Despite years of dedicated research, scientists are still not sure what the first ”living” cell would have looked like. One of the most well-known hypotheses is the RNA world hypothesis, which assumes that, in the beginning, life relied on RNA molecules instead of DNA as information carriers and primitive enzymes. The population of such RNAs is made up of self-replicating molecules (replicases) that could make copies of themselves and parasite molecules that could only be copied by replicases. In this study, we further investigated the interplay between these hypothetical prebiotic RNA species, since it plays a crucial role in generating diversity and complexity in prebiotic molecular evolution. We compared two approaches that are commonly used to investigate such simple prebiotic systems, representing different modeling and observation scales—namely, microscopic and macroscopic. In both cases, we were able to obtain consistent results.The origin of life remains one of the major scientific questions in modern biology. Among many hypotheses aiming to explain how life on Earth started, RNA world is probably the most extensively studied. It assumes that, in the very beginning, RNA molecules served as both enzymes and as genetic information carriers. However, even if this is true, there are many questions that still need to be answered—for example, whether the population of such molecules could achieve stability and retain genetic information for many generations, which is necessary in order for evolution to start. In this paper, we try to answer this question based on the parasite–replicase model (RP model), which divides RNA molecules into enzymes (RNA replicases) capable of catalyzing replication and parasites that do not possess replicase activity but can be replicated by RNA replicases. We describe the aforementioned system using partial differential equations and, based on the analysis of the simulation, surmise general rules governing its evolution. We also compare this approach with one where the RP system is modeled and implemented using a multi-agent modeling technique. We show that approaching the description and analysis of the RP system from different perspectives (microscopic represented by MAS and macroscopic depicted by PDE) provides consistent results. Therefore, applying MAS does not lead to erroneous results and allows us to study more complex situations where many cases are concerned, which would not be possible through the PDE model. Understanding how life might have begun is a fundamental problem in biology, requiring input from almost every discipline ,2,3. OneThe concept of information in molecular biology was developed based on Shannon’s theory ,8. ShannSince much of the historic process of the origin of life has been lost and it is not possible to track back to our universal ancestor through the path of evolution, various theories on prebiotic evolution have been proposed. Among these, the RNA world hypothesis is probably the most extensively studied. It postulates an intermediate stage of life sustained by autocatalytic replication, where RNA played both genetic and enzymatic roles . The disApart from the many serious obstacles involved, including but not limited to the supply of RNA building blocks and template replication in the absence of specialized enzymes, another challenge is the maintenance of genetic information in RNA sequences over many rounds of imperfect replication . In ordeThe hypercycle theory was first formulated and studied in terms of ordinary differential equations (ODE) assuming the presence of a perfect mixed medium ,28,29. LRP model for short)) proposed by Hogeweg and Takeuchi aP is lowSimple prebiotic systems are usually investigated by dynamical models, which can basically be discrete or continuous in time and may also take spatial extension into account. Continuous time models are formalized as ordinary differential equations (ODE), whereas spatially structured ones can be treated in continuous space by partial differential equations (PDEs) or as cellular automata (CA) ,38,44 inIt is worth mentioning that the efforts made towards understanding the synthesis of catalytically active RNA replicators and their population dynamics were thoroughly reviewed in . Here, tUnfortunately, there is a wide gap between the discrete and continuous mathematical models proposed for the replicase–parasite system so far. The formulations existing in the literature are rather simple and focus on selected aspects of the RP model dynamics ,38,45,46Since no differential equation models that can coherently reflect the complexity of the replicase–parasite system exist in the literature, we introduced and thoroughly analyzed a comprehensive and sophisticated PDE model in which molecules—namely, replicases, parasites, and resources—are described as density functions. Additionally, the affinities of replicases and parasites towards replicases were assumed to be fixed and, in contrast to the abovementioned ODE models ,38,45,46First, an analysis of a well-mixed system assuming constant densities of all molecules was conducted and, as a result, the general inequality  that mus earlier  was fulfequality  was no lHowever, since mutations occur randomly, despite the fact that parasites that are better recognized by replicases are locally preferred, if the area covered by the population was large enough, during PDE simulation, there were some places that retained a low value of ed above  was agaiIn the MAS simulations of the RP system conducted in this work (see Scenarios 1–8), it could be noticed that parasites evolved locally towards having a higher probability of being replicated , the propagation of the parasites with a high value of the Furthermore, Scenarios 1–8 concerning MAS simulations were carried out mainly to investigate waves as a means of survival of the system. They focused on parameter range analysis in order to check their influence on the system collapse versus survival. The results of the simulations clearly show that the diffusion constant Additionally, Scenario 9 was conducted to show that a stable coexistence of replicases and parasites is possible in a well-mixed system with no mutation modeled using MAS, where inequality  is fulfiEscherichia coli, where RNA was replicated by self-encoded RNA replicase . To the best of our knowledge, it is the first such comprehensive and sophisticated model that can coherently reflect the complexity of the RP system. We carefully analyzed the abovementioned PDE model both analytically and through extensive simulation experiments. Next, in order to identify the general principles and rules governing their dynamics and evolution, we thoroughly compared this model with a simplified version of the multi-agent system described by the corresponding set of parameters. We showed that approaching the description and analysis of the RP system using different modeling and observation scales (microscopic and macroscopic) allowed us to obtain consistent results. From this, it follows that, by applying a multi-agent approach, we can not only consider many complicated cases, such as the formation of protocells or self-assembled membranes, which would not be possible in the PDE model, but also avoid erroneous outcomes, since MAS and PDE lead to convergent results. Additionally, this clearly showed that mathematical modeling methods can be successfully applied for the description of RNA world dynamics and evolution.The simulation results obtained for both models showed that natural selection favored better templates. Despite the fact that efficient parasites were winning locally, which meant that the system was locally unstable as natural selection favored parasites with a higher affinity, the system was able to survive due to some places where, because of random mutations, parasites were mild or in a small minority, and the population was able to restore itself. Therefore, in the simulations involving both models, we could observe spatial patterns resembling traveling waves, as seen in previous research ,41.As a continuation of the research reported in this paper, one may consider taking into account further mathematical and simulation analyses of the proposed PDE model, especially in the context of the period of the cycle, which can describe the oscillations of the system between growth and decay.


