One of the most significant discoveries resulting from the sequencing of the human genome is the realization that a large portion (over 85%) of the genome is transcribed into RNA, yet less than 2% of it encodes protein-coding genes. This non-coding RNA (ncRNA) constitutes several groups, including well-known examples like tRNA and rRNA, as well as small RNA, long non-coding RNAs (lncRNAs), and circular RNA (circRNA). Among these, lncRNAs are the most abundant group of non-coding RNAs produced by the genome. While many newly discovered ncRNAs are yet to be fully characterized and validated, it is believed that a significant proportion of them serve important functions—yet many of these remain unclear.The functions of ncRNAs are often influenced by factors such as their localization, sequence, and secondary structure. They can regulate protein expression and activity at various levels, leading to a diverse array of biological effects. Regardless of the specific type of ncRNA, their mechanism of action primarily involves interactions with macromolecules, including DNA, RNA, and proteins.There is increasing evidence supporting the interaction of long non-coding RNAs (lncRNAs) with DNA, as well as their role as transcriptional activators or repressors in gene regulation. LncRNAs have the ability to target specific regions of the genome for epigenetic modifications. They can directly bind to purine-rich double-stranded DNA sequences through Hoogsteen base pairing, forming RNA–DNA triplex structures . These cBioinformatics analysis has been utilized to predict the existence of numerous triplex-forming motifs in the human genome. Interestingly, these motifs tend to accumulate in gene regulatory regions, particularly in the promoter region . This suThe RNA–DNA triplex structure can regulate gene transcription in two main ways. Firstly, it can increase chromatin accessibility and facilitate transcription by altering chromatin conformation. Although this mode of regulation has been suggested based on bioinformatics analysis and preliminary experimentation, more in-depth experimental evidence is needed. Secondly, the RNA–DNA triplex structure can recruit transcription factors or epigenetic modulators, thereby influencing gene transcription in both directions. Two independent studies have shown that the vast majority of lncRNAs positively regulate gene expression through RNA–DNA triplexes, suggesting that the first mode of regulation is more predominant ,5.In our previous study, we proposed that the stable triplex structure formed by AP006284.1 lncRNA could alter chromatin conformation and promote the transcription of target genes. We hypothesized that this stable triplex structure weakens the interaction between DNA and histones, increasing the accessibility of chromatin and enhancing transcription , and it Studies have also shown that RNA–DNA triplex structures can recruit chromatin-modifying enzymes or affect chromatin remodeling. For example, the lncRNA PARTICLE forms a triplex structure with the promoter region of MAT2A, recruiting methyltransferases and polycomb repressor complexes to inhibit MAT2A transcription . AnotherOverall, the RNA–DNA triplex structure represents a fascinating mechanism by which lncRNAs can regulate gene expression. Further experimental studies are necessary in order to fully elucidate the functional significance and mechanisms of RNA–DNA triplexes in gene regulation.ncRNAs are also involved in post-transcriptional regulation through RNA–RNA interactions, where they interact based on the principle of Watson–Crick base pairing. ncRNAs regulate mRNA expression through various mechanisms, including affecting mRNA stability, splicing activity, modifications, capping, and translation efficiency. For example, lncRNAs can act as miRNA sponges, indirectly derepressing the expression of mRNAs targeted by the miRNAs. Additionally, ncRNAs can modulate translation by binding to ribosomes or mRNA transcripts during translation.One of the most well-known RNA–RNA interactions is the interaction between small RNAs, such as siRNAs and miRNAs, and the RNA-mediated silencing pathways. These interactions have shed light on eukaryotic gene regulation and revealed novel host defenses against viruses and transposons. MicroRNAs (miRNAs) are small, non-coding RNAs containing about 23 non-coding nucleotides that downregulate protein-coding transcripts by base pairing with their target mRNAs. They play crucial roles in developmental and pathological processes in both animals and plants . miRNAs miRNAs are typically incorporated into the RNA-induced silencing complex (RISC), where they guide the complex to target mRNAs through base pairing. AGO proteins in the RISC complex recruit factors that induce translational repression, mRNA deadenylation, and mRNA decay . miRNA-bAnother important RNA–RNA interaction is competitive endogenous RNA (ceRNA), which was first proposed by Salmena and colleagues . ceRNAs Overall, RNA–RNA interactions play crucial roles in post-transcriptional regulation, with ncRNAs acting as important mediators in these interactions.RNA–protein interactions are a common phenomenon that plays a crucial role in the cellular functions of non-coding RNAs (ncRNAs). Understanding the molecular mechanisms underlying the assembly and regulation of ncRNA–protein complexes is essential for comprehending their cellular functions. Compared to proteins, lncRNAs are generally larger, more complex, and prone to intermolecular interactions. Due to their length, ncRNAs can engage in both short- and long-range interactions, resulting in complex folded structures and the recruitment of various proteins.On the one hand, proteins play crucial roles in regulating RNA transcription, processing, and stability in order to ensure proper gene expression. They are involved in multiple steps of RNA metabolism, contributing to the intricate network of gene regulation. On the other hand, it is important to note that ncRNAs also have an impact on protein function. They interact with proteins through their complex tertiary structures, acting as scaffolds or molecular chaperones. These interactions play a pivotal role in the recruitment and assembly of protein complexes, as well as the regulation of their activities. As a result, ncRNAs are able to perform a wide range of biological functions and contribute to various cellular processes . trans–regulation). In the cytoplasm, they can affect protein assembly and activity by forming complexes with proteins, participating in various biological processes such as signal transduction and enzyme activity. For example, Huarte’s research revealed that LincRNA-p21 can repress transcription by physically associating with hnRNP-K, which is required for the proper genomic localization of hnRNP-K at repressed genes and regulates p53-mediated apoptosis [The proteins that ncRNAs regulate are distributed throughout the nucleus and cytoplasm. In the nucleus, ncRNAs can act as scaffolds for transcription factors or chromatin remodeler proteins, regulating transcription or chromatin accessibility, respectively , recruiting and sequestering RBPs, inhibiting their activity, or altering their localization in the cytoplasm and nucleus . The assIn recent years, there has been a growing recognition of the role of proteins in undergoing phase separation and phase transition, which are involved in important physiological and pathological processes. RNA, although traditionally considered a “supporting player,” has the ability to influence the phase transition of proteins through its interactions with them. While RNA has mostly been studied in its supportive role in phase separation and phase transition, it has the potential to actively drive these processes. A recent study by Lu and colleagues has shed light on the ability of RNA molecules with expanded CAG repeats (eCAGr) to undergo sol–gel phase transitions. This cytoplasmic RNA gelation process sequesters eEF2, leading to disruptions in global protein synthesis. This finding highlights the potential of RNA to actively participate in the phase separation and phase transition processes, exerting a significant impact on cellular activities .These insights demonstrate that RNA’s involvement in phase separation and phase transition is not limited to a mere supporting role. It possesses the potential to drive these processes and play a more significant role than has been previously recognized. Further research in this area will help us to better understand the intricate interplay between RNA, proteins, and phase separation, contributing to our knowledge of cellular function and dysfunction.In summary, ncRNAs regulate diverse biological processes and play regulatory roles in various biological processes and diseases. They achieve their functions through interactions with DNA, RNA, and proteins. LncRNAs can regulate gene expression at both the transcriptional and post-transcriptional levels, and participate in the formation and activity regulation of protein complexes. The interplay between ncRNAs and macroproteins is vital for organism development and the coordination of biological processes, including metabolism, the maintenance of genome integrity, immune responses, and disease and stress responses.


Escherichia coli, at a single‐nucleotide resolution. A strictly linear relationship is observed between the RNA pyrophosphohydrolase processing ratio and scores assigned to the first three nucleotides of the primary transcript. Stem‐loops associated with PNPase targetomes exhibit a folding free energy that is negatively correlated with the termination ratio of PNPase at the 3′ end. More than one‐tenth of the RNase E processing sites in the 5′‐untranslated regions（UTR） form different stem‐loops that affect ribosome‐binding and translation efficiency. The effectiveness of the SRPS elements is validated using a dual‐fluorescence reporter system. The findings highlight a multi‐layer and quantitative regulatory method for optimizing the stoichiometric expression of genes in bacteria and promoting the application of SRPS in synthetic biology.Selective RNA processing and stabilization (SRPS) facilitates the differential expression of multiple genes in polycistronic operons. However, how the coordinated actions of SRPS‐related enzymes affect stoichiometric regulation remains unclear. In the present study, the first genome‐wide targetome analysis is reported of these enzymes in  The selective RNA processing and stabilization (SRPS) facilitate differential expression of multiple genes in polycistronic operons. The discovery presents a multilayer, quantitative, and ubiquitous posttranscriptional regulatory mechanism as well as elements to precisely optimize gene expression stoichiometry. Moreover, this finding suggests a novel strategy of ‘polycistronic operon plus SRPS elements’ to address the challenge of ‘balanced expression of multiple genes in chassis cells’ in synthetic biology. However, there is also a need for the introduction of multiple genes encoding metabolic pathway enzymes into chassis microorganisms for the synthesis of natural and synthetic products. To prevent the accumulation of hazardous intermediates or bottlenecks that result in growth inhibition or inadequate yield, genes must be expressed at adequately balanced levels to produce these compounds at commercially relevant levels. Similarly, because excess subunits that do not have binding partners may misfold or aggregate and drive proteotoxicity, manipulation of multi‐subunit proteins  frequently necessitates the coordinated expression of several genes to produce subunits at the appropriate stoichiometries. A key challenge in synthetic biology is the development of precisely regulated and well‐characterized molecular components to construct optimal biological pathways or functioning protein complexes. It is nearly impossible to predict the strengths of promoters and ribosome‐binding sites (RBSs) required to balance and coordinate the expression of multiple genes.Natural products are compounds with great value in the biological, pharmaceutical, and industrial fields. However, many difficulties are encountered when culturing the original strains of natural products. Synthetic biology, which involves cloning synthetic gene clusters into chassis cells, is an important method for synthesizing natural products. Synthetic gene clusters are often cloned into host cells such as products.2, 3 is a practical strategy to simultaneously regulate several genes. Moreover, it reduces noise in stochastic gene expression, facilitates complex assembly, and minimizes the demand for gene transcriptional resources. The relative expression of each open reading frame (ORF) in the operon is then set by altering post‐transcriptional processes such as selective RNA processing and stabilization (SRPS), where the primary mRNA transcribed as an operon is first processed by nucleases into segments, and stability variations among the segments contribute to differential gene expression. Unlike transcriptional termination, which generates only a staircase‐like transcript abundance (TA) pattern, SRPS leads to more complex TA patterns. For example, it has been reported that 12 genes within the Clostridium cellulolyticum cip‐cel operon, which is regulated by SRPS, exhibit highly skewed TAs (ratios of 100:110.2:8.6:8.1:38.1:5.0:3.9:2.0:2.6:2.2:3.2:4.7). SRPS has been reported in several operons of gram‐negative and gram‐positive bacteria. The differential RNA sequencing (RNA‐seq) technique, which