at CureVac and at Imperial College London, whereas BioNTech/Pfizer and Moderna use modified uridine triphosphate (UTPs)41.As part of iterative model development, the QbD bioprocess model can be improved in several ways, including (1) incorporating additional CQAs and linking these to CPPs using mathematical equations, (2) adding first-principle QbD models for downstream unit operations, i.e. tangential flow filtration and chromatography purification, and (3) adapting the model for larger-scale production and purification, for example by fitting the kinetic model parameters to the RNA synthesis at larger scales or, if needed, by changing the model architecture to more accurately describe larger scale RNA synthesis. All these three model improvements are currently hindered by the lack of publicly available data since this is a new type of product and production process. An example of product CQA that can be added to the model in the future is the 5′ capping efficiency. Inclusion of the 5′ capping efficiency CQA in the current version of the model has not been prioritised because the commercially available 5′ cap analogue, CleanCap  yields 5′ capping efficiencies of ≈95% which is considered high enough for the effective translation of the RNA into vaccine antigen in human cellsThe mechanistic model is advantageous over statistical and data-driven models in data-scarce environments, strengthens process understanding and showcases cause–effect relationships. However, uncertainty quantification may be less robust when using mechanistic models compared to multivariate statistical modelling when sufficient experimental data is available.43 can be used to establish causality between CQAs and CPPs using mechanistic modelling terms. For instance, statistical DoE has established that there is an optimum in Mg concentration to maximise RNA yield. This could be the effect of a complex interplay of enzyme saturation, Mg-facilitated RNA degradation, and precipitation out of solution through magnesium PPi. All of these have different impacts on the safety and efficacy CQAs downstream. MB-DoE and stochastic Global Sensitivity Analysis could then be used to pinpoint the most probable reason. Thereafter, including additional measurements such as NTP concentration and solution turbidity measurements throughout the course of the reaction could be used to infer the most likely physical cause. With the current model, one cannot dismiss model parameters without jeopardising predictive power, nor include additional terms without overfitting. As more biochemical and bioprocess knowledge become available, insignificant parameters can be fixed or constrained in parameter estimation so that more physical parameters can be investigated without overfitting the data.A key pillar of QbD is product and process understanding. As more data becomes available, model discrimination and model-based DoE (MB-DoE)Yet, due to the high cost of experiments, simultaneous mechanistic and statistical model building might not be feasible. To this end, it might be beneficial to start out with screening experiments to build a first mechanistic DS, which is able to map larger PP spaces with fewer data. As more data becomes available, it can be combined with data-driven techniques into a hybrid model to minimise plant-model mismatch. As the mechanistic model should capture most non-linearities, the data-driven technique could even be linear and relatively inexpensive.In conclusion, a QbD qualitative methodology and quantitative mechanistic model has been developed and applied for facilitating the rapid and high-quality production of RNA vaccines against emerging infectious diseases. The new qualitative methodology identified critical PPs (CPPs) and related these to critical quality attributes (CQAs) of the RNA vaccine transcript. The mechanistic bioprocess model mapped the value of the RNA drug substance effective yield over a four-dimensional CPPs space. This way, the first DS of an RNA vaccine synthesis bioreactor was obtained facilitating the optimal control of the production process.This QbD framework incorporates disease-agnostic prior knowledge, experimental data, production process understanding, bioprocess modelling and can serve as a “pre-qualification” for accelerating the pre-clinical and clinical development and the regulatory approval process. By combining such a QbD framework with the RNA vaccine production platform, vaccines and vaccine candidates can be produced for future outbreaks faster and at consistent high-quality. The QbD framework follows an iterative development cycle and this QbD model can be improved and implemented to enable vaccine production against pandemics substantially faster. This can be catalysed by cross-disciplinary collaboration between academia, industry and regulatory authorities.See Supplementary Table 48 and expert knowledge.To quantitatively assess the impact of PPs on CQAs, a new methodology was developed which accounts for the magnitude, direction and type of the CPP-CQA relationship. The magnitude of the impact was rated from zero (low) to three (high), and PPs rated with two or three were considered CPPs. The direction and type of CPP-CQA relationship were characterised either by a positive slope, a negative slope, or a peak behaviour, labelled with plus, minus, or plus–minus, respectively. The ratings provided in Supplementary Table 2+, NTP4−, H+, HEPES− (buffer) and PPi4−. These five free solution components can form the following ten complexes: HNTP3−, MgNTP2−, Mg2NTP, MgHNTP−, MgPPi2+, Mg2PPi, HPPi3−, H2PPi2−, MgHPPi− and HEPES. This system naturally gives rise to differential-algebraic equations (DAE)  a degradation term48 (Eq. (20 (Equations (M1)–(M5) and (E1)–(E10) under Model equations in Supplementary Information). It is assumed that: (1) temperature is constant, (2) the DNA template has the correct sequence, (3) 5′ RNA cap analogue does not change the mechanisms of the synthesis hence its concentration is neglected, (4) the four NTP4− concentrations are equimolar and (5) DTT and spermidine concentrations remain at optimal values throughout the reaction.The model aims to link the grouped effective RNA yield CQA to 4 CPPs. To relate RNA concentration