To date, more than 170 chemical modifications have been characterized in RNA, providing a new layer of gene expression regulation termed the ‘epitranscriptome’. RNA modification detection methods and tools advance the functional studies of the epitranscriptome. According to the detection throughput and principles, existing RNA modification detection technologies can be categorized into four classes, including quantification methods, locus-specific detection methods, next-generation sequencing-based detection technologies and nanopore direct RNA sequencing-based technologies. In this review, we summarize the current knowledge about these RNA modification detection technologies and discuss the challenges for the existing detection tools, providing information for a comprehensive understanding of the epitranscriptome. Improved methods for detecting chemical modifications of RNA will help to understand the epitranscriptome, the collection of RNA modifications in a cell. Chemical modifications to RNA molecules do not alter the sequence, but can nonetheless greatly affect RNA function. More than 170 RNA modifications have been discovered over the past 60 years and their effects are analogous to epigenetic modifications of DNA. Xiaoyu Li and co-workers at Zhejiang University School of Medicine in Hangzhou, China, reviewed existing tools for detecting RNA modifications. These tools, which are developed based on the inherent chemical properties of RNA modifications, vary in sensitivity, quantification, specificity and accuracy. The researchers also summarize the recent progress in nanopore direct RNA sequencing based detection technology and are optimistic that future advances will enable detection of different modifications simultaneously in the single molecules. Most of the RNA modifications have been identified in abundant non-coding RNAs, including ribosomal RNA (rRNA), transfer RNA (tRNA), and small nuclear RNA (snRNA).RNA is a single-stranded molecule consisting of four nucleotides: adenosine (A), guanosine (G), cytidine (C) and uridine (U). RNA, both an important and conserved macromolecule, not only participates in the flow of genetic information but also regulates gene expression. Beyond sequence information, chemical modifications add to the complexity of RNA, emerging as a new layer of gene expression regulation. Since the first chemical modification was characterized 60 years ago, more than 170 RNA modifications have been characterized7. To date, more than ten chemical modifications have been mapped in a transcriptome-wide manner, including N6-methyladenosine (m6A), N6, 2′-O-dimethyladenosine (m6Am), 5-methylcytosine (m5C), 5-hydroxymethylcytosine (hm5C), inosine (I), pseudouridine (Ψ), N1-methyladenosine (m1A), 2ʹ-O-methylation (Nm), N4-acetylcytidine (ac4C), N7-methylguanosine (m7G) and dihydrouridine (D). Different chemical modifications play distinct regulatory roles in RNA metabolism and function. For instance, m6A, the most abundant internal messenger RNA (mRNA) modification, influences RNA metabolism in multiple ways, including stability, splicing, translation, localization and RNA secondary structure11. m5C in mRNA influences mRNA export, RNA stability, and translation, and m5C in tRNA is essential for maintaining structural stability and translational fidelity19. Inosine preferentially exists in double-stranded RNA (dsRNA) regions and affects codon recoding, splice-site choice, microRNA (miRNA) biogenesis and targeting efficiency22. Ψ is required for proper rRNA folding, tRNA structure stabilization and snRNP  biogenesis32. In addition, introducing Ψ into mRNA increases protein production and alters translation35. m1A at position 58 in tRNA is conserved and vital for stabilizing tRNA tertiary structure, and m1A in mRNA influences translation47. Nm is essential for accurate and efficient protein synthesis52. Internal m7G increases mRNA translation efficiency and augments miRNA biogenesis54. ac4C in mRNA promotes translation, and ac4C in rRNA can affect rRNA biogenesis59.The development of detection technologies advances the investigation of the functional roles of RNA modificationsRNA modification detection technologies provide not only resources for a comprehensive understanding of the epitranscriptome but also tools for functional studies. Hence, in this review, we will summarize the current knowledge about these existing RNA modification detection technologies and discuss the challenges for these existing detection tools. The detection technologies are categorized according to the detection throughput and principles into four classes: quantification methods, locus-specific detection methods, next-generation sequencing-based detection technologies and nanopore direct RNA sequencing-based technologies.The identification and quantification of new modified nucleotides requires powerful RNA modification quantification methods. Based on the principle that modified nucleotides possess distinct chemical properties from the originals, several RNA modification quantification methods have been established, including two-dimensional thin-layer chromatography (2D-TLC), dot blot, and liquid chromatography–mass spectrometry (LC–MS). These approaches can be used to quantify the modification abundance in specific RNA species and require highly purified RNA due to the lack of sequence information.62. In detail, isolated RNA is first partially digested into oligonucleotides using RNase A, T1, or T2 and then labeled with 32P using T4 polynucleotide kinase (T4 PNK). Finally, 5ʹ-32P-NMP is acquired by nuclease P1 digestion and further separated by 2D-TLC. The nucleotides can be determined by assignment to the standards by comparing their retardation factor (Rf) values . Consequently, this approach can be applied to both abundant non-coding RNAs (rRNA and tRNA) and less abundant mRNA. In addition, it can also detect RNA modifications in specific tRNA or snRNA sequences, which can be isolated by gel purification or hybridization methods. Furthermore, this approach does not require expensive instruments and thus can be run inexpensively. However, this method also has certain drawbacks, including the requirement of a radioactive reagent, the bias caused by RNase digestion and discrepant 32P labeling efficiency for the modified nucleotides66.2D-TLC is a widely used RNA modification detection method according to the distinct mobilities of different nucleotides in the solvent68. However, the lack of absolute quantification and locus information limits a wider application of this approach.Dot blot assay is established and applied to detect and quantify modification levels in RNAs using a specific antibody. In detail, isolated RNAs are stalled to the polyvinylidene fluoride (PVDF) or nitrocellulose membrane directly without electrophoretic size separation. A specific antibody for target modification is incubated with the membrane, followed by secondary antibody hybridization and subsequent signal trapping Fig. . Compari69.High-performance liquid chromatography (HPLC) is an advanced column chromatography technology to separate nucleosides according to their distinct polarities. Prior to HPLC analysis, RNA or oligonucleotides are digested and dephosphorylated to single nucleosides by nuclease P1 and alkaline phosphatase. The UV absorbance and retention time of the nucleosides are recorded by a UV detector, which can be used to identify and measure the abundance of the modified nucleosides  and product ion are capable of determining a certain nucleoside  Primer extension; (2) RNase H-based approaches; (3) electrospray ionization-mass spectrometry (ESI-MS)-based approaches; and (4) semiquantitative PCR- or qPCR-based approaches.1A, Ψ, and m1G. In principle, a 5ʹ-labeled specific RT primer is hybridized with the RNA of interest and extended by reverse transcriptase. Hence, this approach relies on prior knowledge of the modification type and sequence information of the target RNA. Without modifications, reverse transcriptase can reach the 5ʹ end of RNA and generate full-length cDNA. When encountering modified nucleotides, the extension of reverse transcriptase is blocked immediately upstream of the modified site. Then, the RT products are separated using denaturing polyacrylamide gels, and the terminal position of truncated cDNA indicates where the modification occurred , has been developedTLC Fig. . This ap6A in diverse RNA species . In this method, the RNA of interest is first reverse-transcribed at both low and high dNTP concentrations. The truncated and full-length cDNA are amplified by specific primers. The PCR products are analyzed by gel electrophoresis, and the Nm status can be determined by comparing the intensity of the longer PCR product with that of the shorter product  has been developed93. Because m6A can both hinder the elongation activity of DNA polymerases and reduce the nick ligation efficiency of SplintR ligase, in SELECT, cDNA products formed from m6A-containing RNA templates are dramatically reduced, hence significantly increasing detection robustness. Based on qPCR, SELECT is a powerful tool to quantify the m6A fraction in a linear manner  carbodiimide (CMC), and the Ψ-CMC adducts interfere with Watson-Crick base pairing during reverse transcription. Under optimized RT conditions, Ψ–CMC adducts can be read through and induce mutation/deletion in cDNA. Such mutation/deletions alter the melting curves of the qPCR products, thus enabling the locus-specific detection of Ψ modification Fig. 95.99. Compared with other locus-specific methods, the ESI-MS-based approach does not rely on prior knowledge of sequence information and thus is able to detect and quantify RNA modification in a de novo manner. Therefore, this approach has been widely applied for modification detection in abundant RNAs, including tRNA, rRNA and snRNA109. Recently, this approach has been used to detect modifications in miRNA and cap modifications in mRNA110. The major limitation of this approach is the requirement for highly sensitive ESI-MS. In addition, given the detection principle, the requirement of starting material is large; thus, this method is only suitable for abundant RNA species.In contrast to the LC–MS-based nucleoside quantification strategy described above, in this approach, isolated RNA samples are first digested into 5–15 nucleotide fragments by selective endoribonucleases, such as RNase T1, RNase A, and RNase U2. Then, the oligonucleotides are separated by HPLC, and the sequence ladders from the oligonucleotides are further generated through ESI-MS, which can be used for sequence reconstruction and modification identification Fig. . Hence, With the benefit of advances in next-generation sequencing, an increasing number of RNA modification sequencing technologies have been developed. Such technologies represent powerful tools to map modified nucleotides in a transcriptome-wide manner and promote the elucidation of the regulatory roles of RNA modifications. Chemical modifications alter the inherent features of the original nucleotides, including base-pairing performance in reverse transcription, chemical reaction activities, enzymatic reaction activities and binding affinities with specific proteins or antibodies. Therefore, coupled with next-generation sequencing, these properties of modified nucleotides can be used to characterize RNA modifications throughout the transcriptome. The existing sequencing technologies can be categorized according to the detection principles into four classes: 1) direct sequencing technology, (2) chemical-assisted sequencing technology, (3) antibody-based sequencing technology, and (4) enzyme/protein-assisted sequencing technology , and 3-methyluridine (m3U) in RNA prior to cDNA synthesis, and high-confidence methylation sites in the tRNA transcriptome can thus be identified by comparing the data for parallel sequenced, demethylase-treated and untreated samples  for dTTP under RT conditions can also facilitate m6A detection. In principle, compared with 4SeT-A, 4SeT-m6A pairing is unfavorable and results in aborted cDNA synthesis opposite m6A sites, thereby allowing for the discrimination