In Arabidopsis, mutations in writers, erasers, and RNA methylation readers have affected the plant’s phenotype. It has also been demonstrated that methylated TRANSLATIONALLY CONTROLLED TUMOR PROTEIN 1-messenger RNA moves from shoot to root while unmethylated TCTP1-mRNA does not. Methylated RNA immunoprecipitation, in conjunction with next-generation sequencing, has been a watershed moment in plant RNA methylation research. This method has been used successfully in rice, Arabidopsis, Brassica, and maize to study transcriptome-wide RNA methylation. Various software or tools have been used to detect methylated RNAs at the whole transcriptome level; the majority are model-based analysis tools . Finally, the limitations and future prospects of methylation of RNA research have been documented.RNA methylation is an important post-transcriptional modification that influences gene regulation. Over 200 different types of RNA modifications have been identified in plants. In animals, the mystery of RNA methylation has been revealed, and its biological role and applications have become increasingly clear. However, RNA methylation in plants is still poorly understood. Recently, plant science research on RNA methylation has advanced rapidly, and it has become clear that RNA methylation plays a critical role in plant development. This review summarizes current knowledge on RNA methylation in plant development. Plant writers, erasers, and readers are highlighted, as well as the occurrence, methods, and software development in RNA methylation is summarized. The most common and abundant RNA methylation in plants is N6-methyladenosine (m The presence of an m3C methylation mark is linked with transcripts’ instability and high turnover rates , and METTL14 were recognized in animals  RNAs during infection to generate host resistance. Inhibition of AtALKBH9B increased the relative abundance of m6A marks on AMV RNAs, impairing the systemic invasion of the plant  homolog (ALKBH) proteins . ALKBH ehe plant . Anotherpression .6A-containing mRNAs, including TTG1 (TRANSPARENT TESTA GLABRA1), ITB1 (IRREGULARTRICHOMEBRANCH1), and DIS2 (DISTORTED TRICHOME2), which are involved in trichome development. It has also been studied that ECT2 is responsible for mRNA stability  . In Plantability . A detait growth . HoweverLittle is known about writers, erasers, and readers in plants; therefore, more future studies are required to elucidate the in-detail role of these proteins in plants.36A (6-methyladenosine) methylation was first reported in 1979 in maize, oat, and wheat , pseudouridine, and C-U editing of mitochondrial and chloroplast mRNA are commonly studied in RNA methylation  is translated in target cells and changes root growth  biogenesis, which demonstrates that m6A methylation is necessary to maintain levels of mature miRNAs and their precursors  during fruit ripening. In fruits of tomato, the ripening-deficient Colorless non-ripening (Cnr) mutant, which harbors DNA hypermethylation, approximately 1100 transcripts display increased m6A levels. Further analysis confirmed that the increase in m6A methylation in Cnr mutant fruit is associated with the decreased expression of RNA demethylase gene ALKBH2 , 1-methyl guanosine (m1G), and 1-methyl adenosine (m1A) in plants. The roles of these marks in photosynthesis, response to cold, osmotic stress, etc., have been predicted using gene ontology analysis . Around 4-6 m6A sites per transcript were identified in both organelles. These methylation marks directly influence gene expression, as the negative correlation between m6A methylation and gene expression has been observed in both organelles. The high levels of RNA methylation in chloroplast and mitochondrial RNAs suggest the role of RNA methylation in organelles’ functioning. Considering the importance of chloroplast and mitochondria in photosynthesis and energy metabolism, understanding the role of RNA methylation in these organelles will answer many unsolved questions  through various genome-editing technologies  has been developed for cucumber (Cucumis sativus L.) and pumpkin (Cucurbita moschata). The Cucume database contains information about the m5C and m6A sites of different tissues and the vascular exudates. The Cucume database also includes graft-transmissible mRNAs identified in previous studies using heterografts. This database will help in understanding of the role of cucurbit RNA methylation in RNA mobility  methyltransferase is responsible for inducing N4‐methylcytidine (m4C) type methylation in 16S chloroplast rRNA. The CMAL mutant showed reduced chloroplast biogenesis, altered photosynthetic activity, and stunted growth. The CMAL‐ overexpression lines grew better than the wild type in the presence of abscisic acid (ABA). 4C methylation in 16S chloroplast ribosomal RNA  signaling-related genes. This study indicates that all these CMAL-mediated processes are essential for plant development and hormone signaling . Another46A loci are present in the poly-A tail of mRNAs and mainly occur in the R(m6A)C (R=A/G) sequence pattern  CH (H=A/C/U) as an extended consensus motif. This study also reveals that m6A in Arabidopsis is enriched around the stop codon, within 3′ untranslated regions (3′ UTRs), and around the start codon  is left intact. This method is widely used in the research of DNA methylation , immunoprecipitation of antibody-bound methylated RNA, elution of RNA, cDNA synthesis, and finally, sequencing and data analysis. The whole procedure of meRIP-Seq is given in Arabidopsis thaliana, Brassica, rice, maize and so on  method has been reported as antibody free RNA methylation detection method. In this method, metabolic labeling with propargyl-selenohomocysteine in along with click chemistry is used to detect N6A and m5C sites in mRNA with single nucleotide precision in the same sequencing run (MePMe-seq). MePMe-seq overcomes the problems of antibodies for enrichment and sequence-motifs for evaluation . In this5.25.2.1The initial studies on RNA methylation in 1975 used the radioisotope incorporation method . First, 5.2.2LC-MS is regularly used to detect RNA methylation . A proce5.2.3Arabidopsis thaliana to detect the 2′-O-methylation in small RNAs. (Methylation of the 2′-hydroxyl-group of ribonucleotides (2′-O-methylation) has been noticed in various RNAs in eukaryotes. This method has used the finding that 3′-terminal RNA methylation of the 2′-hydroxyl-group of ribonucleotides 2′-O-methylation can inhibit the activity of poly(A) polymerase, an enzyme that can add the poly(A)-tail to RNA. A method by which the 2′-O-methylation level of small RNAs, such as microRNAs (miRNAs) can be directly quantified based on the poly(A)-tailed RT-qPCR technique. This method has been successfully used in ll RNAs. . In this5.2.46A) for RNA methylation detection. Then the signals from the dot blot images can be quantified by ImageJ  and the secondary antibody. The specific band visualization by chemiluminescence. This method can also detect methylation in other RNAs like tRNAs, rRNAs, etc.  provide kits to detect RNA methylation using ELISA.66AViewer, a graphical user interface (GUI) platform, is the best option for methylation peak analysis and visualization from sequencing data. m6AViewer is a novel m6A peak-calling algorithm that identifies high-confidence methylated residues with high precision  methylated genes based on corrected methylation profiles (RNA methylation transcriptome data analysis, i.e., epitranscriptome data analysis, varies depending on the final goal of the experiment. Epitranscriptome data generated using antibody and immunoprecipitation-based techniques follows the same principle as ChIP-Seq . In ChIPrecision . Anotherprofiles . The deeprofiles . The inf76A is generally less than 0.1% of the total RNA. Most researchers prefer to use mRNA for assay, but recently it has shown that miRNA, tRNA, and snRNA also undergo methylation in plants (For most of the RNA methylation experiments, high input of total RNA is required as an abundance of mn plants . Highly n plants .86A. However, the signal modulations caused by this method is yet to be determined. Addressing these questions will significantly expand our knowledge and broaden the horizons of RNA methylation of plants.Several questions in field of plant RNA methylation remain unanswered. For example, how methylation of RNA can regulate gene expression in plants? How methylation affects the stability of cellular RNA in plants? Are reader, writer, and eraser proteins conserved among all plants? Recently, it also became possible to map RNA methylation directly by sequencing native RNAs using nanopore technologies . This haConceptualization: HS. Writing—original draft preparation: HS, AD, and UK. Writing—review and editing: HS, AD, UK, and JH. Visualization/figures: HS, AD, and UK. Funding acquisition: UK and JH. All authors contributed to the article and approved the submitted version.