to total initial NTP and Mg concentration, all buffer component concentrations need to be tracked. It is assumed that the main free species present in solution affecting transcription and degradation kinetics are: MgE) (Eqs.  to 7)).).2+, NTPE) (Eqs. ) modifiem48 (Eq. ) and (c)m48 (Eq. ). Algebr−18 M PPi , for further details see the SI document. The initial concentrations of total Mg, NTP and T7RNAP depend on the model input. (2) The solution concentrations that appeared in the kinetic terms were solved for at each time step using scipy.optimise.fsolve.The system of DAEs was solved explicitly by breaking up the problem into two: (1) The differential equations expressing the total solution components were solved as initial value problems using an in-house fourth-order Runge–Kutta solver. The initial conditions of the ODE system are set to 0 M for RNA, 0.04 M for HEPES, the equivalent of 7.5 pH for total protonated components and 1 × 1028. Biological knowledge was used to set Nall, the length of the RNA chain, to 10,000 bases. Similarly, [Mg2PPi]eq was found to be 1.4 × 10−5 mol/L and the values of dissociation equilibrium constants were taken to be Keq,0 to Keq,9 respectively. To reduce overfitting, nac, nba, nMg and nRNA were set to 1. The phenomena of enzyme degradation and Mg2PPi precipitation were ignored for now, and hence kd and kprecip set to 0, as they did not improve model performance. The residual six parameters kapp, K1, K2, kac, kba and kMg were then estimated using the scipy.optimise.curve_fit local solver function in Python 3 through least-squares error minimisation, with initial guesses kapp 1.3 × 10−3 K1 20 K2 100 kac 1 × 106 kba 1 × 106kMg 2 The model was fitted and validated with a set of 51 experimental data samples with three replicates each obtained from saRNA synthesis experiments using wild-type, non-modified UTPs−8 M T7RNAP where the kinetic parameters were quasi-randomly generated, using Sobol sequences, in a uniformly distributed range at ±10% around the optimal kinetic parameter values which were generated using parameter estimation.The described model was implemented in gPROMS , in which the Global Systems Analysis entity was used to perform variance-based sensitivity analysis. Therein, 80,000 simulations were run at 0.075 M Mg, 0.04 M NTP and 1 × 10® statistical Design of Experiments software. Time, initial Mg, initial NTP and initial T7RNAP concentrations were included as factors and scaled to unit variance. Then, two separate models were fitted using MLR, one using the four factors as linear predictors and one that also included square terms in the Mg and NTP concentrations as well as an interaction term consisting of the product of Mg and NTP concentrations.The 51 averaged data samples from the parameter estimation were uploaded to the MODDEFor determining the probabilistic 2D DS, each Mg/NTP point was Monte-Carlo simulated 50 times using a random normal uncertainty, with a standard deviation of 20% around the optimal kinetic rate constant model parameters.8 $/mol and 2.5 × 105 $/mol, respectively. These cost values are representative of these products and over time it is expected that these raw material purchase prices will decrease due to technology maturation and economies of scale, as the RNA vaccine platform technology will be used to produce other vaccine product leading to an increased demand for these raw materials.GMP grade T7RNAP and wild-type, unmodified NTP costs were obtained from Roche Diagnostics International Ltd. as 1.35 × 10Further information on research design is available in the Supplementary InformationReporting Summary


TARDBP, FUS, and C9ORF72 mutations. Specifically, we focus on recent studies that describe toxic RNA as the potential initiator of disease, disease-associated defects in specific RNA metabolism pathways, as well as how RNA-based approaches can be used as potential therapies. Altogether, we highlight the importance of RNA-based investigations into the molecular progression of ALS, as well as the need for RNA-dependent structural studies of disease-linked RNA-binding proteins to identify clear therapeutic targets.Amyotrophic lateral sclerosis (ALS) is a progressive and fatal neurodegenerative disease that affects upper and lower motor neurons. Familial ALS accounts for a small subset of cases (<10–15%) and is caused by dominant mutations in one of more than 10 known genes. Multiple genes have been causally or pathologically linked to both ALS and frontotemporal dementia (FTD). Many of these genes encode RNA-binding proteins, so the role of dysregulated RNA metabolism in neurodegeneration is being actively investigated. In addition to defects in RNA metabolism, recent studies provide emerging evidence into how RNA itself can contribute to the degeneration of both motor and cortical neurons. In this review, we discuss the roles of altered RNA metabolism and RNA-mediated toxicity in the context of  TARDBP, FUS, and C9ORF72 that cause amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), numerous investigations have demonstrated the effect these mutations have on RNA metabolism, as well as how RNA itself contributes to pathogenicity (Since the discovery of mutations in genicity . Mutatiogenicity . HoweverTARDBP encodes the TAR DNA-binding protein (TDP-43), which was initially discovered as a protein that binds pyrimidine-rich regions (TAR) within the HIV-1 gene, repressing its expression  domains within TDP-43 , numerouMuch is known about how specific point mutations confer changes to the function of TDP-43. Until recently, the lack of a high-yield protocol to produce pure, full-length TDP-43 has plagued the field. However, a major breakthrough by the Chiti group has made this possible , openingSince TDP-43 has roles in almost every aspect of RNA metabolism , it is nDrosophila model of ALS, overexpression of wild-type and mutant TDP demonstrated loss of actively translated futsch mRNA (MAP1B in humans)  can mitigate this translational deficit and remodel RNA granules to restore the translation of Futsch/Map1b to rescue synaptic defects   . The diralidated . This realidated . Further defects . The sam defects . The obs defects . An indeDennd4a) .There is also evidence that