of m6A from A. Furthermore, with the assistance of the m6A demethylase FTO through high-throughput sequencing, m6A can be precisely identified within the mammalian transcriptome at single-nucleotide resolution  installing biotin tags to enrich modified transcripts; (2) altering the base-pairing features to induce misincorporation or truncation in reverse transcription; and (3) chemical-induced cleavage followed by specific adaptor ligation Fig. .Fig. 4Sc4 and subsequently exposed to low pH to generate abasic sites at m7G positions, which can be tagged with biotin molecules for further streptavidin pulldown . In this method, m6A is first oxidized to hm6A by the demethylase FTO, and hm6A is further converted to N6-dithiolsitolmethyladenosine (dm6A) through DTT-mediated thiol addition. The free sulfhydryl group of dm6A enables biotin to be conjugated to the m6A-modified transcripts, thereby facilitating streptavidin enrichment and sequencing , total RNA is treated with NaBHown Fig. 53. Direcing Fig. 127.5C, Ψ, m7G, ac4C, D and m6A. For inosine detection, the direct sequencing approach is disturbed by background noise. To overcome this limitation, a selective chemical labeling reaction with acrylonitrile is adopted, and the formed N1-cyanoethylinosine (ce1I) blocks reverse transcription, resulting in truncation of cDNA. Coupling this chemical reaction with sequencing, referred to as inosine chemical erasing sequencing (ICE-seq), can achieve base-resolution inosine detection throughout the transcriptome , instead of CMC. The presence of an azido group enables conjugation with biotin through a click reaction. Hence, the Ψ-containing RNA can be pre-enriched before sequencing, and this method is named CeU-seq86. The pre-enrichment step enables CeU-seq to identify thousands of Ψ sites in the mammalian transcriptome  can map internal m7G modifications at nucleotide resolution in tRNA and rRNA137. To achieve m7G detection in mRNA, m7G-seq conjugates a biotin molecule to the generated abasic sites, thus enabling pre-enrichment of m7G-containing RNA. Moreover, the biotinylated sites induce misincorporations during cDNA synthesis; therefore, transcriptome-wide base-resolution m7G mapping can be achieved . Furthermore, the Rho-adducts block reverse transcriptase elongation and thus can be identified by analyzing induced RT-stops in sequencing data  analog, Se-allyl-L-selenohomocysteine, the cellular RNAs could be modified with N6-allyladenosine (a6A) at supposed m6A-generating sites. Furthermore, a6A-containing RNAs can be enriched by a specific antibody, and a6A sites are converted to N1,N6-cyclized adenosine (cyc-A) through the iodination-induced cyclization reaction. As cyc-A induces misincorporations in cDNA synthesis, m6A can be mapped at base resolution by detecting mutation signals in sequencing data . Since cyclized a6m6A can induce misincorporation in RT, the technology, named m6A-SAC-seq, achieves quantitative, transcriptome-wide mapping of m6A at single nucleotide resolution141.The detection strategy of altering the base-pairing properties of nucleotides by chemical treatment has been widely applied to the transcriptome-wide detection of various modifications, including inosine, mome Fig. 128. Bisuata Fig. 129,130. ome Fig. . In addiome Fig. 131. As dved Fig. 54. In adata Fig. 138. ac4Cion Fig. 139. In aata Fig. 140. With7G, m3C and Ψ. Nm is resistant to alkaline hydrolysis and thus can be mapped at single-base resolution by analyzing read-ends information in sequencing data. Since RNA fragmentation is random and irregular, this strategy requires rather high read coverage and is limited to highly abundant RNAs144. To overcome this limitation, ribose oxidation sequencing (RibOxi-seq) and Nm-seq have been developed. In these two methods, RNA is treated with iterative oxidation–elimination–dephosphorylation (OED) cycles to remove unmodified nucleotides, and the non-methylated ends cannot be ligated to linkers for sequencing library construction146. The selective cleavage and ligation allow detection of low-stoichiometry 2ʹ-O-methylation sites using this approach. Similarly, m7G and m3C are resistant to NaBH4-aniline treatment, and the generated 5′-phosphate end during aniline cleavage could be exploited for selective ligation to enrich modified fragments148. This positive selection strategy facilitates transcriptome-wide detection of m7G and m3C. In addition, upon hydrazine-aniline treatment, RNA can be specifically cleaved at m3C-modified sites and resistant to cleavage at Ψ-modified sites. Based on this approach, hydrazine-aniline cleavage sequencing (HAC-seq) and HydraPsiSeq have been developed to detect m3C and Ψ, respectively  and miCLIP (methylation iCLIP) can enrich target sites by immunoprecipitation, thereby enabling identification of the direct targets of m5C methyltransferases , the cytidine deaminase APOBEC1 is fused to the m6A-binding YTH domain and thus leads to C-to-U deamination at sites adjacent to m6A residues. Furthermore, m6A residues can be identified by analyzing C-to-T mismatches in sequencing data  promotes binding of inosine in RNA instead of cleavage. Taking advantage of this approach, Endonuclease V immunoprecipitation enrichment sequencing (EndoVIPER-seq) can enrich A-to-I edited transcripts from cellular RNA164.In addition to antibody immunoprecipitation, some enzymes or RNA modification-related proteins can also be utilized for affinity capture or editing modification-containing transcripts, thereby enabling transcriptomic RNA modification detection Fig. . For insses Fig. 160,161. ata Fig. 162. ReceE. coli RNA endoribonuclease MazF can specifically cleave the unmethylated 5ʹ-ACA-3ʹ motif but not the 5ʹ-m6ACA-3ʹ motif165. Taking advantage of this specificity, RNA-endoribonuclease–facilitated sequencing (m6A–REF-seq)/MAZTER-seq allows quantitative profiling of m6A at single-nucleotide resolution  of m6A-modified sites.Similar to chemical-induced hydrolysis, some endonucleases also show distinct cleavage efficiency between modified and unmodified transcripts, which can be utilized to enrich modification-containing transcripts and identify modified nucleotides. For instance, the ion Fig. 166,167. 171. Mechanistically, single-stranded RNA is driven through the nanopore by the motor protein and thus causes ionic current changes for a set of k nucleotides residing within the pore , which enables decoding of the nucleotide sequence by computational analysis  and distinct detection principles for different modifications, next-generation sequencing-based detection technologies cannot map diverse RNA modifications simultaneously. The development of the Oxford Nanopore Technologies (ONT) sequencing platform shows promise in overcoming these challenges. In contrast to next-generation RNA sequencing, nanopore sequencing can sequence RNA directly without the requirement of additional reverse transcription and PCR amplification, thus decreasing the biases caused by these steps170. Furthermore, modifications can be identified by the algorithms previously trained with modified and unmodified kmer contexts or comparison with the nonmodified control samples. These computational algorithms have allowed the identification of several modifications, including m6A, m7G, m5C, hm5C, Ψ and Nm, using nanopore direct RNA sequencing180. In addition, owing to the long sequencing length, the sequence information and modification landscape of SARS-CoV-2 can be determined simultaneously by nanopore direct RNA sequencing182. However, there are still many challenges and limitations for nanopore RNA sequencing-based detection technology. First, the sequencing error rate (~1–4%)168 is still much higher than that of next-generation sequencing (~0.1-1%). Since some algorithms exploit systematic base-calling errors to identify RNA modifications, the high sequencing error rate increases the complexity for base calling and modification identification. Second, the throughput of nanopore RNA sequencing is relatively low (1–3 Gb per flow cell), and the costs of direct RNA sequencing are high. Recently, several studies have shown that accurate modification detection requires high sequencing coverage (at least 30X)180. Hence, the sequencing depth and costs limit broader application of this strategy. Third, nanopore direct RNA sequencing requires a large amount of RNA (~500 ng polyA+ RNA), and thus, it is not suitable for samples that are difficult to obtain. Therefore, efforts are still needed to improve nanopore RNA direct sequencing, including the development of more powerful nanopore proteins and robust computational algorithms.To identify RNA modification from the nanopore RNA direction, robust computational algorithm analysis is vital. There are two major analysis strategies, including alterations of raw signal intensity analysis  and base-called “error” feature analysis 183. In addition, synthetic RNAs can also be developed for reducing false positives in sequencing-based technologies. For instance, an elegant study, which utilizes an unmodified IVT RNA library resembling the endogenous transcriptome as a negative control, can precisely and quantitatively map m6A and m5C through calibration184.Since all existing detection methods confer both advantages and drawbacks, appropriate detection strategies should be chosen by considering the abundance of RNA modifications, RNA types of interest , the amount of starting material, etc. For instance, direct sequencing methods preserve the modification status but are often confounded by other factors, including RNA secondary structure, SNPs, and sequencing errors. Direct sequencing methods also require a high sequencing depth to detect modifications in low-abundance RNA. Antibody-based detection technologies are powerful and capable of detecting low-abundance modifications in rare RNA species. However, due to the enrichment step, the stoichiometry information of modification is lost, and large starting materials are needed. In addition, since antibodies may have intrinsic bias on RNA sequences and secondary structures, caution is needed to eliminate false-positive signals. Chemical treatment-based approaches have been widely applied to RNA modification detection owing to their high labeling specificity. For this strategy, both high labeling efficiency and mild reaction conditions are required for transcriptome-wide modification detection and quantification. Metabolic labeling or RNA modification-related protein-based approaches provide an alternative strategy to characterize modified residues but can only be applied to cultured cells. Therefore, appropriate negative controls are necessary to reduce false positives, including modification status manipulation (enzyme knockdown/knockout), chemical or enzyme treatment, and differential RT conditions. Orthogonal locus-specific detection methods are also necessary to validate the modification sites and status. In addition, synthetic modified spike-in controls preserve stoichiometry information during sequencingNanopore direct RNA sequencing has been applied to the detection of several RNA modifications, showing promise for the simultaneous identification of distinct modifications in a single molecule. However, the accuracy and sensitivity of nanopore direct RNA modification sequencing are limited, and the simultaneous detection of different modifications is still unachieved. Further efforts should be made to improve the instrument to reduce the signal noise and develop robust computational analysis to identify multiple modifications simultaneously. Finally, the development of RNA modification detection technologies for low-input and single-cell samples is still an urgent need for facilitating understanding of the roles of RNA modifications in physiological and pathological processes. Improvement in integrating existing RNA modification detection strategies with single-cell RNA sequencing platforms shows promise. Collectively, recent progress in detection technologies promotes functional studies of RNA modifications, and we anticipate that further advancement will lead to a more comprehensive understanding of the epitranscriptome.