A structure of the RRM1-ZnF1 region in complex with RNA shows how the tandem domains cooperate to sandwich target RNA and specifically recognize a GG dinucleotide in a non-canonical fashion. While the RRM1-ZnF1 domains act as a single structural module, RRM2 is connected by a flexible linker and tumbles independently. However, all three domains participate in RNA binding and adopt a closed architecture upon RNA binding. Our data highlight how cooperativity and conformational modularity of multiple RNA binding domains enable the recognition of distinct RNA motifs, thereby contributing to the regulation of alternative splicing. Remarkably, we observe surprising differences in coupling of the RNA binding domains between the closely related homologs RBM5 and RBM10.The RNA-binding motif protein RBM5 belongs to a family of multi-domain RNA binding proteins that regulate alternative splicing of genes important for apoptosis and cell proliferation and have been implicated in cancer. RBM5 harbors structural modules for RNA recognition, such as RRM domains and a Zn finger, and protein-protein interactions such as an OCRE domain. Here, we characterize binding of the RBM5 RRM1-ZnF1-RRM2 domains to  The RNA binding protein RBM5 regulates alternative splicing of genes implicated in cancer. Here the authors show structural mechanisms how multiple RNA binding domains of RBM5 cooperate to recognize specific target RNA sequences. The regulation of AS is complex and involves recognition of cis-acting RNA elements in the pre-mRNA by trans-acting protein splicing factors, which can favor or impair the recruitment of core splicing factors to the correct splice sites7.Alternative splicing (AS) greatly expands the protein repertoire encoded by the genome, whereby alternatively spliced isoforms can be translated into proteins with distinct, often antagonistic functions. Dysregulation of AS events has been correlated with numerous human diseases, including cancertrans-acting RNA binding protein, which is down-regulated in a variety of cancers11 and suggested to regulate metastasis by directly altering expression and activation of proteins important for this process13. RBM5 belongs to a larger protein family that includes the related multidomain RNA binding proteins RBM6 and RBM10, which share a similar domain organization and exhibit high sequence similarity with RBM5   is a ly) Fig. . Interes19 and of apoptosis-related genes including the cell-death receptor Fas20 and the initiator Caspase-221. In the latter, it promotes formation of the pro-apoptotic Caspase-2 isoform by interacting with a U/C-rich element immediately upstream of the splicing repressor element ln10022. RBM5, RBM6, and RBM10 31, an OCRE domain (OCtamer REpeat)32 and a glycine-rich domain (G-patch) at the C-terminus  region, two RNA recognition motif (RRM) domains (RRM1 and RRM2)nus Fig. . The RRMNP2 Fig. , which m35 are necessary in case of Fas AS regulation35 while protein–RNA interactions via its RNA binding domains (RRM1 and RRM2) are important for Caspase-2 AS regulation36.Given the presence of multiple domains in the RBM5 family proteins it is unknown what the contributions of the different domains for RNA binding, interactions with other proteins and biological function of the protein are. Consistently, it has recently been shown that protein-protein interactions mediated via OCRE domain37 while RRM2 can recognize both CU and GA rich RNA sequences with low micromolar affinity38. CLIP-Seq23 derived consensus motifs reflect previously identified U/C-rich RBM5 regulatory elements21 and known ZnF137 and RRM238 binding motifs. In contrast, RNAcompete experiments identified consensus motifs that appear to mainly reflect the binding preferences of ZnF1 39. Of note, the different top scoring motifs identified using CLIP-Seq 23 show little sequence overlap, although the RNAcompete consensus motif is quite similar to one of the CLIP-Seq motifs (AAGGAAAG). There are some interesting similarities for the RNA binding preferences of RBM5 and its homolog RBM10. Similar to RBM5, the tandem RRM1-ZnF1 domains of RBM10 have been shown to recognize a CUGUGGA-motif40, while CLIP-Seq23 and PAR-CLIP41 identified C-rich sequences shown to be preferentially bound by RRM240. Solution NMR structures of the unbound RBM10 RRM1, ZnF1, and RRM2 domains , and of the RBM5 ZnF142 and RRM238 domains have been reported. Given the variety of cognate RNA sequences reported it is important to dissect the structural mechanisms of how multiple RNA binding domains recognize these RNA ligands.The N-terminal region of RBM5 contains three RNA binding domains, namely RRM1, ZnF1, and RRM2, connected together by linker regions , and isothermal titration calorimetry (ITC) to study the structure, dynamics, and contribution of the three RNA binding domains of RBM5 to RNA recognition. A crystal structure of the tandem RRM1-ZnF1 domains in complex with RNA, reveals how the RNA is sandwiched between the two domains and recognized in a non-canonical way. SAXS and NMR analyses of the RRM1-ZnF1-RRM2 region show that the RRM1-ZnF1 domains act as a single module, while RRM2 is more dynamic. ITC binding data highlight the affinity contribution of the individual domains which work together cooperatively. Using paramagnetic relaxation enhancement (PRE) NMR experiments coupled with SAXS, we unveil the dynamic interplay of the different RNA binding domains of RBM5 for RNA binding. Our findings highlight the distinct domain contributions towards RNA recognition of RBM5 and underline differences with its related homolog RBM10.cis-regulatory RNA motifs, we investigated the roles of the three RNA binding domains (RRM1-ZnF1-RRM2), which are connected by linkers L0  and L1   Fig. . To stud15N-labelled single RRM1 (residues 94-184) and RRM2 (residues 231–315) domains, as well as the tandem RRM1-ZnF1 (residues 94–210) and RRM1-ZnF1-RRM2 (residues 94–315) domains, both harboring a protein stabilizing point mutation C191G43 (henceforth denoted as RRM1-ZnF1S and RRM1-ZnF1S-RRM2). A superposition of 1H-15N HSQC spectra of RRM1-ZnF1S-RRM2 with those of the individual RRM1 and RRM2 domains shows minor spectral changes for both RRM1 and RRM2 . This construct indeed shows only minor chemical shift differences when compared to RRM1-ZnF1S-RRM2  values (obtained from 15N R1 and R1ρ experiments) for RRM1 and ZnF1 are comparable in both constructs, i.e. with and without linker L1 . Taken together, these data are indicative of a strong coupling of the RRM1 and ZnF1 domains.First, we assessed whether inter-domain interactions exist between the different RNA binding domains of RBM5. We therefore expressed and purified RM2 Fig. , c. In cins Fig. , d. ThesRM2 Fig. , d. Thes L1 Fig. . Experim2+-SAD phasing . NMR titration of GGCU_12 into the three RNA binding domains shows binding in fast to intermediate exchange on the NMR chemical shift timescale  or RRM2 (L1-RRM2) upon binding to GGCU_12 RNA versus those seen for RRM1-ZnF1S-RRM2 in the presence of GGCU_12 RNA  while the affinity is only further increased 2-fold with the addition of the weaker binding RRM2 (RRM1-ZnF1S-RRM2: KD = 133 nM)  Fig. . While tS with RNA. Crystals of RRM1-ZnF1S bound to GGCU_10 RNA , we compared titrations of GGCU_12 RNA and CU_9 RNA which comprises a pyrimidine-rich region alone without the GG dinucleotide  or a 12-fold increase in affinity compared to the RRM1 construct. In conclusion, these ITC experiments clearly show that both RRM1 and ZnF1 are important for recognition of the GG dinucleotide in GGCU_12  units) both in HeLa and in HEK 293T cells, equivalent levels of expression of either of the mutants resulted in more limited (FAFA) or negligible (KERE) effects  or ZnF1 , which strongly impair RNA binding. We first compared their splicing activities to that of the wild-type protein using assays in which expression vectors of these proteins were co-transfected in HeLa or HEK 293 cells with a minigene reporter. While overexpression of RBM5 affected AS of 221 Fig. , includi221 Fig. . While o47. The SAXS-derived pairwise distance distribution, p(r), of the three RRM1-ZnF1S-RRM2 domains in the absence and presence of GGCU_12 shows significant compaction upon RNA binding and a reduction of the maximum pairwise distance Dmax of the particle (Dmax = 78 Å for RRM1-ZnF1S-RRM2 apo and 75 Å for the RNA complex)  Fig. . Our NMRex) Fig. . Taken tex) Fig. ,  it is Next, we measured long-range domain/domain distances using NMR paramagnetic relaxation enhancement (PRE) experiments. For this, several single cysteine mutants were generated in the different RBM5 domains and conjugated to single nitroxyl spin labels. Spin labeling at a total of four different positions was successful  and gave rise to PRE effects in the absence and presence of RNA  as templates. Together with PRE-derived distance restraints, chemical shift perturbations observed in RRM2 upon RNA binding were also incorporated as ambiguous RNA/RRM2 distance restraints during structure calculations. The structure calculation converged well, with the 10 lowest energy structures showing a backbone Ccaspase-2 derived pre-mRNA sequence. Using our RRM1-ZnF1-RNA crystal structure and solution NMR structure of apo-RRM238, together with our NMR-based PRE and SAXS experiments, we present a model of RNA recognition where all three RNA binding domains of RBM5 contribute to the overall affinity. ITC and NMR data provide details of a cooperative mode of action of the RNA binding domains of RBM5.In this study, we investigated the RNA binding properties of RBM5 to provide insights into its complex modes of target pre-mRNA recognition involving distinct but synergistic contributions by the RNA binding domains. We present the crystal structure of RBM5 RRM1-ZnF1 tandem domains in complex with a 40, even though the length and sequence of linker L0 is rather similar between the two proteins . Notably, our data show that the GG dinucleotide in caspase-2 pre-mRNA is recognized by both the RRM1 and ZnF1 domains of RBM5, as seen by comparing binding affinities of RRM1 alone and tandem RRM1-ZnF1 domains to the pyrimidine-rich CU_9 RNA and the GGCU_12 RNA (Table Previous studies focusing on RanBP2-type zinc fingers (including RBM5) indicate a binding specificity for ssRNAs containing GGU sequence motifs with micromolar affinitiesNA Table .S tandem domains in complex with a caspase-2 derived RNA (GGCU_10), containing both a GG dinucleotide and a pyrimidine-rich sequence shows that the RNA is sandwiched between the two domains with specific RNA recognition mediated by both the RRM1 and ZnF1 domains. We show that RRM1 and ZnF1 residues important for RNA binding also impact the activity of RBM5 in the NUMB exon 9 reporter assays. Note, that the in vitro studies focus only the RNA binding domains, while the splicing assays are performed with full-length protein, where additional domains present may contribute to splicing activity via protein-protein interactions.Surprisingly, our crystal structure of RRM1-ZnF138. In the canonical RRM-RNA interaction, the aromatic residues at positions 2 and 5 of RNP2 and RNP1 stack with two adjacent nucleotides in the RNA, while the aromatic residue at position 3 of RNP1 is usually inserted between the sugar rings of this RNA dinucleotide . The RNA is involved in hydrophobic interactions with position 2 of RNP2 even in the absence of an aromatic residue 37 and RRM2 (C/U rich)38. However, RBM5 RRM2 on its own binds GGCU_12 with a rather weak, high micromolar affinity  causes pre-mRNA splicing defects in round spermatids affecting male fertility in mice51. A superposition of 1H,15N-HSQC NMR spectra of the RRM2 R263P mutant and wild-type proteins shows severe protein folding defects in the mutant protein , indicating cooperative binding of the domains. Cooperativity in multidomain proteins is common and has been found for many multidomain RNA binding proteins56. While the individual RRM domains  have limited capacity to recognize RNA in a sequence-specific manner, their combination with additional domains can increase sequence specificity, and at the same time strengthen RNA binding affinity by creating a larger binding interface30. The relatively short linker L0 and the coupling between RRM1 and ZnF1 suggest that RRM1-ZnF1 acts as an independent module for specific RNA recognition. RRM2 acts as an independent RNA binding module as well, where the flexible linker L1 may enable local diffusion and initial RNA scanning by RRM2.Our ITC data show that the combination of three RNA binding domains indeed leads to an increase in the overall binding affinity  cells for expression. To produce unlabeled, 15N- or 13C-15N uniformly labeled protein samples, the cells were grown either in Luria-Bertani (LB) or M9 minimal media supplemented with 15NH4Cl/13C-glucose and 50 μg/ml Kanamycin. For constructs containing the zinc finger (ZnF1) domain, the cultures were supplemented with 