has been used to unveil the first global landscape of SRPS regulation in C. cellulolyticum, suggests that SRPS is a conserved global regulatory mechanism. However, the quantitative regulation of TA by SRPS remains unknown, which seriously hampers its application in synthetic biology.Organizing multiple related genes into operons, as prokaryotes naturally do,10 is  and are divided into two main classes: endoribonucleases (endoRNases) and exoribonucleases (exoRNases), which regulate RNA stability by cleaving the RNA internally or attacking RNA from either its 5′ or 3′ end, respectively. Although the transcription and translation processes may be quite similar in different bacteria, their RNase activities are different. EndoRNases initiate the SRPS. RNase E, an important endoRNase in E. coli, with orthologs widely distributed in many species, including bacteria, cyanobacteria, and plant chloroplasts. However, there is no ortholog of RNase E in Bacillus subtilis RNases Y and J play critical roles in regulating the abundance of mRNAs in B. subtilis. Besides, RNase III is a unique endoRNase that shears RNA within double‐stranded regions. Furthermore, exoRNases play a complementary role by rapidly degrading decay intermediates. ExoRNases are divided into two main categories: 3′‐to‐5′ and 5′‐to‐3′ exonucleases. There are several 3′‐to‐5′ endoRNases  in E. coli. However, there is no 5′‐to‐3′ endoRNase in E. coli. The first 5′‐to‐3′ endoRNase, RNase J, was identified in B. subtilis. The RNase J family of proteins is widely distributed in archaeal and bacterial species. For example, RNase J in Synechocystis PCC 6803 participates in the response to environmental stress.Ribonucleases (RNases) are key players in SRPS,29 andE. coli is an important model microorganism that is widely used as a host for the biosynthesis of specialty chemicals in pharmaceutical, chemical, and agricultural applications. The model of the SRPS process in E. coli is as follows: The primary endoRNase that initiates the SRPS process in E. coli is RNase E (encoded by rne), whose endonucleolytic activity at the internal sites is greatly enhanced as the 5′‐triphosphate of the transcript is converted to 5′‐monophosphate by RNA pyrophosphohydrolase . Several 3′‐to‐5′ exoribonucleases  then digest the products originating from the initial cleavage. Among these, the stem‐loop structure plays a key role in RNA stability; it is generally located in the 3′‐untranslated region and 5′‐UTR of mRNA, and its stability affects the processing efficiency of nucleic acid exonucleases. The stability of the stem‐loop is determined by ΔG. With the emergence of RNA‐seq techniques that allow for the global detection of RNase cleavage sites, several targetomes of endoRNases and exoRNases have been determined, such as RNase E in Salmonella enterica; RNase Y in Streptococcus pyogenes,Staphylococcus aureus, and B. subtilis; and 3′‐to‐5′ exoRNases in S. pyogenes. Nevertheless, the genome‐wide targets of RppH, endoRNases, and exoRNases in E. coli and the mechanisms by which these enzymes interact to quantitatively regulate gene expression have never been investigated.E. coli str. K‐12 substr. MG1655 as a model and its SRPS‐related gene mutants (the Δrne‐3071(ts), Δpnp, Δrnb, and Δrnr strains; we used published transcriptome data for these), we first revealed the genome‐wide targetomes of these enzymes in E. coli at a single‐nucleotide resolution, by integrating the methods of simultaneous 5′ and 3′ end sequencing (SEnd‐seq), identification of specific cleavage positions . Following that, we revealed the interplay between RppH, endoRNase (RNase E), and exoRNases . We quantitatively characterized SRPS processes and depicted a quantitative regulation model of SRPS in E. coli. At the post‐transcriptional layer, the process efficiency  of RppH is regulated by the first three nucleotides at the 5′ end of the primary transcript, and thus determines the cleavage efficiency of RNase E by the cascade; the stability of the stem‐loops formed by the 3′ nucleotide sequences of the processed mRNA fragments positively affects the termination of the 3′‐to‐5′ exoRNase activities. These synergistic actions collectively modulate the TA in E. coli. At the translational layer, different endoRNase cleavage sites in the 5′‐UTR lead to differential stem‐loop formation in the 5′‐UTR, which affects ribosome‐binding and thus modulates translational initiation efficiency. The effectiveness of these SRPS elements was validated using a dual‐fluorescence reporter system. To further explore and utilize SRPS elements, we constructed the first database of prokaryotic SRPS sites . Thus, our findings revealed the molecular mechanism of SRPS and identified a multi‐layered, quantitative, and ubiquitous regulatory mechanism. The quantification of these SRPS elements allows for their application in precisely optimizing the stoichiometric expression of target genes in synthetic biology.Using r these),48 we 22.1E. coli, we first detected their genome‐wide targetomes (SRPS enzymes)  in the SEnd‐seq data, 666 positions processed by RppH were identified  were identified. In addition, positions 2604, 1685, and 374 were trimmed using PNPase, RNase II, and RNase R, respectively  upstream of the RppH targetome Figure . These rto 5′‐Pi.50rne ends of the RNase E targetome. First, there was a conserved motif “RN↓WUU”  around the 5′ rne end, which was in accordance with previous findings  upstream of the 3′ rne end, indicating the existence of a putative RNA structure  PCR . A total of 20.1% of 3′ rne ends corresponded to 3′‐to‐5′ exoRNase trimming start positions, while 70.6% of 3′ rne ends corresponded to stop positions  between the minimum MFE values of the 3′ rnb start and stop sites , indicating that most targets of PNPase were fully degraded. Here, the 5′ end of the short fragments, which are generated by an endoRNase and fully degraded from its 3′ end by PNPase, was identified as the 5′ pnp start , as compared to 5′ rnb (n = 134) or 5′ rnr (n = 147) start sites. In total, 125 5′ pnp start sites were located 50–300 nt upstream of the 3′ pnp start sites  The transcript was first sheared by RNase E, following which the 3′‐to‐5′ exoRNases digested the transcript from the exposed 3′ end and stopped at the 3′ d Figure . In the d Figure .2.3 we concluded that 588 polycistronic operons in E. coli utilized transcript unit information. To investigate the effects of SRPS enzymes on polycistronic transcripts, their processing positions inside each polycistronic operon were determined (Table rne end and 3′ rnr stop site (over 80%)  Table . These f2.4in vitro studies and has a sequence preference for the 5′ ends of its substrates. To quantitatively investigate the function of RppH in SRPS, we evaluated the relationship between the first three nucleotides of TSS and the ratio of transcript processing by RppH. We assigned an appropriate score to the first three RNA nucleotides at RppH processing sites based on sequence preferences in vitro and observed a noticeable positive correlation  than that for 5′‐PPPi RNA.While RppH requires at least two unpaired nucleotides, it prefers three or more nucleotides, as determined by several G of the stem‐loop as determined by the 3′ end sequence varied widely, which quantitatively determined the associated TA ratio in C. cellulolyticum. In addition, the stem‐loop structure protects transcripts from degradation by 3′‐to‐5′ exoRNases such as PNPase and RNase II in E. coli. In the present study, we investigated whether this quantitative relationship exists in E. coli and the underlying practical molecular mechanism. First, we selected 42 3′ pnp stops in which there was a 3′ pnp start located less than 300 nt downstream. Second, the correlation between the end ratio at the 3′ pnp stop and the ΔG of stem‐loops upstream of the same position was assessed, which was negative  was exposed when RNase E was cleaved at the position closest to the 5′ boundary of the downstream ORF  of the 5′ eno, bcp, and srlD) were selected to test this hypothesis   ligated to mcherry resulted in higher fluorescence intensity. All three cases showed that the mCherry fluorescence intensity with a shorter 5′‐UTR was higher than with a longer 5′‐UTR (UTR‐2) both in the WT and the Δrne‐3071(ts)  strain. The probable reason would be the partial shearing of UTR‐2 by the RNase E in WT, which resulted in a short UTR and therefore induced a higher expression. In summary, RNase E cleavage at different positions on the 5′‐UTRs regulates their translation efficiency.Three examples  of RppH is determined by the first three RNA nucleotides at the 5′‐terminus. After the initial endonucleolytic cleavage by RNase E, which generates SRPS intermediates with 5′‐monophosphate ends, RNase E can proceed in the 5′‐to‐3′ direction, generating additional processed transcripts. Because the affinity of RNase E for 5′‐monophosphate RNA is significantly higher (over 20‐fold) than that for 5′‐triphosphate RNA, the first three RNA nucleotides on the 5′‐terminus of the primary transcript determine the RNase E cleavage efficiency by the cascade. The RNase E‐processed transcripts are then degraded by exoRNases , and the stability of the stem‐loop formed by 3′ nucleotide sequences on RNase E‐processed transcripts determines the termination efficiency of exoRNases at the 3′ terminus. This synergistic action ultimately modulates the TA in E. coli. At the translational layer, cleavage of the 5′‐UTR by RNase E leads to the formation of different stem‐loops, which affects the affinity of the RBSs for the ribosome and thus modulates the translation efficiency.Based on the above conclusions, we propose the following quantitative regulatory model for SRPS in hate RNA,38 the2.7Figure G values of −25.4 (stem‐loop1) and −9.3 (stem‐loop2) kcal·mol–1 were individually inserted between the reporter genes gfp and mcherry, to assess their effects on exoRNase processivity. To assess the influence of RNase E cleavage on the TA, the RNase E cleavage site was located between the stem‐loops and mcherry. When the RNase E cleavage site was inserted, the more stable stem‐loop  significantly enhanced the expression of the gfp that was located upstream of the stem‐loop  and stem‐loop2 (Y2) expanded  . Because a higher TSS score corresponds to a higher ratio of transcripts processed by RppH, the difference in gfp expression caused by the stability of the stem‐loop is related to the processing efficiency of RppH. This could be attributed to the fact that the more the gfp transcripts were processed by RppH, the more they were cleaved by RNase E, and the more their stem‐loops at the 3′‐terminus were exposed. The gfp transcript was swiftly digested by 3′‐to‐5′ exoRNases without a stable stem‐loop, resulting in minimal gfp expression. Additionally, the mCherry fluorescence intensity was affected by the stem‐loops, resulting in a significant change in the ratio of GFP to mCherry  and was unaffected by RppH processing . The ProSRPSite database provides genome‐wide processing sites of various SRPS‐related enzymes from diverse species, such as RNase E in E. coli and S. enterica; RNase Y in S. pyogenes, B. subtilis, and S. aureus; and 3′‐to‐5′ exoRNases in E. coli and S. pyogenes. Moreover, the RNA processing sites of unknown enzymes identified using differential RNA‐seq in several species were included in the database. The data from ProSRPSite are beneficial for elucidating the mechanisms of SRPS in different species, and their availability should accelerate the application of SRPS in synthetic biology.To further explore and utilize SRPS elements, we constructed the first database of prokaryotic SRPS sites  reducing noise in stochastic gene expression, 3) facilitating protein complex assembly, and 4) minimizing the demand for gene transcriptional resources, it also presents a potential conflict between the equimolar stoichiometry of transcripts within an operon and non‐equimolar stoichiometry of enzymes or subunits that are frequently necessary. One strategy employed by the cell to resolve this conflict is SRPS. Although we understand many of the major processes involved in SRPS, we still lack a quantitative predictive understanding of the gene expression stoichiometry, which severely limits the applications of SRPS elements in synthetic biology.Balanced expression of multiple genes in chassis cells is a central challenge in synthetic biology. Although a polycistronic operon offers the advantages of 1) regulating several genes simultaneously,11 2) E. coli, at single‐nucleotide resolution  located near the processing sites , which differed from that of RNase Y, with the preference of a G at position −1 relative to the cleavage site in S. pyogenes. We then employed an RNA‐seq‐based comparative approach to juxtapose the RNase E‐dependent RNA 3′ ends with 3′‐to‐5′ exoRNase targetomes and discovered that the RNase E‐dependent RNA 3′ ends were mostly derived from PNPase trimming and RNase II nibbling following RNase E processing  carried by different stem‐loops  more quickly and efficiently. Studies have shown that programmed RNA decay can modulate synthetic circuit resource allocation. Moreover, translation is one of the most energy‐consuming processes in any cell (related processes account for more than 40% of the cell's energy budget) and different genes in the same transcript can have translation efficiencies that differ by up to 100‐fold. The translational initiation rate modulated by SRPS, rather than the elongation rate, is regarded as a