With the increasing understanding of various disease-related noncoding RNAs, ncRNAs are emerging as novel drugs and drug targets. Nucleic acid drugs based on different types of noncoding RNAs have been designed and tested. Chemical modification has been applied to noncoding RNAs such as siRNA or miRNA to increase the resistance to degradation with minimum influence on their biological function. Chemical biological methods have also been developed to regulate relevant noncoding RNAs in the occurrence of various diseases. New strategies such as designing ribonuclease targeting chimeras to degrade endogenous noncoding RNAs are emerging as promising approaches to regulate gene expressions, serving as next-generation drugs. This review summarized the current state of noncoding RNA-based theranostics, major chemical modifications of noncoding RNAs to develop nucleic acid drugs, conjugation of RNA with different functional biomolecules as well as design and screening of potential molecules to regulate the expression or activity of endogenous noncoding RNAs for drug development. Finally, strategies of improving the delivery of noncoding RNAs are discussed. In the gene expression process, RNA is at a central position between DNA and protein, which is the product of DNA transcription and also serves as the blueprint for the translation of proteins. However, only less than 2% of RNAs can be translated into proteins , while tAccording to the different regulatory roles, ncRNA transcripts have been categorized into two families: housekeeping ncRNAs and regulatory ncRNAs. Housekeeping ncRNAs contain transfer RNA (tRNAs) for carrying amino acids, ribosomal RNAs (rRNAs) for mRNA reading and decoding, small nuclear RNAs (snRNAs) for RNA splicing and small nucleolar RNAs (snoRNAs) for RNA modification. Another family of ncRNAs is regulatory ncRNAs, which are regulators of gene expressions at epigenetic, transcriptional, and post-transcriptional levels. Regulatory ncRNAs consist of microRNAs (miRNAs), small interfering RNAs (siRNAs), long noncoding RNAs (lncRNAs), circular RNAs (circRNAs) and PIWI-interacting RNAs (piRNAs). Based on the molecular length, regulatory ncRNAs were generally classified into two subclasses: long (or large) ncRNAs (>200 nucleotides) (nt) and short  ncRNAs (<200 nt). Linear long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) are distinguished by their shapes. Small ncRNAs (sncRNAs) include microRNAs (miRNAs), small interfering RNAs (siRNAs) and PIWI-interacting RNAs (piRNAs)  3]..3].ncRNAs play important regulatory roles in physiological processes. The interaction between ncRNA and target sequences leads to significant effects in different cellular programs, such as RNA maturation, RNA processing, signaling, gene expression, and protein synthesis . On the ncRNA-based drugs, which refer to ncRNAs or their derivatives used as drugs, can interact with multiple biological targets  within cells and exert therapeutic abilities. As the effect of ncRNAs in various diseases has been increasingly revealed, it is possible to design novel ncRNA-based drugs for clinic use. ncRNA-based drugs, different from the current pharmacotherapy such as small molecule drugs or antibody drugs, offer a brand new perspective in drug design. The simple structures, the specific complementary binding mode and the post-transcriptional regulation made ncRNAs attractive therapeutical molecules. ncRNA molecules could be endogenous or exogenous, natural or artificial modified, which endow ncRNAs different roles in disease theranostics.The emerging links between noncoding RNAs and diseases have provided opportunities not only for understanding the prognostic mechanism, but also for finding novel diagnostic biomarkers.Many miRNAs, particularly circulating miRNAs in serum, plasma, urine, and saliva, have already successfully served as biomarkers for many different diseases. In the context of cancer, the oncogenes (or “onco-miRs”) such as miR-126, miR-17-92 cluster, miR-210 and miR-21 are highly expressed in cancer cells and promote cancer development ,14,15,16For instance, increased levels of miR-21 have been reported as clinical diagnostic and prognostic marker in various cancers. The serum levels of circulating miR-122, miR-22, and miR-34a were correlated with liver injury in HIV patients and may serve as biomarkers . After tOn the other hand, some endogenic ncRNAs control the expression of disease-related genes. The aberrant expression of these ncRNAs induces the emergence of diseases, making these ncRNAs potential targets for drug development. Aside from disease-associated miRNAs and lncRNAs, intron–exon junctions and repetitive RNAs are also considered as therapeutic targets for drug design . TherefoThe agents used for targeting ncRNAs can be either small molecules or synthetic oligonucleotides. Traditional small molecules targeting specific RNAs are still promising as a therapeutic strategy for diseases. However, due to the similarity of intracellular nucleic acid structures, it is challenging to find small molecules that can both selectively bind the target and be specifically active to the target. Therefore, it is crucial to screen small molecules against specific ncRNAs as drugs. Early this year, Aguilar et al. established a screening strategy that can identify ncRNA binding drug-like compounds with functional effects on phenotype. With this strategy, the RNA-targeted compounds with disruption in activity can be systematically screened .Alternatively, it is much easier to design an oligonucleotide than a small molecule. The complementary base-pairing recognition makes some “undruggable” targets for small molecules accessible. There are several classes of oligonucleotides for targeting ncRNAs. The most common strategies are antisense oligonucleotides (ASOs) and duplex RNAs that elicit RNA interference. Other oligonucleotides-based agents, such as ribozyme and CRISPR-cas13, could target and degrade the ncRNA in cells.Nucleic acid therapeutics use synthetic oligonucleotides to modulate gene expressions for therapeutic effects . Since 1The discovery of ncRNAs of different structures and functions has promoted the mechanistic understanding and possible therapeutic interventions. There are several strategies to design ncRNA theraputics, such as inhibiting RNA activity by siRNA or antisense RNAs, targeting proteins by aptamers, reprogramming genetic information by trans-splicing ribozyme, targeting promoter sequences by saRNA to trigger gene production, gene modifying by CRISPR guide RNAs . These tShort interfering RNA (siRNA) is a class of double-stranded RNA with 20–25 nt, which triggers the RNA interference (RNAi) pathway in mammalian cells. siRNAs are used as gene silencing tools for investigating gene function and as nucleic acid drugs ,33. To dMicroRNAs (miRNAs) are evolutionary conserved, endogenous single-stranded, small ncRNAs, influencing target mRNA expression via RNAi . miRNAs RNA activation (RNAa), unlike RNAi pathway induced by miRNAs and siRNAs, is a process where dsRNA triggers gene production by targeting promoter sequences. Small activating RNA (saRNA) is a class of small dsRNA oligonucleotides synthesized by homologous sequences close to or within gene promoters, which can positively and reversibly upregulate the target genes by RNA-induced transcriptional activation (RITA) complex in an Ago2-dependent manner ,37,38. MASOs are short, single-stranded RNA or DNA oligonucleotides that bind complementarily to the target mRNA. ASO drugs usually follow three types of mechanisms: (1) recruiting RNase H and promoting degradation of mRNA, (2) sterically blocking the interaction between mRNA and ribosome, (3) alternative splicing of pre-mRNA in exon skipping or exon inclusion manner ,41. By nAptamers are single-strand oligonucleotides with tertiary structure that can bind and modulate specific proteins. The screening system known as systemic evolution of ligands by exponential enrichment (SELEX) is used to produce aptamers in vitro. Aptamers are called chemical antibodies because the synthetic molecules act similarly to antibodies . PegaptaRibozymes are RNA molecules with enzymatic activity. They bind to the target RNA and lead to RNA cleavage in a recycling manner  RibozymeThe rise of CRISPR -Cas (CRISPR-associated gene) systems has significantly improved the performance of genome editing. The CRISPR system is composed of (1) a noncoding RNA known as guide RNA (gRNA) for targeting the sequence to be engineered, (2) the endonuclease Cas to introduce site-specific breaks in DNA or RNA . The CRIXeno nucleic acids (XNA) are artificial nucleic acids with chemical modifications on the natural sugar backbone of DNA and RNA . XNA sucVery recently, several biotechnology companies including Alltrna, ReCode Therapeutics, Shape Therapeutics, Tevard Biosciences and hC Bioscience started the pursuit of of tRNA-based therapeutics. The companies are focused on designing tRNAs to bypass premature termination codons and incorporate desired amino acids instead. The premature termination codons, which cause truncation of translated protein, are responsible for an estimated 11% of all inherited disease. Suppressor tRNAs (sup-tRNAs) can read through a premature stop codon to restore production of full-length proteins. It may open the door for an entire new class