RNA molecules participate in many important biological processes, and they need to fold into well-defined secondary and tertiary structures to realize their functions. Like the well-known protein folding problem, there is also an RNA folding problem. The folding problem includes two aspects: structure prediction and folding mechanism. Although the former has been widely studied, the latter is still not well understood. Here we present a deep reinforcement learning algorithms 2dRNA-Fold to study the fastest folding paths of RNA secondary structure. 2dRNA-Fold uses a neural network combined with Monte Carlo tree search to select residue pairing step by step according to a given RNA sequence until the final secondary structure is formed. We apply 2dRNA-Fold to several short RNA molecules and one longer RNA 1Y26 and find that their fastest folding paths show some interesting features. 2dRNA-Fold is further trained using a set of RNA molecules from the dataset bpRNA and is used to predict RNA secondary structure. Since in 2dRNA-Fold the scoring to determine next step is based on possible base pairings, the learned or predicted fastest folding path may not agree with the actual folding paths determined by free energy according to physical laws. More and more studies have shown that ribonucleic acid (RNA) participates in many important biological processes ,2,3,4. TReinforcement learning is a computational method of understanding and automating goal-oriented learning and decision-making. The difference between it and other computational methods (deep learning) is that it emphasizes that the agent learns from direct interaction with the environment. Now reinforcement learning and deep learning are more and more combined together, referred as deep reinforcement learning methods. Deep reinforcement learning has now shown extraordinary results in many fields, such as AlphaGo  and AlphFrom the perspective of exploring RNA folding path in secondary structure formation, here we propose an RNA folding path learning algorithm based on deep reinforcement learning, called 2dRNA-Fold. 2dRNA-Fold selects residue pairing step by step according to a given RNA sequence until the final secondary structure is formed. Then, we can find a folding path with the largest probability that can be considered as the fastest folding path of the RNA because of the special scoring function used.The flow chart of 2dRNA-Fold, a folding-path learning algorithm for RNA secondary structure based on deep reinforcement learning, is shown in The main features of reinforcement learning are the agent and the environment. The environment is the world where the agent is in and interacts with. In each step of the interaction, the agent will see all or part of the state of the world, and then decide the action to be taken. The agent can also get a reward from the environment, which tells it the current state of the world. The goal of the agent is to maximize its cumulative return. Reinforcement learning is a way to maximize returns through learning .In reinforcement learning, the optimization policy function can be updated according to the policy gradient. The policy gradient can be written as:The gradient The reinforcement learning algorithm used by 2dRNA-Fold is the Advantage Actor-Critic (A2C) , and so s, take an action a, and then forever after acting according to the policy s and always act according to the policy π weighted sum over all actions). If the advantage function is less than zero, it means that the current action is not as good as the average action, which makes feedback with positives and negatives. Another superiority of advantage function is that the variance is small when training, because it uses the approximation method, but there will be some deviations in the calculated policy gradient. All in all, the advantage function makes the training process more stable than using the traditional trajectory return.Here, Monte Carlo tree search (MCTS) ,30 is a (1)SelectionSelection is the most important step in Monte Carlo tree search. Start from the root node and go down its child nodes until the last leaf node. In the process of going down, there is a scoring function that will score each child node, so each time the child node with the highest score is selected to go down. This scoring algorithm generally uses the Upper Confidence Bounds to Trees (UCT) algorithm . The sco(2)ExpansionIf the leaf node is the final state, then the tree search process ends. Otherwise, create a child node under the node and select one of the child nodes.(3)SimulationFor fast simulation of the currently selected child node, random strategies can be used, and the final result can be obtained in a short time.(4)BackpropagationUsing the simulation results of the current child node the information  A single base cannot form a pair with multiple bases; (2) At least three bases apart along sequence between bases that form a pair. Therefore, after filtering according to the above constraints, we can get a set that can form base pairings, which is called action space in reinforcement learning. As shown in For a given target RNA sequence, we will perform 1000 episodes of folding simulation. In each episode, each step of the base-pairing selecting process will perform 2000 Monte Carlo tree searches, and finally select the next possible base pair with the highest score, update its own state and action space, and then use the current state node as the root node to perform the next Monte Carlo tree search. This iterates until it reaches the final state judged by the value function, and then the episode ends. As mentioned in the input section above, the third part of the input data describes the current state, represented by a matrix of Unlike machine learning or deep learning, which only needs to provide training data to train the model, the training of reinforcement learning algorithms generally requires an interactive environment for learning. OpenAI