determinant of translation efficiency.The SRPS mechanism has the following characteristics: Compared to other regulatory mechanisms, such as engineered promoters,62 proG) of the stem‐loops formed at the 3′ end of the RNA fragment and the degradation termination rate of the exonuclease in the stem‐loop , which not only provides genome‐wide SRPS sites of various enzymes in diverse species but also lays a solid foundation for the application of SRPS in synthetic biology. Our discovery presents a multi‐layer, quantitative, and ubiquitous post‐transcriptional regulatory mechanism as well as elements to accurately optimize gene expression stoichiometry. In addition, it provides a solution for generating “polycistronic operon plus SRPS elements”, to overcome the classic challenge faced by synthetic biology researchers, that is, “balanced expression of multiple genes in chassis cells”.In conclusion, we present the first single‐nucleotide resolution genome‐wide targetomes of SRPS‐related enzymes in 4−1, respectively.The strains and plasmids used in this study are listed in Table pnp from E. coli MG1655 were generated using the Wanner method. The primers and plasmids used are listed in Tables pnp gene were ligated with the Kan resistance gene by means of PCR, using pCL1920 as the template and the primers pcl1920‐pnp‐F/R. The PCR product thus obtained was electrotransformed into MG1655 cells. The transformants were selected based on their Kan resistance and verified by means of PCR and sequencing. For complementation, the pnp genes were cloned into linearized pCL1920 using an In‐Fusion HD Cloning Kit . The correct plasmid was then transferred into the corresponding deletion strain.Mutants of Δr method.73 The Subsequently, the processed paired end reads of SEnd‐seq were extracted from the total library by searching for the special 5′‐adapter ‘AAACTCTCCACGTNNNN’. The total and processed paired end reads of SEnd‐seq were mapped to the reference E. coli genome NC_000913.3 using Hisat2 (v2.1.0). Only paired end reads that were uniquely mapped to the same strand and had an insert length of <10000 nt were used for further analysis. The paired end RNA‐seq reads were mapped to the same reference genome. Gene expression was determined using FeatureCounts (v1.6.4) and the FPKM for each gene was calculated using the following formula:The list of select SEnd‐ and RNA‐seq results is shown in Table 48 SubFinally, the average FPKM for each gene calculated from all replications was used for further analysis.i RNA) was ligated to a special 5′‐adapter using T4 RNA ligase I , while the primary RNA (5′‐PPPi RNA) was not. A custom shell script was then used to search for adaptor‐labeled reads in the total library, thereby specifically extracting processed RNA as a processed library. The parameters for identifying the processing positions of RppH, which refers to the method for identifying the TSS using SEnd‐seq data, were as follows: in the alignment results of processed libraries, 1) only positions with >3 reads starting at this position, with an increase of at least 30% in read coverage from its upstream to its downstream were retained; 2) Candidate processing positions within five bases in the same orientation were clustered together, and the position with the largest number of reads was used as the representative processing position; 3) The positions were retrieved from at least two samples; 4) Only processing positions located within 5 nt of the identified TSS were defined as the RppH processing sites, while the others were determined to be processed by other enzymes , while end‐counts with a log2‐fold change  ≥1 and false discovery rate <0.05 were counted. Additionally, these positions were further filtered using two parameters: the proportion of ends at a position in the WT strain was 1) counted as the number of reads starting or ending at this position divided by the coverage at this position, which was set to ≥2%; and 2) threefold the proportion of ends in Δrne. Finally, positions that overlapped with those processed by the other enzymes described above were identified as the 5′ rne end, while there was no more restriction for the 3′ rne end. When at least two consecutive positions were identified within a window of 5 nt, the position with the highest proportion of ends in the WT was chosen for further analysis , based on the criteria: log2FC ≥1 or ≤−1 and false discovery rate <0.05. Subsequently, the positions with a proportion of ends in the WT of ≥2% and a ratio between the WT and Δexornase proportions of ends of ≥3, or positions with a proportion of ends in the Δexornase of ≥2% and a ratio between the Δexornase and WT proportions of ends of ≥3 were further analyzed. Finally, the 3′ ends of the transcripts that were more abundant in the WT were identified as 3′ exornase stops. The 3′ and 5′ ends that were more abundant in the Δexornase strain were identified as the 3′ and 5′ exornase start sites, respectively. Similarly, when there were at least two positions in a window of 5 nt, the position with the highest proportion of ends in the reference strain was used for further analyses  using a sliding window of 50 nt, with a 100 nt center at the processed positions. The average MFE was calculated for each position in the 100 nt window around the processed positions.Nucleotide motifs surrounding the processing positions of SRPS enzymes were generated using WebLogo (v3.6.0). RNAfold (v2.4.14) was used to calculate the MFE (Δrne end was compared with the 3′ exornase stop (trimming stop positions of exoRNases), which is located less than 5 nt downstream or upstream of the 3′ rne end. Second, the 3′ rne end, which corresponds to the 3′ exornase stop, was compared with the 3′ exornase start (trimming start positions of exoRNases) located downstream. The maximum distance between the trimming stop and start positions was set to 300 nt for PNPase and RNase R, and 10 nt for RNase II. Third, the 3′ rne end was compared with the 3′ exornase start, allowing a ±5 nt shift. Finally, the 5′ rne end was compared with the 3′ exornase start located less than 10 nt upstream. Additionally, two fragments generated by RNase E were detected (the 5′ rne end was located less than 300 nt upstream of the 3′ rne end). These comparisons were performed using AWK software (v4.0.2).The processing positions of RNase E were compared with those of the exoRNases. First, the 3′ The processing ratio of the RppH processing sites was calculated using the following formula:The sum of the processed 5′ ends around the processed positions was represented as the sum of the 5′ end‐counts, which were retrieved from the alignment results of the processed paired end reads from 4 nt upstream to 4 nt downstream of the processed positions. The sum of the 5′ ends around the processed sites was calculated in the same manner; the only difference was that the 5′ end‐counts were retrieved from the alignment results of the total paired end reads. The average processing ratios of all the replicates for each position were calculated. Finally, the average processing ratio of the processed positions was calculated with the same first three nucleotides, and the correlations between these processing ratios and the sum of the scores of the first three sequences. Plots were generated using OriginPro 2020 software .42 3′ pnp stop sites that paired with 3′ pnp start were selected, to calculate the end ratios of these positions using the following formula:pnp stop site to 5 nt downstream of the 3′ pnp start site that was paired with the 3′ pnp stop site. The 3′ ends of the transcripts around the 3′ pnp stop site were counted as the sum of the 3′ ends of the 11 nt centered on the 3′ pnp stop site. The average MFE of the sequences was then predicted located 50–100 nt upstream of the 3′ pnp stop site. Plots were generated using OriginPro 2020 software.The total 3′ ends of the transcripts were counted as the sum of the 3′ ends 5 nt upstream of the 3′ The FPKM of each gene was calculated as described above. If the FPKM values of two adjacent genes within the same operon were both ≥2 and their ratio was ≥3 or ≥1/3, or if one of them was <2 and the absolute value of their difference was ≥10, the operon was determined to be a complex‐operon exhibiting a highly skewed TA landscape.600 values of 2.5–3, were collected and total RNA was extracted from them using the TaKaRa MiniBEST Universal RNA Extraction Kit . Total RNA was separated on 1% agarose‐formaldehyde gels and transferred onto nylon membranes  using a Bio‐Rad vacuum blotter . RNA was UV‐crosslinked to the membranes and DIG‐labeled RNA probes were generated for detection, using the DIG Northern Starter Kit . Hybridization was performed overnight at 68 °C, in Easy Hyb buffer. Target bands were visualized by means of chemiluminescence using CDP‐Star. A RiboRuler High Range RNA Ladder  was used to assess the size of the RNA transcripts and 5S rRNA was used as a loading control. Each northern blot was performed in triplicates.Cultures of MG1655 and its mutants at log‐phase, with ODg, 4 °C for 10 min. Total RNA was isolated using the TaKaRa MiniBEST Universal RNA Extraction Kit , and the RNA concentration was verified using Qubit 4 . cDNA was synthesized using the PrimeScript RT Reagent Kit with gDNA Eraser . A SYBR Premix Ex Taq II Kit  was used for qRT‐PCR and the reactions were run on a LightCycler 480 II Sequence Detection System . The primers used to amplify the target genes are listed in Table gapA was used as the reference gene. The results were analyzed using the 2−ΔΔCT method.Cells at log‐phase were harvested by means of centrifugation at 10000×rne end in the 5′‐UTR and located less than 200 nt downstream of the TSS. Alternatively, the operon that covered at least two 5′ rne ends in the same IGR, and located within a distance of <200 nt between adjacent 5′ rne ends. Second, all of these transcripts with different 5′‐UTR lengths were required to exist in the total RNA library.The operons satisfying the following criteria were retrieved: First, the operon that covered at least one 5′ Total RNA (2 µg) was used to synthesize first‐strand cDNA with the cDNA synthesis primer SP1, using Transcriptor Reverse Transcriptase . The product was purified using the High Pure PCR Product Purification Kit . A homopolymeric A‐tail was added to the 3′ end of the first‐strand cDNA using recombinant terminal transferase and dATP. Nested PCR was performed using the cDNA as a template, to obtain gene‐specific products. The PCR products were cloned into the pMD19‐T vector  for further sequencing.lac promoter, TSS, 5′‐UTR, and mCherry. The TSS was verified using 5′ RACE. The 5′‐UTRs of bcp, eno, and srlD were fused with mcherry, cloned into pBBR1MCS‐2, and transformed into the RNase E inactivation strain. pBBR1MCS‐2 is a low‐copy number plasmid with a lac promoter and T7 terminator. The fluorescence intensity of mCherry was detected at excitation and emission wavelengths of 580 and 610 nm, respectively, using a Synergy H1 microplate reader . The final fluorescence intensity was normalized to the OD600 value.To assess the effect of the 5′‐UTR on the translation efficiency of downstream genes, an mCherry reporter system was constructed  and mcherry (encoding red fluorescent protein) genes were expressed in a single operon with a lacI promoter. Different stem‐loops harboring intergenic regions were introduced into the site between the two genes. The RNase E cleavage site was located between the stem‐loops and mcherry, to assess the influence of RNase E cleavage. The fluorescence intensity of mCherry was detected at excitation and emission wavelengths of 580 and 610 nm, respectively, while that of GFP was detected at excitation and emission wavelengths of 482 and 515 nm, respectively.A dual‐fluorescence reporter system was constructed to evaluate the effects of the stem‐loop and RNase E cleavage sites. The Analysis of the Correlation Between the Processing Ratio and First Three Nucleotide Sequences of the Transcript In Vitro which resulted in eight TSSs with different scores . The primers used for this are listed in Table The first and second A bases after the TSS of the lacI promoter in the plasmid pBR2‐gfp‐loop1/2(+/−)‐mCherry were mutated to C, G, or T using the QuikChange site‐directed mutagenesis method,74 whiThe measurements were performed in triplicate and independently repeated at least three times, except otherwise described. Data were given as mean ± standard deviation (s.d.). Statistical analysis was performed with GraphPad Prism 8.0 software . Two‐tailed Student's t‐test was applied to detect differences between the two groups with a single independent factor. P < 0.05 was considered statistically significant.The SEnd‐ and standard RNA‐seq datasets from this study can be found in the National Center for Biotechnology Information Gene Expression Omnibus, under Accession Number GSE117737.The authors declare no conflict of interest.D.L., H.L., and Y.W. contributed equally to this work. R.H. designed research; D.L. and J.C. performed experiments; H.L. and R.H. analyzed data; D.L. constructed the database; R.H., H.L., and D.L. wrote the paper.Supporting InformationClick here for additional data file.Supporting Table 1–2Click here for additional data file.Supporting Table 3Click here for additional data file.Supporting Table 4Click here for additional data file.Supporting Table 5Click here for additional data file.Supporting Table 6Click here for additional data file.Supporting Table 7‐8Click here for additional data file.