specific TDP-43 mutations or overexpression may lead to alterations in non-sense-mediated decay (NMD) . NMD is Here, we only discuss RNA-binding in the context of cytoplasmic gain-of-function as it relates to TDP-43 and its disease-related mutants. However, there is powerful recent work that discusses the loss of nuclear TDP-43 function following its depletion from the nucleus . It is iGallus gallus chicks  may seem to contradict this hypothesis of RNA-induced toxicity. The naturally occurring C-terminal cleavage product of TDP-43 (TDP-25) leads to significantly more cell death in Neuro2A cells when compared to TDP-43, yet TDP-25 does not colocalize with RNA in cells . Thus, ipotentially lead to toxicity. Elucidating the neurodegenerating mechanism of ALS-causing mutations may be even stronger. Most TDP-43 mutations are in the C-terminal domain and do not directly interfere with RNA processing or binding functions  protein was first identified as part of a fusion protein resulting from a chromosomal translocation that is common in human myxoid liposarcomas , and the zinc-finger (ZnF) domain (amino acids 269–454) were resolved in complex with various RNA motifs . This stvia promiscuous RNA-binding. Indeed, a triplet of K→A mutations within the RRM lead to a loss of FUS-ΔNLS ability to compartmentalize itself within either the nucleus or cytoplasm  mRNA and protein are particularly enriched in FUS inclusions  and helps maintain FUS solubility in the nucleus  within the first intron of the gene  translation, ribosomes bind to the sense and antisense repeat RNA and elongate to produce dipeptide repeat (DPR) proteins. Owing to ribosomal frameshifting, two unique DPRs are produced from each HRE RNA strand, with a common DPR expressed by both  ] . These fDue to the complex nature of this mutation, as well as the focus of this review, the following discussion will be limited to an assessment of how the HRE RNAs and DPRs alter RNA processing pathways, as well as how the repeat RNA itself induces neuronal toxicity.in vitro (n RNA under various conditions (in vivo. Future investigations may need to probe the distinct roles of soluble (unstructured) and insoluble (highly structured) HRE RNA in conferring cellular toxicity.The GGGGCC HRE RNA has been shown to form G-quadruplexes in vitro , and thein vitro . Few stunditions , though nditions , though in vivo remain unknown.Due to the variable length of the five DPRs, as well as their relative insolubility when purified, it has been difficult for investigators to derive their crystal or nuclear magnetic resonance structures. However, some headway has been made using techniques like circular dichroism to elucidate the relatively disordered secondary structure of the DPRs . To dateClearly, the field of investigating the HRE RNA and DPR structures is wide open. Advancements therein will provide extremely useful data to inform future investigations into their toxic role in disease.The first indication that the HRE RNA may contribute to alterations in RNA metabolism came with the observations that the HRE foci (punctate inclusions containing the HRE sense and antisense RNA) sequester various RNA-binding proteins (RBPs) . The hypvia overexpression or exogenous addition) reduce translation  after incubation with NSC34 cells yielded a variety of poly(PR)-interacting proteins that regulate translation and ribosome assembly . Indepennslation . Indeed,nslation .The potent effect of poly(GR) and poly(PR) on translation becomes quite relevant with the “apparent” discovery of NMD defects in C9ALS. Multiple recent studies have proposed that NMD function is altered by the HRE, whether it be due to DPR toxicity or HRE RNA . NotablyIn addition to the strong RBP sequestration caused by the HRE RNA and DPRs, there is also evidence that the biogenesis of ribosomes may be disrupted by this mutation. Poly(GR) and poly(PR) colocalize with ribosomal proteins in C9ALS patient tissue , which hDrosophila models that overexpress the HRE RNA or GGGGCC RNA that contains intermittent stop codons (“RNA only”)  . While tA only”) . This obDrosophila model fail to demonstrate RNA-induced toxicity? The answer is simple: Pom121 is not conserved in fruit flies  have been utilized widely in the laboratory and clinical settings. ASOs are heavily modified DNA sequences that are designed complimentary to a target RNA. Upon annealing, they induce RNase H-dependent degradation of their target while the ASOs themselves remain intact . ASOs aged cells , have bein vitro and in vivo studies have clearly demonstrated that simple knockdown or overexpression of wild type TDP-43 is terribly cytotoxic. One workaround that may prove effective in the realm of TDP-43 pathogenesis comes with the use of bait oligonucleotides (Targeted knockdown of TDP-43 and FUS, however, may not be as useful. While it is a valid hypothesis that reducing their abundance would decrease their propensity for pathogenesis, both TDP-43 and FUS have important biological functions. Knocking down these mRNAs may open a whole new can of worms, where loss of their endogenous function may be just as toxic as their disease-related gain of function. Numerous leotides . Introduleotides . Althougleotides .Targeting downstream molecular events that follow TDP-43 loss of function, such as Stathmin2 mis-splicing, are open to candidate therapies and may allow at least some mitigation of TDP-43 loss-of-function toxicity . FinallyDecades of research and independent investigations have provided clear evidence that RNA biology is commonly disrupted in the context of ALS. Yet our understanding of RNA metabolism alterations caused by each mutation lacks a unifying link. Indeed, the missing link may be found by investigating RNA metabolism in sALS, where the vast majority of cases are caused by hitherto unknown mechanisms. If a prominent, causative mechanism for sALS pathogenesis is identified within the realm of RNA biology, only then can the field clarify the bidirectional role for RNA in ALS neurotoxicity and neuroprotection.BLZ wrote the manuscript. JDR edited the manuscript. Both authors contributed to the article and approved the submitted version.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.


Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality in the world. Despite considerable progress in the diagnosis, treatment and prognosis of CVDs, new diagnostic biomarkers and new therapeutic measures are urgently needed to reduce the mortality of CVDs and improve the therapeutic effect. RNA methylations regulate almost all aspects of RNA processing, such as RNA nuclear export, translation, splicing and non-coding RNA processing. In view of the importance of RNA methylations in the pathogenesis of diseases, this work reviews the molecular structures, biological functions of five kinds of RNA methylations  and their effects on CVDs, including pulmonary hypertension, hypertension, vascular calcification, cardiac hypertrophy, heart failure. In CVDs, m6A “writers” catalyze the installation of m6A on RNAs, while “erasers” remove these modifications. Finally, the “readers” of m6A further influence the mRNA splicing, nuclear export, translation and degradation. M5C, m1A, m6Am and m7G are new types of RNA methylations, their roles in CVDs need to be further explored. RNA methylations have become a new research hotspot and the roles in CVDs is gradually emerging, the review of the molecular characteristics, biological functions and effects of RNA methylation on CVDs will contribute to the elucidation of the pathological mechanisms of CVDs and the discovery of new diagnostic markers and therapeutic targets of CVDs. Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality in the world, and the pathological mechanisms are complicated . CVDs rePulmonary hypertension (PH) refers to a hemodynamic and pathological state in which the pulmonary artery pressure rises beyond the threshold, which can lead to right heart failure. The hemodynamic diagnostic criteria of PH is mean pulmonary artery pressure ≥25 mmHg . PH can Hypertension is a clinical syndrome characterized by increased systemic arterial blood pressure , which can be accompanied by damage of heart, brain, kidney and other organs . HyperteVascular calcification is a common pathological manifestation of atherosclerosis, hypertension, diabetic vascular disease, vascular injury, chronic kidney disease and aging. The main symptoms of vascular calcification are increased vascular wall sclerosis and decreased compliance, which easily lead to myocardial ischemia, left ventricular hypertrophy and heart failure . ThromboCardiac hypertrophy mainly occurs in the case of long-term myocardial pressure overload, the total amount of myocardium increases and the contractility strengthens. Cardiac hypertrophy enables the heart to maintain normal blood circulation. Cardiac hypertrophy leads to the increase of myocardial oxygen demand, and the blood supply of coronary artery is often unable to supply, resulting in myocardial ischemia. This eventually lead to a decrease in myocardial contractility . The maiHeart failure refers to the failure of cardiac systolic function and (or) diastolic function to fully discharge venous blood from the heart, eventually leads to venous system blood stasis, insufficient arterial blood perfusion . Heart fFor patients with CVDs, it is recommended to take preventive measures to reduce the risk, such as lifestyle changes. On the other hand, traditional drugs such as renin angiotensin blockers, lipid-lowering drugs, beta receptor inhibitors and antithrombotic drugs are used to prevent disease progression . For a lRNA methylations regulate almost all aspects of RNA processing, including nuclear export, RNA translation, splicing and non-coding RNA processing . BecauseMethyl modifications of RNAs form post transcriptional regulatory mechanisms, which can fine tune gene expression by altering the interaction between RNAs and other components of cells. RNA methylations involve the “writers”, “erasers”, and “readers” . AlthougM1A, m6Am and m7G are new types of RNA methylations. M1A is a highly abundant post transcriptional modifications of tRNAs and rRNAs in eukaryotes. M1A modification affects the regulation of mRNA translation. M6Am is an evolutionarily conserved mRNA modification, which is different from m6A in function. M6Am does not alter mRNA transcription or stability, but negatively affects the cap dependent translation of methylated mRNAs . M7G metIn view of the fact that RNA methylations have become new research hotspots and their functions has been emerged in CVDs are gradually emerging, this work focuses on the roles and mechanisms of RNA methylations  in CVDs, and reveals the molecular characteristics, biological functions and the effects on these diseases.Methylation is an important modification of nucleic acids and proteins, which regulates gene expression and closure. It is closely related to many diseases, such as cancer, aging, Alzheimer’s disease (AD) . SimilarFurthermore, RNA methylations exist in many RNA species. For example, besides mRNA, m5C also exists in ribosomal RNAs (rRNAs), transfer RNAs (tRNAs) and some non-coding RNAs . In addiM6A refers to the methylation of nitrogen-6 adenosine base . Up to nFat mass and obesity-associated protein (FTO) is the first m6A eraser discovered in mRNAs . FTO catM5C exists in RNA by introducing a methyl group into the fifth carbon atom of cytosine . M5C is in vitro. This suggests that TET2 may affect translation by affecting tRNA methylation, and reveals the roles of TET enzyme in regulating gene expression . Then, Am can be further methylated at N6 to generate m6Am . Unlike FTO controls the reversible methylation of m6Am mRNAs during snRNA biogenesis. The biogenesis of snRNA begins with the formation of an initial M1 subtype of a single methylated adenosine , and then the M1 subtype is transformed into a dimethyl M2 subtype . The relative levels of M1 and M2 subtypes were determined by FTO, which selectively demethylated M2 subtypes, suggesting that FTO plays an important role in the biogenesis of snRNA . ObviousMost eukaryotic mRNAs have a methyl group and a positive charge at the N7 position of the 5′ terminal guanine nucleoside. M7G RNA methylation is a kind of modification that makes the seventh N of RNA guanine adds methyl under the action of methyltransferases . StudiesMETTL1 mediated methylation enhances the let-7 miRNA processing by destroying the inhibitory secondary structure in pri-miRNA transcripts . METTL1 RNAs have the dual identity of information molecules and regulatory molecules. They not only transmit DNA genetic information to proteins, but also regulate many biological processes. The post