of tRNA therapies .Traditional small molecule and monoclonal antibody drugs require the recognition of complex spatial conformations of target proteins, but many disease-associated target proteins, whose surface lack some specific hydrophobic pockets, cannot be screened for highly active and high-affinity drug molecules. Thus, these diseases are considered to be unavailable for drug discovery. In contrast, noncoding RNAs like miRNA or siRNA are simpler to design and can theoretically be used to down-regulate the expression of almost all the genes in the body through Watson-Crick base pairing with target mRNAs, offering a broader therapeutic scope than small molecule drugs ,55. DespAs the siRNA and miRNA gene silencing activity is not dependent on ribose 2′-OH, the chemical modification of 2′-OH has little effect on biological function of small nucleic acid drugs. Currently, the dominant modification strategy is to replace 2′-OH with other chemical groups, including 2′-O-methyl (2′-O-Me), 2′-fluoro (2′-F), 2′-O-methoxyethyl (2′-O-MOE), etc. Modification of ribose 2′-OH can significantly enhance the ability of RNA to resist nucleases, while being able to reduce RNA-mediated immunogenic responses ,62,63. TBase modification of NTPs is a common method for introducing additional functionality and increasing chemoselectivity. C5′ of the pyrimidine ring and C2′ of the purine ring are common modification sites. Substitution of base analogues such as pseudouridine, 2-thiouridine, N6-methyladenosine (m6A) and 5-methylcytidine can enhance the stability of RNA while reducing its innate immunogenicity. It has been shown that m6A promotes primary miRNA processing and X-inactive specific transcript (XIST)-mediated transcriptional repression . Zhang ePhosphate backbone modifications improve the stability of nucleic acids primarily by replacing the phosphodiester bond with other types of bonds. Phosphorothioate (PS) is the most widespread strategy and this modification primarily uses the replacement of a non-bridging oxygen of phosphodiester by a sulphur atom. PS-modified nucleotides are more resistant to nucleases and are able to prolong the half-life of drug. PS was first applied in the modification of antisense oligonucleotides (ASO). Although the modification of siRNA could enhance the stability, it is also accompanied by the side effect of increased toxicity and reduced gene silencing. This may be because siRNAs only tolerate limited modifications to remain RNA-induced silencing complex (RISC) compatible. In addition to PS, other residues have been also successfully used to replace phosphodiester groups in oligonucleotides, including dithiophosphate (PS2), methyl phosphate (MP), methoxypropyl phosphate (MOP), and peptide nucleic acid (PNA). Although less popular than PS, these modifications have the same potential for clinical application development ,70,71.Various biomolecules have been conjugated to noncoding RNAs such as siRNA to give RNA-X conjugates. Biomolecules including antibodies, membrane receptor ligands, targeting peptides and aptamers conjugated to siRNA were functional with respect to target recognition and membrane penetration  72]. Du. Du72]. Coupling RNAs to receptor ligands can effectively increase their specific targeting ability as well. N-acetylgalactosamine  is a sugar derivative of galactose presented on damaged glycoproteins. It is able to bind specifically to the trimeric desialic acid glycoprotein receptor (ASGPR) (Kd = 2.5 nM), which is highly expressed on the surface of liver cells. The strategy of modifying GalNAc on siRNA for targeted delivery has been widely used ,77,78. RAlthough naked oligonucleotides are susceptible to degradation by nucleases, the stability can be greatly increased by forming spherical nucleic acids via nanotechnology, which allows the co-delivery of nucleic acid drugs with other types of drugs. Shi et al. constructed a siRNA vesicle-based drug delivery nanostructure, which was obtained by the self-assembly of siRNA-disulfide-poly (N-isopropylacrylamide) (siRNA–SS–PNIPAM) diblock copolymers. By exploiting temperature changes and intracellular reduction reactions, the loading of chemotherapeutic drugs into siRNA vesicles and the release of drugs from cells can be effectively controlled. Results from MCF-7 cancer cells and mouse tumor models showed that Dox-loaded siRNA nanovesicles could effectively inhibit tumor cell growth . Zhang eAntibodies are biomolecules with high specificity, high stability and long in vivo half-life. Combination with antibodies can significantly improve the pharmacokinetics of drugs and reduce off-target rates. Antibody-drug conjugates (ADCs) have been rapidly developed in recent years. Antibody-siRNA conjugates (ARCs) have been successfully used to deliver siRNA to target cells expressing specific antigens, which is an effective strategy for RNA delivery . Zhang’sSmall molecules that are capable of targeting or regulating RNAs are important intracellular molecular tools, for the understanding of underlying principles of specific RNA-involved interactions, also for demonstrating clinical efficacy ,88. For Small molecule compounds targeting oncogenic miRNAs were considered to hold potentials for cancer therapy . Using lAmong various small molecule targeting strategies, RIBOTAC is entering the limelight as a novel intracellular regulatory tool. Similar to proteolysis-targeting chimera (PROTAC), RIBOTAC is a bifunctional small molecule, with one part specifically targeting RNAs secondary or tertiary structures, and the other part recruiting and activating RNaseL enzymes to induce degradation of noncoding RNAs . By usinStudies have shown that approximately 50% of the nucleotides in RNA targets are structured or non-classically paired, and therefore approximately half of the sequences cannot be targeted by sequence recognition methods . In addiDr. Matthew D. Disney and his team first developed a strategy to design structure-specific ligands based on RNA sequences, and named it Inforna. Inforna integrated a selection-based strategy , a statistical approach, and the structural information about RNA targets of interest to allow computational and high-throughput screening of small molecules with the strongest affinity for structural sites. Compared to traditional methods, Inforna-based screening is more systematic and reliable, and is now being used for the development of new targets in RNA structural regions .Based on the Inforna platform, Disney developed the earliest RIBOTACs small molecules in 2018. The initial RIBOTACs consisted of two parts, one of which was Targaprimir-96 (TGP-96) small molecule that specifically targets the primary transcript of miR-96 (pri-miR-96), and the other was a short 2′-5′ poly(A) oligonucleotide (2′-5′ A4) that recruits RNaseL enzyme. The results showed that after targeted degradation of Pri-miR-96 by RIBOTACs, downstream miR-96 expression was downregulated, which in turn inhibited the expression of the pro-apoptotic FOXO1 transcription factor and induced apoptosis in breast cancer cells . In 2019Compared with traditional nucleic acid drugs, RIBOTAC is a small molecule with smaller molecular weight, higher stability and better membrane penetration effects. At the same time, RIBOTAC uses structural regions as its own targeting sites, which greatly broadens the RNA drug-forming targets and provides more possibilities for the development of novel RNA-targeted drugs. RIBOTAC molecules are capable of degrading the target RNA by recruiting RNaseL enzymes after targeting. Therefore, RIBOTAC molecules theoretically only require a catalytic number of drugs to initiate target degradation with less chance of acquiring drug resistance and better safety ,108. DesNoncoding RNA-based nucleic acid drugs as next-generation drugs serve as an alternative approach for therapy. In particular, RNA-based drugs can theoretically target any gene of interest, which is “undruggable” for most small molecules and antibodies. Another feature is that the development of new nucleic acid drugs is simple and fast. It is straightforward to design new drugs towards target sequences based on Watson-Crick base pairing. In addition, nucleic acid-based therapies are more precise and efficient due to the sequence-target mechanism, but less susceptible to drug resistance ,115.Despite the outstanding properties, nucleic drugs also have some challenges. For example, the inherent instability of nucleic acid drugs makes it sensitive to either nucleic acid endonucleases or exonucleases and thus they are highly susceptible to degradation. In addition, how to effectively deliver oligonucleotides to target sites is another challenge. The instability, negative charge and hydrophilic nature of oligonucleotide hinder the diffusion through cell membranes ,57,58. ILipid nanoparticles (LNP) are one of the most established vectors for nucleic acid drug delivery and have gained much attention as a delivery platform for COVID-19 mRNA vaccines ,120. In With the continuous development of RNA modification and regulation technology, noncoding RNA-based nucleic acid drugs will have a wide scope for development. A variety of diseases caused by non-druggable proteins will have new treatments, and we expect more nucleic acid drugs to be approved and marketed in the future to bring benefits to patients.


