Alcanivorax is one of the most ubiquitous alkane-degrading bacteria in the marine ecosystem. Small RNAs (sRNAs) are usually at the heart of regulatory pathways, but sRNA-mediated alkane metabolic adaptability still remains largely unknown due to the difficulties of identification. Here, differential RNA sequencing (dRNA-seq) modified with a size selection (~50-nt to 500-nt) strategy was used to generate high-resolution sRNAs profiling in the model species Alcanivorax dieselolei B-5 under alkane (n-hexadecane) and non-alkane (acetate) conditions. As a result, we identified 549 sRNA candidates at single-nucleotide resolution of 5′-ends, 63.4% of which are with transcription start sites (TSSs), and 36.6% of which are with processing sites (PSSs) at the 5′-ends. These sRNAs originate from almost any location in the genome, regardless of intragenic (65.8%), antisense (20.6%) and intergenic (6.2%) regions, and RNase E may function in the maturation of sRNAs. Most sRNAs locally distribute across the 15 reference genomes of Alcanivorax, and only 7.5% of sRNAs are broadly conserved in this genus. Expression responses to the alkane of several core conserved sRNAs, including 6S RNA, M1 RNA and tmRNA, indicate that they may participate in alkane metabolisms and result in more actively global transcription, RNA processing and stresses mitigation. Two novel CsrA-related sRNAs are identified, which may be involved in the translational activation of alkane metabolism-related genes by sequestering the global repressor CsrA. The relationships of sRNAs with the characterized genes of alkane sensing (ompS), chemotaxis , transporting  and hydroxylation  were created based on the genome-wide predicted sRNA–mRNA interactions. Overall, the sRNA landscape lays the ground for uncovering cryptic regulations in critical marine bacterium, among which both the core and species-specific sRNAs are implicated in the alkane adaptive metabolisms.Alkanes are widespread in the ocean, and  Alcanivorax, as one of the most widespread and abundant hydrocarbon-degrading bacteria in the ocean, is particularly good at metabolizing various alkanes, regardless of aliphatic, branched, halo- and cyclo-alkanes [Alcanivorax play critical roles in the marine ecosystem through regulating the alkane cycling.Alkanes, as a multifaceted issue, fuel human society but threaten ecological balance. In the ocean, alkanes are ubiquitous and can originate from both natural and anthropogenic activities, such as cyanobacterial biosynthesis , geother-alkanes ,4,5,6,7.Alcanivorax based on the cultivation and culture-independent methods [A. borkumensis and A. dieselolei species [A. dieselolei B-5 [For decades, a number of studies have focused on the isolation, diversity and distribution of  methods ,8,9,10,  species ,7,11,12. species . In parttrans- and cis-acting sRNAs), directly interacting with proteins  and intrinsic functions  [It has been well established that sRNAs are widely distributed both in various gram-positive and gram-negative bacteria, and they play diverse and widespread roles in almost all cell processes ,15,16,17c sRNAs) ,19. Givec sRNAs) , transieBacterial sRNAs are usually regarded as the ‘dark matter’ in a genome due to their enormous diversity but difficulty to identify. Firstly, most sRNAs are poorly conserved except those within phylogenetically close species , resultiA. dieselolei B-5. Furthermore, we combined gene expression responses to alkane and targets analysis to predict the sRNA candidates and corresponding regulatory mechanisms in alkane metabolisms. The results provide insights into sRNA-mediated alkane adaptability of the prevalent marine alkane-degrader.The dRNA-seq is an emerging technology for the global mapping of transcription boundaries and transcript origins with single-nucleotide resolution ,34,35. O6), indicating that both rare and abundant transcripts are detected. Therefore, the dRNA-seq could capture the 5′-end of transcripts with different sizes and varied expression levels at the single-nucleotide resolution in this study.To verify the accuracy of 5′-end of transcripts in dRNA-seq, we first compared the start sites of highly conserved tRNAs predicted by two independent methods (tRNAscan-SE 2.0 and ARAGORN) ,37 with To gain comprehensive profiles of the sRNAs, we combined the ANNOgesic prediction and manual inspection based on the dRNA-seq and ssRNA-seq data ,39. FinaTo understand the origin patterns of sRNAs in the genome, considering the accurate TSSs and PSSs obtained by dRNA-seq, we first classified the sRNAs based on their 5′-end origins into two types: ts-sRNA (transcription starting sRNA) and ps-sRNA (processing sRNA) . The ts-A. dieselolei matches the RNase E-specific recognition motifs in diverse bacteria very well  at the downstream 2-nt (+2 position) of cleavage sites, and a 5-nt consensus motif of “RN↓WUU”  was identified , reminisery well , includienterica , Vibrio cholerae  and evenPCC 6803 . This hibacteria . These rA. dieselolei. This finding is in congruence with recent evidence in different bacteria, such as sRNAs from 3′UTRs in Salmonella enterica [Escherichia coli [Xanthomonas campestris [To understand the sources of the sRNAs in the B-5 genome, they were also classified based on their locations relative to the annotated genes. As a result, the sRNAs were distributed in diverse genomic locations and can be divided into three major types: inter-sRNA (intergenic sRNA), asRNA (antisense sRNA) and intra-sRNA (intragenic sRNA) . The intenterica , 5′UTRs hia coli ,26, and mpestris .p > 0.05), while the asRNAs (244 ± 81-nt) and inter-sRNAs (193 ± 84-nt) had notably longer and shorter sizes than others, respectively  than our marine-derived strain B-5 (61.6%) [The inter-sRNAs have the lowest GC content (53.9% for average), which is comparable to rRNAs (55.4%) but significantly lower than other types of RNAs (58.4–61.8%). Except for the inter-sRNA, all other types of sRNAs had similar GC contents (58.4–59.8%), which are significantly lower than CDSs (61.8%), higher than rRNAs (55.4%) and comparable with tRNAs (60.1%) . However (61.6%) ,47. Alth (61.6%) , our resp < 0.001) in this study , which is closely correlated with the structural folding stability of RNA . Except is study , indicatA. dieselolei. It is noted that these features may provide some valuable parameters for the bioinformatic prediction of sRNAs in the genome, and the differences caused by the species-dependent specificity should be fully considered.Taken together, the sequence- and structure-based features of sRNAs are less influenced by their origin patterns , but are closely correlated with their location patterns  in E. coli, and the persistent existence of sRNAs was attributed to their protective secondary structures instead of the ribosome binding [The ORF Finder prediction showed that about one-third of the asRNAs (35/113) or inter-sRNAs (12/34) have coding potential with at least one small ORF . Ribo-se binding . Another binding , the lim binding  and the  binding , these sAlcanivorax suggested that only 4 of them, i.e., 6S RNA, 4.5S RNA, M1 RNA and tmRNA, are widely distributed in the Alcanivorax genus  and four different riboswitches . All of ax genus B. In facEscherichia coli and Bacillus subtilis [A. dieselolei and is conserved in Alcanivorax with varied sequence similarities , in E. coli and B. subtilis (one pRNA), and in Legionella pneumophila (without pRNA) [Alcanivorax in the ever-changing marine environments.The 6S RNA is an important global riboregulator at the transcriptional level through mimicking the open DNA promoter to sequester the RNA polymerase (RNAP) in subtilis . Since tsubtilis . Here, tlarities B. Gene sanivorax . The upstabolism . The zapbacteria , implyinam stem” D, which i 6S RNA . Importai 6S RNA . In facteselolei , which aut pRNA) ,34,47. TE. coli, is the core component of SRP for delivering inner membrane proteins to the translocon or the insertase for membrane insertion [A. dieselolei showed 80–88% sequence identities with that in most of the Alcanivorax species  RNA, termed 4.5S RNA in nsertion ,56. The  species B, and al species . However species . The sec species D, which A. dieselolei, which has widespread homologs with high similarities in Alcanivorax representative strains (rsmI gene encoding 16S rRNA (cytidine (1402)-2’-O)-methyltransferase is located upstream of the M1 RNA gene, but the downstream genes are not conserved in Alcanivorax , which plays a vital role in rescuing the stalled ribosomes on defective mRNAs in bacteria [Alcanivorax tmRNAs shared a conserved upstream gene encoding a hypothetical protein in an opposite transcriptional direction  was also identified in A. dieselolei tmRNA (E. coli and Alcanivorax species (E. coli and Asp-Thr/Ser for Alcanivorax)  . Moreovenchanged . DiffereA. dieselolei.In addition to the above four core sRNAs, some Rfam-annotated locally distributed sRNAs that are conserved in some bacteria are also identified from Alcanivorax and conservatively located at the upstream of an urtA gene encoding a putative urea ABC transporter substrate-binding protein  and cobalamin (vitamin B12), respectively ,64. Althanivorax B. Based anivorax , we deduting way , the aboon peaks A, and twon peaks , indicatbacteria ,65,66.E. coli in a microarray-based transcriptome study about twenty years ago [A. dieselolei, located between the genes map  and rpsB    , which iectively . Meanwhictor Ts) , which ilendidus . Given tA. dieselolei, we also identified two species-specific CRISPR RNAs (crRNAs) of the CRISPR-DR4 family, which is characterized by the same repeat with palindrome to form a 6-bp stem-loop with high GC contents  the Alcanivorax species, especially in A. dieselolei and A. xenomutans (~60%) (Alcanivorax species with 75–100% sequence identities (p < 0.05 or 0.01) with their 16S rRNA-based phylogenies  may be overly conservative, particularly for the inter-sRNAs. Therefore, BLASTN analysis with a looser threshold (≥60% identity and ≥50% coverage) was further performed on the inter-sRNAs against all of the RefSeq_Representative_genomes of Oceanospirillales in the NCBI database. Similarly, most of the inter-sRNAs (27/34) are still Alcanivorax-specific, which is just distributed in several Alcanivorax species  and its antisense