Adar1 p110/Aadr2 double KO mice. However, the molecular mechanisms underlying RNA editing of AZIN1 remain unknown. Here, we showed that Azin1 editing levels were increased upon type I interferon treatment, which activated Adar1 p150 transcription, in mouse Raw 264.7 cells. Azin1 RNA editing was observed in mature mRNA but not precursor mRNA. Furthermore, we revealed that the two coding sites were editable only by ADAR1 p150 in both mouse Raw 264.7 and human embryonic kidney 293T cells. This unique editing was achieved by forming a dsRNA structure with a downstream exon after splicing, and the intervening intron suppressed RNA editing. Therefore, deletion of a nuclear export signal from ADAR1 p150, shifting its localization to the nucleus, decreased Azin1 editing levels. Finally, we demonstrated that Azin1 RNA editing was completely absent in Adar1 p150 KO mice. Thus, these findings indicate that RNA editing of AZIN1 coding sites is exceptionally catalyzed by ADAR1 p150 after splicing.Adenosine-to-inosine RNA editing is catalyzed by nuclear adenosine deaminase acting on RNA 1 (ADAR1) p110 and ADAR2, and cytoplasmic ADAR1 p150 in mammals, all of which recognize dsRNAs as targets. RNA editing occurs in some coding regions, which alters protein functions by exchanging amino acid sequences, and is therefore physiologically significant. In general, such coding sites are edited by ADAR1 p110 and ADAR2 before splicing, given that the corresponding exon forms a dsRNA structure with an adjacent intron. We previously found that RNA editing at two coding sites of antizyme inhibitor 1 (AZIN1) is sustained in  Post-transcriptional modifications increase protein diversity from the limited information encoded in the genome. Adenosine-to-inosine RNA editing, which occurs in dsRNA structures, is such a modification with an estimate of more than 100 million sites in human transcripts , 3. AdenAdar1 KO mice (Adar1−/− mice), Adar1 p150–specific KO (Adar1 p150−/−) mice, and Adar1 knock-in mice harboring an editing-inactive E861A point mutation (Adar1E861A/E861A mice) all show embryonic lethality with increased expression of IFN-stimulated genes (ISGs) (Ifih1-encoded melanoma differentiation–associated protein 5 (MDA5) or its downstream mitochondrial antiviral signaling protein (MAVS) (Adar1 p110–specific KO (ADAR1 p110−/−) mice show a high mortality rate during their early postnatal days without overexpression of ISGs, which are caused by RNA editing–independent functions (Adar2 KO (Adar2−/−) mice die within 3 weeks after birth because of progressive seizures (s (ISGs) , 21, 22.n (MAVS) , 24. In unctions . In addiseizures , 26. Sucseizures .2+ permeability of the receptor , were responsible for type I IFN treatment in vitro. For this purpose, we stimulated mouse macrophage-like Raw 264.7 cells, which express both ADAR1 p110 and p150 at a comparable level, with IFNβ1 (B). As expected, the expression of ADAR1 p150, which is driven by the type I IFN–inducible promoter, was significantly increased, whereas the expression of ADAR1 p110 was unaffected (B–D). This treatment did not affect the expression of Azin1 mRNA and AZIN1 protein (E and F). We subsequently examined editing levels at two editing sites of mature Azin1 mRNA. Although the editing ratios at E/E and S/G sites were ∼12% and ∼33%, respectively, under normal conditions, the editing levels of these two sites significantly increased to ∼22% and ∼51%, respectively, after type I IFN treatment (G). In contrast, no substantial RNA editing was detected in both sites in precursor mRNA that contained downstream intron 11 (In11) (H). These results suggest that RNA editing at Azin1 coding sites is IFN responsive, depending upon the amount of ADAR1 p150, and these sites are edited after mRNA maturation.To examine the molecular mechanisms underlying Azin1 RNA editing, we first investigated whether two editing sites, which are located in exon 11 (Ex11) of the in1 gene A, were rth IFNβ1 B. As expaffected , B–D. Th protein , E and Freatment G. In con1 (In11) H. These Adar1/Adar2 double-KO (A1/A2 dKO) Raw 264.7 cells, which were previously established (A). After each ADAR was induced in A1/A2 dKO Raw 264.7 cells, we showed that each EGFP-tagged mADAR protein was expressed at the expected size (A) and then sorted GFP-positive cells by adjusting the expression of each mADAR protein using the intensity of GFP fluorescence. After RNA extraction from the sorted cells, editing levels were examined. We found that the ADAR2-specific S/G site of Nova1 was editable by only ADAR2 in this system (B). In addition, Y/C and Q/R sites of Blcap mRNA, known ADAR1-target sites, were preferentially edited by ADAR1 p110 in addition to ADAR2, which could edit these sites to some extent (C). Given that RNA editing of Blcap coding sites requires ECS in the upstream intron (B). This ADAR1 p150–mediated RNA editing was not detected in precursor Azin1 mRNA containing In11 (C).Next, we transfected a plasmid containing enhanced GFP (EGFP)–tagged mouse ADAR1 (mADAR1) p110, p150, or mADAR2 into ablished . As prevablished , 46, 47 ablished A. After ted size A and thes system B. In adde extent C. Given m intron , 34, ADAm intron B. This Aing In11 C.Figure A and B). In these cells, the editing ratios at E/E and S/G sites of AZIN1 mRNA were increased to ∼15% and ∼30%, respectively, when ADAR1 p150 was forcibly expressed. In contrast, such an increase in RNA editing was not observed when ADAR1 p110 or ADAR2 was overexpressed (B). These results indicate that AZIN1 coding sites are editable in vitro only by ADAR1 p150 at the mature mRNA stage, which is conserved between human and mouse cell lines.To examine the conservation of AZIN1 RNA editing between humans and mice, we transfected a plasmid containing HaloTag-fused human ADAR1 (hADAR1) p110, p150, or hADAR2 into human embryonic kidney 293T (HEK293T) cells. Of note, the editing level of AZIN1 coding sites was less than 2% under normal conditions, which most likely reflects the low expression of ADAR1 p150 in HEK293T cells in which ADAR1 p110 is relatively abundant , A and Bxpressed B. These A). After each mCherry-fused reporter mRNA was expressed in Raw 264.7 cells, we investigated whether RNA editing occurred at the two sites in Ex11. No RNA editing was detected in both In10-spliced transcripts derived from an mEx10–In10–Ex11 reporter construct and transcripts derived from an mEx11 reporter construct . In contrast, we observed that E/E and S/G sites were edited at levels of 1.5% and 6.4%, respectively, under normal conditions, which increased to 4.2% and 16.5%, respectively, in the presence of type I IFN in In11-spliced transcripts derived from an mEx11–In11–Ex12 reporter construct . Although In11 was efficiently spliced out, we further examined RNA editing in In11-containing transcripts, which were derived from both precursor mRNA and mature mRNA with In11 retained, resulting in no detection of substantial RNA editing (C and D). These data suggest that a dsRNA structure is formed between Ex11 and Ex12 after the splicing of In11, which inhibits RNA editing of AZIN1 coding sites.ADARs recognize a dsRNA structure as a target. The editing sites in coding regions are usually located in a dsRNA structure formed within a single exon or between the editing site–containing exon and ECS in an adjacent intron , 43, 44.Fig. 3A and B). To validate the dsRNA structure, we introduced four sequential point mutations into the predicted complementary region facing the editing sites in Ex12 of an mCherry-fused mEx11–In1–Ex12 reporter construct and expressed this mutant construct in Raw 264.7 cells (A). As expected, no substantial RNA editing was observed at E/E and S/G sites, regardless of IFNβ1 treatment (B). Taken together, these results indicate that the dsRNA structure formed between Ex11 and Ex12 is essential for RNA editing of the AZIN1 coding site, and In11 prevents this RNA editing by inhibiting the formation of a stable dsRNA structure.Considering RNA editing of AZIN1 coding sites is conserved between humans and mice, the dsRNA structure required for this RNA editing is also likely conserved. In this regard, the conservation of In11 is relatively low, whereas the predicted dsRNA structure formed between Ex11 and Ex12 is highly conserved between humans and mice , A and B.7 cells A. As expreatment B. Taken A). Given that Zα domain–mediated Z-RNA binding affects the RNA editing activity of ADAR1 p150 in vitro and in vivo (B). After sorting GFP-positive cells by adjusting the