Gym  is such In the RNAWorld2D environment, the secondary structure of the native RNA only contains standard base pairings A-U and C-G and wobble base pairing G-U, but it allows pseudoknots. Here we also provide two modes to choose from, the one with pseudoknots and the one without pseudoknots. Depending on the mode selection, the action space  will be different. At the beginning of each episode, it will initialize the RNA secondary structure to an open strand, and the action space is all possible base pairings . The current RNA state is an  network  extensioWe selected several RNA molecules with a length of 30 nucleotides and used 2dRNA-Fold to learn and visualize their fastest folding paths. In the following we discuss these results separately.We also selected a longer RNA molecule to test the capabilities of 2dRNA-Fold for learning the fastest folding path of longer RNAs. Here we chose one famous riboswitch RNA molecule with PDB ID 1Y26, the length is 71 nucleotides, its sequence is:tructure  is“ to build a folding-path tree. Since the folding-path tree is very large with many nodes, we show the learned folding path more intuitively in One thing worth noting here is that because 1Y26 contains three pseudoknots, our action space does not contain pseudoknots, but the native secondary structure we used in training has not been processed, that is, removing these three pseudoknots, 2dRNA-Fold cannot correctly predict out these three pseudoknots.We trained a model on the RNA molecule with ID PDB_00972. The RNA sequence is CAUGAGGGAUUACCCAUGUGAGGAUUACCCA, the length is 30, and the secondary structure is “(((((.........))))((.. .......))”, the native base pairs are , , , , , . The predicted fastest secondary structure folding path is shown in Then we used this model to visualize the intermediate results in the step of selecting the first base pairing to reveal the effect of the Monte Carlo tree search. The secondary structure of the RNA and its initial action space are shown in We input the initial state data into the 2dRNA-Fold model, through the policy network we can get a probability matrix of It takes a long time to train 2dRNA-Fold model. Compared with the time taken to train the neural network, a lot of time is mainly consumed in the process of Monte Carlo tree search.However, in each episode and every step, we have to perform 2000 Monte Carlo tree searches. We tried to reduce the number of Monte Carlo tree searches. However, through experiments, we found that it would seriously affect the learning efficiency, so we adopted a multi-threaded approach to speed up training.We mainly perform multi-thread acceleration in the whole folding simulation part. Here, 20 threads are used, that is, 20 episodes of folding simulation are performed at the same time, but they share the same policy/value network parameters. When these 20 simulations are over, the generated data are put into the database, and then are sampled to train the neural network. After the neural network is trained, it continues to use 20 threads to perform folding simulation. As shown in 2dRNA-Fold can be used to predict the fastest folding path and secondary structure of a new RNA at the same time when it is trained for a large number of RNA molecules by folding-path learning. The diagram of multi-RNA molecules folding path learning is shown in As a first try, we used 770 RNA sequences less than 50 in the bpRNA  databaseWe performed 21,650 episodes of folding simulation on this training set and visualized the sampling probability of each sample on the training set, as shown in We pick one RNA (bpRNA_RFAM_25409) from the test set to see if 2dRNA-Fold learned the rules from the multi-RNA training as same as that we used to learn with a single RNA before. The sequence of the RNA is AUUCAAAUAACCAAAAUCCUCGGAAGGGGAUUAAAACG, the length is 38 nucleotides, and the secondary structure is “..............(((((((....))))))))......”. The predicted fastest folding path is shown in What’s more, in the secondary structure prediction mode, 2dRNA-Fold takes nonstochastic sampling in the selection part of Monte Carlo tree search to avoid different prediction results each time and the predicted folding paths are the most likely folding paths or the fastest folding paths. If we do not remove this randomness, we can obtain multiple different potential folding pathways, but in this case these paths may be not the most likely folding paths or the fastest folding paths. The other two possible folding pathways for the above example (bpRNA_RFAM_25409) are shown in Finally, it should be pointed out that in 2dRNA-Fold the scoring to determine next step is based on possible base pairings, the learned or predicted folding path may be not completely in agreement with the real folding paths that are determined by free energy according to physical laws. So, it may be better to incorporate free energy into the scoring function, so that the results obtained can be more consistent with the experimental studies. However, currently accurate calculation of free energy is difficult. But anyway 2dRNA-Fold can be easily extended to use scoring function based on free energy if there is an accurate method to calculate free energy of RNA secondary structure.In summary, we developed a deep reinforcement learning algorithm called 2dRNA-Fold to learn the fastest folding paths in RNA secondary structure formation. 2dRNA-Fold uses a neural network combined with Monte Carlo tree search to select residue pairings. The learned fastest folding paths of some RNAs show that 2dRNA-Fold can quickly find the key base pairs that determine the framework of the native secondary structures. Furthermore, 2dRNA-Fold can also be used to predict RNA secondary structure if it is sufficiently trained on large dataset. Since our method does not combine physical mechanisms such as energy and thermodynamics, the identified fastest folding paths for RNAs may not be the actual folding paths.