100 μΜ ZnCl2 solution for proper folding of the domain. The protein was expressed by growing cultures to an optical density (OD) of 0.6–0.8 at 37 °C before induction with 0.5 mM IPTG and grown overnight at 18 °C (RRM1-ZnF1S-RRM2) or 20 °C . For RRM1-ZnF1, RRM1-ZnF1S, RRM1-GGS-ZnF1, and RRM1-ZnF1S-L1, the cultures were grown for 3 h at 37 °C after induction. The cells were harvested by centrifugation and resuspended in buffer containing 20 mM Tris pH 7.5, 500 mM NaCl, 10 mM Imidazole  or 20 mM Hepes-Na, pH 7.5, 500 mM NaCl, 1 M Urea  or 20 mM sodium phosphate pH 7.0, 500 mM NaCl, 1 M Urea (RRM1-ZnF1S-RRM2). All buffers were supplemented with 2 mM β-Mercaptoethanol (BME) and 1 mM AEBSF protease inhibitor. Subsequently, cells were lysed using sonication and lysates were centrifuged at 35000 × g for 45 min to separate cell debris and supernatant. For RRM2, the supernatant was loaded onto a 3 ml bench top Ni2+ affinity column equilibrated with lysis buffer, washed with 30 ml of lysis buffer and eluted in the same buffer supplemented with 500 mM Imidazole. The eluted protein was incubated with TEV protease overnight at 4 °C for cleavage of fusion tag during dialysis to remove excess Imidazole. The protein was loaded again onto Ni2+ affinity column to remove uncleaved protein and the fusion tag from cleaved protein of interest and the flow-through was collected. The protein was further purified over a size exclusion chromatography column  equilibrated with buffer containing 20 mM MES pH 6.5, 100 mM NaCl, 1 mM DTT. For RRM1, a cation exchange purification step  was introduced before size exclusion chromatography, where the protein was diluted to contain 50 mM NaCl, loaded on the column and eluted with a linear gradient of buffer containing 20 mM Tris pH 7, 1 M NaCl. For RRM1-ZnF1, RRM1-ZnF1S, RRM1-GGS-ZnF1, and RRM1-ZnF1S-L1, the supernatant was diluted 5-fold to contain 100 mM NaCl and purified over SP-Sepharose  using a linear gradient of buffer containing 2 M NaCl. The eluted protein was diluted 3-fold to contain 10 mM potassium phosphate, pH 7.4, 75 mM NaCl, loaded on a homemade 15 ml hydroxyapatite (HA) column and eluted with a 2-step gradient of buffer containing 12% w/v (NH4)2SO4. Finally, the protein was purified over a size exclusion column in buffer containing 20 mM MES pH 6.5, 400 mM NaCl, 1 mM DTT. For RRM1-ZnF1S-RRM2, the supernatant was loaded on a 3 ml Zn2+ affinity bench top column, equilibrated with lysis buffer, washed with buffer containing 1 M NaCl for removing non-specifically bound nucleic acids and sequentially eluted with buffer with pH adjusted to 6.0, 5.5, 5.0, 4.5. The protein appeared to be mostly pure in fractions with pH 5.5-4.5. TEV cleavage was done in 10 mM Na phosphate pH 7, 400 mM NaCl, 2 mM ΒΜΕ buffer. The protein was diluted 8-fold to contain 50 mM NaCl and purified over a 6 ml Resource S column with a linear gradient from 50 mM NaCl to 1 M NaCl. The eluted peak fractions were concentrated to 1 ml protein solution, after adjusting the final salt concentration to 400 mM NaCl and finally purified over a size exclusion column with buffer containing 20 mM MES pH 6.5, 400 mM NaCl, and 1 mM DTT. The purity of final protein samples was checked on Coomassie-blue stained sodium dodecyl sulfate polyacrylamide gels (SDS-PAGE). The final concentrations of RBM5 protein constructs were calculated using NanoDrop 1000 spectrophotometer (Thermo Scientific) with absorbance measured at 280 nm using extinction coefficients of 18450 M−1cm−1, 16960 M−1cm−1, 13980 M−1cm−1, 8480 M−1cm−1, and 1490 M−1cm−1 for RRM1-ZnF1S-RRM2, RRM1-ZnF1S-L1, RRM1-ZnF1S, RRM1, and RRM2, respectively.The DNA sequence encoding various constructs of RBM5 including RRM1 (residues 94-184), RRM2 (residues 231-315) and RRM1-ZnF1-RRM2 (residues 94-315) were cloned into modified pET24d vector containing an N-terminal His-Thioredoxin tag upstream of a TEV protease cleavage site, while RRM1-ZnF1 (residues 94-210) was cloned into pET28a vector. All point mutations were generated using overlapping PCR and the results were confirmed by Sanger sequencing. The primers used to generate all constructs in this study are listed in Supplementary Table −1 cm−1, 87300 L mol−1 cm−1, 103600 L mol−1 cm−1 and 133400 L mol−1 cm−1 for CU_9, GGCU_10, GGCU_12, and eGGCU_12, respectively.RNA oligonucleotides for crystallization, NMR and ITC were purchased from IBA GmBH, Germany. The RNA concentrations were calculated using NanoDrop 1000 spectrophotometer (Thermo Scientific) with absorbance measured at 260 nm using extinction coefficients of 75800 L mol2O for lock. Spectra were processed in NMRPipe/Draw59 and analyzed in CCPN Analysis60. Protein backbone resonance assignments were obtained using 3D HNCA, HNCACB, CBCA(CO)NH and HNCO61 and side-chain resonance assignments using HCCH-TOCSY and H(CCO)NH62. Aromatic resonances were assigned using 2-D 1H−13C HSQC, HBCBCGCDHD and HBCBCGCDCEHE experiments63. Additionally, to check for presence of inter-molecular NOEs in the protein–RNA complex, a 2D ω1-filtered NOESY, aliphatic and aromatic 3D ω1-filtered edited 13C NOESY experiments in 100% D2O were recorded at protein: RNA ratio of 0.8:1. To see the dispersion of RNA signals in free versus protein bound form, 2D 1H-1H TOCSY spectra were recorded. The chemical shift assignments for ZnF1 (ID:17387) and RRM2 (ID:18017) from the BMRB repository were used to assist in the assignment process, wherever necessary.All spectra were recorded at 298 K on AVIII 600, AVIII800, or AVIII + 950 Bruker NMR spectrometers equipped with cryogenic triple resonance gradient probes using Topspin v3.2. Samples contained 0.05–0.6 mM protein in 20 mM MES pH 6.5, 100 mM /400 mM  NaCl, 1 mM DTT supplemented with 10 % DS, RRM1-ZnF1S-L1, and RRM1-ZnF1S-RRM2 were not stable at high concentrations at 100 mM NaCl in free form, the respective RNA was added to the protein at 1:1 ratio at 400 mM NaCl and subsequently diluted to 100 mM NaCl.Since RBM5 RRM1-ZnF115N R1 and R1ρ relaxation were performed65 to study the dynamic properties of the different protein constructs. NMR data were recorded at 298 K for 15N-uniformly labeled proteins at 0.24 mM concentration for wild-type RRM1-ZnF1, 0.57 mM for RRM1-ZnF1S, 0.16 mM for RRM1-GGS-ZnF1, 0.20 mM for RRM1-ZnF1S-L1 and 0.33 mM for RRM1-ZnF1S bound to RNA complex on AVIII600 Bruker NMR spectrometer and for RRM1-ZnF1S-RRM2 free and bound to RNA at 0.3 mM on AVIII800 Bruker NMR spectrometer. For wild-type RRM1-ZnF1 and C191G mutant, RRM1-GGS-ZnF1, RRM1-ZnF1S-L1, and RRM1-ZnF1S-RNA complex, 15N R1 data were measured with 10 different relaxation delays and two duplicate delays, 21.6/21.6, 86.4, 162, 248.4, 345.6, 518.4, 669.6, 885.6/885.6, 1144.8, 1382.4 ms and 15N R1ρ data were determined by using 10 different delay points with two duplicate delays, 5/5, 10, 15, 20, 40, 80, 100/100, 130, 160, 180 ms. For RRM1-ZnF1S-RRM2 in free form, 15N R1 data were measured with 10 different relaxation delays and two duplicate delays, 21.6/21.6, 86.4, 162/162, 432, 540, 675, 810, 1080, 1350, 1620 ms and 15N R1ρ data were determined by using 12 different delay points with two duplicate delays, 5/5, 10, 15, 20, 30, 50, 75, 80, 100/100, 115, 130, 160 ms. For RRM1-ZnF1S-RRM2 - GGCU_12 RNA complex, 15N R1 data were measured with 11 different relaxation delays and one duplicate delay, 0, 80, 160, 240/240, 400, 560, 800, 960, 1200, 1440, 1600 ms, and 15N R1ρ data were determined by using 11 different delay points with one duplicate delay, 5, 10, 15, 20/20, 30, 40, 50, 60, 80, 100, 120 ms. Duplicate time points were used for error estimation. The transverse relaxation rate R2 for each residue was estimated by correction of the observed relaxation rate R1ρ with the offset Δν of the radio-frequency field to the resonance using the relation R1ρ = R1 cos2θ + R2 sin2θ, where θ = tan−1(ν1/Δν). The correlation time (τc) of the protein molecule was subsequently estimated using R2/R166. The experiments were acquired as pseudo-3D experiments and converted to 2D data sets during processing in NMRPipe59. The relaxation rates and error determination were performed by using PINT67. Cross-peaks with low intensity or extensive overlaps were removed from the data analysis.Experiments to measure 12E6/hexanol stock was prepared in buffer supplemented with 10 % D2O68 and subsequently mixed in a 1:1 ratio with 15N-labeled 0.4 mM RRM1-ZnF1S. The dipolar couplings were extracted from 2D in-phase–anti-phase (IPAP) HSQC experiments recorded under both isotropic and anisotropic conditions69. Long-term stability of the alignment, was assessed by comparing the 2H- quadrupolar splitting of D2O cosolvent before and after the experiment. NMR spectra were processed with NMRPipe59 and the signal splittings were extracted from peak positions in CCPN Analysis. Only residues forming secondary structure or involved in Zn2+ coordination (in case of ZnF1) were used for further analysis. PALES software70 was used for the analysis of RDCs whereby the magnitude of alignment tensor (Da) and rhombicity (R) were calculated using the principal components of traceless matrix -given by PALES and the absolute value of RDC. The Cornilescu Q factor was used to determine the quality of the fit of experimental versus back-calculated RDCs71.For all residual dipolar coupling (RDC) measurements, a 6% C15Ν-labeled RRM2 or 0.5, 0.75, 1-fold of CU_9 into 0.1–0.2 mM 15Ν-labeled RRM1F142A/F144A and recording 1H-15N HSQC spectra in SEC buffer 2, supplemented with 10% D2O. Due to low stability of free RRM1-ZnF1S, RRM1-ZnF1S-L1, and RRM1-ZnF1S-RRM2 at 100 mM NaCl, GGCU_12 RNA was added in excess to 15N-labeled 0.2 mM protein in buffer containing 400 mM NaCl. The protein–RNA complex was then diluted to a 100 mM NaCl concentration, and subsequently concentrated using a 0.5 ml Amicon centrifugal filter concentrator with 3.5 kDa cut-off. To obtain a comparable spectrum of the free protein at 100 mM NaCl, the protein sample was diluted to ≈0.05 mM concentration. CSPs were calculated for backbone amide peaks of 2D 1H-15N HSQC correlation experiments using the equation, NMR RNA titrations were performed by adding 0.5, 1, 2-fold of GGCU_12 RNA into 0.1 mM S-RRM2 . 1H-15N HSQC spectra were recorded for 0.15-0.25 mM 15N-labeled protein with or without GGCU_12 RNA and used to obtain the ratio of peak intensities (Ipara/Idia) of the paramagnetic and diamagnetic (after addition of 10-fold excess of ascorbic acid) and transferred into distances restraints as described72.For PRE measurements, a natural solvent-exposed cysteine residue was mutated to serine (C230S). The free radical 3-(2-Iodoacetamido)-PROXYL spin label was attached to specifically engineered cysteine residues in RRM1-ZnF115N-labeled RRM2 upon titration of CU_9 were used to generate ambiguous distances between the RNA residues 6–12 of GGCU_12 (CUUCUCU) and the protein residues which satisfy a threshold criterion , and all missing residues of the protein and RNA was generated. Since a high-resolution structure of RRM2-RNA complex is not available, chemical shift perturbations in The template generated using this approach was randomized by random rotations of the phi/psi angles of the linker L1 followed by a short three-step simulated annealing energy minimization. To avoid clashes between the structured parts at the end of the randomization process, a random rotation was only accepted for a residue if the van der Waals (vdW) interaction energy of the molecule did not increase by more than 10% from the starting vdW energy. This led to 1750 randomized structures with proper geometries out of 2000 randomization processes started. This random pool of structures was minimized in a second step by a longer three-step Cartesian simulated annealing protocol including the PRE and chemical shift perturbation distance restraints.74 and the structure was solved by Auto-Rickshaw platform using MR-SAD76. The missing residues were built using Coot model building software77 with multiple rounds of model building and refinement with Refmac software78 from CCP4 suite79. The RRM1-ZnF1S –RNA complex (GGCU_10 RNA) was prepared by addition of 1.2-fold molar excess of RNA over protein and subsequent purification over a size exclusion column to remove excess RNA. The complex crystals were obtained in 0.05 M sodium cacodylate pH 6.5, 18 mM CaCl2, 2.7 mM spermine, 9% 2-propanol at 4 °C as thin joint needles. Addition of 10% glycerol produced thicker separated needles, which were flash frozen in mother liquor containing 30% glycerol. The dataset was collected at ID30b beamline at ESRF Grenoble, France, and the crystals diffracted to a resolution of 3.5 Å. The crystals showed severe radiation damage, pseudo translation, and twinning. The dataset was integrated in space group P21 and was solved by molecular replacement with the individual structures of RRM1 and ZnF1 domains from the RRM1-ZnF1 structure as search models. Four molecules are found in the asymmetric