expression level of wildtype ADAR1 p150 and W197A mutant proteins using the intensity of GFP fluorescence, we compared RNA editing levels. This analysis revealed that no significant alteration of RNA editing at two sites of Azin1 mRNA was detected (C). This finding indicates that Z-RNA binding capacity is dispensable for Azin1 RNA editing.ADAR1 p150 possesses a Zα domain and an NES in the N terminus, which is not present in ADAR1 p110 A. Given  in vivo , 48, we  in vivo , 49, and in vivo , the W19 in vivo B. After detected C. This fA and B). Then, we quantified RNA editing levels after adjusting the expression level of wildtype ADAR1 p150 and NES_del mutant using the intensity of GFP fluorescence. This demonstrated that RNA editing levels at two coding sites were significantly reduced (C). Next, we inserted an NES to the N terminus of ADAR1 p110 (A) and expressed an EGFP-tagged mADAR1 p110 NES_ins mutant in A1/A2 dKO Raw 264.7 cells. A part of the mADAR1 p110 NES_ins mutant was observed in the cytoplasm and not detected in the nucleolus where wildtype ADAR1 p110 is localized (A and 5B). Accordingly, we found that RNA editing levels at two coding sites were significantly increased (C). Taken together, the intracellular localization of ADAR1, but not binding capacity to Z-RNA, is critical for regulating Azin1 RNA editing.The intracellular localization of mADAR1 p150 is affected by multiple domains, including an NES, a part of a Zα domain, a nuclear localization signal, and a part of the C terminus , 50, 51. reduced C. Next, AR1 p110 A and expocalized A and 5B.ncreased C. Taken Adar1 p110−/−/Adar2−/− mice  mice by deleting the type I IFN–inducible promoter and exon 1A, as previously reported (A). We found that Adar1 p150−/− mice showed embryonic lethality, which was rescued by concurrent deletion of the Ifih1 gene as previously observed (Adar1 p150−/−Ifih1−/− mice (B and C). Accordingly, we observed that ADAR1 p150 proteins were selectively lost in all organs of mutant mice examined at P0 (D). Given that RNA editing of Blcap coding sites requires ECS in the upstream intron . Accordingly, although Blcap RNA editing was absent in Adar1 p110−/−/Adar2−/− mice (Adar1 p150−/−Ifih1−/− mice examined were comparable to those in wildtype mice (E and F). In contrast, the editing level of Azin1 coding sites was substantially absent (less than 1%) in all organs of Adar1 p150−/−Ifih1−/− mice examined at P0 (G and H). In addition, we found that E/E and S/G sites were edited by less than 6% in the brain of wildtype mice where ADAR1 p110 is abundant, whereas ADAR1 p150 was expressed at its lowest level , as we previously reported  formed between Ex11 and Ex12, In11 has sequences that might hybridize with Ex11 .Mice were housed with food and water available ad libitum in a light- (12 h on/12 h off), temperature- (23 °C ± 1.5 deg. C), and humidity- (45 ± 15%) controlled environment as previously described , 71.Ifih1−/− mice were maintained in our laboratory as previously described  containing 10% (v/v) fetal bovine serum at 37 °C in the presence of 5% COus study .Adar2 gene obtained from DNAFORM, into a pEGFP-C1 vector using EcoRI–BamHI restriction enzyme recognition sites. To create pEGFP-C1-mADAR1 p150 NES_del and pEGFP-C1-mADAR1 p110 NES_ins plasmids, site-directed mutagenesis was performed by using a PrimeSTAR mutagenesis basal kit (Takara Bio) and pEGFP-C1-mADAR1 p150 as a template with the following primers: 5′-TGCTGACCAGAGTCCGGAGCAGAAG-3′ and 5′-GGACTCTGGTCAGCACCTCTCCATGG-3′, and 5′-CATCAGTGCTGAAATCAAGGAGAAG-3′ and 5′-ATTTCAGCACTGATGCTCAGCTCCCG-3′, respectively. The coding region of hADAR1 p110, ADAR p150, and ADAR2 was amplified by PCR from plasmids containing these genes, which were obtained from Open Biosystems. The resultant PCR products were inserted into a pFN21A HaloTag CMV Flexi Vector (Promega) using SgfI–PmeI restriction enzyme recognition sites, which were termed pHaloTag-hADAR1 p150, pHaloTag-hADAR1 p110, and pHaloTag-hADAR2, respectively. The corresponding regions of a partial mouse Azin1 gene were amplified from mouse tail genomic DNA by using Phusion Hot Start Flex 2X Master Mix (New England Biolabs) and the following primers: 5′-TATTCTCGAGTAGAAAAAAATGGGAGTGAT-3′ and 5′-AATAATGGATCCCAATCACTGAATGACATCAT-3′ for mEx10–In10–Ex11, 5′-TATTCTCGAGCCAAATACAAGGAAGATGAGCC-3′ and 5′-AATAATGGATCCCAATCACTGAATGACATCAT-3′ for mEx11, and 5′-TATTCTCGAGCCAAATACAAGGAAGATGAGCC-3′ and 5′-AATAATGGATCCAACCCAGTTAATGGGCTTCCA-3′ for mEx11–In11–Ex12. The resultant PCR products were inserted into a pmCherry-C1 (Clontech) using XhoI–BamHI restriction enzyme recognition sites. To insert four point mutations (underlined in the primer sequences) in Ex12 of a mEx11–In11–Ex12 reporter construct, site-directed mutagenesis was performed using a PrimeSTAR mutagenesis basal kit (Takara Bio) with the following primers: 5′-AGACAGCAAAACCACTGAAGCTTAAACAG-3′ and 5′-TCAGTGGTTTTGCTGTCTTCTTGGCTCAG-3′. All the constructs were verified by Sanger sequencing.The following plasmids, pEGFP-C1-mADAR1 p150, pEGFP-C1-mADAR1 p150 W197A, and pEGFP-C1-mADAR1 p110 were previously reported . Similar2 on the logarithmic x-axis, which was defined as the GFP-negative fraction. Then, the cell fraction within a range of 102 to 105 on the x-axis was sorted. For microscopic analyses, A1/A2 dKO Raw 264.7 cells were maintained in chamber slides, and plasmids were transfected using Lipofectamine 2000 (Thermo Fisher Scientific) in the presence of NATE (InvivoGen), a transfection enhancer. Twenty-four hours after transfection, cells were fixed with 4% paraformaldehyde for 30 min at room temperature and stained with DAPI Fluoromount-G (Southern Biotech) to observe intracellular localization using an Olympus BX63 fluorescence microscope. For treatment with type I IFN, Raw 264.7 cells were cultured with 100 ng/ml recombinant mouse IFNβ1 (BioLegend). After 24 h, partial mouse Azin1 expression plasmids were transfected using Lipofectamine 2000 in the presence of NATE in some experiments.The following plasmids, pEGFP-C1-mADAR1 p150, pEGFP-C1-mADAR1 p150 W197A, pEGFP-C1-mADAR1 p150 NES_del, pEGFP-C1-mADAR1 p110, pEGFP-C1-mADAR1 p110 NES_ins or pEGFP-C1-mADAR2, were transfected into A1/A2 dKO Raw 264.7 cells using a Neon Transfection System (Thermo Fisher Scientific) with the following parameters: pulse voltage, 1750 V; pulse width, 25 ms; and pulse number, 1, as previously described . Twenty-g and 4 °C for 10 min. Each supernatant was transferred to a 1.5 ml tube and stored at −80 °C until use. To prepare cell lysates, collected cells were lysed with lysis buffer 2 , frozen in liquid nitrogen, and then thawed on ice five times. After centrifugation at 20,000g and 4 °C for 10 min, the supernatants were transferred to new 1.5 ml tubes and stored at −80 °C until use.Tissue lysates were prepared as previously described . In brieEach lysate was separated by SDS-polyacrylamide gel electrophoresis and transferred to polyvinylidene difluoride membranes (Bio-Rad). Immunoblotting was performed by using the following primary antibodies: mouse monoclonal anti-ADAR1 antibody , mouse monoclonal anti-ADAR2 antibody , rabbit polyclonal anti-AZIN1 antibody , mouse monoclonal anti-GFP antibody , rabbit polyclonal anti-Halo Tag antibody , and mouse monoclonal anti-GAPDH antibody . Chemiluminescence was detected by using ImmunoStar Zeta (Fujifilm) or Pierce ECL Western Blotting Substrate (Thermo Fisher Scientific).Total RNA was extracted from isolated organs or collected cells using TRIzol reagent (Thermo Fisher Scientific) by following the manufacturer’s instructions. After measuring the RNA concentration using a NanoDrop One (Thermo Fisher Scientific), total RNA samples were stored at −80 °C until use.The preparation of ion amplicon libraries for the quantification of RNA editing sites has been previously described , 14. In As previously described , 18, 71,Potential secondary dsRNA structures were calculated using an RNAfold web server , 18, 72.t test or Tukey’s honest significant difference test was used as indicated in each figure legend. All values are displayed as the mean ± SD. No significance is displayed as NS, whereas statistical significance is displayed as p < 0.05 (∗), p < 0.01 (∗∗), or p < 0.005 (∗∗∗).For statistical analyses, an unpaired two-tailed Student’s The data generated during all experiments are available from the corresponding authors upon reasonable request.This article contains The authors declare that they have no conflicts of interest with the contents of this article.