Pestivirus, is required for the cap-independent translation of viral genomic RNA. Thus, it is essential for viral replication and pathogenesis. We applied a combination of high-throughput biochemical RNA structure probing (SHAPE-MaP) and in silico modelling approaches to gain insight into the secondary and tertiary structures of BVDV IRES RNA. Our study demonstrated that BVDV IRES RNA in solution forms a modular architecture composed of three distinct structural domains (I–III). Two regions within domain III are represented in tertiary interactions to form an H-type pseudoknot. Computational modelling of the pseudoknot motif provided a fine-grained picture of the tertiary structure and local arrangement of helices in the BVDV IRES. Furthermore, comparative genomics and consensus structure predictions revealed that the pseudoknot is evolutionarily conserved among many Pestivirus species. These studies provide detailed insight into the structural arrangement of BVDV IRES RNA H-type pseudoknot and encompassing motifs that likely contribute to the optimal functionality of viral cap-independent translation element.The internal ribosome entry site (IRES) RNA of bovine viral diarrhoea virus (BVDV), an economically significant  Pestivirus, family Flaviviridae, that includes the causative agents of economically significant diseases of cattle, pigs, and sheep. The BVDV infections in cattle lead to decreased fertility and milk production, slow foetal growth, diarrhoea, respiratory symptoms, reproductive and immunological dysfunction [Pestivirus A) of low-virulence and type 2 (Pestivirus B) of high-virulence, have been recognized and are estimated to cause considerable economic loss [Bovine viral diarrhoea virus (BVDV) is a member of the genus function . Accordifunction , two maimic loss . Besidesmic loss ,5.The BVDV genome is a positive-sense single-stranded RNA 12.5 kb) that codes for one open reading frame (ORF). The coding region is preceded by the distinctly structured 5′ untranslated region (5′ UTR), which folds into the Internal Ribosomal Entry Site (IRES) . By defi kb that in vitro analysis involving BVDV IRES, 40S rRNA, and eIFs suggested that the ribosome makes multiple contacts with the BVDV IRES. Similar observations have been made for HCV IRES RNA [According to previous structural studies, the viral IRES RNAs fold into intricate secondary and tertiary arrangements that provide a structural scaffold for cellular initiation factors and trigger conformational changes in the 40S ribosomal that drive translation initiation by an active mechanism . For exaIRES RNA , outliniIRES RNA ,23. AddiIRES RNA .in vitro synthesized constructs that contain all essential elements supporting the cap-independent translation, revealed that BVDV IRES RNA folds into three distinct structural domains (I–III). Two single-stranded regions of domain III were shown to support the formation of an H-type pseudoknot, thus corroborating earlier studies that predicted the existence of a pseudoknot in BVDV IRES RNA and in related pestiviruses [Pestivirus, which confirmed pervasive evolutionary conservation of the IRES region, and provided evidence for the presence of a pseudoknot in all studied viruses.In this study, we investigated the secondary structure of BVDV IRES RNA using selective 2′-hydroxyl acylation analysed by primer extension and mutational profiling (SHAPE-MaP) ,29. Our iviruses ,31. To oiviruses  and SimRiviruses  on a tri2+ ions that induce reverse transcriptase to read through the bulky 2′-O-adducts and insert mutations [SHAPE-MaP is a high-throughput biochemical probing technique that can be used to characterize RNA secondary structure at a single-nucleotide resolution . In SHAPE-MaP, unpaired nucleotides (nts) are acylated more readily at the 2′-hydroxyl ribose moieties by an electrophilic reagent, e.g. 1-methyl-7-nitroisatoic anhydride (1M7)  than thoutations . The resin vitro synthesized transcripts; the short transcript (560 nts) included the BVDV IRES RNA sequence only; the long transcript (1296 nts) additionally contained the EGFP cassette (nts 561–1296), and the MS2 hairpin (nts 523–543) at its 3′ end. The IRES RNA constructs used in this study were derived from BVDV-1 strain NADL genomic RNA and contain RNA sequences that were outlined as necessary for supporting the expression of EGFP protein by the cap-independent translation [p-value 0.022) . Since we obtained a more comprehensive mapping output for the long transcript, we used these data for modelling the BVDV IRES RNA.We performed structural probing of BVDV IRES RNA on two nslation ,38. BVDVnslation   within each domain being assigned letter codes . Domain The residues immediately downstream of SL IIIe and the residues near the AUG codon (nts 429–431) were proposed previously to be involved in a H-type pseudoknot ,22. In tThe single-stranded region immediately downstream of the pseudoknot harbours the AUG codon. We noted that