unit. To mitigate the effect of pseudo translation on the Rfree values, the Rfree set was chosen in thin shells during refinement in Phenix, and the structures were refined with grouped B-factors, non-crystallographic symmetry restraints, and considering the h, -k, -l twin law. Due to low resolution of the data and the poor electron density because of the crystal pathologies, we could confidently model RNA in only one of the molecules of the asymmetric unit. All structures were visualized using Pymol v2.3.1 or Chimera v1.12.RRM1-ZnF1 protein crystallized at a concentration of 10 mg/ml in a drop containing 0.1 M BICINE pH 9.0, 20 % PEG 6000 at 4 °C as very thin joint needles. After buffer optimization and using an additive screen, the thin needles were optimized to obtain thin plates in buffer containing 0.1 M BICINE pH 8.5, 12% PEG 6000, 0.1 M cesium chloride. Crystals were flash frozen in mother liquor supplemented with 20% ethylene glycol. Several datasets for the crystals were collected at ID23-1 and ID23-2 beamlines at ESRF, Grenoble. Datasets from best diffracting crystals were then processed with XDS software packageS-RRM2 with or without RNA was injected for each run. A 1.2-fold molar excess of GGCU_12 RNA was added to the protein sample followed by an incubation period of 30 min on ice before sample injection. Elution profiles were collected for 30 min with a flow rate of 0.5 ml/min and data were collected using absorbance UV detection at 280 nm, right-angle light scattering (RALS) and refractive index (RI). The molecular weights of separated elution peaks were calculated using OmniSEC software . As a calibration standard, 4 mg/ml bovine serum albumin was used before all experiments.All measurements were made with a Malvern Viscotek instrument (TDA 305) connected to an Äkta purifier equipped with an analytical size-exclusion column  equilibrated in buffer containing 20 mM MES pH 6.5, 100 mM NaCl, 1 mM DTT at 4 °C. A sample volume of 100 µl containing about 2–4 mg/ml of RRM1-ZnF1S were performed at 25 °C on the BioSAXS beamline BM29 with a 2D Pilatus detector at the European Synchrotron Radiation Facility (ESRF), Grenoble. Fifteen frames with 1 sec/ frame exposure time were recorded, using an X-ray wavelength of λ = 0.9919 Å, in flow mode at concentrations ranging from 1 mg/ml – 8 mg/ml. The dedicated beamline software BsxCuBE was used for data collection and processing. 1D scattering intensities of samples and buffers were expressed as a function of the modulus of the scattering vector Q = (4π/λ)sinθ with 2θ being the scattering angle and λ the X-ray wavelength. Downstream processing after buffer subtraction was done with PRIMUS80. Rg was determined using Guinier approximation and from p(r) curve and crystal structure validation was done using CRYSOL81. Measurements for RRM1-ZnF1S-RRM2 in apo at concentrations of 0.5 mg/ml and 1 mg/ml and for the RNA-complex at concentrations ranging from 0.6 to 4.8 mg/ml were performed at 5 °C using Rigaku BIOSAXS 1000. A purification of the protein–RNA complex using size-exclusion chromatography was performed to remove excess RNA before measurement. Primary data processing was done with Rigaku SAXSLab v 3.0.1r1 with eight frames with 900 sec/ frame exposure time recorded and data treatment was done with PRIMUS. All data were plotted using GraphPad Prism v6.SAXS measurements with RRM1-ZnF1ITC experiments were performed either with MicroCal iTC200 or PEAQ-ITC calorimeters . Prior to recording data, the protein samples were dialyzed overnight in buffer containing 20 mM MES pH 6.5, 100 mM NaCl, and 2 mM BME. The cell was filled completely with 10–30 µM protein and depending on the affinity, the syringe was filled with different concentrations of the respective ligand, ranging from 100 to 300 µM. A series of 26 injections of 1.5 µl titrant or 39 injections of 1 µl were made into the protein at 25 °C. The data were processed with either Origin (iTC200) or PEAQ-ITC Analysis software (PEAQ-ITC). The data were fit to a one-binding site model.RG6-NUMB minigene23 were co-transfected with 2 μg of plasmids expressing either T7 beta-galactosidase as control or T7-RBM5 (wild type or mutants) using Lipofectamine 2000. RNA was isolated 24 h after transfection and the pattern of alternative splicing was assessed by RT (with oligo-d(T)/random hexamer) and AMV Reverse Transcriptase (Promega) and PCR with GoTaq (Promega) using the primers- RG6 S: GGATTACAAGGATGACGATGACAAGGG, RG6 AS: GTCACCTTCAGCTTCACGGTGTTGTG. Inclusion/skipping products were detected at 414/270 bp. Image quantification was performed using Fiji (Image J). The expression of the proteins was assessed by western blot using antibodies raised against the T7 epitope  and GAPDH . The GADPH signal was detected using mouse HRP secondary antibody .Transfection assays were carried using 400,000 HeLa or HEK 293T cells seeded in 35 mm diameter tissue culture plates. 20 ng of the Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


About 4.5% of non-synonymous SNPs which result in C>U changes in RNA, and about 5.4% of such SNPs labelled as pathogenic, were identified as probable sites for APOBEC3A/G editing. This suggests that the variant proteins created by these DNA mutations may also be created by transient RNA editing, with the potential to affect human health. Those SNPs identified as potential APOBEC3A/G-mediated RNA editing sites were disproportionately associated with cardiovascular diseases, digestive system diseases, and musculoskeletal diseases. Future work should focus on common sites of RNA editing, any variant proteins created by these RNA editing sites, and the effects of these variants on protein diversity and human health. Classically, our biodiversity is thought to come from our constitutive genetics, epigenetic phenomenon, transcriptional differences, and post-translational modification of proteins. Here, we have shown evidence that RNA editing, often stimulated by environmental factors, could account for a significant degree of the protein biodiversity leading to human disease. In an era where worries about our changing environment are ever increasing, from the warming of our climate to the emergence of new diseases to the infiltration of microplastics and pollutants into our bodies, understanding how environmentally sensitive mechanisms like RNA editing affect our own cells is essential.Intra-organism biodiversity is thought to arise from epigenetic modification of our constituent genes and post-translational modifications after mRNA is translated into proteins. We have found that post-transcriptional modification, also known as RNA editing, is also responsible for a significant amount of our biodiversity, substantively expanding this story. The APOBEC  family RNA editing enzymes APOBEC3A and APOBEC3G catalyze the deamination of cytosines to uracils (C>U) in specific stem-loop structures. In this paper, we explore the ability of post-transcriptional modifications, or RNA editing, to contribute to human biodiversity.Our biodiversity has long been thought to come from alternative splicing and post-translational modification of the thousands of proteins encoded in the human genome. There are about 20,300 protein-encoding genes in the genome, of which 19,267 have an approved HUGO gene name (as of 04/07/23).”5 There are two well-studied families of RNA editing enzymes which catalyze single nucleotide substitutions.6 The ADAR (adenosine deaminase acting on RNA) family deaminates adenosine to guanosine-analog inosine (A>I) in specific double-stranded RNA contexts.7 Similarly, specific APOBEC  family enzymes deaminate cytosine to uracil (C>U) in single-stranded contexts.2RNA editing has been described as “any site-specific alteration in an RNA sequence that could have been copied from the template, excluding changes due to processes such as RNA splicing and polyadenylation.8 For a long time, these enzymes were primarily known for their ability to edit single-stranded DNA (ssDNA) produced by viruses such as HIV (APOBEC3G) and parvovirus (APOBEC3A).1 Perhaps because of this, known APOBEC3-mediated RNA editing sites are rare. In a dataset taken from a paper by Asaoka et al., only about 0.05% of bases across 2343 genes were considered APOBEC3A/G RNA editing sites.9 We contend that, beyond their effects on viruses, these enzymes may also affect human health via the editing of healthy human mRNA and subsequent creation of protein variants.Two such APOBEC-family enzymes are APOBEC3A and APOBEC3G. The ability of APOBEC3 enzymes to edit RNA is a relatively recent discovery, starting in 2015 with a paper by Sharma et al.10 Specifically, an optimal target contains a tri-or tetraloop, with the edited cytosine at the 3’ end of the loop and a pyrimidine 5’ to it  has been found in mice, and a link between RNA editing and autoantigen creation in autoimmune diseases like systemic lupus erythematosus (SLE) has been suggested in humans.18There is a growing body of evidence supporting the influence of C>U RNA editing on human health. APOBEC3-mediated RNA editing sites have been found in genes with known relationships to neoplasms, hypertension, and nervous system disorders, including amyotrphic lateral sclerosis (ALS), Alzheimer’s, Huntington’s, and Parkinson’s disease.20 Although at least one study attempted to collate known APOBEC3A/G editing sites, no comprehensive database like RADAR yet exists for C>U editing events, and studies on APOBEC3 editing tend to remain narrowly focused on one or two diseases at a time.9However, this area of research is still relatively young. Most articles and tools on RNA editing still focus on ADAR-mediated A>I editing. For instance, the publicly available RADAR database catalogues A>I RNA editing sites with manual annotations, and this tool has been used alongside data from The Cancer Genome Atlas to identify protein variants that may affect tumor cell viability and drug sensitivity.21 In this paper, we will compare this list of predicted APOBEC3A/G-mediated C>U editing sites with publicly available data on human protein variants from the CinVar database, particularly pathogenic variants, to help open the conversation on the extent to which C>U RNA editing may contribute to human disease.To address this gap in knowledge, we have developed RNAsee , a program that combines machine learning and rules-based methods to predict APOBEC3A/G mediated RNA editing sites in transcripts of human genes.To assess the performance of RNAsee in predicting APOBEC3A/G editing sites, we benchmarked the random forest and rules-based models on the proportional set, which contains a 468:1 ratio of non-editing to editing sites (see Performance benchmark for RNAsee). First, we calculated area under the receiver operator characteristic (AUROC) metrics for the rules-based and random forest models . The AURUsing a score threshold of >= 10 for the rules-based model and probability threshold of > 0.5 for the random forest model, we found recall and precision metrics for the rules-based and random forest models, plus two consensus models created using set operations (intersection and union) on the output of these two models. These metrics were calculated for the testing and proportional sets, which differed in the number of non-editing sites they included (see Performance benchmark for RNAsee). The recall of each model is shown in 22 We pared this set down to only include single nucleotide polymorphisms (SNPs) which were exonic and non-synonymous. Then, we examined the relative frequency of different polymorphisms  and G>A (21.1%) were by far the most frequently observed polymorphisms. Both of these types of SNPs may result in C>U changes in RNA, depending on which DNA strand is transcribed. Therefore, to determine which of these SNPs could correspond with C>U editing, we needed to refer to the RNA sequences.9 The remaining sites are likely novel.We examined coding sequence files for each gene with an SNP. Entries in genes with no good coding sequence file found were excluded, leaving 617,363 SNPs across 9228 genes. This set is the set of “all exonic SNPs”. Of these, 101,565 (16.5%) were associated with a C>U RNA change and were included in the set of “all C>U SNPs.” We used the most sensitive RNAsee model, the union model, to analyze each cytosine in that set. RNAsee returned 4600 SNPs, 4.5% of all C>U SNPs, as potential APOBEC3A/G-mediated RNA editing sites. These SNPs were included in the “RNAsee predicted” set. Of these 4600 sites, 62 (1.34%) are known APOBEC3-mediated RNA editing sites included in the Asaoka et al set.Each entry was annotated with a pathogenicity tag, which we sorted into three bins: “pathogenic,” “benign,” and “unspecified.” The proportions of these tags in the set of all exonic SNPs, all C>U SNPs, and all predicted editing sites were calculated . Of the Of 101,565 sites in the all C>U set, 72917 (71.8%) were associated with at least one disease, condition, or