Sugar transport through the plasma membrane is one of the most critical events in the cellular transport of nutrients; for example, glucose has a central role in cellular metabolism and homeostasis. The way sugars enter the cell involves complex systems. Diverse protein systems participate in the membrane traffic of the sugars from the extracellular side to the cytoplasmic side. This diversity makes the phenomenon highly regulated and modulated to satisfy the different needs of each cell line. The beautiful thing about this process is how evolutionary processes have diversified a single function: to move glucose into the cell. The deregulation of these entrance systems causes some diseases. Hence, it is necessary to study them and search for a way to correct the alterations and utilize these mechanisms to promote health. This review will highlight the various mechanisms for importing the valuable sugars needed to create cellular homeostasis and survival in all kinds of cells. Sugar typically refers to simple carbohydrates that include monosaccharides such as fructose and glucose and disaccharides such as sucrose and lactose . In natuGlucose enters the cell by its corresponding transporters to fuel glycolysis. Pyruvate is obtained from this process to enter the mitochondria and forms acetyl coenzyme A, which enters the Krebs cycle. This group of enzymes produces the needed electrons to fuel the respiratory chain, which produces adenosine triphosphate (ATP). One of the biological functions of carbohydrates is to attach to several other molecules and complete their natural process. Metabolically, sugars are the precursors of fat storage.In mammals, sweetness perception is initiated when sweeteners interact with taste-receptor proteins in taste buds. Then, a signal is generated and translated into communication with the nervous system to identify it. The brain may contain a different group of cells with a food-oscillatory function. Food palatability may shift the neuronal activity from the medial hypothalamus to the limbic and reward-related areas. Complex signaling systems initiate all this communication.The extracellular environment is not static because many elements make it up, and they are constantly changing so that cells are continuously exposed to a tremendous mixture of environmental states. For its adaptation, the cells must correctly communicate and transmit messages between them. It is only easy to conceive life with compartmentalization. Biological membranes are boundaries between environments, dividing the extracellular, intracellular space, and subcellular organelles. Prokaryotic or eukaryotic cells use lipid membranes, proteinaceous channels, and transporters.Cellular membranes are dynamic structures with many roles in cellular processes by specific signal-transduction duties, so they can localize, amplify, and direct signals. Their intrinsic properties depend on lipid and protein interrelations. For years, protein interactions have been regarded as the main factor responsible for stabilizing membrane macro- and microdomains. The protein–lipid bilateral exchange has been considered mutually exclusive; nevertheless, they cooperate in creating membrane structural and functional heterogeneity .Membrane proteins are internalized and then degraded or recycled back into the membrane. The actin cytoskeleton regulates membrane dynamics to deform the membrane, promoting invagination, tubulation, and scission of transport carriers in the secretory and endocytic pathways, coordinately holding transport carrier biogenesis and transport.Membrane transporters are a large and miscellaneous group of proteins characterized by a complicated mesh of receptors, channels, carriers, translocators, and pumps. All of them are considered indispensable. They belong to the major facilitator superfamily (MFS). Solute carriers (SLC) are one of three significant superfamilies of membrane transporters, including uniporters, exchangers, and symporters. SLC proteins are a group of transmembrane proteins that mediate solute in and out flux across membranes. They use substrate and ion gradients to promote substrate transport . They caAnother communication system with the outside of the cell is the transmembrane-receptor (TM) proteins that interact with an extracellular molecule, producing an intracellular signal which spreads and amplifies through the canonical molecular transduction pathways and propagates the signal through phosphorylation, activating the signal from downstream. Examples are receptors for insulin, cytokines, neurotransmitters, and sex hormones, to mention some . Ion-specific transmembrane channels also sense ion concentrations between the cytoplasm and the extracellular environment. These changes can be monitored by the membrane channels provoking an instant, local response, altering the location and function of membrane proteins. Some examples are the sodium, potassium, and chloride channels, which are very important to maintain life .The Most Recent Common Ancestor (MRCA) is an ancestor of the sugar transport family, which probably goes far back in evolutionary history. This family encompasses transporters through the diversity of life, from humans to bacteria, accepting only sugars or related compounds . Sugar transporters (ST) belong to a large family of membrane proteins containing transmembrane α-helices. The oxidation of glucose is the primary source of obtaining cell energy and requires a transport system into the cell, given that glucose cannot diffuse through due to its high molecular weight. Transport is possible through solute carrier proteins; by a process called facilitated diffusion, which takes place down the gradient from high concentration to low concentration. The orchestrated action of various hormones such as glucagon, estradiol, adrenaline, cortisol, thyroid, growth hormone, and insulin achieve glucose normal range levels.SLC5 family), glucose transporters , Sugars Will Eventually be Exported Transporter , ATP-binding cassette transporters (ABC), fungal sugar facilitators (FfZ family), putative sugar transporters (SLC45 gene family), (SLC37 gene family), Nucleotide sugar transporters (SLC35 gene family), tonoplast monosaccharide transporter (TMT), vacuolar glucose transporter (VGT), sucrose transporters (SUC or SUT) plant transporters, and more.Nowadays, there are diverse transporter proteins in living beings that mediate the entrance of sugars through the lipid layer: Sodium-dependent glucose transporters . The structure contains a central group of seven helices  supported by a ring of other helices. Cytoplasmic loops of TM1, TM2, TM5, TM7, and TM9 form a hydrophilic cavity comprising the inner gate of the sugar-binding site. These transporters are of great importance in the physiology of animals, and they are present in mammals’ gastrointestinal tract and renal tubules  [5]. OthThe mammalian monosaccharide transporters or glucose-transport proteins (GLUTs) belong to a family of integral membrane proteins that catalyze the facilitated diffusion (transport down a concentration gradient) of hexose and pentose sugars into and out of cells. They contain 12 hydrophobic, α-helical TM connected by hydrophilic loops. These loops are of different lengths; specifically, they join domains 6 and 7 of the protein. Some GLUTs also mediate the transport of dehydroascorbic acid, urate, or myoinositol. The physiologic substrates for some GLUTs are not known. The GLUTs are expressed in nearly all mammalian cells. However, most cells express one GLUT isoform as the major monosaccharide transport protein and lower levels of one or more isoforms .The human SLC2 protein family contains 14 currently known isoforms, and each member has a distinct tissue-specific expression and binding affinities for individual hexose substrates, considered uniporters. A facilitative glucose-transport system operates in a concentration gradient and is grouped into three classes. These classifications are due to the specific differences in the structure of the proteins .Class 1: transporters 1, 2, 3, 4, and 14; Loop 1–2 is longer than the other five extracellular loops and contains only one glycosylation site. These transporters have an N-linked asparagine with glycan polymers that differ in length, allowing them to move throughout the protein. They express a motif in loops 7 and 8 that confers the possibility of changing conformationally during the sugar transport.Class 2: transporters 5, 7, 9, and 11; transporters belonging to this class have glycation on the exofacial arginine in the fifth exofacial loop, which connects the ninth and tenth transmembrane spans. This class of Gluts does not have a glycation site on the first loop as the other two classes do. Something that distinguishes class 2 GLUTs is the absence of the tryptophan residue TM10. The transporters from this class prefer fructose over glucose and alternative non-hexose as substrate.Class 3: transporters 6, 8, 10, 12, and 13 (HMIT); this class of proteins differs structurally from the other two classes. These proteins have an N-linked glycation site on the asparagine in the first loop and a single glycosylation site on an exofacial loop that connects TM 9 and 10, opposite to what happens in classes 1 and 2. They also have intracellular sequences, the kind of dileucine motifs, on the N-terminal domains, which make up part of intracellular targeting and compartmentalization . Th. Th85]. RAG1AP1), may lead to altered glucose levels, affecting TRPV2 targeting. HsSWEET1, expressed in β-cells, may contribute to glucose homeostasis and, thus, like GLUT2, may play a critical role in glucose-induced insulin secretion. Given the importance of sugar homeostasis in humans, it is necessary to study the physiological role of SWEETs and their regulation, including metabolic diseases ..107].The hypoxic microenvironment in some autoimmune diseases upregulates the GLUT1 expression by either T-cell-specific knockouts or small-molecule GLUT1/glycolysis inhibitors, by which improvement can be seen in arthritis rheumatoid, lupus, or psoriasis .Combining metabolic inhibitors, such as 2-deoxy-D-glucose (2-DG) and metformin, has given promising results in preventing chemotherapy resistance .SGLT2 inhibitors have been very useful in controlling high pressure and protecting the heart  Figure .The inhibition of transporters can also be addressed by manipulating transcription factors or intervening in the mechanism that transports them to the membrane. Sugar transport is highly regulated and expressed differentially in tissues and different organisms depending on the function they control. Until now, various aspects of these transporters have been characterized in detail, such as the distribution in tissues, its specificity to the substrate, its kinetics, and in the case of some, their physiological role. However, there is still much to know, such as mechanisms that regulate its synthesis, incorporation into intracellular vesicles, translocation, internalization, degradation, etc. The detailed knowledge of these systems of transport and their regulation in the future will allow us to design more efficient therapeutic strategies in the case of their dysfunction.