sRNA (32 genera), and 6S RNA (14 genera)  detected in e strain . As a res (~60%) . Notablyentities A,B. Intelogenies , indicatlogenies B. Exceptlogenies B, indica species B. In fac species , suggest species . Only th genera) . FurtherOverall, the expression levels of the 549 sRNAs showed very broad ranges, spanning 6–7 orders of magnitudes under the alkane and non-alkane conditions . The topMore than half (54.8%) of the 549 sRNAs showed similar expression levels, irrespective of alkane or acetate as the sole carbon and energy source, while 127 sRNAs (23.1%) were notably up-regulated and 121 (22.1%) were down-regulated in treatments of hexadecane compared to acetate . HoweverA. dieselolei may globally switch the expression of related genes at the transcriptional level through the core 6S RNA to adapt the alkane utilization. Besides the 6S RNA riboregulator, other key protein-regulators may also participate in the process of transcriptional expression switching between distinct carbon sources. For instance, the H-NS (histone-like nucleoid structuring protein) DNA-binding protein (B5T_00005), as a major genome-wide transcriptional silencer [Among the actively responded sRNAs, there are three core sRNAs, i.e., tmRNA, M1 RNA and 6S RNA , which csilencer , was alsPseudomonas [Alcanivorax. Another key global regulator CsrA  is widely distributed in this genus and shows a highly consistent synteny of ‘tRNA-CsrA-Aspartate kinase’ with neighboring genes  through molecular mimicry [By far, the most well-known alkane metabolism regulatory mechanism mediated by sRNA is the multi-tier regulating strategies of the Crc/Hfq system in udomonas . Howeverng genes , implyin mimicry . As a ke mimicry ,77,78,79A. dieselolei (rubA (B5T_04349) in the electron transfer during alkane hydroxylation, aldH (B5T_00039) and exaA (B5T_01640) in the aldehyde and alcohol oxidations, proB (B5T_03726) in the proline synthesis—which is the key component of the lipopeptide biosurfactant generated by A. dieselolei B-5 [fadB (B5T_01660) and acdA (B5T_04219)  . In addiT_04219) , similarsubtilis ,77. ThesA. dieselolei (hfq (B5T_00774). Hfq is the most important chaperone interacting with sRNAs in most bacteria [A. dieselolei and A. xenomutans , chemotaxis , transporting (ompT1–3), hydroxylation , regulation  and other related processes  were used to define candidate sRNAs directly related to the alkane metabolism key genesaccording to one of the following three criteria: (i) intra-sRNAs originating from the key genes, (ii) asRNAs of the key genes and (iii) intra-sRNAs or inter-sRNAs potentially targeting the key genes through the trans-acting mode based on predicted interactions  and cross-membrane transport (ompT3), indicating that multiple processes of alkane metabolism might be closely linked and co-regulated by sRNAs. Moreover, other key genes in alkane transport (ompT2 and ompT1) and chemotaxis (mcp and cheW2) are also predicted to be directly targeted by intergenic or intragenic trans-acting sRNAs , and alkB2 is also the parental mRNA of three sRNAs, in which sRNA761554 from the 3′UTR may antagonize the neighboring gene expression of a transcriptional regulator of the MarR family to indirectly influence the iron metabolism of A. dieselolei. With AlmA as the key hydroxylase of long-chain alkanes [cis- and trans-acting interactions with it . Among all the above 21 candidate sRNAs, there is only one broadly conserved sRNA (sRNA4204314) across the genus Alcanivorax  in future.Therefore, sRNA may play a central role in alkane metabolism through diverse and flexible ways. The intricately mixed relationships among sRNA, mRNA and protein, like ‘one-to-many’ or ‘many-to-one’, collectively constitute the competitive alkane metabolic networks in A. dieselolei B-5 is a type strain of Alcanivorax isolated from the surface water of the Bohai Sea [T). It was cultivated in the artificial sea-water medium (ASM) using alkane n-hexadecane  and non-alkane acetate  as the sole carbon sources, respectively. The mid-log phase (OD600 ~ 1.0) bacterial cells were harvested at 4°C by centrifugation, then the pellets were lysed in 1 mL of TRIzol Reagent , and all samples were subsequently stored at −80 °C until RNA extraction. Three biological replicates were collected for each condition. The total RNA was extracted using a TRIzol reagent according to a previous description [The ohai Sea , and thecription . More deThree types of RNA-seq libraries, including differential RNA-seq (dRNA-seq), fragmented strand-specific RNA-seq (ssRNA-seq) and ribosome profiling sequencing (Ribo-seq), were prepared to target the 5′-ends of transcripts with single-nucleotide resolution to determine the expression ranges and levels of transcripts, and to evaluate the coding potential of sRNA candidates, respectively.The dRNA-seq libraries were prepared according to a previous description which could distinguish the primary and processed transcripts through a 5′P-dependent terminator exonuclease (TEX) treatment , and a pA. dieselolei B-5 [For all of the RNA-seq data, the sequencing reads were quality checked according to Illumina standards and then converted into FASTQ format. The READemption tool (version 2.0.1) was used to perform the read mapping with its subcommands of ‘create’ and ‘align’ against the reference genome of olei B-5 ,92. WhenTo visualize the mapped results of dRNA-seq and corresponding ssRNA-seq, the READemption subcommand ‘coverage’ was used to generate strand specific coverage files based on the read alignments with default parameters. Finally, the data were visualized in the Integrative Genomics Viewer  , with thThe ANNOgesic pipeline (version 1.1.4) was used to predict and annotate numerous features through integrating a suite of tools based on the dRNA-seq and ssRNA-seq data , and allTo detect the sRNA candidates, we first used the ANNOgesic’s subcommand ‘annogesic srna’ to scan all the possible sRNAs (both intergenic and CDS-overlapped sRNAs), and added a parameter of ‘--detect_srna_in_cds’ with the inputs of the transcripts, coverage files, TSSs, PSSs and genome annotation. Meanwhile, the promoter table and terminator file generated by the subcommands of ‘annogesic promoter’ and ‘annogesic terminator’, together with the parameters of ‘--compute_sec_structures’ and ‘--srna_database_path’, were also included in the analyzing process to assist the filter of sRNAs. After that, each of the obtained sRNA candidates was manually verified based on the distributions, abundances and peak shapes of the mapped reads in the IGV visualization.In addition, to overcome the potential bias of the ANNOgesic results due to the selection of parameters, we also manually inspected the distribution of all the mapped reads in IGV across the whole genome to complement the missed sRNA candidates. As a result, only the sRNA candidates that fit all the following criteria are included in the final sRNAs list: (i) sRNAs have mapped reads both in the dRNA-seq and ssRNA-seq results ; (ii) the sRNAs’ abundances normalized by total reads number in ssRNA-seq are ≥10 in at least one carbon source condition (to exclude the very low abundance); (iii) the peak plots of sRNAs are with good shapes, i.e., there are TSSs or PSSs at the 5′-ends, and the 3′-ends are with terminators, PSSs or sharp coverage decreases (to lower the interference from degrading fragments and overlapped transcripts) ,47.https://rna.usegalaxy.eu/, accessed on 12 March 2022) [http://rna.tbi.univie.ac.at//cgi-bin/RNAWebSuite/RNAfold.cgi, accessed on 13 May 2022) was used to predict and visualize the centroid secondary structures of sRNAs with the default parameters [The CD-HIT was used to cluster the primary sequence similarity of sRNAs, with thresholds of over 70% sequence identity and 90% sequence length  . The lench 2022) ,97. To rch 2022) ,26. Plotch 2022) , and thehttps://meme-suite.org/meme/tools/meme, accessed on 13 July 2020) [http://weblogo.threeplusone.com, accessed on 25 February 2022). The above sequence extraction is carried out using a customed Perl script.The putative promoter motifs were detected by scanning the 50-nt upstream of TSSs using MEME (ly 2020) ,99. The ly 2020) . The conwww.bioinformatics.org/sms2/orf_find.html, accessed on 4 December 2022). Only the ORFs, with lengths ≥ 20 aa and with complete start (AUG) and stop codons , were retained as potential sORFs. Moreover, the filtered high quality reads of Ribo-seq were mapped to the detected sRNAs using Bowtie2 [Considering most of the intra-sRNAs should have coding potential in the same frame as the corresponding CDSs, they were therefore excluded from this analysis. The open reading frames (ORFs) in the asRNAs (n = 113) and inter-sRNAs (n = 34) were predicted using the ORF Finder . The 15 reference genomes of Alcanivorax were downloaded from NCBI Datasets . Then, BLASTN was used for similarity searches of the Rfam-annotated sRNAs against the reference genomes with relative loose parameters  for the identity and coverage [Alcanivorax according to a previously described threshold [All the sRNA candidates were aligned and annotated within the Rfam database .The IntaRNA 2.0 program, which enables fast and accurate prediction of RNA–RNA interactions by integrating seed constraints and interaction site accessibility , was useA. dieselolei B-5. These sRNAs can originate from nearly everywhere in the genome through promoter-driven transcription or post-transcriptional RNA processing. RNase E likely plays a key role in the processing and maturation of sRNAs. Most of the sRNAs have no sORF-coding potential and are species-specific distributed in Alcanivorax. Some core sRNAs, including 6S RNA, M1 RNA and tmRNA, are first revealed as responding to alkane, which can likely reprogram the global gene expressions at multiple levels of transcription, post-transcription and translation to benefit the alkane utilization. Two novel CsrA-related sRNAs may also complementarily regulate alkane metabolism through sponging the global translation repressor CsrA, in concert with other mechanisms. Species-specific sRNA-mediated regulations may be widely involved in key processes of alkane sensing, chemotaxis, transporting and hydroxylation. Altogether, both core and local/specific sRNAs may collaboratively reshape the gene expressions at various levels through diverse mechanisms to optimize the alkane utilization in B-5 (Summarized in Here, combining the advantages of dRNA-seq and RNA size selection strategy, we comprehensively captured the high-resolution sRNAs landscape in the marine-alkane-degrading bacterium