phenotype in ClinVar. To determine which areas of health are most likely to be affected by APOBEC3A/G-mediated RNA editing, we decided to survey these conditions.We labelled each SNP with the MeSH subject headings that most closely matched its associated conditions. In total, there were 6,534 unique conditions associated with C>U SNPs. 575 conditions (8.8%) could not be matched with a good single MeSH equivalent and were labelled “Not found.” In total, 7181 SNPs (9.3%) were associated with “Not found” at least once, including 401 SNPs considered pathogenic.The top-level subject headings corresponding to the initial MeSH labels were found for each SNP, with each top-level term labelling an SNP no more than once. We counted the number of times each top-level subject heading or “Not found” was associated with a SNP in the all C>U and RNAsee predicted sets. Because this work is focused on the potential effects of RNA editing on human health, we focused on those SNPs labelled pathogenic. The top ten most common top-level MeSH subject headings associated with pathogenic SNPs in the all C>U and RNAsee predicted sets are shown in In both sets, the most common associated heading was “Congenital, Hereditary, and Neonatal Diseases and Abnormalities.” Because ClinVar pathogenic variations are thought to be causally linked to their associated conditions, all conditions associated with these variations should either be congenital or a result of a de novo tissue mutation, for instance, in a neoplasm. “Nervous System Diseases” and “Nutritional and Metabolic Diseases” were the second and third most common subject headings in both groups, which may mean C>U RNA editing is most likely to negatively influence human health in these two areas. The proportions of most subject headings were similar between the two groups, with only slight differences in their relative rankings.To further elucidate the potential effects of APOBEC3-mediated RNA editing on human health, we wanted to examine the proportion of diseases with any possible influence from APOBEC3-mediated RNA editing. To do this, we counted the number of third-level MeSH headings (grandchildren of top-level headings) with at least one predicted editing site. To find an upper bound on the number of conditions affected, we also counted the number associated with at least one C>U SNP .Almost all top-level types of non-traumatic, non-infectious conditions recognized by MeSH had at least one grandchild term associated with at least one predicted editing site. Some types of diseases had as few as 4% of their grandchild terms with an associated editing site , whereas 72% of third-level nutritional and metabolic diseases were associated with predicted editing sites. Notably, some top-level headings with a large number predicted editing sites had a lower percentage of diseases with associated predicted editing sites.15 Transient increases in RNA editing could result in the production of variant proteins, which could affect recovery time and result in sequelae following periods of inflammation.We searched a list of known non-synonymous DNA SNPs to see which of those polymorphisms, and the resultant variant proteins, could also be caused by APOBEC3A/G-mediated RNA editing. We found that about 4.5% of known SNPs which result in C>U mRNA changes are also potential APOBEC3A/G editing sites. If APOBEC3A/G RNA editing regularly occurs at even a fraction of these sites, this could result in meaningful effects on human health. It has previously been demonstrated that interferon-rich environments, such as inflamed tissues, increase APOBEC3-mediated RNA editing in a variety of cell types.10 APOBEC3A was already known to extensively edit DNA in neoplasms, which lends credence to this theory.10 Interestingly, neoplasms were only the sixth most common subject heading associated with editing sites predicted by RNAsee. Therefore, APOBEC3A/G DNA editing is likely a factor in pathogenic SNPs and the creation of variant proteins beyond its known activity in neoplasms. RNA editing could have a likewise unknown role in health.Over 40% of exonic, non-synonymous human SNPs recorded in the ClinVar database are associated with a C>T change on one strand. This is far more than expected by random chance alone; counting the number of non-synonymous single nucleotide changes a codon can undergo, only 14.4% are C>T or G>A. This high percentage may be due, in part, to rare uncorrected C>U DNA editing events catalyzed by enzymes such as APOBEC3A/G and AID (activation-induced cytidine deaminase).23 Therefore, if many of these SNPs were indeed originally caused by APOBEC3A/G ssDNA editing, RNA editing likely occurs at the same sites.When the union method of RNAsee was benchmarked, it returned about 3.2% of cytosines in the proportional set as potential APOBEC3A/G editing sites, including false positives. However, when run on C>U sites in the ClinVar database, it identified 4.5% (4600) of C>U SNPs and 5.4% (1046) of pathogenic C>U SNPs as potential editing sites. This higher percentage supports the idea that some of the C>T and G>A polymorphisms recorded in ClinVar result from APOBEC3A/G DNA editing, leading to a sample that was biased towards higher APOBEC3A/G editing affinity. It was previously found that APOBEC3A was more active against RNA than DNA in cancer cells, and this activity occurred at similar sites in both.When compared to SNPs associated with C>T changes on one strand, sites associated with C>U changes in RNA were underrepresented. By random chance, half of C>T and G>A changes should be on the correct strand to result in C>U RNA changes, but only 16.5% of SNPs actually resulted in C>U changes. This could be explained if the enzymes which catalyze C>U deamination have easier access to or higher affinity for a DNA strand and segment that is being transcribed. This could also result from evolutionary pressure against the appearance of good C>U editing sites in RNA if C>U RNA editing events are generally deleterious.15 These periods of increased enzyme expression provide increased opportunities for APOBEC3A/G-mediated mRNA editing events to occur, particularly in optimal stem-loop structures.10We believe that these results, alongside existing evidence, support the significance of APOBEC3A/G RNA editing to human health . Strong 12 Beyond these known sites, we have found that over 40% of non-synonymous SNPs in the ClinVar database are associated with a C>T change on one DNA strand, much higher than expected by random chance and possibly indicative that C>U ssDNA editing has contributed greatly to the set of known human SNPs  describing the optimal substrates for RNA editing enzymes, (2) finding the conditions in which these enzymes are most active, and (3) identifying the effects of variant RNA or proteins created by RNA editing on protein diversity and human health. In this way, we can add to our basic understanding of human molecular biology.Classically, our biodiversity is thought to come from our constitutive genetics, epigenetic phenomenon, transcriptional differences, and post-translational modification of proteins. Here, we have shown evidence that RNA editing, often stimulated by environmental factors, could account for a significant degree of the protein biodiversity leading to human disease. In an era where worries about our changing environment are ever increasing, from the warming of our climate to the emergence of new diseases to the infiltration of microplastics and pollutants into our bodies, understanding how environmentally sensitive mechanisms like RNA editing affect our own cells is essential. Future research will apply our analysis to the transcriptomes and proteomes of specific disease conditions in order to further clarify the functional and predictive role of APOBEC-mediated C>U RNA editing in human disease.1 We extracted the annotated set of DNA mutations from ClinVar on May 18, 2022 via FTP.2 All variants which were not SNPs (“Type” of “single nucleotide variant”) were excluded. Two records were provided for each variant corresponding to the GRCh37 and GRCh38 reference genomes. Records corresponding to GRCh37 were excluded. Finally, because this study was intended to examine RNA editing as a potential source of protein diversity in human cells, all DNA variants which would not result in protein variants (including non-exonic or exonic but synonymous variants) were excluded.The ClinVar database, maintained by the National Center for Biotechnology Information (NCBI), collects information regarding DNA variants found in patient samples, including assertions of clinical significance and evidence to support those assertions.For each SNP, we extracted the following information: the gene, allele ID, any amino acid change resulting from the mutation, assertion of clinical significance, rsID, original and mutant nucleotides, and list of phenotypes associated with the variant. To get a baseline for the frequency of different SNPs, we used the original and mutant nucleotides (found in the “ReferenceAlleleVCF” and “AlternateAlleleVCF” columns) for each SNP. Entries with non-specific nucleotides (e.g. A>X) or like-to-like nucleotide changes (e.g. A>A) were excluded. Entries without any associated amino acid change recorded were also excluded. We also binned the clinical significance into three categories  based on whether the significance tag contained the words “pathogenic,” “benign,” or neither.3 An attempt was made to download a human coding sequence file for any gene with at least one SNP which matched the inclusion criteria. If said file did not exist or was incompatible with the data contained within ClinVar , variants in that gene were excluded. Out of 622,222 SNPs matching the inclusion criteria, 617,363 exonic single nucleotide polymorphisms across 9228 genes were analyzed. The amino acid change recorded in each entry and the corresponding codon in the coding sequence file were used to determine whether each SNP could be associated with C>U RNA editing. Those DNA polymorphisms that resulted in a C>U change in the transcribed mRNA were included in the C>U set and analyzed using RNAsee. In total, 101,565 sites were assessed for their likelihood of being APOBEC3A/G editing sites.Coding sequence files were obtained from the CCDS.https://github.com/ram-compbio/RNAsee. We previously developed RNAsee to identify potential APOBEC3A/G editing sites. RNAsee v1 was a rules-based method that used a simple stem-identification algorithm and a minimum free energy score assigned by ViennaRNA to rank cytosines within a gene from most to least likely to undergo RNA editing.5 During the development of RNAsee v2, we decided to utilize the set of known APOBEC3-mediated RNA editing sites published in Asaoka et al. to both improve and benchmark RNAsee.6 All cytosines identified in Asaoka et al.were considered editing sites, and all other cytosines in the same genes were considered non-editing sites.6RNAsee is a publicly available Python package. It can be found at We refined the rules-based model to score sites based on sequential features commonly observed in this dataset in addition to the presence and strength of a stem-loop structure. We developed a novel scoring algorithm, and we trained a scoring threshold for the rules-based model for best F1 score on a subset of the dataset.We also added a machine learning model to RNAsee. Four types of classification models were initially considered: support vector machines, logistic regression, decision tree, and random forest. We also considered different methods of vectorizing the sequence surrounding the potential editing site based on number of nucleotides included (a 25- or 15-nucleotide stretch surrounding the site) and method of encoding nucleotides (4 or 2 binary integers per nucleotide). Finally, we attempted to accommodate the highly imbalanced class sizes between editing and non-editing sites (1:468) by downsampling non-editing sites and/or using SMOTE-based up-sampling of editing sites. Ultimately, based on initial benchmarking performance and area under the receiver operator curve (AUROC) figures, we chose a random forest model that takes as input a 25-nucleotide stretch surrounding a cytosine, vectorized into 2 binary integers per nucleotide (isPurine and pairsGC), trained on a set of sites downsampled for non-editing sites and with an increased proportion of non-editing sites that received high scores when scored by the rules-based modelRNAsee v2 includes two primary methods and two consensus methods for editing site identification:Rules-based model. The rules-based model assigns a score to cytosines if, and only if, they are found at the 5’ end of a 3–4 nucleotide loop in a stem-loop structure. Scores are calculated based on the strength of the stem (3*(the number of GC pairs) + (the number of AU pairs)) and the presence or absence of specific sequential features (+2 for a uracil in the loop or