Analysis of allele-specific expression is strongly affected by the technical noise present in RNA-seq experiments. Previously, we showed that technical replicates can be used for precise estimates of this noise, and we provided a tool for correction of technical noise in allele-specific expression analysis. This approach is very accurate but costly due to the need for two or more replicates of each library. Here, we develop a spike-in approach which is highly accurate at only a small fraction of the cost.Caenorhabditis elegans. Our new approach, controlFreq, enables highly accurate and computationally efficient analysis of allele-specific expression in (and between) arbitrarily large studies at an overall cost increase of ∼5%.We show that a distinct RNA added as a spike-in before library preparation reflects technical noise of the whole library and can be used in large batches of samples. We experimentally demonstrate the effectiveness of this approach using combinations of RNA from species distinguishable by alignment, namely, mouse, human, and R package controlFreq (github.com/gimelbrantlab/controlFreq).Analysis pipeline for this approach is available at GitHub as  AI analgulation . Besidesprinting , X-chromprinting , and autprinting .Qllelic, that takes advantage of the production of two or more RNA-seq libraries for each RNA sample, i.e. technical replicates  as a spike-in before starting library construction.Non-allelic RNA spike-in standards have been previously used to access technical noise in transcription levels measurements in bulk RNA-seq , and estThe analytical part of our approach relies on the uniformity of the spike-in RNA aliquots across all libraries. We extend our previously described idea of processing data from replicates to the spike-in portions of all the libraries in a batch. Crucially, we experimentally show that allele-specific noise properties of the spike-in portion reflect technical noise of the whole sample. Thus, spike-in acts as the standard for estimating technical noise in each library. Adding a fraction of spike-in RNA to the main sample of interest (e.g. mouse RNA to human RNA at 1:10 ratio) removes the need for additional libraries, while only marginally increasing total depth of sequencing, as opposed to doubling or tripling experimental costs.In this article, we revised and expanded the procedure described in Here we describe the new procedure, which operates with lots of samples at once  and assiBeta-binomial family of distributions is commonly used to model an “overdispersed” binomial distribution. One way to parameterize this family is using the real parameters The variance of this distribution isQ. Given parameters α and β as The second part is the variance of the binomial distribution with Q are 1 and n. When Q approaches n, α, and β become close to 0, and the probabilities of all outcomes except for 0 and n tend to 0. The limit distribution assigns probability n. When Q approaches 1, α and β tend to Two edge cases of the possibility range for Q < 1. While most samples give overdispersed distributions, some could be modeled better with underdispersed ones (see Subsection 2.1.4 for more details), and clamping them to one point Q = 1 does not make computational sense. Fortunately, we can overcome the problem by extending the probability mass formulas past the edge Q = 1, essentially extrapolating the family of beta-binomial distributions into hypergeometric distributions following . Thus If all of iQ estimates from the first run show significant dispersion, the fitting algorithm can be iterated. In the grand scheme, we alternate between fitting Q’s, then fitting Q fitting is run using the gradient ascend algorithm. After each Q-fitting step, iQ values are compared to previous estimates, and iterations are repeated until convergence.Otherwise, if jQ. To do that, we counter the previous estimate with a different one. If one includes the sample j in the jQ will be underestimated. Moreover, on the simulated data we see that the overestimated and underestimated In case of small number of samples, we need to take care of the overestimation of  Fig. 3c;. Using tInformation on RNA-seq datasets generated for this work, including sources of RNA, library preparation methods, sequencing, and data processing is summarized in β-mercaptoethanol (Sigma). Human peripheral blood monocytes from deidentified donors were appropriately consented for generating genomic sequencing data for unrestricted deposition in public databases. Caenorhabditis elegans strains N2 and CB4856 (Hawaiian) were maintained at 20°C on nematode growth medium plates with Escherichia coli OP50 bacteria , with DNase treatment using TURBO DNA-free kit (Ambion). RNA integrity was assessed using Bioanalyzer and was quantified using Qubit RNA HS Assay. DNase-treated RNA from N2 and Hawaiian strains was mixed in proportions described in Biological replicates for human and mouse clones see  were madFor experiments 1 and 2 see , aliquotDe novo SNPs were annotated with dbSNP v151.Imputation on genotype calls for donor samples in experiment #3 was performed on autosomes only. VCFTools was used to split files per chromosome. Per chromosome genotype files were submitted to the TopMed imputation server  with the following parameters: imputation = minimac4-1.0.2; phasing = eagle-2.4; panel = TOPMED.vR2; Mode: Quality Control & Imputation. About 131 562 sites were excluded prior to imputation, leaving 572 955 sites for imputation. About 131 452 of the excluded sites were due to monomorphic sites. After imputation, only markers with a R2 value of ˃9 were kept. in silico chimeric pseudogenome reference files “containing” more than one organism, we first create a set of respective pseudogenome reference files for each of them, namely, pseudo-reference fasta files for both alleles , allelic VCF files  and reference GTF files. The procedure is described in detail in in silico chimeric pseudogenome with STAR, setting sjdbOverhang parameter to the advised data-specific len(read)-1.To receive github.com/gimelbrantlab/controlFreq). The unit of overdispersion analysis is a table with allelic read counts per gene. In order to obtain that kind of tables, we first align the reads to both pseudogenomes with STAR (version 2.7.9a), which supports allele-aware alignment (–outSAMattributes vA vG) when provided prearranged reference fasta and vcf (see section above for details). For further analysis, we select reads aligned at positions that include at least one SNP to determine the allele, and filter out those whose SNP signals or alignment positioning on two haplotypes don’t show consistency. The remaining reads are assigned to one of the alleles, according to SNP calls and alignment quality. Finally, we assign allele-resolved reads to genes and calculate gene counts using featureCount (version 2.0.2) (with parameters –countReadPairs -B -C). Autosomal genes were used for iQCC values calculation.Note that we have revised recommendations on allelic counts tables creation relative to Qllelic, to minimize effect of noise coming from statistically dependent counts for nearby variants while preprocessing RNA-seq data  or from replicates quantity (spike-in case), and utilizes the assumption of shared initial state of allelic proportions in RNA prep in both scenarios (see ults see .C. elegans (N2 and Hawaiian (CB4856)) performed as a spike-in just as well as N2xHawaiian F1 heterozygous cross , it has the same limitation as Qllelic, namely, the assumption of overdispersion similarity between replicates, even as it no longer requires similarity in library sizes. However, when multiple replicates are available (as spike-ins), the new approach enables confident detection of outliers, allows us to remove the assumption of similarity of overdispersion in all samples, and eliminates expectations of library size similarity. Overall, the analysis is highly robust to fluctuations.When An important practical feature of the spike-in approach is that the spike-in RNA must be identical only within the batch of samples being analysed. Once the overdispersion corrections are calculated for the batch of samples, they can be correctly compared to samples from any other batch, including those using entirely different spike-ins . This means that spike-in RNA can be prepared as needed, and does not have to come from a single source. It should be possible to use other spike-ins in parallel. For example, existing designs using sequins  or ERCC C. elegans RNA as a spike-in standard for mammal samples when using library protocol with ribo-depletion. Otherwise, spike-in material can be produced in any convenient way; we posit that once the “snapshot” of allelic proportions is captured for the whole library, the origin of library components does not matter. For example, we used “synthetic F1s”—mixes of RNA from homozygous C. elegans strains  that it has significant density of polymorphisms (the higher the better) and (ii) its nature enables it to undergo the same library preparation process as the main sample. So, e.g. bacterial RNA should not be used for poly-A-based libraries, or ains see  as a spiains see . Notablyains see . One conains see , the oveThe allelic counts depend on read length, both in terms of total sequence data and unique alignment rate: e.g. with 50PE reads, total allelic counts are about 1/4 of the counts with 150PE reads . The desWe note that the experiments we described are focused on poly-A-enriched bulk RNA sequencing. While we expect that application of this approach can be extended into a variety of other experimental settings where accurate measurement of allelic signal is desirable, a pilot study should be performed in each particular case to test the applicability of spike-in methodology and tune it accordingly. A clear example of an application is capturing technical noise in single-cell RNA-seq; this is a question of great importance since otherwise , no techbtad254_Supplementary_DataClick here for additional data file.


Previous work on murine models and humans demonstrated global as well as tissue-specific molecular ageing trajectories of RNAs. Extracellular vesicles (EVs) are membrane vesicles mediating the horizontal transfer of genetic information between different tissues. We sequenced small regulatory RNAs (sncRNAs) in two mouse plasma fractions at five time points across the lifespan from 2–18 months: (1) sncRNAs that are free-circulating (fc-RNA) and (2) sncRNAs bound outside or inside EVs (EV-RNA). Different sncRNA classes exhibit unique ageing patterns that vary between the fcRNA and EV-RNA fractions. While tRNAs showed the highest correlation with ageing in both fractions, rRNAs exhibited inverse correlation trajectories between the EV- and fc-fractions. For miRNAs, the EV-RNA fraction was exceptionally strongly associated with ageing, especially the miR-29 family in adipose tissues. Sequencing of sncRNAs and coding genes in fat tissue of an independent cohort of aged mice up to 27 months highlighted the pivotal role of miR-29a-3p and miR-29b-3p in ageing-related gene regulation that we validated in a third cohort by RT-qPCR. Understanding and controlling the molecular hallmarks of age-related processes in higher organisms promises to greatly improve the quality of life . For humThese observations indicate a systemic and orchestrated exchange of information and molecules between organs. For example, extracellular vesicles (EVs) are membrane vesicles mediating the horizontal transfer of genetic information between different cell types. Specifically, EVs are postulated to play an important role ,15 in, eThese and other issues complicate the analysis of EVs and their molecular cargos. Especially in the context of EVs in cancer, common pitfalls in purification have been summarized by Schekman and co-workers , with thTMS) data [The main aim of our study was to provide a data resource of small non-coding RNAs included in EV cargo and freely circulating in plasma (fc-RNAs) in mice of different ages and to identify differences between the molecular information in these fractions associated with ageing that might advance our understanding of the systemic ageing process. Therefore, plasma fc-RNA and EV-RNA of individual mice were sequenced for noncoding RNA profiling and contrasted by computational approaches. To demonstrate the use of this resource, we conducted pathway and comparative analyses using original Tabula Muris senis (MS) data ,10 and pTo uncover age-related dynamic processes and to model the information exchange involved, we sequenced both non-coding fc-RNAs and non-coding EV-RNAs from individual mice. The molecular profiles are available at five time points across the average lifespan between two and 18 months in two to four replicates per age group and biospecimen type (Supplementary Table S1). For the fc- and EV-RNA samples, we sequenced an average of 38 million reads per sample and mapped them to ten different noncoding RNA classes. Our analysis covered a total of 80,688 different noncoding RNAs, with piRNAs, circRNAs, lincRNAs and miRNAs being the classes with the highest number of different features . The firWe thus asked whether unique ageing trajectories within and between noncoding RNA classes exist, either enclosed into/bound to EVs or freely circulating in plasma. One indicator is the proportion of variance in the RNA counts that can be explained by available sample covariates, i.e. either by age of the mice, the specimen type and donor mice identity, or linear combinations of such. Depending on the RNA class we observe varying results with respect to the separation in the fc- and EV-fraction in a 2-dimensional UMAP embedding, with a clear segregation in the scaRNAs . Compared to the other RNA classes, tRNA fragments and miRNAs however showed the highest fraction of variance explained by age . In compThe largest age-related differences appear for rRNAs, where the overall amount increases for free circulating molecules with ageing but the EV loading of rRNAs decreases. This notable difference in the specimen types also explains the high proportion of variance attributed to the sample type annotation . Our data further indicate a strong ageing signal in EV-and fc fractions, with varying strengths, again depending on the RNA class . As our Taken together, our analyses suggest that non-coding RNAs exhibit specific age trajectories, both qualitatively and quantitatively. Moreover, the data pinpointed substantial differences in the case of fc-RNAs and EV-RNAs, where the highest correlation with age was observed for tRNA fragments. This poses the question of whether loading of EVs follows biologically relevant environmental mechanisms. To potentially discover such patterns, we next performed