Individual social ties have been an important source of competitive advantages for hightech firms in the knowledge economy. However, the existing cross-level studies have mainly investigated the impact of HRM systems on traditional individual attitudinal or behavioral outcomes, and few studies have examined the effect of SHRM on individual social ties. Based on the data collected from 363 knowledge employees working in 64 high-tech firms in China, we examine the cross-level relationships among collaborative HRM practices, climate for cooperation and employee intra-organizational social ties. The hierarchical linear model results show that organizational-level collaborative HRM practices have significant positive effects on the number and strength of individual-level intra-organizational social ties, and the climate for cooperation mediates the positive cross-level relationship between collaborative HRM and individual intra-organizational social ties. This study makes three contributions to the literature. First, with a cross-level model, this study helps us better understand how collaborative HRM acts as an approach to manage individuals’ social capital formation. Second, this study makes contribution to the social network literature by showing how organizational contextual factors  affect employee individual social ties. Third, based on the AMO model, this paper developed a more clear construct and a three-dimension measurement of the collaborative HRM. Over the past two decades, scholars have widely recognized the essential role that strategic human resource management (SHRM) plays in driving organizational effectiveness . RecentlIndividual social tie  is the social links between actors , which iScholars have highlighted the roles of HRM practices in influencing the way employees interact with their colleagues . For insIn this study, we propose that the collaborative HRM, as an HRM configuration investing in interpersonal relationships rather than in individuals , focusinThe number and strength of employees’ social ties are two frequently-used indicators to describe a social network . The numOur research model is shown in Our research will make three contributions. First, with a cross-level model, this study helps us better understand how collaborative HRM acts as an approach to manage individuals’ social capital formation. This not only expands the literature on strategically-targeted HRM with a multilevel perspective , but alsThe pioneer literature studies on collaborative HRM are primarily concerned with information sharing and building cooperative relationship between collaborators, such as enterprises and their suppliers , 2002. AWe adopted the widely used ability-motivation-opportunity approach  companies in n a firm , 2002, oOverall, 126 out of the 213 firms that agreed to participate in the survey returned the questionnaires (59.1% return rate). Consequently, 126 HR managers, 287 line managers, and 548 employees completed the questionnaire. We included only firms that provided data for HR managers, line managers, and at least three core knowledge employees, thus reduced the number of qualified firms to 64, with 5.7 employees (range 3–17), 3.3 line managers (range 1–13) and 1 HR manager per firm on average. The sampled firms were, on average, 11.8 year old , hiring 815 employees . Furthermore, among the sampled core knowledge employees, 56.0% were male and 44.0% were female, averaging 27.4 years of age and 2.2 years of work experience at their current respective firms, while 98.0% had either college degrees or higher qualifications.The measurement of collaborative HRM concentrates on employees’ abilities, motivation, and opportunities for collabation and network building. These items were adapted from three studies that explicitly use the concept of collaborative HRM . At the p < 0.001) was used to extract three factors by principal component maximum variance rotation method. The results showed that the cumulative variance contribution rate of the three factors was 73.49%, and the factor loading of each item was greater than 0.6. As shown in 2/df = 2.482，RMSEA = 0.075，CFI = 0.966，TLI = 0.954，SRMR = 0.033) is shown in Since this paper uses the scale of collaborative HRM for variable measurement, it is necessary to test the reliability and validity of this construct. Therefore, a total of 268 valid questionnaires were collected from HR managers and line managers. Exploratory factor analysis (KMO = 0.92，(j) for the collaborative HRM index was 0.9, indicating that the aggregation was justified  was 0.09, while the average of the Rwgustified .(j) was 0.85 and the ICC(1) was 0.16, which can justify such aggregation to the organization-level in this study  fits the data better than the 2-factor modela , 2-factor modelb , and the 1-factor model . Therefore, these results suggest that our measures’ exhibit discriminant validity among these constructs, since each measure is conceptually distinct.We conducted a series of confirmatory factor analyses (CFA) to test the measurement model specifying collaborative HRM, climate for cooperation, and employees’ intra-organizational social ties as separate factors. We next ran Harman’s single-factor test using all the items at the individual level to examine the potential common method variance problem . The resWe use SPSS 23.0 to perform the descriptive statistics and correlations, and Mplus 8.3 to test the cross-level mediation-upper mediator model.First, we applied Kurtosis and Skewness to check the data normality following To test the significance of the indirect effects, A Monte Carlo simulation with 20,000 replications was conducted to test the 95% bias-corrected confidence interval (CI), using the web estimator provided by The mean, standard deviation, and correlation coefficient matrix of the variables are shown in b = 0.574, p < 0.05) and tie strength . Therefore, Hypothesis 1 is supported.The results of hypothesis test are shown in b = 0.338, p < 0.001).In Model 2, we estimated the direct effect of the independent variable  on the mediating variable . The regression results show that a collaborative HRM system is positively related to the climate for cooperation  and tie strength .In Model 3, we examined the effect of the mediating variable  on the dependent variable . Our results indicate that the climate for cooperation was significantly associated with tie number  and tie strength , whereas the relationship between collaborative HRM and tie number decreased from 0.574 (p < 0.05) to 0.242 (p > 0.05), and the relationship between collaborative HRM and tie strength decreased from 0.406 (p < 0.01) to 0.296 (p < 0.05). Results showed that the indirect effects between collaborative HRM and tie number  and tie strength  through climate for cooperation were significant. The overall results of the proposed model indicated an acceptable level of model fit . Consequently, we concluded that a climate for cooperation mediates the relationship between collaborative HRM, and the number and strength of employees’ intra-organizational social ties. Thus Hypothesis 2 is supported  has a significant positive relationship with tie number  for the publication of any potentially identifiable images or data included in this article.ZS and XL: conceptualization, methodology, and writing-original draft preparation. MZ and XL: data curation, software, writing, reviewing, and editing. MZ, YY, and WS: visualization, investigation, supervision, and validation. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


RNA viruses are characterised by extremely high genetic variability due to fast replication, large population size, low fidelity, and  a lack of proofreading mechanisms of RNA polymerases leading to high mutation rates. Furthermore, viral recombination and reassortment may act as a significant evolutionary force among viruses contributing to greater genetic diversity than obtainable by mutation alone. The above-mentioned properties allow for the rapid evolution of RNA viruses, which may result in difficulties in viral eradication, changes in virulence and pathogenicity, and lead to events such as cross-species transmissions, which are matters of great interest in the light of current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemics. In this review, we aim to explore the molecular mechanisms of the variability of viral RNA genomes, emphasising the evolutionary trajectory of SARS-CoV-2 and its variants. Furthermore, the causes and consequences of coronavirus variation are explored, along with theories on the origin of human coronaviruses and features of emergent RNA viruses in general. Finally, we summarise the current knowledge on the circulating variants of concern and highlight the many unknowns regarding SARS-CoV-2 pathogenesis. RNA viruses are obligate intracellular parasites characterised by extremely high genetic variability and phenotypic diversity, facilitating infection of an extensive range of hosts ,2,3. SigOrthocoronaviridae among the Coronaviridae family, were shown to be distributed among various animals and capable of causing diseases with a broad range of symptoms and degrees of pathology [The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019 and the ongoing pandemics once again emphasised the importance of elucidating molecular mechanisms of RNA genome evolution and potential viral emergence ,14,15. Cathology ,17,18. Hathology ,19,20,21The first part of this review article aims to summarise the main RNA virus characteristics necessary for understanding the mechanisms and causal agents that govern RNA virus variation. The second part applies the above-mentioned concepts to the evolution of human coronaviruses with an