a purine following the cytosine and −2 for a guanine in the loop). A site is considered an editing site if it receives a score of greater than nine.Random forest model. The random forest model was created as an instance of the RandomForestClassifier class from scikit-learn.7 The model takes as input a 50-bit vector representing 15 nucleotides preceding and 10 following a cytosine. Every nucleotide is represented by 2 bits; one for whether that nucleotide is a purine and another for whether it can participate in GC pairing. The model was trained and tested using a 70–30 training/testing split of known editing and non-editing istes. A site is considered an editing site if its probability of being an editing site is over 0.5.Consensus models. The intersection and union models combine the outputs of the two primary methods using simple set operations. The intersection method outputs only sites returned by both primary models, and the union method outputs all sites returned by either primary model.Two datasets were used to benchmark RNAsee: the testing set and the proportional set. The former included all sites in the random forest model’s testing set. It contained a 3:1 ratio of non-editing to editing sites. Because this ratio is greatly different from that found in the whole Asaoka et al. dataset, we also created the proportional set. This set contains all sites found in the testing set, plus additional non-editing sites to bring the ratio of non-editing sites to editing sites to 468:1, similar to the original ratio.During benchmarking, each model was used to predict which cytosines in both datasets are APOBEC3 editing sites. The number of non-editing and editing sites in each model’s predictions were tallied and used to calculate metrics including sensitivity and precision. For the primary models, AUROC figures were also calculated.Because this paper focused on fully exploring the potential effects of APOBEC3A/G-mediated RNA editing on human health, we decided to use the union model, which showed the highest sensitivity (82.8%). For each SNP in the C>U set, the original cytosine in question and its sequential context was passed as input to the union model. All sites returned by the union model were considered potential APOBEC3A/G editing sites.The diseases and phenotypes associated with each site in the C>U set were extracted. All unique conditions were extracted into a single list. For each term in this list, the most relevant MeSH subject heading  was found. MeSH mappings were used to associated supplemental concept records with tree terms unless a mapping contained a qualifier that suggested the supplemental concept record was not a child of the mapped term . Each term was then associated with the chosen subject heading and every parent subject heading up to the top level. If no single subject heading encapsulated the given term, that term was associated with the phrase “Not found.” “Not found” was treated as a top-level subject heading for analysis purposes. Finally, the terms were re-associated with the SNPs.The number of times each subject heading appeared across all SNPs was counted. Each subject heading was counted up to once per SNP, even if that SNP was associated with multiple conditions corresponding to that subject heading. Using the same method and same list of condition-subject heading associations, the number of times each subject heading appeared across the union set was also calculated. Additional counts were also generated for the subsets of pathogenic, benign, or unspecified SNPs in each set. Analysis was primarily carried out on the counts of top-level subject headings.Additional figures were calculated for the percentage of diseases with at least one C>U SNP and/or editing site associated. For the purposes of this work, the third level of MeSH subject headings (grandchildren of top-level terms) were considered to represent individual diseases. For each top-level term and select second-level terms, the number of associated third-level terms was counted. If at least one C>U SNP or editing site mapped directly to a third-level term or to child terms of that third-level term, the term was counted as having a C>U SNP or editing site. The percentages of diseases with at least one associated C>U SNP and diseases with at least one editing site were then calculated by dividing these counts by the total number of third-level terms.


The synergistic effect produced by NRPC and bimetallic compounds increases the number of active sites at the electrode/electrolyte interface and thus speeds up the OER process.Proper design of multifunctional electrocatalyst that are abundantly available on earth, cost-effective and possess excellent activity and electrochemical stability towards oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) are required for effective hydrogen generation from water-splitting reaction. In this context, the work herein reports the fabrication of nitrogen-rich porous carbon (NRPC) along with the inclusion of non-noble metal-based catalyst, adopting a simple and scalable methodology. NRPC containing nitrogen and oxygen atoms were synthesized from polybenzoxazine (Pbz) source, and non-noble metal(s) are inserted into the porous carbon surface using hydrothermal process. The structure formation and electrocatalytic activity of neat NRPC and monometallic and bimetallic inclusions  were analyzed using XRD, Raman, XPS, BET, SEM, TEM and electrochemical measurements. The formation of hierarchical 3D flower-like morphology for NRPC/NiMn was observed in SEM and TEM analyses. Especially, NRPC/NiMn proves to be an efficient electrocatalyst providing an overpotential of 370 mV towards OER and an overpotential of 136 mV towards HER. Moreover, it also shows a lowest Tafel slope of 64 mV dec Nevertheless, their scarcity and high cost hamper their usage on a large scale. Therefore, it is essential to design a cheap and eco-friendly electro catalyst that effectively boosts the reaction kinetics of HER and OER , manganese nitrate tetrahydrate [Mn(NO3)2·4H2O], polyvinylidene fluoride (PVDF) and N, N-dimethylformamide (DMF) were purchased from Duksan Chemicals Co., Ltd. Gyeonggi-do Republic of Korea. All chemicals were used without further purification.Phenylethylamine, hydroquinone and paraformaldehyde were purchased from Sigma Aldrich . Potassium hydroxide (KOH), sodium hydroxide (NaOH), dimethyl sulfoxide (DMSO), nickel nitrate hexahydrate . Then, the polymer was converted into the carbon material through carbonization, by heating up to 600 °C, with a heating rate of 2 °C/min. After which, the carbonized material was mixed with twice the amount of aqueous KOH and kept overnight. It was then filtered, dried at 120 °C and activated by heating in a tubular furnace up to 800 °C, following a heating rate of 3 °C/min. Following activation, the products underwent multiple washings with 1 M HCl and deionized H% yield. .3)2·4H2O was prepared and the synthesized NRPC (10 mg) was dispersed into this solution. After which, NH4F (18 mM) was added to the dispersed solution and kept under ultra-sonication for 1 h. In the meantime, a basic solution containing NaOH and Na2CO3 in 1:2 ratio was prepared and added to the ultra-sonicated solution and stirred vigorously for 4 h. Then, the solution was aged overnight and sealed in an autoclave at 90 °C for 12 h. The obtained product was washed and dried completely to obtain NRPC/Mn. Similar procedure was followed to synthesize NRPC/Ni and NRPC/NiMn, where Ni(NO3)2·6H2O was used for synthesizing NRPC/Ni, and a mixture of Mn(NO3)2·4H2O and Ni(NO3)2·6H2O were used for synthesizing NRPC/NiMn  measurements were conducted at different scanning rates from 5–100 mV s−1. Linear sweep voltammetry (LSV) was performed at a constant rate of 5 mV s−1. Tafel plots were fitted by considering the linear part of overpotential versus current density in log scale (log |j|), from which the Tafel slopes were calculated. All the potentials were calibrated versus reversible hydrogen electrode (RHE) using the equation given below:Electrochemical measurements were carried out using the CS2350 electrochemical workstation in a three-electrode system at room temperature for both HER and OER. Ni foam/NRPC, NRPC/Mn NRPC/Ni and NRPC/NiMn were used as the working electrode, a standard Hg/HgO electrode was used as the reference electrode and a Pt foil was used as the counter electrode. Furthermore, a 1 M aq. KOH solution was used as the electrolyte. For OER measurements, the system was saturated with OThe pH of the electrolyte used was found to be 13.4.−1. Nitrogen sorption isotherms were measured at −197 °C using a Micromeritics ASAP 2000. Before the experiments, the samples were dried at 120 °C and evacuated for 8 h in flowing argon at the flow rate of 60 standard cubic centimeters per minute at 140 °C. Surface area, pore size, and pore volumes were obtained from isotherms using the conventional Brunauer–Emmet–Teller (BET) and Barrett–Joyner–Halenda (BJH) equations. XPS spectra were achieved using a K-Alpha (Thermo Scientific Seoul korea). CasaXPS software  was used for the deconvolution of the high-resolution XPS spectra. All the instrumentation analyses were performed at the core research support center for natural products and medical materials of Yeungnam University.The prepared materials, i.e., NRPC, NRPC/Mn, NRPC/Ni and NRPC/NiMn were characterized by various physicochemical techniques such as field emission scanning electron microscopy (FESEM) with energy-dispersive X-ray spectroscopy (EDS), high-resolution transmittance electron microscopy (HRTEM), X-ray diffraction (XRD), Raman spectroscopy, nitrogen adsorption/desorption isotherms and X-ray photoelectron spectroscopy (XPS). FESEM was carried out on a Hitachi S-4800 equipped with EDX at an accelerating voltage of 4 kV. TEM/HRTEM images were performed with an FEI-Tecnai TF-20 transmission electron microscope with an operating accelerating voltage of 120 kV. XRD measurements were carried out using a PANalytical X’Pert3 MRD diffractometer with monochromatized Cu Kα radiation (λ = 1.54 Å) at 40 kV and 30 mA and were recorded in the range from 10 to 80° (2θ). Raman spectrum was recorded on the XploRA Micro-Raman spectrophotometer (Horiba) with a range between 500 and 4000 cm−1, indicated as ‘D’ band, which is an indication of the degree of disorderness, and the other at 1586 cm−1, indicated as ‘G’ band, which arises due to graphitization. The ratio of ID and IG provide the value of ‘R’, indicating the degree of disorderness  . It is observed that the catalytic property of NRPC/NiMn is efficient towards OER and HER due to the following reasons: the hierarchical three-dimensional flower-like morphology of NRPC/NiMn can speed up the transport of electrons and ions through increased surface area; the anchoring of these metal oxides on carbon spheres avoids agglomeration of these particles, providing excellent structural stability of NRPC/NiMn; and the inclusion of hetero-atom-doped carbon (NRPC) and bimetallic compounds results in providing more active sites at the interface of the electrode and electrolyte due to variable oxidation and reduction states of the metals and synergistically improves the OER process [In a similar way, the electrochemical activity of HC/NiMn towards HER was also analyzed in 1 M aqueous KOH, employing three-electrode system. NiFe2O4 2 mV and N process ,53,54,55−2. Moreover, a stable polarization curve was observed even after 2500 cycles. All these results indicate that the prepared NRPC/NiMn material is suitable for both OER and HER performance, with increased activity and electrochemical stability.The electrochemical stability of NRPC/NiMn towards HER performance was measured using chronoamperometry at two different current densities and polarization curves, represented in −1 for OER. Moreover, it also exhibits excellent performance, stability and cost-effectiveness, making it a promising option to be used in energy-related technologies. The NRPC/NiMn electrocatalyst can also be used to develop new types of fuel cells, solar cells and other energy technologies. The results obtained from this study provide new insights in the development of new methods for the production of clean and renewable fuels.In this study, we have demonstrated the synthesis of a new electrocatalyst, NRPC/NiMn, that exhibits excellent electrocatalytic performance for hydrogen evolution in water-splitting reactions. The NRPC/NiMn electrocatalyst was prepared using a simple and scalable method, and it is preapred from inexpensive and abundant materials. The nanoflower structure of the catalyst plays an important role in enhancing the electrochemical performance, as it provides more active sites by enlarging the surface area and enables increased contact with the electrolyte. In addition to its excellent performance, the NRPC/NiMn electrocatalyst is also stable and cost-effective. It was able to maintain its activity for over 20 h when used continuously. The morphology of NRPC/NiMn electrocatalyst, as observed by SEM and TEM analyses revealed a uniform distribution of Ni and Mn on the NRPC surface. This uniform distribution is essential for the high performance of the catalyst, as it ensures that there are no areas that are depleted in Ni or Mn. Overall, the NRPC/NiMn electrocatalyst is a promising new candidate for use in water-splitting applications, with an overpotential of 370 mV and a Tafel slope of 64 mV dec