transcriptional modifications of RNAs lay the foundation for this diverse functions .2+ dependent form  can mediate the intergenerational transmission of paternal acquired phenotypes, including stress and metabolic disorders . For exaIn addition to cancers, methylation of m6A on transcripts play key roles in the regulation of downstream molecular events and other biological functions, such as maladjustment of m6A modification related to bacterial resistance, viral replication and pluripotency of embryonic stem cells. In recent years, many studies have found that abnormal methylation of m6A is associated with CVDs, including heart failure, cardiac hypertrophy, aneurysm, vascular calcification and pulmonary hypertension .Sepsis is a highly heterogeneous syndrome with different immune states and pathological processes. According to different RNA epigenetics, immune status and biological process, three different subtypes of sepsis have been identified . In viewCVDs are the most common cause of death in developing and developed countries. Although primary prevention has improved, the incidence rate of CVDs has been increasing in recent years . TherefoPH induced by hypoxia is a fatal disease, and there is still a lack of effective treatment . In humaHowever, in vitro. M6A affects the circRNA-miRNA-mRNA network during hypoxia. For example, the levels of circXpo6 and circTmtc3 are down-regulated in PH induced by hypoxia. These two m6A circRNAs, which are related to hypoxia mediated PH, have certain significance in the study of PH pathological mechanisms and treatment  induced by hypoxia. The study of PASMCs and hypoxic rat models show that the level of METTL3 is up-regulated, and the down-regulation of METTL3 reduces the proliferation and migration of PASMCs. In addition, the m6A binding protein YTHDF2 in PASMCs are up-regulated significantly under hypoxia. YTHDF2 recognizes the PTEN mRNA modified by METTL3 and promotes the degradation of PTEN. The decrease of PTEN further leads to the excessive proliferation of PASMCs by activating the PI3K/Akt signaling pathway. Therefore, the METTL3/YTHDF2/PTEN axis plays an important role in the proliferation of PASMCs induced by hypoxia, which provides a new diagnostic and therapeutic target for the treatment of hypoxic PAH .The SETD2 catalyzes the trimethylation of lysine 36 on histone 3 (H3K36me3) and is involved in hypoxic PAH . The expThe roles of m6A methylation in hypertension have not been fully elucidated. However, the high-throughput sequencing show that the methylation of m6A is more abundant in the coding region, 3′UTR and 5′UTR of mRNAs, and the motifs of m6A are relatively conservative. Compared with the control, the mean abundance of m6A in spontaneously hypertensive rat pericytes is decreased. The specific distribution of m6A in the pathogenesis of hypertension suggests that m6A plays an important role in the pathogenesis of hypertension .METTL14 may be a potential target for clinical treatment of atherosclerosis. METTL14 can enhance the expression of FoxO1 by enhancing its m6A modification, and can also induce endothelial cell inflammation and atherosclerotic plaque formation . METTL14The importance of m6A in various physiological and pathological mechanisms is gradually emerging, and it also plays an important role in vascular calcification . Chen etThe level of m6A in primary cardiomyocytes is up-regulated with the development of cardiac hypertrophy . In the PIWI-interacting RNAs (piRNAs) are highly expressed in cardiac hypertrophy, but the underlying mechanism remains unclear. At present, a large number of mammalian mRNAs are modified by m6A, and the m6A is also an important posttranscriptional regulation mechanism for heart disease discovered in recent years . StudiesThe imbalance of epigenetic modifications and abnormal gene expression are important mechanisms of heart failure, and the m6A plays an important role in the development of heart failure. For example, model mice with cardiomyocyte knockout of RNA demethylase FTO have impaired cardiac function . About aThe FTO is closely related to cardiac contractile function. The decreased expression of FTO in the failing mammalian heart and hypoxic cardiomyocytes result in the increased level of m6A in RNAs . IncreasHeart failure with preserved ejection fraction (HFpEF) is a heterogeneous disease with complex pathogenesis . CompareAs a competitive endogenous RNA, MALAT1 regulates the hypoxia induced cardiomyocyte proliferation, apoptosis and cell cycle progression by regulating miR-200a-3p/PDCD4 axis . Heart fIn this review, the mechanisms, molecular structures, biological functions of five kinds of RNA methylations  and their effects on CVDs were summarized. RNA methylation “writers” catalyze the installation of m6A on RNAs, and “erasers” remove these modifications. Finally, “readers” of m6A affect the mRNA splicing, export, translation and degradation . In addiThe research progress of epigenetics and CVDs always lags behind the research in cancers. The mechanisms of epigenetics in cancer has reference value for the research of CVDs . RNA metRNA methylations have been confirmed to participate in the development of CVDs . RNA metin vivo research of animal models, develop the RNA methylation targeted therapy to provide new potential options for the treatment of CVDs. Fourth, combined with the exosomes or vesicles secreted by cells, explore the feasibility of RNA modifying factors encapsulated in 100 nm exosomes for therapy of CVDs.In the future, research on RNA methylations and CVDs should focus on the following aspects. First, a complex regulatory network model of single RNA modification in a single CVD should be constructed. With the deepening of research, gradually improve the network model to find new targets for disease diagnosis and treatment. Second, expand the sample size of clinical cases, repeatedly verify the experimental phenomenon to avoid errors in the experimental results. Thirdly, combined with the