A. thaliana leaf nuclei during hypoxia stress was investigated. It was observed that in ncb-1 mutants with a complete absence of CBs, the accumulation of poly(A+) RNA in the leaf nuclei was lower than that in wt under stress. Moreover, unlike in root cells, CBs store less RNA, and RNA retention in the nuclei is much less intense. Our results reveal that the function of CBs in the accumulation of RNA in nuclei under stress depends on the plant organ. Additionally, in ncb-1, retention of introns of mRNA RPB1 (largest subunit of RNA polymerase II) mRNA was observed. However, this isoform is highly accumulated in the nucleus. It thus follows that intron retention in transcripts is more important than CBs for the accumulation of RNA in nuclei. Accumulated mRNAs with introns in the nucleus could escape transcript degradation by NMD (nonsense-mediated mRNA decay). From non-fully spliced mRNAs in ncb-1 nuclei, whose levels increase during hypoxia, introns are removed during reoxygenation. Then, the mRNA is transferred to the cytoplasm, and the RPB1 protein is translated. Despite the accumulation of isoforms in nuclei with retention of introns in reoxygenation, ncb-1 coped much worse with long hypoxia, and manifested faster yellowing and shrinkage of leaves.Retention of RNA in the nucleus precisely regulates the time and rate of translation and controls transcriptional bursts that can generate profound variability in mRNA levels among identical cells in tissues. In this study, we investigated the function of Cajal bodies (CBs) in RNA retention in  Low-O2 Escape Syndrome) which includes the accelerated growth of aboveground organs, lateral root formation, aerenchyma formation, regulation of stomatal opening and closing, and hyponastic or epinastic growth. The second defence mechanism, called dormancy, consists of waiting out unfavourable conditions by the plant—LOQS (Low-O2 Quiescence Syndrome) and involves a slow activation of carbohydrate resources to survive unfavourable conditions with reduced metabolism [A. thaliana, 49 hypoxia response genes (HRGs) have been identified [ADH1) and pyruvic decarboxylase (PDC) [ethylene response sensor 1), ERS2, ETR1 (ethylene resistance 1), ETR2, and EIN4 (ethylene insensitive 2 and 4) are activated. This results in the appearance of the C-terminus of EIN2 in the nucleus, which triggers the transcriptional cascade leading to the expression of group VII ERFs (ethylene response factor) transcription factors which, then, regulate HRGs [Frequent occurrence of floods resulting from climate change, including global warming in recent decades, has led to a reduction of 10 to 40% in crop yields ,3. The 1tabolism ,10,11,12tabolism ,15,16,17tabolism . In A. tentified . The keyse (PDC) ,21,22,23se (PDC) . This gaate HRGs ,26,27,28ate HRGs ,29.A. thaliana. Recently, it has been shown that the level of gene expression is influenced not only by the number of transcripts but also by their localization in the cell, for example, the spatial regulation of gene expression by the separation of mRNA transcription and translation by the nuclear envelope. The precise regulation of transcript transport from the nucleus to the cytoplasm prevents the translation of defective or unnecessary mRNAs in the cell [A. thaliana during hypoxic stress, and after the removal of stressful conditions, the accumulated transcripts move to the cytoplasm during reoxygenation [A. thaliana. The analysis of RNA bound to RNA Ser2P polymerase II and nuclear RNA demonstrates that genes encoding ribosomal proteins, metabolic enzymes, and some components of the photosynthetic apparatus are synthesized and retained as partially or completely processed transcripts in the nucleus during hypoxia, a situation reversed by reaeration. However, the mechanism of transcript retention in the nucleus still remains incomprehensible. The accumulated mRNAs lack significant sequences or features that can explain their temporary retention or release from the nucleus [The mechanisms regulating gene expression in response/adaptation to abiotic stress in plants are not fully understood. At the transcriptional level, phospho-regulation of the CTD domain of RNA polymerase II is an integral part of various biotic and abiotic stress signalling pathways, including microbial-associated molecular patterns/innate immunity signalling ,31 osmotthe cell . Additiothe cell . Moreovegenation . The selgenation  revealed nucleus .+) RNA in the nucleus during hypoxic stress in the root cells of three species: A. thaliana, L. luteus, and A. cepa [A. thaliana ncb-1 mutants devoid of CBs show a lower tolerance to hypoxia than the wild type, which might be connected to a much lower accumulation of poly(A+) RNA in the nucleus of ncb-1 in comparison to the wild-type. The presence of active RNA polymerase II and SR proteins has not been found in CBs so far, which suggests that they are not the site of transcription and splicing. This means that CBs can participate in the later stages of the post-transcriptional regulation of gene expression (PTGR) in plant cells exposed to abiotic stress. CBs are associated with the localization of certain types of RNA in the nucleus. However, it is not known how this phenomenon is related to other PTGRs, including alternative splicing, alternative polyadenylation, and interference [A. thaliana has shown that approximately 40% of the transcripts contain intron sequences. However, these transcripts often contain premature termination codons (PTCs) [Our recent results indicate the function of Cajal bodies (CBs) in the accumulation of poly ,53,54. TA. thaliana subjected to hypoxia stress.In this study, we determined the roles of Cajal bodies, in nuclear RNA accumulation and intron retention in the RPB1 mRNA, in leaves of ncb-1 with a complete absence of CBs coped much worse with long hypoxia (5 days), and manifested faster yellowing and shrinkage of leaves compared to wt plants , a higher transcript level was found in the mutant. Reoxygenation caused a strong reduction in ADH1 expression in both wt and ncb-1. It thus follows that the mutant reacts to the oxygen deficiency is delayed, which may affect growth conditions.To determine how plants respond to induced stress, we examined the expression level of the hypoxia response gene ADH1 . It was +) RNA during hypoxia stress in the roots of A. thaliana and Lupinus luteus [+) RNA distribution and accumulation and whether CBs may be involved in this process, U2B”:GFP  and ncb-1 mutants A. thaliana were studied. For this purpose, microscopic analysis and the measurements of the amount of poly(A+) mRNA in nuclei, CBs, and cytoplasm were performed (+) RNA, which can participate in the translation, was present in the form of an extensive network between chloroplasts (+) mRNA was observed after 5 days of hypoxia vs. normoxia, 2.6 times in wt and over 5.5 times in ncb-1  RNA was more than twice as high in wt than in ncb-1 (+) mRNA was lower by only approximately 30% in wt and as much as over 70% in ncb-1 compared to normoxia. This indicates that the quantity of mRNA recovers more quickly to physiological levels in wt.We previously demonstrated the participation of CBs in the accumulation of poly RNA was detected in the nucleoplasm, except in the nucleolus (+) RNA was observed during oxygen deprivation conditions, similar to the cytoplasm (+) RNA accumulated in the nuclei of wt (ncb-1 (+) RNA in the nucleus decreased in wt and increased in ncb-1 (+) RNA in the nucleus in the A. thaliana roots [A. thaliana cells, regions of the nucleus with strong signals and CBs usually overlap. In contrast to root cells, a precise accumulation of poly(A+) RNA in CBs was only occasionally observed in the leaves (+) RNA in CBs slightly decreased during hypoxia, but after 5 days of stress, it was the highest (+) RNA in the cytoplasm during prolonged hypoxia and reoxygenation respectively, which may affect the condition of plants.In the nucleus, homogeneous signal of poly RNA in both genotypes were related to the level of transcription. To this end, immunolocalization and the amount of phosphorylated Ser2 in the CTD domain of RPB1, the largest subunit of RNA polymerase II (the active form of the polymerase) (ncb-1 was observed (+) RNA accumulated in the nucleus, the amount of active polymerase II was still decreasing  RNA in the nucleus is not accompanied by a strong increase in the level of transcription  A–F, wereobserved B,B’. In creasing E,E’. Thein ncb-1 G. Conseqin ncb-1 F and onl1 (ncb-1 F’. The ocription G.ncb-1 (+) RNA during hypoxia. The level of RPB1 expression of E8E9 was almost twice lower after one day of hypoxia in both the ncb-1 and wt compared to the control conditions  for both tested plants. However, the expression level for the E8E9 amplicon was twofold higher for wt than for the ncb-1 mutant, which directly corresponds to the amount of active RNA polymerase II.Subsequently, we checked whether changes in the level of RNA polymerase II are reflected at the RPB1 (largest subunit of RNA polymerase II) transcription level (AT4G35800) A. Expresncb-1 B,D. The nditions B. A furtncb-1  showed two and three PTCs in intron 2 and 3 mRNA RPB1, respectively  retention in RPB1 mRNA using PCR . RI is tncb-1 . Additiointron 3 . Subsequ 2 and 3 . Transcra groups C. The ela groups C. These a groups C,D. In tectively . mRNAs wectively D. Under re mRNAs B,D.ncb-1 during hypoxia was observed  RNA show the overall response of the transcriptome by changing its localization and quantity in leaf cells subjected to hypoxia. We previously demonstrated strong poly(A+) RNA retention, including coding transcripts in nuclei and CBs, in roots of Lupinus luteus and A. thaliana submerged in water [A. thaliana mesophyll cells, retention of poly(A+) RNA in nuclei was observed with decreasing transcription levels. However, this phenomenon was observed only under prolonged hypoxia in wt. The level of retention of poly(A+) RNA in leaves was lower than that in roots [Marsilea vestita