The ribosome is a macromolecular complex composed of RNA and proteins that interact through an integrated and interconnected network to preserve its ancient core activities. In this review, we emphasize the pivotal role played by RNA-binding proteins as a driving force in the evolution of the current form of the ribosome, underscoring their importance in ensuring accurate protein synthesis. This category of proteins includes both ribosomal proteins and ribosome biogenesis factors. Impairment of their RNA-binding activity can also lead to ribosomopathies, which is a group of disorders characterized by defects in ribosome biogenesis that are detrimental to protein synthesis and cellular homeostasis. A comprehensive understanding of these intricate processes is essential for elucidating the mechanisms underlying the resulting diseases and advancing potential therapeutic interventions. RNA-binding proteins (RBPs) are a broad group of proteins that specifically bind to RNA molecules, forming ribonucleoprotein particles (RNPs) through interactions with their RNA-binding domains (RBDs), including RNA recognition motifs (RRMs), K homology (KH) domains, double-stranded RNA-binding domains (dsRBDs), intrinsically disordered regions (IDRs) and others . RBPs plWhile contemporary eukaryotic ribosomes consist of approximately equal parts protein and rRNA macromolecules, accumulating evidence over time suggested that rRNA plays a central role in controlling peptide bond catalysis and ensuring the fidelity of genetic code reading. Initially met with skepticism, this perspective has now gained widespread acceptance, leading to the recognition of ribosomes as ribozymes. Indeed, the atomic structures of the large ribosomal subunits in different living species consistently reveal the absence of ribosomal proteins (RPs) at the core of the peptidyl transferase center (PTC) ,13,14,15(1)Stabilizing one of the numerous diverse structures that RNA molecules can fold into, thus slowing down the folding process towards the most functional structure ,28.(2)Promoting conformational changes in RNA upon binding to even relatively small peptides, expanding the range of possible RNA structures, as exemplified by “riboswitches” .(3)Preventing RNA misfolding through chaperone-like activity ,31.It has been proposed that the essential functions performed by the RNA component of ribosomes during protein synthesis can be traced back to its ancestral role in the prebiotic environment of primordial Earth. According to the “RNA world” hypothesis, this primordial environment favored the emergence of biological replicators composed of RNA molecules exhibiting behavior similar to genetic algorithms; namely, RNA molecules that could iteratively replicate, mutate, and be selected based on improved fitness resulting from acquired mutations ,24,25,263–104 polymerized amino acids, occurred through an extensive series of intermediate stages. Each of these steps was based on the reciprocal interaction between rRNA and RPs. Indeed, the Accretion Model of ribosomal evolution depicts rRNA that recursively accreted and froze over time, increasing in mass [In addition to these reasons, the inherent versatility of proteins, in comparison to RNA molecules, enables them to adopt a wide variety of shapes and three-dimensional structures. This expands their range of functions to the extent that they play a significant role in shaping the skeletons and structures of cells, tissues and organisms. Collectively, the examples described above provide a rationale for the evolution of living systems that led to the emergence of proteins, of which some were capable of binding RNA. Support for this hypothesis is derived from phylogenetic studies demonstrating that ribosomal proteins with RNA-binding activity are among the most ancient and universally conserved proteins ,33. It i in mass ,46,47. T in mass . A three in mass . It shou in mass . Expandi in mass ,17,41,51The present-day ribosomes consist of both small (30S and 40S) and large (50S and 60S) subunits in prokaryotes and eukaryotes, respectively, and they exhibit a highly conserved three-dimensional structure in all living cells, as shown in Their reciprocal association forms the 70S complex in prokaryotes and the 80S complex in eukaryotes, creating the integral A (aminoacyl), P (peptidyl) and E (exit) sites. The A site accepts incoming aminoacyl-tRNA, the P site holds the tRNA with the growing peptide chain and the E site accommodates the deacylated tRNA before it departs from the ribosome. The precise order in which amino acids are incorporated into nascent polypeptide chains depends on the accuracy with which the genetic information in the mRNA is encoded by the ribosomes through the coordinate action of the mRNA, tRNA and various translation factors. Achieving this accuracy requires the ribosomes to undergo a complex and energy-intensive process of biogenesis. In eukaryotes, ribosome biogenesis initiates in the nucleolus, which is a specialized nuclear region for ribosome production that involves all three primary RNA polymerases. RNA polymerase I transcribes rDNA to produce the 47S polycistronic precursor pre-rRNA (35S in yeast), which undergoes further processing to yield mature rRNAs: 18S, 5.8S and 28S. RNA polymerase II generates a class of messenger RNAs (mRNA) known as 5′-Terminal-Oligo-Pyrimidine (TOP)-mRNAs, encoding ribosomal proteins (RPs) and ribosome biogenesis factors (RBFs). These motifs help coordinate the regulation of all ribosome biogenesis and translation components . RNA polBased on what is described above, the cooperative interaction between RPs and rRNAs must be considered a prerequisite for the optimal functioning of the RNP complex. Due to the structural complexity of both the molecular machinery and the mechanisms controlling its correct function, it is generally difficult to define a single path for the appearance of the pathological phenotype resulting from their impairment. Indeed, although all ribosomopathies share defects in the production of ribosomes, their appearance can originate from mutations of genes that control different molecular mechanisms ranging from the transcription and modification of pre-ribosomal RNA (pre-rRNA) to its processing until ribosome assembly. As a rule, homozygous mutations of genes coding for RPs and RBFs are lethal, while their single-copy mutations can generate a group of heterogeneous diseases, defined as ribosomopathies, including Diamond-Blackfan anemia (DBA), Shwachman–Diamond syndrome (SDS), X-linked subtype of dyskeratosis congenita (DKCX) and 5q- myelodysplastic syndrome (5q- MDS) ,114,115.GATA1, HSP70 and TSR2 [RPS19 gene, which encodes for RP eS19 [RPS19 gene reported so far may break some of these hydrogen bonds while other mutations appear to alter the surface electrostatic properties of the protein perturbing interactions between negatively charged rRNA and the positively charged surface of Rps19 [DBA corresponds to a hereditary, uncommon form of pure red blood cell aplasia characterized by the incapability of erythropoiesis. The disease condition typically appears within the initial year of one’s life, with 95% of DBA cases diagnosed before 2 years of age . Fifty-fand TSR2 ,130. The RP eS19 ,132. Thi RP eS19 . Both coof Rps19 ,135. Conof Rps19 . The netof Rps19 ,137,138.of Rps19 .SBDS gene. In these cases, other mutated genes such as DnaJ heat shock protein family (Hsp40) member C21 (DNAJC21), elongation factor-like 1 (EFL1) and signal recognition particle 54 (SRP54) are associated with an SDS-like phenotype [Shwachman–Diamond syndrome (SDS) is a rare autosomal recessive disorder characterized by bone marrow failure, exocrine pancreatic dysfunction and a predisposition to leukemia. The age of onset is heterogeneous, being able to occur in the prenatal, neonatal and infantile periods . Approxihenotype . The exphenotype ,146,147.henotype ,149. SBDhenotype . The inthenotype ,150. Thehenotype ,151. Subhenotype ,149. Addhenotype ,153.The ancestral origins of the ribosome and its evolutionarily conserved essential functions make it possible to consider it as an excellent reference model for understanding the evolutionary meaning of RNA–protein interactions that are useful for the emergence of living organisms. Indeed, the primitive heart of the ribosome made up of RNA with ribozyme functions has represented a source for the synthesis of new proteins from which those capable of binding the RNA itself have, in turn, represented a driving force to further improve its enzymatic activity and reading fidelity. Their functions, however, are not attributable to the interaction of a single and specific couple of RNA/proteins but are the result of an intricate interconnected network composed in eukaryotes by 79 ribosomal proteins (RPs) and four ribosomal RNAs (rRNAs). The complexity of a system also results from its ability to keep the different and distant sites that compose it in communication with each other. Remarkably, the system is further complicated in its understanding by the numerous factors participating both in the assembly process and in the surveillance mechanism for the preservation of ribosomal integrity. Understanding the specific contribution of each RNA–protein interaction to the proper functioning of the above represents one of the major challenges in the current ribosome research which can be prosecuted through interdisciplinary and integrated approaches ranging from structural, phylogenetic and mathematical methods. Looking ahead, utilizing emerging methods such as in vivo single-molecule tracking  and cryo


The increasing availability of RNA structural information that spans many kilobases of transcript sequence imposes a need for tools that can rapidly screen, identify, and prioritize structural modules of interest.RNA Structural Content Scanner (RSCanner), an automated tool that scans RNA transcripts for regions that contain high levels of secondary structure and then classifies each region for its relative propensity to adopt stable or dynamic structures. RSCanner then generates an intuitive heatmap enabling users to rapidly pinpoint regions likely to contain a high or low density of discrete RNA structures, thereby informing downstream functional or structural investigation.We describe https://github.com/pylelab/RSCanner).RSCanner is freely available as both R script and R Markdown files, along with full documentation and test data ( RSCanner identifies and analyzes modules of high BPC across any RNA target of interest, regardless of length. RSCanner scans an RNA secondary structure model in windows, calculates relative BPC, and applies a Shannon entropy (SE) filter derived from base-pairing probabilities to identify regions where a high degree of secondary structure coincides with regions that are conformationally restricted or conformationally variable. In doing so, RSCanner enables the user to quickly pinpoint regions of high and low density of discrete RNA structures, which can significantly facilitate the choice of candidate regions for biochemical and structural analysis, as well as aid in downstream experimental design.We previously introduced a strategy for computing stable BPC along the length of long RNA molecules such as viral genomes, thereby providing a means for selecting transcript modules for subsequent structural investigation . In the in silico calculation  genomic RNA structure . First, RSCanner allows the user to define several parameters. The sliding window size for BPC and/or SE smoothing and the bin size for the histogram can be changed to generate graphs of varying granularity. The lower and upper nucleotide bounds for visualization can also be altered, allowing the user to zoom in on specific regions of interest on the RNA. Furthermore, the stringency for filtering structure counts can be adjusted by individually varying the cut-off values for BPC and SE. For example, to increase the stringency, the user could increase the BPC cut-off value from the 25th to the 75th percentile  and/or dIn addition to the HCV structure, we also applied RSCanner to other structured RNAs to illustrate its applicability to cases with and without experimental constraints: the HIV RNA genome, the SARS-CoV-2 Spike subgenomic RNA (sg-Spike), and the GRS1 long noncoding RNA (lncRNA GRS-1) . The SHAbtad111_Supplementary_DataClick here for additional data file.