Exosomes are natural nanocontainers actively secreted by the body’s cells and transmitting molecular signals of various types to recipient cells. Cellular mechanisms of exosomes’ biogenesis involve specific sorting of RNA for incorporation into them. As a result, the molecular composition of exosomes is closely related to the donor cell’s functional state, and this makes exosomes an important diagnostic and prognostic marker in a number of diseases . The ability of exosomes to transport biologically active molecules and to protect the cargo from degradation makes them nearly ideal candidates as delivery carriers of RNA in therapeutic or prophylactic regimes. Potential of exosomal surface functionalization enables improved targeting to specific organs, tissues and cells. However, the development of an effective technology for RNA’s loading into exosomes cannot be considered resolved. This review is focused on experimental data on the use of exosomes as vehicles for the delivery of therapeutic and prophylactic RNAs. We briefly consider the biogenesis and functions of exosomes, focusing on those biological properties that make them formidable candidates in the race to develop effective delivery carriers. Furthermore, we describe various techniques of cargo loading into exosomes. Prospects of exosomes application as therapeutic delivery system for siRNAs, miRNAs, and long RNAs are considered. RNA-based therapeutics and vaccines are rapidly progressing areas of medical science and making their first steps towards clinical implementation. Agents with potential medical applications include both short and long RNAs. Therapeutic short RNAs include siRNAs, miRNA mimics, and antisense miRNA antagonists ,2. PotenMessenger RNA vaccines have been developed recently as a new tool for immunotherapy for oncological diseases ,7. They Despite obvious advances in this area over the past few years, many challenges remain for RNA-based therapy and prophylaxis. For siRNAs and miRNAs, eventual off-target effects with silencing of unexpected genes are a concern, despite powerful bioinformatic tools for sequence design ,11. For Although early work was published demonstrating in vivo protein expression from naked, exogenously delivered mRNA template , it is nCurrently, there are a number of both viral and non-viral experimental solutions for RNA delivery to cells. Non-viral systems include various cationic polyplexes, lipid nanoparticles, and cell-penetrating peptides .Effective RNA delivery vehicles should meet multiple requirements. They should: bind RNA with high capacity and protect it from nuclease cleavage; be reasonably stable, biodegradable, and non-toxic for the organism; and should effectively pass through numerous biological barriers in the body .In recent years, exosomes have attracted the serious attention of researchers as a very promising nanocarrier for the delivery of short and long RNAs. In terms of cell biology, exosomes are extracellular vesicles secreted by cells, 30–120 nm in size, which mediate intercellular communication. When formed in the endosomal compartment, exosomes selectively include a multitude of biologically active host cell molecules  ,22. SubsIn this review, we analyze experimental data on the use of exosomes as vehicles for the delivery of therapeutic and prophylactic RNAs. We briefly consider the biogenesis and functions of exosomes, focusing on those biological properties that make them formidable candidates in the race to develop effective delivery carriers. Furthermore, we describe various techniques of cargo loading into exosomes. We then discuss recent publications on the application of exosomes as an experimental means of delivering siRNAs, miRNAs, and long RNAs. Prospects and current challenges in this area of medical biotechnology are covered.Unlike other types of extracellular vesicles  which bud directly from the plasma membrane , exosomeThe precursors of exosomes are formed as secondary invaginations of the endosomal membrane, with their subsequent detachment into the lumen of the endosome and the appearance of a multivesicular body [MVB], containing multiple intralumenal vesicles (ILV) . The molEndosomal sorting complexes required for transport (ESCRT) executes multiple events of cell membrane remodeling, resulting in reverse topology membrane scission ,36. ESCRAn ESCRT-independent pathway of exosome formation employs sphingolipids [ceramide], tetraspanin CD63, and heat shock proteins ,44.Like protein loading, RNA loading into ILVs also involves several mechanisms. Non-specific, random loading of cellular RNA can occur through RNA binding to the outer (cytoplasmic) surface of the MVB limiting membrane. Incorporation of these RNAs into ILVs is based on the affinity of RNAs to the raft-like regions in the outer layer of the MVB membrane. ,46. For Currently, molecular mechanisms of exosomal RNA sorting are not completely clear. In general, many aspects of RNA biology including RNA distribution and trafficking depend on interactions with multitude of RNA-binding proteins (RBP) ,50. VariThe involvement of several RBPs in the loading of RNA into exosomes has been experimentally demonstrated, including Annexin A2, heterogeneous nuclear ribonucleoproteins A2/B1 (HNRNPA2B1) , YBX1 5, ALIX 5, major vUpon completion of MVB formation, it either fuses with a lysosome , or is tKnowledge of the mechanisms of recipient cell exosomal uptake is of great importance for the development of exosome-based delivery vehicles. Clearly, natural exosomes are capable of preferentially binding specific target cell types and inducing specific phenotypic or biochemical changes. For instance, exosomes from primary neurons are internalized only by other neurons, whereas those from neuroblastoma cells are internalized predominantly by glial cells . SimilarExosomal internalization by recipient cells apparently occurs via several endocytic pathways, such as a clathrin-dependent pathway; a caveolin-dependent pathway; macropinocytosis; and a lipid raft-mediated pathway. Direct membrane fusion is also possible (reviewed in: ). While The essential steps of biogenesis, release and uptake of exosomes are summarized in Exosomes are natural containers which perform cell-to-cell transport of multiple biologically active molecules including several types of RNA. Progress in exosome purification methods , as It has been firmly established by extensive research ,89 that Crucially, exosomes overcome the immunogenicity issue, which is an insurmountable hurdle for many other delivery vehicles ,91. ExosExosomal surfaces can be engineered to enhance their targeting to certain cell types. For example, exosomal membrane protein Lamp2b has been fused to the rabies virus RVG peptide which is known to bind specifically to neurons. In result, RVG-exosomes acquired the ability to cross the blood–brain barrier in mice and deliver siRNA therapeutics to brain tissue . Similar−/− mice , during which molecules diffuse through the lipid bilayer of the exosome. This approach permits sufficiently effective