emphasis on the emergence of SARS-CoV-2. Finally, the unresolved questions and long-term consequences of the rapid RNA virus evolution are discussed as we witness the aftermath of one of the largest globally known pandemics.Expression of Animal Virus Genomes, suggesting a division of viruses into six classes according to the method of transmission of their genetic information from one generation to another (mRNA synthesis) and the style of expression of their genetic information (structure of nucleic acid) [In 1971 David Baltimore published an article titled ic acid) . The oriic acid) . This clic acid)   [Riboviria was later extended to include nearly all RNA viruses and reverse-transcribing viruses, with a total of six realms recognised up to this day [In 2019 and 2020, the International Committee on Taxonomy of Viruses (ICTV), a global organisation responsible for developing virus taxonomy and nomenclature, introduced a new fifteen-rank classification hierarchy of virus taxonomy to replace the previously used five-rank hierarchy of species, genus, subfamily, family, and order. The 15-rank classification includes eight primary ranks  and seven secondary ranks, which resemble those used in Linnean systems . Realm R, and V) ,29. The this day ,31. Eventhis day . With tiRNA viruses are primarily infectious agents of Eukarya . No knowCoronaviridae family [Even though RNA viruses are known for their large structural and functional diversity, their genome sizes exhibit relatively small variations with differences within only one order of magnitude. While the smallest RNA viruses measure around 2 kb in size, the maximum observed genome length of 30–40 kb is reached among the e family ,35. Thise family ,34,36,37e family ,39. A unRNA-dependent RNA polymerase is an essential enzyme for viral RNA replication because it catalyses RNA replication from an RNA template. It is a process generally not typical in eukaryotic cells, except in events such as RNA-mediated silencing pathways and telomere formation . TherefoCoronaviridae family [Canonical posttranscriptional processing necessary for the maturation of the primary transcript in cellular organisms includes obtaining a 5’-cap, splicing, and generation of a 3’ poly-A tail . By mimie family ,50.While the degree of independence of transcription and posttranscriptional processing in RNA viruses is a result of the intrinsic viral characteristics, translation is ultimately dependent on the host cell translation machinery. No matter if their genome may be used directly as mRNA or if the transcription of the minus strand must occur first, the translation of the resulting viral mRNA is completely dependent on cellular ribosomes. Since the cellular translation machinery is rather complex, viruses exhibit numerous tactics of manipulation, from an attack on regulatory signalling pathways to targeting specific steps of the translation process itself, comprehensively reviewed by Jaafar et al. (2018) and Jan et al. (2016) ,52. FurtThe concept of quasispecies was first developed by Manfred Eigen and Peter Schuster in the search of an adequate model for the origin and evolution of early life forms, defining quasispecies as a distribution of closely related replicative units centred around one or several (degenerate) master copies ,57,58. BThe most important mechanisms responsible for genetic change and, therefore, the rapid evolution and quasispecies nature of RNA viruses are mutations, recombination, and gene segment reassortment ,68,69. I−3 to 10−5 per nucleotide polymerised, leading to the introduction of many mutations at every replication cycle due to the absence of 3’ to 5’ exonucleolytic proofreading activity [RNA-dependent RNA polymerase was identified as one of the viral hallmark genes responsible for critical functions in virion structure and genome replication, but is missing from the cellular genome ,24,74,75activity . Howeveractivity . Low repactivity .Picornaviridae family (such as the poliovirus) as an ‘exception’, but it was soon found to be relatively common, especially among Baltimore class IV (+) ssRNA viruses [Recombination is a process of exchange of genetic material between different non-segmented genomes, which is one of the main drivers of RNA virus heterogeneity . The rol viruses ,81,82,83 viruses . However viruses . Two mec viruses ,82,85.On the other hand, homologous recombination occurs between two similar RNA molecules with substantial sequence homology at the same or comparable sites on the paternal strains, while nonhomologous recombination is used to refer to genetic crossover between RNA molecules with no sequence homology . It shouAnother form of genetic exchange sometimes referred to as pseudo-recombination, a feature of all segmented RNA viruses, is gene segment reassortment. Reassortment is a consequence of the co-infection of a single host with two or more segmented viruses, which may result in progeny that contains novel genome combinations derived from both parental genomes ,93. EarlFlaviviridae family [High mutation rates observed in RNA viruses can be considered pillars of fast RNA virus evolution . Howevere family ,106,107.e family ,108.Riboviria, which belong to the order Nidovirales, family Coronaviridae, and subfamily Orthocoronavirine [Orthocoronavirinae subfamily into four major genera  and 26 subgenera, with 52 currently recognised species [Human coronavirus 229E (HCoV-229E), human coronavirus OC43 (HCoV-OC43), Human coronavirus NL63 (HCoV-NL63), Human coronavirus HKU1 (HCoV-HKU1), Middle East respiratory syndrome-related coronavirus (MERS-CoV), severe acute respiratory syndrome coronavirus (SARS-CoV) and, as of most recently, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [Coronaviridae taxonomy to accommodate emergent viruses (Coronaviruses are a group of viruses with the (+) ssRNA genome of Baltimore class IV under the realm of navirine . Phyloge species . Coronav species . Even th species . However species . There aS-CoV-2) ,109. Maj viruses .Severe acute respiratory syndrome-related coronavirus  among RNA genomes of different strains of coronaviruses was shown during mixed infection of susceptible cells, with some of the recombinants appearing to go through multiple crossover events ,135,136.On the other hand, the discontinuous transcription and generation of subgenomic mRNAs by RNA-dependent RNA polymerase used by coronaviruses as a strategy of gene expression resembles the mechanism of copy-choice RNA recombination via template switching and creates a predisposition for such events to occur ,72. TherWith current evidence supporting the hypothesis that bat coronaviruses are gene sources for Alpha-CoV and Beta-CoV, while bird coronaviruses are gene sources for Gamma-CoV and Delta-CoV, recent studies showed that the most recent common ancestor of all coronaviruses dates to around 8100 BC, with no concluding proof of whether it first occurred in bats and later jumped to birds or vice versa ,141. AllRhinolophidae family, which are hosts to bat SARS-CoV [Sarbecovirus subgenus, several of the bat SARS-like coronaviruses, such as WIV1 and WIV16, can bind human and bat ACE2 alike [The most likely natural reservoirs of sarbecoviruses are various horseshoe bats from the SARS-CoV ,141,144.SARS-CoV ,155. It SARS-CoV . Even thE2 alike ,158. WhiE2 alike ,159. OneE2 alike ,151. TheE2 alike ,154. FurE2 alike . A uniquE2 alike ,162,163.The emergence of SARS-CoV-2 in December 2019 was followed by its swift spread in the human population and continuously increasing transmissibility leading to the evolution of newly evolved variants. Mutations causing single amino acid change, such as D614G in spike protein gene and P323L in RNA-dependent RNA polymerase protein nsp12, were shown early on to be associated with higher viral loads and higher infectivity of the haplotypes that harbour them, leading to enhanced viral fitness but no change in pathogenesis ,165. TheCoronaviridae family showed that there are still many unknowns regarding viral pathogenesis, especially events leading up to potential cross-species transmission. Furthermore, the evolution of SARS-CoV-2 and its variants demonstrated the importance of the quasispecies concept and the need to revise rather old-fashioned constructs, such as the existence of the ‘wild type’ genome, when it comes to RNA viruses. There are many unknowns regarding the further trajectory of the ongoing SARS-CoV-2 pandemic. Despite the relative success of the SARS-CoV-2 vaccines (which is out of the scope of this review), it must be emphasised that the expectations for vaccine efficiency were a lot higher during 2020 based on then-observed limited diversity between viral genomes [Even though evolutionary processes are generally rather slow and long-lasting when observed on a level of cellular organisms, characteristics of the RNA viruses described in this review article enable real-time monitoring of some of the key evolutionary mechanisms. The increase in genetic variability could be considered one of the prerequisites for exploring new niches, while the plasticity of RNA viruses is best described by the multitude of ways utilised for processes such as genetic recombination and mutation. The emergence of three highly pathogenic RNA viruses from the  genomes . On the  genomes . The hig