development. Stored RNAs whose translation is essential and is post-transcriptionally regulated after processing and transport to the cytoplasm are expressed at specific times and in specific cells of the gametophyte during spermatogenesis [+) RNA in the nuclei of leaves decreases during reoxygenation and increases in the cytoplasm. This finding supports the hypothesis that after stress, retained poly(A+) RNA is transferred from the nucleus to the cytoplasm for translation. However, this phenomenon is much less intense than that found in the root cells. Our results reveal that the distribution of poly(A+) RNA under stress could be dependent on the metabolism of cells and the amount of RNA in cells and plant organs. If nuclear RNA retention is an important component of the oxygen deprivation condition response, weaker changes in spatial regulation of gene expression in leaves may result from the fact that this is usually the last of the plant organs to submerge during floods and their hypoxia compared to roots in soil is therefore lower.It has been shown that many genes fine-tune transcription in response to abiotic stresses in plants . Our resin water ,41. The in water . Therefoin roots  and nevein roots . Retentiogenesis . Nuclearogenesis . However+) RNA in the nucleus was observed in wt vs. ncb-1, indicating a CBs function in this process. In wt, the amount of poly(A+) RNA decreased in CBs in mesophyll cells during the first stages of hypoxia. In the plants submerged for 5 days in water, the poly(A+) RNA increased compared to 3 days of stress. At the same time, in contrast to root cells, a stronger concentration of poly(A+) RNA in CBs vs. surrounding nucleoplasm was observed less frequently. This indicates that the function of CBs in RNA retention in the leaves’ nuclei could be different from that in the roots and may rely on regulating the quantity of transcripts in the nucleus in a way other than only the accumulation/storage of transcripts. Recently, Rudzka et al. (2022) [ncb-1 with a complete absence in CBs. mRNA RPB1 is one retained in the nucleus, in Lee and Bailey-Serres (2019) [ncb-1 under control and oxygen-deprived conditions. The transcripts with introns are the majority of the total RPB1 mRNA pool during hypoxia in ncb-1. The FISH results with a probe for intron 3 revealed that this isoform is strongly retained in the nucleus. Despite the lack of CBs, isoforms with introns accumulate strongly in nuclei, especially during stress. Additionally, the percentage of isoforms estimated by PCR revealed that more than 80% of the amount detected in nuclei by the probe to exon 8 detects mRNA with introns, so the accumulation of the form without introns is low in ncb-1. This result may be due to a lack of CBs and the need to provide RNA polymerase II under stress survival. Our research shows that intron retention in transcripts is more important than CBs for the accumulation of RNA in nuclei. It has been shown in plants [Marsilea vestita and in CBs in Larix decidua [+) RNA in leaves’ nuclei. This is because we observed a stronger signal of poly(A+) RNA in wt vs. ncb-1. Under hypoxia, fully mature RPB1 mRNA was retained in the nucleus. Lowering the amount of mRNA in nuclei and CBs with a simultaneous increase in the amount in the cytoplasm during reoxygenation suggests their use in translation. In conclusion, CBs in plants are involved in mRNA retention in the nucleus; however, the strength and mechanism of this phenomenon may depend on the type of cells, plant organ, and isoform of RNA.A stronger accumulation of poly(A. (2022)  showed t. (2022) . Our ress (2019)  studies n plants ,58 and in plants  that mRNn plants ,61. In pn plants  in micro decidua . Our resncb-1. AS generates PTC-harbouring nonsense isoforms and is frequently referred to as unproductive alternative splicing (UAS), and mRNAs harbouring in-frame PTCs may trigger a nonsense-mediated mRNA decay (NMD) pathway [A. thaliana were found to generate at least one splicing variant that was upregulated upon NMD impairment [It has been shown that mutation of some core spliceosomal components  had a strong effect on exon skipping and RI events regulated by abiotic stress conditions ,63,64. W pathway . Almost pairment ,69. Howepairment ,71. The ncb-1 nuclei during hypoxia, introns are removed after reoxygenation. Then, the mRNA is transferred to the cytoplasm, and RPB1 is translated. This process enables intense transcription, allowing recovery after stress.The retention of introns in mRNA has been described in a few abiotic stress and model plants ,56,72,73ncb-1, ethylene biogenesis is impaired. There are probably lower levels of hormones in the mutant that regulate the response to hypoxia. It has been shown that mutant ERFs exhibit poorer survival under reduced aerobic conditions [ncb-1, which causes a greater energy deficit than in wt. There is a stronger reduction than at wt, the level of transcription, the quantity of poly(A+) RNA, and their retention in the nuclei. Prolonged stress affects the AS mRNA of RPB1, resulting in a significant increase in the quantity of isoforms with introns and a decrease in the level of fully spliced mRNA. Despite the post-transcriptional splicing of mRNA with introns during reoxygenation, the levels of RNA polymerase II in ncb-1 were lower than those in wt. This leads to ncb-1 leaves being more yellow and curly after 5 days of stress but does not cause necrosis; however, after reoxygenation, ncb-1 are lower and have fewer flower stems.In conclusion, our results indicate that splicing defects resulting from the lack of CBs reduce abiotic stress tolerance. Our findings also indicate the mechanism of this phenomenon. In nditions . The resArabidopsis thaliana ecotype Columbia-0, U2B”’:GFP and ncb-1 [2/s light for 14 days in a long-day growth chamber. Hypoxia was induced by submerging plants in pots in plastic containers filled with tap water (the oxygen concentration in the water: 8.7 mg/L) so that the water level was approximately 5 cm above plants, and the plants were grown in the same conditions but under low light (30–60 µM m2/s).nd ncb-1  mutants For sample preparation, the small pieces of leaves were fixed in 4% paraformaldehyde  and 0.25% glutaraldehyde  in PBS pH 7.2 overnight at 4 °C. The material was dehydrated in increasing ethanol concentrations containing 10 mM dithiothreitol (DTT) , supersaturated and then embedded in BMM resin  with 10 mM DTT) at −20 °C under UV light for polymerization. The embedded material was cut on Leica UCT ultramicrotome into semithin sections (1.5 µm), which were placed on microscope slides coated with Biobond . Before performing immunocytochemical reactions, the BMM resin was removed by washing the sections in pure acetone twice for 15 min and then in water and, finally, in PBS pH 7.2.The sections were incubated with the primary antibody  1:50 in 1% BSA overnight at 4 °C, followed by the secondary antibody 1:250 in 1% BSA for 1 h at 35 °C. After incubation, the sections were washed twice with PBS for 5 min, and then the nuclei were stained with Hoechst 33342  1:2500 for 10 min. Sections were then rinsed with PBS solution and RNAse-free water for 5 min and dried for approximately 20 min. The control reaction was performed in the same manner, except that the primary antibodies were omitted.2HPO4 3 mM NaH2PO4, 2.7 mM KCl, 0.1% Tween 20, 80 mM EGTA , 5% formaldehyde  for 30 min at room temperature. Next, the samples were treated twice for 5 min each in 100% methanol, three times for 5 min each in 100% ethanol and incubated for 30 min in ethanol: xylene (1:1)  with gentle agitation. The samples were then washed in fixation buffer without formaldehyde and fixed in 5% formaldehyde 29 at a concentration of 50 pmol/mL.In situ hybridization was performed as previously described in Germain et al. (2010) . Small pArabidopsis thaliana leaves was isolated using the TRIzol reagent  and the Direct-sol RNA MiniPrep kit .Total RNA from 100 mg 18 primer. The samples were incubated in a thermocycler at 25 °C for 10 min, then 55 °C for 30 min, and finally 5 min at 85 °C .Reverse transcription was performed using the Transcriptor First Strand cDNA Synthesis Kit  with oligo d(T)Arabidopsis thaliana (AT4G35800) The sequences of primers are shown in GAPDH (At1g13440), ACT2 (AT3G18780), UBC5 (AT1G63800)) was calculated using the Light Cycler 96 program. In order to determine the PCR efficiencies, standard curves for target and control genes were obtained using a series of cDNA dilutions as a template. In addition to the LC96 system, the LC96 software  was used to interpret the amplification curves and estimate the level of transcript expression.Real-time PCR was performed using the LightCycler 480 SYBR Green Master kit . The primers used in the procedure were designed for the exons and introns of the gene encoding the largest subunit of RNA II RPB1 polymerase for Prior to proceeding with the PCR procedure, the concentration of single-stranded DNA (ssDNA) was measured with a Qubit 4 Fluorimeter  using the Qubit TM ssDNA Assay Kit . A PCR kit with Color OptiTaq DNA Polymerase (EURx) was used for the procedure. A mixture was prepared with a final volume of 25 μL according to the manufacturer’s procedure. The PCR reaction was carried out in a C1000 Touch Thermal Cycler  with the following parameters: initial denaturation at 95 °C for 3 min (1 cycle), denaturation at 95 °C for 30 s, annealing at 56 °C for 30 s, extension at 72 °C for 1 min , final extension at 72 °C for 5 min (1 cycle) and cool down at 4 °C indefinitely. To evaluate the PCR products, electrophoresis was performed on a 2% agarose gel. Documentation was prepared using the ChemiDocTM  tactile imaging system.Elution of the DNA fragments from the agarose gel was performed with the Gene MATRIX Agarose-Out DNA Purification Kit (EURx). Sequencing was performed by Genomed S.A. . The DNA sequences of the individual samples were analysed by software Fast PCR software .2, 0.05% Tween-20.5% sucrose) to which was added 3% BSA . About 80 μL of the mixtures were then spread onto a glass slide and dried at 35 °C. These samples were used for localization of RNA polymerase II and FISH to mRNA of RPB1.For nuclei isolation, about 100 leaves were fixed for 20 min in 4% paraformaldehyde solution . Then the