N = 70) and non-autistic (N = 71) adults on the 10-item multiple-choice RMET to that of a free-report version of the RMET. Both the autistic and non-autistic groups performed much better on the multiple-choice than the free-report RMET, suggesting that the multiple-choice format enables the use of additional strategies. Although verbal IQ was correlated with both multiple-choice and free-report RMET performance, controlling for verbal IQ did not undermine the ability of either version to discriminate autistic and non-autistic participants. Both RMET formats also demonstrated convergent validity with a well-validated adult measure of theory of mind. The multiple-choice RMET is, however, much simpler to administer and score.The Reading the Mind in the Eyes Test (RMET) is a purported theory of mind measure and one that reliably differentiates autistic and non-autistic individuals. However, concerns have been raised about the validity of the measure, with some researchers suggesting that the multiple-choice format of the RMET makes it susceptible to the undue influence of compensatory strategies and verbal ability. We compared the performance of autistic . This test presents participants with photographs of pairs of eyes and asks them to identify the emotion displayed by each pair of eyes from four choices. Some researchers have argued that the multiple-choice format of the RMET may not be an accurate measure of theory of mind, as participants could simply be guessing or using a process of elimination to select the correct answer. Participants may also be disadvantaged if they are not familiar with the specific emotion words used in the multiple-choice answers. We examined whether a free-report (open-ended) format RMET would be a more valid measure of theory of mind than the multiple-choice RMET. Autistic and non-autistic adults performed better on the multiple-choice RMET than the free-report RMET. However, both versions successfully differentiated autistic and non-autistic adults, irrespective of their level of verbal ability. Performance on both versions was also correlated with another well-validated adult measure of theory of mind. Thus, the RMET’s multiple-choice format does not, of itself, appear to underpin its ability to differentiate autistic and non-autistic adults. Recognizing and understanding the perspectives of others—or theory of mind —is imporAnother concern raised about the RMET is that the multiple-choice response format provides respondents with contextual information that influences their test performance . CasselsWe (1) replicated f = .25) at alpha = .05 and power = .80 ; their data were excluded from analyses. Twenty participants who scored 26 or higher on the AQ  but did not confirm an autism diagnosis were also excluded. The final sample comprised 70 autistic  and 71 non-autistic participants . All participants were fluent in English, with 139 participants indicating that English was their first language. No information was collected on participants’ ethnicity and socioeconomic status.As both https://osf.io/93sjm/). On all but one response, at least two of the three raters provided the same score. Disagreements were discussed until consensus. Responses meeting a stringent or lax definition were scored correct; all other responses were scored incorrect. RMET scores range from 0 to 10; higher scores indicate higher levels of theory of mind.The 10-item RMET  presentsThe AQ  is a 50-faux pas, sarcasm, white lie). Scores on this subscale range from 0 to 6; higher scores indicate greater theory of mind. The A-ToM-Q’s social subscale correlates significantly with other theory of mind and criterion-related measures  test  requiresmeasures . Divergemeasures . Discrimmeasures .r = 0.48), WASI-II FSIQ-2 (r = 0.54), and WASI FSIQ-4 (r = 0.53), suggesting the SA-VIQT’s viability as a quick research screening measure of verbal IQ  score ranging from 40 to 160. It is moderately correlated with the Wechsler Abbreviated Scale of Intelligence (WASI-II) , Verbal erbal IQ .RMET performance was examined using a 2  × 2  between-subjects design.This project was approved by the Flinders University Human Research Ethics Committee; participants read a study information sheet and gave informed consent. The study was administered using Qualtrics. Participants provided demographic information and indicated if they had received a formal diagnosis of autism. Two attention checks were used to identify the use of robots or automated systems. Participants completed the AQ and A-ToM-Q social subscale, were randomly allocated to either the free-report or multiple-choice RMET, and then completed the SA-VIQT. Participants received an honorarium as compensation for their time.Two of the authors are practicing clinical psychologists who consult with autistic adults and children.Supplementary Materials (p. 2).As shown in F = 294.78, p < 0.001, ηp2 = .69. The autistic group scored lower than the non-autistic group on both the multiple-choice and free-report RMET, F = 37.35, p < 0.001, ηp2 = .22  = 4.17, p = 0.04, ηp2 = .03, reflected larger differences between the autistic and non-autistic groups on the multiple-choice than free-report RMET.A 2  × 2  between-subjects analysis of variance (ANOVA) revealed a main effect of response format on RMET scores, with higher scores on the multiple-choice than the free-report version,  .22 see . A signir(69) = 0.34, p = 0.003. Unsurprisingly, given the non-autistic group performed near ceiling, their multiple-choice RMET performance was not correlated with verbal IQ, r(35) = 0.07, p = 0.67. For the autistic group, the nonsignificant correlation indicated a moderate effect size, r(32) = 0.33, p = 0.06.For the overall sample, multiple-choice performance was correlated with verbal IQ, r(64) = 0.29, p = 0.02, although the correlation for the autistic group, which performed near the floor, was negligible, r(32) = 0.07, p = 0.69. The correlation for the non-autistic group indicated a moderate effect size, r(30) = 0.45, p = 0.01.Free-report RMET performance was also significantly correlated with verbal IQ for the overall sample, F = 301.81, p < 0.001, ηp2 = .70, and group membership, F = 28.80, p < 0.001, ηp2 = .18, on RMET scores remained. However, the Response Format × Group interaction on RMET scores disappeared, F = 2.89, p = 0.09, ηp2 = .02.Given that verbal IQ and age were significantly correlated with RMET performance, analyses were repeated with verbal IQ and age as covariates. The main effects of response format, sr(70) = 0.59, p < 0.001, with verbal IQ controlled, sr(69) = 0.54, p < 0.001. The free-report RMET had a weak-moderate correlation with the A-ToM-Q in the overall sample, sr(65) = 0.28, p = 0.02. Controlling for verbal IQ, the latter correlation was no longer significant, sr(64) = .22, p = 0.07, but the coefficient was only slightly, and not significantly , lower. The correlation between free-report RMET and A-ToM-Q was significantly weaker than the correlation between multiple-choice RMET and A-ToM-Q , z = -2.18, p = 0.02; however, with free-report RMET performance relatively close to the floor (regardless of group), this pattern is unsurprising.There was a strong correlation between the multiple-choice RMET and the A-ToM-Q in the overall sample, Consistent with In addition, examination of the concurrent validity of both RMET formats revealed that multiple-choice performance correlated strongly with the A-ToM-Q. Although free-report performance was not as strongly correlated, this likely reflects free-report performance being close to the floor. These correlations with A-ToM-Q performance remained consistent after controlling for VIQ. In sum, our findings provide evidence for the concurrent validity of both versions and suggest that the validity of the RMET is not dependent on verbal ability. Given the demanding coding requirements for scoring free-report RMET responses, the multiple-choice RMET is the more accessible, efficient, and economical option.First, we did not obtain evidence that participants had received a formal diagnosis of autism, relying instead on self-reports of a diagnosis and AQ scores. Second, the SA-VIQT, a quick screening measure of VIQ is not as rigorous as a full-scale verbal IQ measure such as the Wechsler scales. Third, although our results provided promising evidence of the RMET’s concurrent validity with the A-ToM-Q, we note that the A-ToM-Q’s stimulus videos depicting social interactions include  the target individuals’ facial expressions. Thus, it is possible that cues from the eye region may contribute to a degree of shared variance between RMET and A-ToM-Q scores. One way to examine this possibility would be to isolate or pixelate the eye region of the characters in the A-ToM-Q stimuli.Our results indicate that both the multiple-choice and free-report versions of the RMET differentiated autistic and non-autistic adults irrespective of verbal ability. However, given its ease of administration, the multiple-choice format offers clear practical advantages over the free-report format.Click here for additional data file.Supplemental material, sj-docx-1-aut-10.1177_13623613231167226 for Response format changes the reading the mind in the eyes test performance of autistic and non-autistic adults by Alliyza Lim, Neil Brewer, Denise Aistrope and Robyn L Young in Autism