The problem of isolating high-quality total RNA from intervertebral discs has no recognized solution yet. This is due to the extremely low content of live cells in the samples and the voluminous intercellular matrix. A variety of published protocols focused on isolating RNA from articular cartilage have recommended the use of expensive equipment, enzymatic matrix cleavage, or cell culture. In our study, we used a combination of the traditional QIAzol protocol  and RNEasy column purification  to obtain high-quality RNA from post-surgical intervertebral disc fragments. Only a mortar and a pestle were used for grinding, making our method particularly accessible. The isolated RNA with a RIN of ~7 is suitable for studying the expression profile of chondrocytes in situ. RNA-seq analysis of three samples demonstrated cell type ratios to be mostly relevant to intervertebral disc tissues, with over 70% of the chondrocytes of the three subtypes having an admixture of blood-related cells. RNA sequencing allows the phenotype to be determined at the level of individual cells and tissue fragments and is crucial for understanding the signaling pathways and mechanisms of disease development . HoweverWorldwide, about half of the adult population eventually experiences lower back pain caused by the degeneration of the intervertebral discs. This disease significantly reduces the quality of life of patients and is a major burden on the economy . StudyinCurrently, active research on the intervertebral disc transcriptome is hampered by the difficulty of isolating RNA, not to mention other factors. All approaches that have been suggested to address this problem can be considered invasive, i.e., requiring enzymatic processing of living cells , which cStudies on RNA isolation from knee cartilage are relatively numerous ,10,11,12Another variant of an expensive but effective solution of the RNA isolation problem is using the technologies of single-cell analysis, the potential of which has been shown by Zhang et al. . AlthougGuided by already published protocols for RNA isolation from cartilage, we set out to develop an affordable protocol for isolating the total RNA from intervertebral disc fragments fixed in liquid nitrogen. This study provides recommendations for efficient grinding of frozen cartilage fragments up to 1.8 g using a mortar and pestle in liquid nitrogen. In addition, we present a modified protocol for the isolation of QIAzol with purification using the RNEasy Plant mini kit and test RNA sequencing results from several samples.This study was approved by the independent ethics committee of the Novosibirsk Research Institute of Traumatology and Orthopedics on 2 October 2020. Informed written consent was obtained from each participant. All experiments and samplings were carried out in accordance with ethical and biosafety protocols approved by the committee.Intervertebral disc fragment sampling was performed at the Novosibirsk Research Institute of Traumatology and Orthopedics according to a previously published protocol . Disc frFor grinding, we selected samples weighing at least 1 g, as the RNA in smaller samples had enough time to degrade due to the delay between resection and freezing. The sample was homogenized using a mortar and pestle, with an awl used for its fragmentation. Throughout the grinding process, the sample was coated with liquid nitrogen. With effective cartilage homogenization being a key step in the entire protocol, we chose to describe it in as much detail as possible. The homogenization process can be divided into three steps based on the crushing techniques involved .First: using the awl as a chisel and the pestle as a hammer, one should crush the sample into small fragments I. Second: using the pestle strictly up and down, one should further crush the fragments. It is necessary to concentrate on one fragment at a time, making medium force beats with a low frequency, with the number of beats and their focus being more important than the pure force II.Third: given that part of the sample has already been ground to a powdery consistency, for further efficient grinding it is necessary to run the pestle along the bottom of the mortar, taking care to trap larger granules and bring them to the side of the mortar III. OnlyIn total, it takes approximately 25 min to homogenize one 1.8 g cartilage sample, assuming the experimenter has already mastered the technique. The ratio of time spent in each step was different depending on the texture and weight of the sample, on average corresponding to 1:2:3.Upon achieving a powdery texture, the sample was grinded with QIAzol until homogeneous, then transferred to a 25 mL test tube, stirred to evenly coat all sides and lid, and left on ice until thawed.QIAzol at a rate of 0.5 mL per 300 mg of the initial sample was added to a test tube with the sample thawed to viscosity. For better proteoglycan purification, the phase separation was carried out twice. After the first time separation, an equal volume of QIAzol was added to the aqueous phase, followed by incubation on ice for 30 min and re-centrifugation with chloroform. After the second time separation, the isolation was further carried out using the RNEasy spin column from the plant mini kit according to the manufacturer’s protocol. RNA elution was performed twice with 25 µL of RNase-purified water to increase the concentration. The aliquots for RIN determination and concentration measurements were selected immediately after isolation. RNA was stored at −80 °C. A complete protocol for RNA isolation is given in The concentration of isolated RNA was measured using a Qubit 4 Fluorimeter with an HS RNA Assay kit . A 2100 Bioanalyzer  and an RNA 6000 pico chip were used to assess the integrity and the length distribution of the isolated RNA. The analysis was carried out in the Genomics Center at the Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of the Russian Academy of Sciences . Agilent software uses an algorithm to evaluate RNA integrity based on capillary electrophoresis data with a specific dye. As a result, the sample is assigned an RNA integrity number (RIN), ranging from 1 to 10, with 1 representing completely degraded RNA . Only saLibraries were prepared with the MGIEasy RNA Directional Library Prep Set  and sequenced using BGI Hong Kong facilities on the DNBseq platform in PE100 mode. Three samples total were sent to Hong Kong and arrived frozen on 3 December 2021. Quality control was performed using the FastQC tool . ContamiMS Excel was used to calculate mean values, standard errors, and correlation coefficients. Exact formulas can be found in 3 and K201 (3, the moment of contamination was logged during the run and was confirmed by the RIN difference (201, K202, and K203, from which the protocol error was deduced. During the protocol’s development, it was found that only non-fragmented samples greater than 1 g in mass can consistently yield high-quality RNA. After homogenization, each such sample was divided into portions of ~300 mg mass, from which the RNA was extracted independently. This approach allows distinguishing between RNA degradation at the sampling/homogenization stage and directly at the isolation stage so that the protocol can be adjusted. In particular, we believe that it is at the RNA extraction stage that samples Rand K201  were confference . Figure RNA from samples 89_RAI, K_20_12_25_03, and N_20_12_25_01 was isolated in September, 2021. The remaining samples were taken for RNA isolation after we analyzed the sequencing results: in June and August 2022.With high accuracy of detection being a requirement, the HS RNA Assay kit for the Qubit fluorimeter was used for concentration measurements. When the protocol was tested on rough samples, the low RNA concentration was the major problem. Re-precipitation led to a loss of RNA quantity and quality. A threshold of 10 ng/µL was set for sequencing. To steadily overcome this threshold, the elution volume was reduced, and pair-wise pooling of the aqueous phases after the second separation and re-elution from the column were introduced. The average concentrations of the derivative samples are shown in 3 and K201, the RIN of samples from the same cartilage varied only slightly, suggesting a predominant effect of sampling and homogenization procedures. For the original 2100 Bioanalyzer data, refer to File S1. The RIN of all derivative samples is given in Excluding RThe Pearson correlation coefficient between weight and mean C was r = 0.49 , suggest3, N1, and R6. One derivative sample per cartilage was selected for sequencing with the following parameters: RIN> 6, C > 10 ng/uL, total RNA > 200 ng.So far, we have obtained the sequencing results of derivative samples K20Quality control with the FastQC tool demonstrated good overall quality, however, with a slight bias in the GC content for some samples. Neither contamination with rRNA nor with sequences derived from other species was observed. The cellular composition analysis in the experimental samples using the BisqueRNA software , with daThe technique for total RNA isolation from intervertebral disc fragments presented in this paper is the only one that allows RNA-seq material to be obtained from cartilage using a mortar and pestle. Until now, it was thought that isolating high-quality RNA from cartilage required either pretreatment with enzymes ,10, poteThe use of the RNEasy spin column ,11 afterThe isolation protocol reported in the study  is very The work  is also Alternatively, separating one large sample into several isolates, as we have done, proves to be effective even if the cartilage has been kept at room temperature for up to 45 min. Despite the increased costs of reagents and columns, our solution is noise-tolerant: it allows one to select a sample that is suitable for sequencing in almost all cases. Also, one can distinguish whether the low RIN is due to contamination during isolation or to the sub-optimal collection and poor homogenization of the material. In the first case, the low quality will only be characteristic of individual samples .Heather et al.  point to