material was chopped as finely as possible with a razor blade in the presence of 750 µL of lysis buffer . The was diluted by adding 3 mL sorting buffer  diluted 1:100 in 1% BSA in PBS pH = 7.2 overnight at 4 °C. Next incubation with goat anti-rat antibodies labelled with Alexa Fluor 546  for 1 h at 36 °C was performed. The control reaction was performed in the same manner, except that the primary antibodies were omitted.For sample preparation, the small pieces of leaves were fixed in 4% paraformaldehyde  and 0.25% glutaraldehyde  in PBS pH 7.2 overnight at 4 °C. The material was dehydrated in increasing ethanol concentrations containing 10 mM dithiothreitol (DTT) , supersaturated and then embedded in BMM resin  with 10 mM DTT) at −20 °C under UV light for polymerization. The embedded material was cut on Leica UCT ultramicrotome into semithin sections (1.5 µm), which were placed on microscope slides coated with Biobond . Before performing immunocytochemical reactions, the BMM resin was removed by washing the sections in pure acetone twice for 15 min and then in water and, finally, in 4xSSC pH 7.0.v/v)  with 30% (v/v) formamide  on isolated nuclei and resin sections. For exon 8 and intron 3 mRNA of RPB1 localisation probe respectively 5′dig-CTGTTT TGGTATGATAAGATTAAAAACTTGTTT3′ and 5′dig-GTAAAATTCATTCGAAAGAAAAGA CGAAC3′. After wash steps in SSC buffer incubation with rabbit anti-digoxygenin antibodies  diluted 1:100 in 1% BSA in PBS pH = 7.2 in 4 °C was performed overnight. Then goat anti-rabbit antibodies with Alexa Fluor 488 diluted 1:500 in 1% BSA in PBS pH = 7.2) were used. DNA was stained for 10 min with Hoechst 33342  diluted 1:2500 in PBS pH = 7.2. The control reaction was performed in the same manner, except that the primary antibodies were omitted.FISH was conducted for a minimum of 16 h after 1 h prehybridisation step using hybridization buffer (50% (+) RNA, RNA polymerase II, and mRNA RPB1 was performed on the basis of 3 independent biological replicates. From 32 to 66 cells or nuclei were analyzed for each antigen and experiments. Statistical analysis was performed using the PAST program. To compare all groups and to determine if there were any significant differences between them, a nonparametric Kruskal–Wallis test was used. To test between which group differences existed, a Mann–Whitney U test with Bonferroni correction was used.A Leica Sp8 confocal scanning microscope equipped with Hc PL APO 63×/1.40 oil objects with 63-fold magnification was used to analyze the results. The photos were recorded using the Leica LAS AF software , Images were acquired sequentially in the blue (Hoechst 33342), green , red  channels. The optical sections were collected at 0.5-lm intervals. For image processing and analysis, ImageJ  software was used. The obtained data were corrected for background autofluorescence determined based on negative control signal intensities. All measurements were conducted at the same magnification, area, and time of laser scanning. The analysis of the quantity of poly(A


Science, Wei et al. report that long-interspersed element-1 (LINE1) RNA is a key target of the N6-methyladenosine (m6A) ‘eraser’ fat mass and obesity-associated protein (FTO). The FTO-LINE1 RNA axis is involved in chromatin regulation and is essential for mammalian development.1In a recent paper published in 6A is the most common RNA modification in mammalian cells and controls, among others, translation, decay and even chromatin state.3 The m6A landscape is shaped by m6A ‘writers’ and ‘erasers’, while the effect of the m6A modification is determined by ‘readers’. FTO is an alpha-ketoglutarate (α-KG)-dependent oxygenase, which functions as an m6A demethylase (‘eraser’), making m6A modifications reversible.4 In addition to m6A, FTO also mediates demethylation of N6,2′-O-dimethyladenosine (m6Am) and N1-methyladenosine (m1A).2 Recent studies have demonstrated an important role for m6A in regulating mouse embryonic stem cell (mESC) fate and controlling early mammalian embryonic development.2 Wei et al. extend these findings by using Fto−/− mESCs and mice to identify physiological substrates of FTO and to investigate its role in early mammalian development.1mFto−/− mESCs, Wei et al. observed changes in m6A levels of chromatin-associated (caRNA) and soluble nuclear fraction RNA, suggesting nuclear FTO substrates. caRNAs, and especially chromosome associated-regulatory RNAs (carRNAs), have recently been shown by the He lab to be targets of the m6A ‘writer’ METTL3 in mESCs.5 As m6A ‘writers’ and ‘erasers’ work together to create an equilibrium of m6A modifications, subsets of carRNAs could also represent FTO substrates. In line, Wei et al. observed hypermethylation of carRNAs in Fto−/− mESCs. The carRNA subset ‘repeat RNAs’ exhibited the strongest correlation between m6A hypermethylation and transcript downregulation, with the repeat RNA LINE1 being most strongly affected. LINE1 elements belong to the LINE family and represent autonomous, active retrotransposons, and LINE1 RNA has been shown to regulate chromatin.6 Wei et al. observed LINE1 RNA-chromatin interaction as well as co-localization and binding of LINE1 RNA and FTO in mESCs, supporting LINE1 RNA as a physiological target of FTO.In 3 2C-like cells share features of the 2-cell state embryo and are characterized by expression of retrotransposons from the murine endogenous retrovirus with leucine tRNA primer (MERVL) family and repression of ESC pluripotency genes.3 Wei et al. observed that Fto−/− mESCs partially recapitulate this phenotype and exhibit a genetic and phenotypic 2C-like state.Interestingly, LINE1 RNA is essential for mESCs and is involved in suppression of the 2-cell (2C) program.6A ‘reader’ YTHDC1 has previously been shown to destabilize m6A-containing carRNAs.5 In line, mechanistic analyses revealed increased binding of LINE1 RNA by YTHDC1 in Fto−/− mESCs. LINE1 RNA was upregulated upon loss of Ythdc1, and Fto knockout (KO) led to enhanced LINE1 RNA decay. These results support the notion that an increase in m6A modifications of LINE1 RNA could lead to destabilization via YTHDC1 binding. Fto KO reduced levels of LINE1 RNA-DNA association and R-loop formation at LINE1 loci. In addition, Fto−/− mESCs showed decreased RNA synthesis and chromatin accessibility. Closed regions were enriched at m6A-modified LINE1 RNA loci. LINE1-containing genes exhibited a stronger decrease in expression upon Fto KO, which was accompanied by an increase in intragenic LINE1 RNA m6A levels, respectively. Genes containing downregulated intragenic LINE1 RNA were particularly affected, arguing for a cis-regulatory role of intragenic LINE1. In line, these gene loci, including early development and pluripotency genes, exhibited a more closed chromatin state. In contrast, 2C genes, such as Dub1 and Zscan4, do not contain LINE1 RNA and exhibited a decrease in expression upon LINE1 RNA-targeted delivery of WT FTO in Fto−/− mESCs. This indicates that 2 C genes are repressed by LINE1 RNA in the WT setting. Overall, Wei et al. show that LINE1 RNA regulates pluripotency genes in mESCs in cis, while 2C genes are regulated in trans. This program seems to be governed by demethylation via FTO and installed by regulating chromatin accessibility. Fto KO, in contrast, induces closed chromatin at pluripotency gene sites and releases the repression at 2C genes, leading to the observed 2C-like state and differentiation defects.The mFto−/− mice led to genotype-specific phenotypes during embryonic development, with FTOmat KO/pat KO embryos not being viable. Analysis of the Ftomat KO/pat KO vs WT morula stage revealed repressed LINE1 RNA, decreased expression of essential regulators of early embryonic development and upregulation of 2C markers. Of note, Fto−/− offspring (F1) of heterozygous Fto+/− mice (P) is viable. The studied phenotypes were observed in the F2 generation after intracytoplasmic sperm injection using female and male Fto−/− animals.In vivo analysis revealed ovarian defects and impaired fertility in female KO mice, i.e. reduced numbers of immature germinal vesicle (GV) oocytes and impaired maturation to the mature MII oocyte stage. GV and MII oocytes exhibited reduced LINE1 RNA levels, more closed chromatin and downregulation of LINE1 RNA-containing genes. Mating of female and male WT and cis and trans achieved? To answer these questions, it will be useful to investigate additional substrates of FTO, including other repeat RNAs identified in this study. In addition, it will be important to understand how the cell type-specific function of FTO is achieved, especially cytosolic versus nuclear function. This and previous studies have highlighted the importance of the m6A landscape for mediating chromatin-regulatory functions of RNA.3 m6A ‘readers’ seem to be important in mediating this crosstalk and are likely to interact with protein complexes involved in DNA methylation and histone modification. But how exactly the FTO-LINE1 RNA axis is involved in these processes remains to be determined. Interestingly, regulation of transposable elements emerges as one of the major targets of this m6A RNA-chromatin axis and plays a role in development. Repeat RNAs, such as LINE1, seem to be highly important to suppress the 2C-like state in mESCs, as shown by Wei et al. and others.3 Interestingly, the 2C-like state is characterized by the expression of MERVL retrotransposon family members. Different retrotransposons are transcribed during embryogenesis, and this pattern seems to partially be controlled by m6A methylation of repeat RNAs, which in turn seem to control chromatin accessibility.In the future, it will be important to further explore the mechanistic link between the FTO-LINE1 axis and YTHDC1. Does FTO demethylate LINE1 RNA in mESCs and during embryonic development to specifically prevent YTHDC1 binding and subsequent LINE1 RNA destabilization and chromatin closure? What other factors are involved in this process, and how are the different functions of LINE1 RNA in cis. In contrast, 2C genes are suppressed by FTO, presumably via LINE1 RNA-mediated regulation in trans. This novel FTO-LINE1 axis is also essential for oocyte and embryo development.In summary, Wei et al. could show that LINE1 RNA is the major target of FTO in mESCs (Fig.