a fine-granular and molecule-centric analysis.Mus musculus included in our analysis, 23052 (28.6%) were stably present in the EV- and fc-fractions (Supplementary Table S2). We then computed the linear Pearson correlation as well as the nonlinear distance correlation for each of the investigated RNAs. While making conclusions on the correlations of single RNA features can be challenging in terms of type-I errors, comparing the different RNA classes and the fc- and EV-fractions globally can support an understanding of linear and non-linear ageing effects. We thus computed an estimate for each RNA whether it was linearly correlated with age, nonlinearly correlated with age, or not correlated with age at all for EV-and fc-fractions separately. Because of the large number of non-coding RNA features included in the study we are potentially facing an overplotting issue and thus computed a density estimation for the two correlation schemes. For both sample types, fc- (p < 10−16). Of note, most of the correlation values observed in our study are not significant. Generally, linear correlation coefficients above 0.5 and −0.5 in our study roughly corresponded to p-values with nominal significance at an alpha level of 0.05 (Supplementary Table S2).Of the 80,668 unique noncoding RNA molecules in pes, fc-  and EV- pes, fc- , the lin, decreasing with age in the EV fraction but increasing in the fc fraction  and negative regulation of myotube differentiation (p = 6×10−7). Here, both categories were significant following adjustment for multiple testing for the fc fraction, in the EV fraction the negative regulation of myotube differentiation remained significant following adjustment for multiple testing. This case indeed indicates that pathways can be significant in both, the fc and EV fraction. Especially one category was highly significant in both: ‘response to hypoxia’ reached a p-value of 4.3 × 10−5 in the fc fraction and of 9.4 × 10−6 in the EV fraction , pointing at an inherent regulatory role of this miRNA family in fat tissue. Among the gene nodes, Nanog could be distinguished by a positive correlation with age. This gene is targeted by mmu-miR-296-5p, which is negatively correlated with age.The core hypothesis of our work stipulates specific loading of EVs, notably considering both possibilities, in- and outside-bound, with non-coding RNAs, first and foremost miRNAs, enabling the control of specific cellular functions and gene regulation in remote cells. To identify tissues most likely affected by the EV- and fc-RNA cargo, we next combined the miRNA data generated in this study with our previously established bulk- and single-cell murine tissue-ageing atlas, TMS ,10. In tTMS . Thus, fTMS  and evalectation . This reectation . This efOf note, the correlation presented in this analysis was indirect. The fc-RNA and EV-RNA fractions were sequenced from another cohort of mice and compared to the gene expression profiles in tissues. To test whether increased levels of these miRNAs have also been detected in gonadal fat tissue of aged mice, we used small RNA sequencing data from gonadal fat tissues from TMS . SpecifiDue to a limited cohort size and potential challenges in using high-throughput screening based on NGS, we performed a further validation experiment. To this end, we investigated a third independent cohort of mice and analysed the relative expression of miR-29 family members along with other dysregulated miRNAs from the network  in the EV-RNA of four young and two old mice using real-time quantitative PCR (RT-qPCR). The RT-qPCR results for the miR-29 family support the sequencing data and provide evidence for an increased abundance of miR-29a/b/c-3p in EV-RNA of old mice . AdditioIn summary, the data presented here provide a valuable resource that can be used as a starting point to study the biology of circulating noncoding RNAs in the context of ageing in general. Especially in combination with published tissue-specific gene expression atlases, these data enable the community to formulate novel hypotheses on distant cell-cell communication and affected tissues during ageing, which serves as a basis for functional studies in the future. At the same time, our analysis suggests a major role of fat in ageing processes, with EV-bound miRNAs of the miR-29 family performing important regulatory events.While our study presents intriguing new insights into the correlation of EV-and fc fractions and the molecular loading of EVs with noncoding RNAs in the context of ageing, it is important to mention the limitations and how they could be addressed. First, the purification of EVs is challenging and has many pitfalls . GeneralIn performing the study, we paid attention to limit confounding factors wherever possible. Because an influence of the sex on organ specific RNA signatures is known ,10, we dCcl6 as a major contributor to adipose cell senescence [IGF1, for example, is a major player regulating ageing on a cellular and organism-wide level [HDAC4 has been shown to be polyubiquitylated and degraded during all types of senescence [CAV2 [The primary aim of our study was to develop a resource for free circulating (fc-RNA) and extracellular vesicle-associated small RNAs (EV-RNA) in ageing. While we observe broad differences between RNA classes upon ageing, pathway and network analysis highlights a limited set of miRNAs with age-related regulatory activity. Missing miRNA target interactions (MTIs) in the regulatory core network  await vanescence . For somde level . The hisnescence ; howevernescence . Similarce [CAV2  and othece [CAV2 . Moreovece [CAV2 . Our datce [CAV2 . Of notece [CAV2 . ConsideTogether with the data presented herein, it is plausible to postulate that the miR-29 family might be one of the mediators for the inhibition of adipogenesis and the induction of a proinflammatory environment in ageing fat tissue. One interesting open task for future studies is to determine the cells of origin of the EVs loaded with miR-29 and clarify the regulatory functions of the miRNA in adipocytes. This finding adds to the knowledge on the importance of extracellular vesicles and the immune system interplay in ageing and immune diseases , togethen=3; body weight (bw): 19–20g), 6 months , 8 months , 12 months  and 18 months . The age range was selected based on our previous results on age-related tissue-specific changes in gene expression that were already evident in mice 18months old  and 18 months . For validation experiments using RT-qPCR, a third independent cohort of female C57BL/6N mice was used . The animals – excluding the existing specimens from TMS – were housed in groups on wood chips as bedding in the conventional animal facility of the Institute for Clinical & Experimental Surgery . They had free access to tap water and standard pellet food  and were maintained under a controlled 12-h day/night cycle. This animal study was approved by the local State Office for Health and Consumer Protection and conducted in accordance with Directive 2010/63/EU and the NIH Guidelines for the Care and Use of Laboratory Animals (NIH Publication #85–23 Rev. 1985).We initially conducted the EV-RNA and fc-RNA isolation workflow on 18 female C57BL/6N mice, 14 of which resulted in a sufficient amount of biological material. Overall, we thus generated non-coding RNA-sequencing samples from mice at the age of 2 months  and xylazine . Subsequently, they were fixed on a heating pad in the supine position. After midline laparotomy, a maximal volume of blood (~700–1000 µL) was taken from the vena cava and transferred into plasma tubes . The blood samples were then centrifuged at 20°C and 10.000 × g for 5 min to remove platelets, large vesicles, and cell debris; the resulting platelet-free plasma was stored at −80°C until further use. After blood collection, gonadal fat tissue of the mice in the third cohort was collected and snap-frozen.Two hundred microlitres of mouse plasma was transferred to a 1 mL open-top thickwall polypropylene ultracentrifugation tube  and diluted with 800 µL of phosphate-buffered saline to prevent the tube from collapsing in the ultracentrifuge vacuum. Samples were centrifuged for 2 h at 4°C at 100,000 × g using Type 50.4 Ti fixed-angle rotor . Supernatants were carefully removed, and the EV-containing pellets were resuspended in 20 µL of phosphate-buffered saline. Samples were stored at −80°C until further analyses. The samples were characterized according to the MISEV2018 criteria , and NTAEV-enriched pellets further referred to as EV fractions and EV-depleted plasma referred to as the free circulating (fc) fraction were used for RNA isolation. All samples  were treated separately, and no samples were pooled. EV- and fc- total RNAs were isolated semi-automated using the miRNeasy Micro kit  and Qiacube isolation robot according to the manufacturer’s recommendations with the addition of 2 µL RNase-free glycogen  to facilitate RNA precipitation. For each sample, at least two replicated sequencing results were available . Total RNA of gonadal fat tissue was isolated using a miRNeasy Mini kit and Tissue Lyser LT according to the manufacturer´s protocol. The RNA concentrations of the EV- and fc-fractions were measured using a Qubit™ microRNA Assay Kit, and fat tissue was measured using a Nanodrop .Isolated EV-RNA and fc-RNA samples were analysed by Agilent small RNA chips, and 2 ng each (EV-RNA and fc RNA) was used for Illumina-compatible library preparation using the D-Plex Small RNA Kit . The kits employ 3´-poly A tailing and template switch-based cDNA generation using unique molecular identifier (UMI)-tagged template switch oligos. After PCR amplification involving 13 cycles, libraries were purified from TBE-PAGEs. Illumina sequencing was carried out on a HiSeq2500 platform using the High Output mode for 96 cycles. Isolated RNA from gonadal fat tissue was sequenced using the MGISeq system with the standard MPS protocol as described previously .Each EV preparation and each EV-depleted sample were tested using NTA to estimate the number of particles in a sample. For that, 1 µl of plasma was diluted in 1199 µL, and 1 µL of the resuspended EV pellets was diluted in 999 µL of phosphate-buffered saline to achieve a final concentration between 20 and 120 particles/frame. Samples were then measured on NanoSight  at a camera level of 15. For each sample, three captures of 30 s were acquired. Videos were then analysed at a detection threshold of 5 using NTA 3.4 software.Three microlitres of each EV sample was transferred to a holey carbon film-coated copper grid (Plano S147–4), blotted for 2 s, and plunged into undercooled liquid ethane at −165°C (Gatan Cryoplunge3). The grid was then transferred to a cryo-TEM sample holder (Gatan model 914) under liquid nitrogen. Low-dose bright-field images were acquired at −170°C using a JEOL JEM-2100 LaB6 Transmission Electron Microscope and a Gatan Orius SC1000 CCD camera.Quantitative real-time PCR of EV-RNA and gonadal fat tissue RNA was used to validate age-related expression differences of selected miRNAs. Reverse transcription was performed using the miRCURY LNA RT Kit  with 100 ng fat tissue RNA and EV-RNA equivalent to EVs from 20 µl plasma as input. qPCR was performed using the miRCURY LNA SYBR Green PCR Kit with miRCURY LNA miRNA PCR Assays specific for selected miRNAs in a 10 µl reaction volume. Uniform isolation and RT efficiency were checked using the manufacturer’s recommended spike-in controls . Expression differences were calculated using the ΔΔCt method with miR-191a and let-7a (in conjunction) as endogenous controls .p < 10−10). Venn diagrams were generated using the eulerr package from R. Mapping the fraction of variance to different parameters was performed using the principal variance component analysis (pvca) package. Splines were computed using the smooth.spline function with seven degrees of freedom. Colour palettes were generated using the RColorBrewer package. Smoothed scatter plots were computed using the smoothScatter function setting the point number to 500. Clustering was performed for the most highly expressed noncoding RNAs (at least 5 reads per million in at least one sample) using the scaled expression matrix (z-score of each feature). The clustering was performed with the hclust function using the Euclidean distance measure. Clusters were extracted by cutting the dendrograms at 1/1.25 of the maximal height. Heatmaps of target genes were computed using the heatmap.2 function. Network visualization was performed using iGraph. As input for the network analysis, targets from miRTarBase [The sample primary processing was performed with miRMaster  using stRTarBase  were usemus musculus and choose the gene ontology categories derived over the miRTarBase. We then selected a p-value threshold of 1 to force miEAA reporting of all nominal p-values, facilitating a direct comparison between the pathway results of the fc- and EV-fraction. We adjusted the p-values using the Benjamini-Hochberg method. As graphical output we present the enrichment plots. These plots describe the enrichment statistics for the input list as solid blue line. In the background, the same distributions computed for random lists (corresponding to the result of non-parametric permutation tests) are shown.For the pathway analysis we used miEAA 2.0. Precisely we performed a miRNA set enrichment analysis of the mature RNAs. To this end, we sorted the correlation value of the EV- and fc-fraction separately with the age and uploaded both sorted lists to miEAA. As organism we selected We predicted the age of samples with respect to three age groups: ‘young’ (2 months), ‘middle’ (6–8 months) and ‘old’ (12–18 months). To this end, the expression patterns were split into 20 individual matrices for each of the 10 noncoding RNA classes and for plasma and EVs. We first normalized the given nonnegative Matrix D by dividing all elements by the maximum value in The first two constraints require all entries of the matrices l solver .We then classified each sample by choosing the index with the highest entry of each column in P and assigned the index as a label to each one. However, the rows of P were invariant to permutations. Here, this means that it is not clear which label corresponds to which age group. Furthermore, a measure of quality for the results was needed. Using the known age, we could construct a ground truth for each sample and calculated the classification accuracy for every permutation. The ground truth used was ‘young’ corresponding to two-month-old mice, ‘middle’ to six- and eight-month-old mice and ‘old’ to twelve- and 18-month-old mice. Finally, we chose the permutation labels that maximize the accuracy and obtain a measure of quality.Click here for additional data file.