penetration into exosomes for some therapeutically important hydrophobic substances, including doxorubicin , curcumiAlternatively, pore formation in the lipid bilayer for the entry of RNA into the exosome is possible. Several physical methods use such a mechanism. The most commonly used option for membrane pore formation is exosome electroporation, in which a mixture of exosomes and cargo RNA is pulsed with an electric field. Electroporation parameters require optimization depending on donor cell type. Typically, applied voltages range from 150 to 700 V ,115, witIn addition to electroporation, other simple methods have been used for the formation of pores in the exosomal membrane to load small RNAs, such as sonication  and freeFurther options are provided by: repeated extrusion of mixtures  through a syringe-based lipid extruder with 200 nm or 400 nm pore size ,134; or Another approach that has been successfully applied for loading RNA into exosomes is transfection of exosomes using commercial liposomal reagents , with formation of exosome/liposome hybrids. Exosome/liposome fusion occurs via co-incubation and can be further enhanced by repeated freezing-thawing of the mixture . The exoHowever, the exosome/liposome hybrid approach has also drawn criticism. After co-incubation, separation of exosomes from unbound lipid vesicles is either not carried out at all ,140, or The aforementioned physical and chemical methods deal with isolated exosomes. Cell-engineering methods involve delivery of the target RNA into donor cells, followed by incorporation of this RNA into exosomes during their biogenesis. Several cell-engineering approaches of this kind have been proposed.In the simplest case, cells are transfected directly with target RNAs: miRNA, ,147; siRTransfection of plasmids, or viral transduction, can be performed to express target RNAs in donor cells, followed by incorporation of these RNAs into exosomes. This approach can be implemented for both miRNA ,152,153 In the two cases above, the RNA  does not carry any exosome-guiding sequence. Apparently, its loading into the exosomes of the donor cell from the cytoplasm occurs through non-specific engulfment as part of secondary invaginations of the endosomal membrane during the MVB formation. With this in mind, the abundance (number of copies) of RNA cargo available for loading is critical. Expression plasmids and viral vectors typically provide a high level of transcription, while the cytosolic concentration of directly delivered RNAs  may be insufficient for its efficient packaging into exosomes of a donor cell. In addition, exogenous RNA delivered to the cytoplasm arrives at the general pool of cellular RNAs and inevitably enters cellular functional RNA pathways, such as gene silencing , protein translation (for mRNAs), and, finally, degradation pathways. This can affect the state of donor cells, including the production/composition of their exosomes. As known, degradation of exogenous RNA can be slowed down by introducing chemically modified nucleotides into it .3.To overcome the aforementioned limitations, more specifically, RNA sequence and a protein that binds and directs it to exosomes can be engineered. For example, a C/D box can be inserted into the 3’ UTR of the target mRNA, which is recognized by archaeal ribosomal protein L7Ae. In turn, the L7Ae sequence is fused with the C-terminus of the CD63 exosomal marker protein. Co-transfection of these two constructs provides highly specific targeting of the mRNA of interest to exosomes .A similar principle was implemented in the Targeted and Modular EV Loading (TAMEL) platform proposed by Hung and Leonard . In it, Various techniques for RNA loading into exosomes are summarized in The first results on successful exosome-mediated siRNA delivery to target cells and subsequent specific gene silencing in them were obtained a decade ago ,114. AlvIn recent years, advantages of exosomes as carriers have been repeatedly used experimentally in works on the delivery [both in vitro and in vivo] of therapeutically significant RNAs  aimed at treating a number of diseases, such as oncology, neurological and neurodegenerative diseases, and infections.Below, we consider the most substantial experimental results of in vivo delivery of therapeutically important RNAs by exosomes, in order to characterize the current state of the art.Successful in vivo experiments on the use of exosome-delivered therapeutic siRNAs have in most cases been aimed at various neurological diseases, including Huntington’s disease, Alzheimer’s disease and spinal cord injury, as well as tumors of various origins. In the case of neurological diseases, RVG-Lamp2b modified exosomes were predominantly used to overcome the blood–brain barrier (BBB) and blood–spinal cord barrier (BSCB) and to deliver siRNA to the brain and spinal cord. In oncological diseases, targeting of siRNA-containing exosomes to tumor tissue can be achieved by modifying them with the RGD-LAMP2b fusion protein. RGD peptide has a high affinity to integrin αvβ3 and specifically delivers exosomes to tumors in vivo . For canRecent data on exosomal delivery of therapeutic siRNA in vivo are summarized in Dysregulation of miRNA profiles is a key component of a wide range of diseases including those of a cardiovascular, infectious, oncological, or neurological nature. Compensation for these pathological disturbances is a promising therapeutic strategy. Unlike siRNAs, miRNAs have multiple targets in the genome to perform “fine tuning” of gene expression. Exosomes are natural carriers which transfer a multitude of donor cell miRNAs. These endogenous exosomal miRNAs themselves can be of high therapeutic significance. For example, MSC-derived exosomes contain more than 150 pre-miRNAs and miRNAs . Such exIn addition, exosomes from various cells (most often MSCs) with exogenously loaded miRNAs have been repeatedly tested experimentally to treat cancer and several neurological disorders, with proven amelioration in disease course. Although the huge potential of mRNA-based therapy is widely acknowledged, current experience in delivery of therapeutic mRNAs by exosomes is rather limited. The large size of mRNA significantly hampers its incorporation into exosomes, in comparison with small RNAs. There is one publication that deals with model mRNA encoding fluorescent protein, loaded into exosomes by exosomal-liposomal hybrid method .More therapeutically relevant, sophisticated cellular engineering techniques of specific mRNA design