However, mRNA has limitations in stability. To overcome the shortcomings of mRNA, circular RNA is emerging as a new modality. In this review, several current methods of manufacturing circular RNA in vitro are introduced and their advantages and disadvantages are reviewed. Furthermore, this study discusses which fields and directions of research and development are needed for the increase in the efficacy and productivity of circular RNA as a therapeutic agent and vaccine formulation. HowevCircRNAs are generally produced by exon skipping or back-splicing in organisms in nature and have been reviewed well in several papers ,5 Figur. PreviouAs circRNAs have various application possibilities in biomedical areas, with some advantages compared to linear RNA such as ribonuclease resistance and efficient ribosome recycling due to start and stop codons in close proximity, circRNA has recently gained attention. Therefore, several start-up companies have received investment funds for circRNA technology, and their studies have been more actively published or filed as patents in recent years.Although in vivo circRNA synthesis is possible for achieving goals for various applications by delivering DNA for transcription and subsequent circRNA formation through back-splicing or other mechanisms , in vitrIn this review, we will briefly introduce in vitro circRNA preparation techniques for biomedical purposes with recently published examples, then focus on the pros and cons of each method and intensively discuss which fields and direction of research and development should be involved.Chemical ligation strategies for oligonucleotides have been developed for decades, and examples for circRNA preparation, such as 5′ or 3′ terminal functionalization with amines, thiols, azides, and others for cyclization , and repFor efficient chemical-based ligation, the ligation junction (5′ and 3′ ends) should be positioned in close proximity by a self-templating effect based on the intrinsic secondary structure  or by teSolid-phase synthesis methods also can be used for circRNA preparation . HoweverCurrently, chemical ligation is not a common method for circRNA preparation due to biosafety concerns and low ligating efficiency compared with other methods such as the permuted intron-exon (PIE) method with nearly 100% efficiency ,17, as dEnzyme-based ligation methods have been commonly used for circRNA preparation. Generally, ATP-dependent T4 DNA ligase ,25, T4 RThe ligase has different preferences for the 5′ terminal nucleotide of the donor (pC > pU > pA > pG) and the 3′ terminal nucleotide of acceptor (A > G ≥ C > U) strands , which sInterestingly, recently Liu et al. reported that circRNA prepared by T4 RNA ligase without extraneous fragments exhibited minimized immunogenicity, whereas circRNAs prepared by a ribozyme-based method were immunogenic . The autPIE methods ,36 have 1-methylpseudouridine (m1ψ) for linear RNAs [Compared to chemical- or enzyme-based ligations, large-size RNAs can be circularized using ribozyme-based methods. For example, Wesselhoeft et al. reported that circRNA up to 5 Kb in length could be prepared by PIE in vitro . Moreoveear RNAs  or N6-meear RNAs  were impear RNAs , as wellear RNAs ,43. Therear RNAs .Recently, circRNA prepared by PIE was applied for vaccine development for SARS-Cov2 and variants ,46. In tAs another example, circRNAs prepared by PIE- or enzyme-based methods were used to prepare adenosine deaminase acting on RNA (ADAR which is for the precise conversion of adenosine to inosine)-recruiting RNAs (arRNAs) for higher editing efficiency and longer periods of RNA editing than their linear counterparts , demonstHowever, as RNA structures for ribozymes or IRES functions are crucial in PIE-based methods, spacer sequences between functional regions or other accessory sequences should be finely tuned for the proper folding of ribozymes for PIE reactions and IRESs for subsequent efficient translation .Self-cleaving ribozymes were also used for circularization in vitro ,48 throuHere, we briefly reviewed the representative in vitro methods for circRNA preparation. Each method has pros and cons as the ligation strategies are completely different  and therTo improve circRNA preparation strategies, we would like to discuss several issues and further efforts that should be undertaken by researchers.Firstly, in vitro transcribed RNAs have several issues that affect in vitro circularization efficiency, such as heterogeneity of the 3′ ends of IVT RNAs and aggregates upon precipitation . These iCircularization efficiency is also strongly dependent on the RNA structure . TherefoFor ribozyme-based methods, antisense and antisense binding sequences (homologous arms at the 5′ and 3′ ends)  can workChen and Lu suggested that unnatural bases or RNA-binding proteins (RBPs) may help to improve the efficiency of ligation by altering the structure of linear RNA precursors . HoweverFor in vivo applications, circRNA purity is an important factor as some reaction contaminants such as dsRNA or DNA can trigger unwanted immunogenicity. For example, TLRs in endosomes and RIG-I-like receptors (RLRs) in cytoplasm detect nonself RNA in endosomes, initiating an inflammatory cascade as well as cytoplasmic dsRNA-responding proteins such as protein kinase R (PKR) . GeneralIn the case of preparing circRNA with PIE methods, the intronic scar should be also considered in terms of immunogenicity  and tranFor biomedical applications, a large-scale production process is required. This scale-up issue was previously discussed by Chen and Lu . As suggIn addition to method optimization, the translation efficiency or function of the prepared circRNA should be comparable with or better than that of its linear modified RNA counterpart by further optimization as shown in recent papers ,45,47.Finally, to develop new strategies for circRNA preparation, we would like to suggest that several requirements mentioned below should be met, in addition to previous suggestions ,11.(1) Less circularization and purification steps for circRNA preparation are required. Even an ethanol precipitation step affects RNA’s quality, suggesting that any additional steps would affect the efficiency and yield of circRNA preparation. The ideal method would be efficient circularization during IVT without additional steps. In this case, a ribozyme-based method would be more suitable.(2) In the case of developing ribozyme-based methods, the intronic scar should be eliminated or minimized to prevent immunogenicity from the intronic scar. As the intronic scar can affect translation efficiency in addition to immunogenicity, the surrounding sequences should be optimized to avoid those effects . If a no(3) During circRNA preparation and purification processes, nicked circRNA populations  should b(4) The efficacy of larger GOI circularization should be improved. As the circularization of linear precursors, especially for large sizes, is entropically disfavored, a ribozyme-based method using an intrinsic self-splicing mechanism would be advantageous over chemical- and enzyme-based methods for preparing large circRNAs .(5) Large-scale production and purification should be possible. If circularization can be completed during IVT as a one-step process, the IVT scale would be proportional to circRNA production.Although major in vitro circRNA preparation methods were developed in the 1990s ,31,58,59


Their RNA binding preferences have been investigated for almost two decades, and even though some RRM domains are now very well described, their RNA recognition code has remained elusive. An increasing number of experimental structures of RRM-RNA complexes has become available in recent years. Here, we perform an in-depth computational analysis to derive an RNA recognition code for canonical RRMs. We present and validate a computational scoring method to estimate the binding between an RRM and a single stranded RNA, based on structural data from a carefully curated multiple sequence alignment, which can predict RRM binding RNA sequence motifs based on the RRM protein sequence. Given the importance and prevalence of RRMs in humans and other species, this tool could help design RNA binding motifs with uses in medical or synthetic biology applications, leading towards the  The interactions between proteins and RNAs are key to many different biological processes and crucial for the proper functioning of the cells. The RNA recognition motif is the most prevalent protein carrying such functions in eucaryotes, so understanding how this protein motif interacts with different RNA sequences is of immediate relevance. However, a general recognition code between this motif and the RNA is not known yet. We have performed a computational analysis to understand how the recognition process works, identify the main binding mode between the protein and the RNA and which are the most relevant amino acids involved in the recognition. This analysis allowed us to build a predictor that estimates the binding between an RNA recognition motif and any RNA sequence. We have named this method RRMScorer and it only needs the sequence of both the protein and the RNA to predict the results. We hope that this new tool will be useful to identify new potential RNA targets or to design new protein mutants to modify their RNA binding capabilities. The RNA recognition motif (RRM) is a well-studied RNA-binding domain that is prevalent throughout organisms, but especially so in eucaryotes, where it plays crucial roles in many aspects of post-transcriptional gene regulation . A singlMany efforts have been made to understand the RNA recognition mechanism, and despite the identification of some RNA consensus sequences for several RRM domains –19, manyHere we introduce RRMScorer, a scoring method that overcomes these limitations by carefully combining information on protein and RNA, structure and sequence data, whilst minimising bias in both areas. A comprehensive analysis of these data reveals RRM-RNA interaction preferences, which are used to generate a score that informs how likely a particular RRM-RNA interaction is. We focus on the canonical binding mode characterized by the binding of the RNA to the surface of the central β-sheet of the RRM fold, , which cin vivo RNA targets of RRMs with as yet unknown function and interactions.This meta-analysis of available RRM-RNA information brings us closer to understanding how this versatile motif works. RRMScorer provides a novel means to decipher a general recognition code for canonical RRMs by using a completely different approach from other RBP predictors, where complex deep-learning networks are trained using high-throughput RNA binding data to determine the consensus for different RRM domains ,22. InstTo gather the RRM data available we started from the Pfam database , where 1From these RRM-RNA complexes, all protein-RNA interactions were computed using an in-house script and stored in a text file also available from the Bitbucket repository . An aminFor the analysis, it was necessary to reduce the bias in the original complete RRM dataset. The protein sequences were extracted from the 1259 RRM domain structures  and to eThe sequence and structure information from the reduced RRM dataset was fed into PROMALS3D  to generThe clean alignment was further enhanced to improve the often poor alignments for the loops and terminal regions. Based on the principle that for amino acids in the loop regions the most important characteristic is how they are connected to the fixed secondary structure elements, these regions were ‘squeezed’ so all the gaps are placed in the middle of the loop regions, or at the extremes for the N and C terminal regions, in case of shorter loops or terminal regions, respectively . This alTo identify and cluster the entries where RNA molecules bind in a similar orientation to the RRMs, we developed a method to compute a pairwise similarity score based on the amino acid positions of the RRM that recognize the nucleotides of the RNAs . This waFor each compared RNA pair, only the alignment with the highest similarity score is retained. A similarity matrix was then constructed from these best scores that includes all the RRM-RNA complexes, and which is later used to identify the different binding modes. The matrix is available as a CSV file from the supplementary information folder in the Bitbucket repository .To select a homogeneous cluster, we grouped all the entries that have a minimum score of 0.25 with at least 25% of the complexes in the cluster. We explored the effect of changing the cutoffs on the cluster generation , and selAfter visual inspection of some of the entries within cluster 0 we verified that the RNAs were bound similarly, and that we essentially captured the canonical binding mode . The entTo conduct the RNA alignment, we used the same method as for the RNA binding modes identification, based on the amino acid sequence positions of the RRM that the nucleotides of the RNA are in contact with. When comparing two RNAs, the sliding window position that generates the highest score corresponds to the best possible alignment for those sequences. To align the 187 RNAs included in cluster 0, we selected the medoid, the entry with the highest similarity scores with respect to all other entries . For the 186 remaining entries we found the best alignment against the medoid and generated the RNA MSA. Gaps were added at the 5’ or 3’ ends of the RNA sequences when required to ensure that nucleotides in the same position were properly aligned. This is necessary due to the different length of the RNA fragments considered, which ranges from 3 to 11 nucleotides. The FASTA file with all the RNAs aligned from cluster 0 is available from the Bitbucket repository .The RRM-RNA scoring method we have developed, RRMScorer, is purely based on statistics derived from the carefully curated multiple sequence alignment. It is an adaptation of the GOR method ,36 that i  has been observed interacting with an amino acid residue in position j . However, selecting all available interactions for the scoring would bias this approach to the amino acids and bound RNAs for the most studied RRMs, which are overrepresented in our dataset. To generalize the approach as much as possible, we first selected a subset of nucleotide and residue positions that interact with each other in at least 20% of the RRM-RNA complexes having different UniProt identifiers in cluster 0, so limiting the analysis to key binding positions. These retrieve all the RRM positions already well known for its importance with respect to RNA binding, such as solvent-exposed RNP1 and RNP2 residues. In total, 30 unique interactions from 20 RRM sequence positions to 5 RNA sequence positions were considered. A data frame was then generated for each of the interactions that incorporates the scores between all the residues and nucleotides in that position, as calculated from Eq 2. To get the final binding score between a target RRM sequence and a target RNA sequence, we take the average from the 30 calculated values. The matrices were rendered and coloured for all the selected interactions E.g., , and areTo cross-validate the scores in as unbiased a manner as possible we followed several steps. First, we removed the complex for which we are calculating the score from the training set. With the remaining entries we calculated the residue-nucleotide preferences for the 30 selected interactions, then calculated the final binding score for the entry being scored, given the amino acid sequence of the RRM, and nucleotide sequence of the RNA. This constitutes the single ‘actual binding’ score for that entry. All the 187 entries used to generate the RRMScorer were taken as the training set, as we have experimental proof that the RNAs bind to their respective RRMs.A randomized test set was generated to validate that the RRMScorer can identify true binders. For each of the training entries an entry was randomly generated, whereby the RRM amino acid sequence is retained, but instead we picked a random nucleotide from the same position in the RNA alignment, but belonging to a complex with a different protein (UniProt Id). With this approach we expect to select nucleotides less likely to bind the tested protein, even though is still possible to pick the same nucleotide as in the original sequence.in vitro technique to quickly analyse the RNA binding preference of RNA binding proteins (RBP)  A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,Shi-Jie ChenAcademic EditorPLOS Computational BiologyNir Ben-TalSection EditorPLOS Computational Biology***********************Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Roca-Martinez and coworkers have performed a computational analysis of RNA recognition motifs (RRMs) and RRM-RNA complexes in an effort to develop a scoring method for predicting and evaluating the probability of interaction between canonical RRMs and single-stranded RNA. The authors use available sequence and structure information to obtain individual scoring matrices for commonly observed interacting positions in RRM and RNA sequences , describing the preference of different nucleobase types to interact with different residue types. While the question of understanding the physicochemical underpinnings of RNA-protein interactions and predicting and sculpting their sequence determinants is extremely timely and important, the comments below should be addressed in detail before the suitability of the manuscript for publication can be adequately assessed.Major comments1. The description of the RRM-RNA scoring approach (p. 6), the very heart of the manuscript, is unclear and sloppy. Equation 2 is not consistent with the text and a proper explanation of the symbols and indices used is missing . Also, the explanations are given in an incomplete way e.g. the sentence “…is related to the number of times adenines interact with any other amino acid residue in position beta1-1” is missing the crucial qualifier “adenines at position 1”. This makes it hard to comprehend how the scores were actually calculated.2. More importantly, the motivation and the physical foundation of the scoring function is not adequately explained. Centrally, the scores do not consider the frequency of amino-acid residues observed at a specific position (see e.g. the second term in Eq. 2), making it not symmetric when considering nucleotides and residues, respectively. In the example given on p. 6, the score should depend on the frequency of arginines interacting with adenines as well as with other nucleotides, but this is not included.3. Also, the scoring function shares resemblance with the standard quasi-chemical approach for defining knowledge-based potentials ), but with important differences. Namely, the authors here normalize the number of occurrences of a given event (e.g. presence of a nucleotide at a given site interacting with a given residue) by the number of all events other than that event  and not the total number of all events . Why is this? The authors are motivated by the GOR method for analyzing secondary structural propensities, but it is not clear that the same formalism is applicable here – namely, in the GOR method one analyzes the linkage between an object (amino acid) and its property, while here one analyzes the propensity of two objects (amino acid and nucleotide) to co-occur in the same context (i.e. contact). This is related to the asymmetry discussed in point 1.4. The rather extensive literature on contact-based statistical potentials for nucleic-acid/protein interactions should be adequately cited and discussed .5. The authors refer to their randomized test set as a negative test set (p. 7). As there is no guarantee that many members of this set are not actual binders – the naming should be changed to something like “background set” or “randomized set”, but certainly not “negative set”. More critically, randomization was only done on the side of the RNA sequences (change of 1 nucleotide in the sequence) and not on the side of RRM sequences – this relates to the asymmetry of the whole approach as discussed above and must be properly defended.6. Defining clusters as all complexes that have a certain similarity score with at least 25% of complexes in the cluster is quite low as a cutoff (p. 5). Of course, if one increases the cutoff, one risks not having sufficient samples for adequate statistics. The authors should defend the choice of their cutoff by providing quantitative evidence that it does not overly impact the scores i.e. the qualitative features of their method.7. For the validation of their scoring method the authors analyze two experimentally studied examples, while extensive data on RRM binding motifs obtained by different experimental methods exists and is not used. See for example the RNAcompete results ) or the Attract database (PMID: 27055826). The authors should validate their results on an as extensive a set of experimental data as possible.Minor comments1. On p. 9, the authors state that “The unbiased number of observed contacts in the training set that is used to calculate the scores is also shown in the preference matrices , below each of the scores”. However, these numbers are not integers, so it is unclear what they actually refer to.  2. Numbers in Figure 2 are not fully consistent with the text .3. It is stated that the "alignment for the RRM-RNA structures" is available in Dataset S6 (p. 4). However, Dataset S6 contains the RRM-RNA similarity matrix.4. In the caption of Table 1 (p. 28), the authors state that “The symbols reflect the score change after the E87N mutation”, however there are no symbols.https://www.biorxiv.org/content/10.1101/2022.09.09.507333v1.full.pdfReviewer #2: The paper describes construction of statistical based potential for scoring rrm domain interactions with specific RNA sequence. They use structural model of binding to identify contacting residues and then create sequence based interaction statistics. The authors acknowledge that the method is limited to already known binding modes of rna to RRM domains or very close to those. The authors validate the approach on leave out training set as well as few novel cases. The problem is still open - but there is a progress. I think the paper can be published. Would be interesting to compare the approach with AF like approach for modeling protein RNA interactions (see the link below). Also can protein rna models from this approach can be used to create alignment? Reviewer #3: The manuscript by Roca-Martinez RNA-recognition motifs discusses ascoring method to estimate binding between an RRM and a singlestranded RNA, and the method aims to predict RRM binding RNA sequencemotifs based on RRM protein sequence. The authors adopt a simplerstatistical approach over deep learning method employed in severalexisting methods for better interpretability. Interesting results ondiscriminatin of high affinity RNAs with the UAG core motif from loweraffinity RNAs are reported.While the reported method serves a useful purpose towards the overalltask of solving the problem of deciphering RNA recognition code ofRRMs, there are a number of significant issues:1. The score described by Equation 2 is not explained and the physicalunerpinning cannot be found. The two log terms summed are the same asproduct of two ratio. But it is not clear what does it mean, and whydoes this make sense? Does it model some eperical binding?Conservation? Not clear. Furthermore, why the denominator in thefirst term comes to be f_n - N_i,R_J ? This is not understandable.2. There are numerous places where the method development depends onvisual inspection. This raises serious issure of reproducibility.3. The model appears to be rather restrictive and works only for cluster 0 and no binding mode change can occur.4. The negative test should be strenthened and should include other entriesif possible.Other issues:1. p.4. "the number of unique postions between both nucleotides" It is not clear if the authors meant positions only in A, only in B, or both?2. Will the results sensitive to the specific threshold of 5A?Minor issues1. Figures seems to be jumping around in order, and it makes it difficult to go back and forth.**********Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, Data Requirements:http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: Reproducibility:https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocolsTo enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at  9 Nov 2022Attachmentresponse_to_reviewers.pdfSubmitted filename: Click here for additional data file. 7 Jan 2023Dear PhD Vranken,We are pleased to inform you that your manuscript 'Deciphering the RRM-RNA recognition code: A computational analysis' has been provisionally accepted for publication in PLOS Computational Biology.Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. 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All press must be co-ordinated with PLOS.Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. Best regards,Shi-Jie ChenAcademic EditorPLOS Computational BiologyNir Ben-TalSection EditorPLOS Computational Biology***********************************************************Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The authors have significantly revised the manuscript and have adequately addressed all of my concerns from the first round.Reviewer #3: The authors have made changes to improve the manuscript. However, a number of important issues have not been addressed adequately.I find it still difficult to understand the origin of Eqn 2 and the physical basis remains unclear. Furthermore, while I appreciate why the authors are are using visual inspection for verifications, the issue of reproducibility largely remain.**********Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #3: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  1223-442824 | ploscompbiol.org | @PLOSCompBiolPLOS Computational Biology | Carlyle House, Carlyle Road, Cambridge CB4 3DN | United Kingdom