and targeting are typically used. Maugeri and co-authors delivered mRNA encoding human erythropoietin (hEPO-mRNA) to epithelial cells HTB-177 using LNP. Then, hEPO-mRNA was revealed in EVs secreted by these donor HTB-177 cells. After intravenous administration of hEPO-mRNA-containing EVs to mice, hEPO protein was detected in plasma and number of organs in 2 h after injection .In Kojima’s group work, catalase mRNA fused with C/Dbox for effective exosomal targeting was engineered . In thisE. coli enzyme HChrR6. This enzyme is capable of converting the prodrug (CNOB) into the cytotoxic drug (MCHB). In transfected cells, HChrR6 mRNA was loaded into HER2-targeting exosomes. When administered in vivo, these exosomes caused effective HChrR6 expression in tumor cells, with subsequent near-complete growth arrest of BT474 xenografts after CNOB prodrug treatment [In another study, exosomes aimed at HER2+ve breast cancer treatment were developed. For exosome targeting to the cancer cells, HEK293 cells were transfected with plasmid encoding EVHB chimeric protein, which contains high-affinity anti-HER2 scFv antibody. This protein is capable of being exhibited on the EV surface. These cells were also transfected with a plasmid-encoding modified reatment .For Shwannoma treatment, 293-T cells were stably transfected with plasmid encoding a cytosine deaminase (CD) fused with uracil phosphoribosyltransferase (UPRT) These enzymes activate “cell suicide” upon administration of the prodrug 5-fluorocytosine (5-FC), which is converted to 5-fluorouracil (5-FU). Exosomes released by these donor cells contained CD-UPRT mRNA. When injected into mouse shwannoma xenografts, in combination with systemic prodrug delivery, exosomes provided significant regression of these tumors .In a recent publication by Tsai et al., exosome-packaged mRNA encoding the S and N proteins of SARS-CoV2 was used to experimentally vaccinate mice. The results obtained by the authors indicate the post-vaccinal appearance of specific anti-S and anti-N antibodies in the blood plasma, as well as T cell responses to N and S proteins . These dThus, exosome-mediated delivery of mRNAs is of proven therapeutic relevance. At the same time, however, complexities present with loading and targeting technologies based on cell engineering complicate clinical implementation. Exosomes are natural nanocontainers actively secreted by the body’s cells, thereby transmitting molecular signals of various types to recipient cells. Cellular mechanisms of exosome biogenesis involve specific sorting of RNA and proteins for incorporation. As a result, the molecular composition of exosomes is closely related to the functional state of the donor cell; this makes exosomes important diagnostic and prognostic markers in a number of diseases . The ability of exosomes to protect their cargo from degradation in the extracellular environment, and to transfer it to recipient cells, makes them effective candidates for the delivery of RNA in therapeutic or prophylactic regimes. The possibility of exosomal surface functionalization enables improved delivery targeting.There is no doubt about the significant potential of exosomes as a carriers of gene therapy agents. In recent years, there has been an explosive growth in research interest in exosome-based drug delivery, and several clinical trials of exosome-encapsulated siRNAs are in progress currently. However, there are certain obstacles to the more active implementation of exosome technology in clinical practice.Despite their proven low immunogenicity, exosomes are always biologically active entities aimed at modulating recipient cell function. For example, tumor-derived exosomes prepare a niche for metastasis . In thisThe question of choosing the type of donor cells requires special attention. Exosomes are not biologically inert particles, but perform regulatory and communicative (including immunomodulatory) functions, and their activity depends on the type and functional state of parental cells. Currently, human MSC, immature DC, and HEK293-T cell lines are most often used as exosome donors. In the future, a “universal exosome donor” could be developed, which is optimal for packaging and delivery of various RNA cargoes. In this sense, of great interest is the work of Kojima and colleagues , who creClearly, further development of specific ligands that determine the dispatching of exosomes to tissues and target cells is necessary. In this regard, there are examples of striking success, such as RVG peptide-based targeting of exosomes to neuronal tissue  or GE11pThe development of a technology for loading of RNAs into exosomes cannot be considered resolved. Plain pore-forming approaches dealing with isolated exosomes are attractive due to their methodological simplicity; however, their usage has often resulted in uncontrolled aggregation and damage to the integrity of both the exosomes themselves and their cargo, especially long mRNA molecules. In general, cell engineering methods seem to be more promising, although currently expensive and difficult to scale.Exosome purification technology and its scalability is also challenging at the moment. It is well known that the vesicular secretome of the cell is heterogeneous. EVs of different types differ significantly in biogenesis, molecular composition, and functions. It is probable that some of them are “debris bags” for the cell, as was postulated in early studies, while others perform specific communicative tasks. At the same time, vesicles of different types overlap in size and buoyant density, and reproducibly obtaining completely purified homogeneous preparations of exosomes is currently extremely complicated. The most effective and specific purification method is the immunoaffinity technique; however, the high cost of antibodies makes it rather difficult to scale it for clinical application. Undoubtedly, further efforts of researchers are needed to optimize exosome purification technologies and their industrial scaling.Finally, more fundamental knowledge is needed regarding the biological roles of exosomes in the body including: kinetics; overcoming of (or limitation by) biological barriers; and molecular mechanisms of endosomal escape. The roles of multiple molecules delivered by exosomes have not been sufficiently studied. For example, the functions of a number of exosomal lncRNAs and circRNAs remain rather elusive, yet such understanding is obviously necessary to realize the therapeutic potential that exosomes represent.