Self-rated health (SRH) is considered a condensed summary of information about bodily conditions that involves people’s biological, cognitive, and cultural status, but has been under-studied in the oldest old population. This study aimed to investigate the association between SRH and all-cause mortality among the oldest-old population in China and to explore potential explanatory factors in this association.The study was based on the Chinese Longitudinal Healthy Longevity Survey (CLHLS) from 1998 to 2018 and included 30 222 participants aged 80 years or older  in the analysis. We used Cox models to assess the association between SRH and mortality in this population and its subgroups, and used the Percentage Excess Risk Mediated approach to identify potential contributing factors.After adjustment of confounders, people with “good” “neutral”, and “bad/very bad” SRH were significantly associated with 8%  = 3%-13%), 23% (95% CI = 18%-29%), and 52% (95% CI = 44%-61%) higher hazard of mortality respectively, compared with those with “very good” SRH. The significant SRH-mortality associations were exclusive to men and those with at least primary education. The adjustment of “regular physical activity”, “leisure activity”, “activities of daily living (ADL)”, and “cognitive function” all led to noticeable attenuation to the SRH-mortality association, with “leisure activity” causing the most attenuation (64.9%) in the “Good SRH” group.Self-rated health is significantly associated with all-cause mortality among the oldest old population in China, particularly among men and the educated, and is considerably explained by regular physical activity, leisure activity, ADL, and cognitive function. We advocate the use of SRH as a simple and efficient tool in research and  health care practices. Self-rated health (SRH), sometimes referred to as self-reported health or self-assessments of health, is considered a condensed summary of information about bodily conditions which involves biological, cognitive, and cultural aspects . NumerouThe oldest-old population (80 years or older) is an increasingly large population in China. The World Health Organization predicted that this population in China will reach over 90 million people in 2050 . This poAlthough SRH is regarded as a subjective indicator of health status with predictive power for mortality in the middle-to-old populations, whether its association with mortality pertains to the oldest-old population in China, and whether and how other factors were involved in this SRH-mortality association remains unknown. Successfully addressing these knowledge gaps could potentially produce useful implications in future health research and routine practices in this under-served population .This study aims to bridge the mentioned research gaps by investigating the association between SRH and all-cause mortality in the oldest old population in China and to determine potential factors that may explain the associations. We further aim to determine whether these associations differ across different socioeconomic groups.We used data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), which consisted of seven waves across 1998, 2000, 2002, 2005, 2008-09, 2011-12, 2013-14, and the latest follow-up in 2018. CLHLS is a prospective, national-wide longitudinal study, with one of the largest cohorts focused on the older population. It was conducted in randomly selected cities and counties that accounted for half of all cities and counties in 23 out of 31 provinces of China, which covers over 85% of China’s population. Details of the cohort study and data quality are described elsewhere . In thisThe Research Ethics Committees of Peking University and Duke University approved the ethics of the study (IRB00001052–13074). All included participants or their legal representatives provided written consent forms in the surveys.In this study, SRH was measured by a single-item question asking respondents at the baseline to rate their own overall health in five categories: “very good”, “good”, “neutral”, “bad”, and “very bad”. We combined “bad” and “very bad” groups into the “bad/very bad” group due to the limited sample size in “very bad” (1.3%).Mortality was the other primary variable of interest in the study’s survival analyses, determined by the participants’ survival status. The vital status and date of death were collected during the survey at each wave. They were based on officially issued death certificates when available, or otherwise from the next-of-kin, or local residential committees who were close to the decedents. The duration of follow-up was calculated by the time interval between the first interview date and the date of death.a priori, including gender , ethnicity , age, marital status , occupation before the age of 60 , education , and residence  at baseline.Other relevant variables were identified We further included seven “health-related factors” in the data analysis: four lifestyle-related factors  and leisure activity, activities of daily living (ADL), and cognitive function. ADL indicated whether participants were capable of or needed help in various daily activities. The cognitive function of participants was measured using the Chinese version of the Mini-Mental State Examination (CMMSE) . These vWe used a two-way table to summarize the demographics and socioeconomic characteristics among respondents in different SRH categories. χ2 and analysis of variance (ANOVA) tests were used to compare the categorical/continuous variables of people in different SRH categories. We used the Kaplan-Meier (KM) curve to illustrate the crude probability of survival over time by different SRH categories. Cox proportional hazard models were used to examine the associations of SRH with mortality. We tested the proportional-hazards assumption by comparing the observed survival curves to the Cox predicted curves in KM plots. The observed values were highly close to the predicted values, suggesting no violation of the assumption , which was calculated by the following formula:PERM = [hazard ratio  – hazard ration-1]The approach has been applied in previous studies to estimate the proportion reduction in the regression coefficients by health-related factors ,19. PERMAll analyses were conducted using STATA 16.1 . Regression coefficients and 95% confidence intervals (CIs) were reported.The Research Ethics Committees of Peking University and Duke University approved the ethics of the study (IRB00001052–13074). All methods were carried out in accordance with The Declaration of Helsinki. All included participants or their legal representatives provided written consent forms in the surveys.). Participants reported “very good” (11.8%), “good” (41.6%), “neutral” (33.8%), and “bad/very bad” (12.7%) SRH levels. The mean age at baseline was 92.2 years. The majority of ethnicity was Han (93.7%), and the majority of occupations were manual workers (94.2%). There were also more female participants (60.2%) than males (39.8%). The univariable analysis suggested that being a male, an urban resident, a non-manual worker, of Han ethnicity, having a higher level of education, and having regular physical activities were associated with generally better SRH . People of different smoking and drinking behaviours were significantly associated with different SRH levels, but the directions were mixed (P < 0.001).Kaplan-Meier survival curve by SRH levels . Our stratified analysis further found that after full adjustment, the significant association in this population was exclusive to men and those with at least primary education.The significant association between SRH and mortality we found is consistent with many other studies across different cultures and populations ,5,20. NoOur study showed that the SRH-mortality associations were influenced by people’s gender and education levels. The gender difference in the SRH-mortality associations has been documented in other studies. The UK Biobank study found SRH to be the strongest predictor for five-year mortality in men but only among the top four predictors for women . HoweverA major contribution of our study is the examination of whether and the extent to which the association between SRH and mortality can be explained by health-related factors. We found that cognitive function, ADL, and lifestyle-related factors—primarily regular physical activity and leisure activities – had the most contributions to the SRH-mortality association. The important role of cognitive function in the association is plausible, since evaluating one’s own health status is by nature an active cognitive process involving considerations of their medical history, health expectation, and peer comparison . PreviouThe critical role of lifestyle factors in the association between SRH and mortality has been inadequately addressed in existing literature. Some studies, nevertheless, explored the associations between SRH and lifestyle per se and produced mixed findings. A cross-sectional study among older Japanese adults found various types of leisure activities to be associated with SRH across gender and work status . In a DaBesides SRH’s relationships with lifestyle factors, a study in Europe found that SRH was also significantly associated with people’s mental health and social functioning . In anotAlthough existing evidence does not clearly delineate the causal pathways among SRH, mortality, and other related factors, studies do consistently imply that SRH is an informative and “inclusive” indicator that not only reflects people’s current health status, but also their exposure to relevant coping resources and health-related behaviours, which could ultimately affect health outcomes . Based oThe comparative advantages of SRH to other health indicators in health care practices are acknowledged. Benyamini et al.  summarizOptimal ways to utilize the information of SRH in real-world health practices, however, need more concrete explorations. Feng et al.  suggesteFor strengths of this study, the large, longitudinal, and nationally representative sample increased its scientific rigour. For limitations, many of the included variables of the study were self-reported . We tried to mitigate this by ensuring the questions’ clarity and comprehensibility for the respondents during survey dissemination. Another study limitation is that some participants in the CLHLS surveys were not included in our analysis due to the missing information on SRH. To address this, our additional descriptive analysis showed that the excluded participants shared similar sociodemographic patterns as the included participants, suggesting no detectable selection bias (Table S2 in the SRH is strongly associated with mortality among the oldest-old population in China, particularly among men and those with at least primary education. Regular physical activity, leisure activity, ADL, and cognitive function contributed largely to the association. We advocate the use of SRH as a simple and cost-effective tool in health research and potentially routine health practices. Further endeavors are needed to explore the underlying mechanisms of the relationship between SRH and health outcomes.