numerous residues within BVDV IRES RNA display hyperreactivity towards SHAPE reagents . These are found within the loops , bulges , single-stranded regions , and near the pseudoknot  . These residues were previously reported as accommodating a C2-endo ribose conformation . This coWe used SuperFold  to compuPestivirus. To this end, we selected 13 representative viral genomes, comprising the ICTV-approved Pestivirus species A-K, as well as Linda virus and Norway Rat Pestivirus, and searched for hits of the Rfam Pestivirus IRES (RF00209) covariance model. Only high-quality (high bit score) hits for each input sequence were considered. Multiple sequence alignment , and consensus structure prediction of these structurally homologous elements revealed rich covariation patterns throughout the entire IRES region . Partition function folding and re-evaluation of the corresponding element in our model revealed that domain II encompasses two entities, i.e. a structurally constrained closing stem, as well as a conformationally flexible apical portion that can fold into either an extended stem-loop or a Y-shaped structure . As these conformations co-exist within a narrow energy range of 1 kcal/mol, we consider the apical portion of domain II is multi-stable. Taken together, earlier models of the BVDV-1 NADL IRES RNA, in particular domain III stay, to a large extent, in agreement with our SHAPE-MaP assisted structural analysis We performed a comparative analysis of our probing-assisted BVDV IRES RNA structure model and previously proposed models, which revealed both commonalities and differences . In the BVDV IRES model that was proposed by Deng and Brock, a largely equivalent architecture of domain III computed by early versions of modern RNA structure prediction algorithms highlights its functional importance . LikewisSHAPE-assisted secondary structure prediction reported the existence of an H-type pseudoknot near the basal stem of Domain III. By analysing the predicted secondary structure, one might expect a co-linear arrangement of SL IIIf and IIIg, resulting in coaxial stacking of the two helices. However, under such a scenario, spatial separation of the complementary pseudoknot interaction regions induced by the putatively coaxially stacked helical stems SL IIIf and SL IIIg might be too large to form the pseudoknot. We were intrigued by the question of whether this pseudoknot interaction is sterically feasible in 3D.in silico.To this end, we performed coarse-grained RNA 3D simulations with a purposefully trimmed construct that encompasses relevant parts of Domain III that are involved in pseudoknot formation. Specifically, 5′ and 3′ portions of the original construct were truncated after positions 179 and 425, respectively. Likewise, the four-way junction that contains SL IIId1, SL IIId2, and the substructure comprising SL IIIa, b, and c, were retained as an apical loop element with a connected backbone. This construct, denoted BVDVsegment_180_425 , features the same structural traits as the original full-length construct. In particular, pKiss single sequence folding predictions suggest the formation of an H-type pseudoknot as expected. Likewise, this truncated construct represents a reasonably small system that allows for detailed tertiary structure analysis We used ernwin to test whether the proposed secondary structure can be embedded in 3D. Operating on secondary structure elements, ernwin employs a coarse-grained sampling approach and distinguishes between stems, bulges, and various loop types, each represented by cylinders that fit the helix axis. 3D structures are built from fragments of already known RNAs, and tertiary structures are guaranteed to always match the input secondary structure. Building on this concept, we constructed initial 3D models of BVDVsegment_180_425, thereby confirming that the proposed H-type pseudoknot is sterically feasible.In parallel, we set out to predict the tertiary structure of BVDVsegment_180_425 with SimRNA. We performed massively parallel replica-exchange Monte Carlo simulations to sample the low-energy portion of the underlying energy landscape, using secondary structure restraints that ensure the formation of all helices predicted for BVDVsegment_180_425, including the pseudoknot. A snapshot of a low-energy conformation is shown in . ClusterRNA viruses are known to use exceptionally thrifty molecular strategies to optimize their replication cycle. They often contain functional RNA motifs within non-coding regions that control crucial steps of their infectivity cycle and structural switches that aid their regulation. The IRES RNA within the 5′ UTRs of BVDV is one of such complex structural domains known to govern the efficiency of cap-independent translation . In thisOverall, BVDV IRES RNA folds into three major structural domains (I–III), each containing well-defined motifs, i.e. hairpins and stems, interspersed among less structurally constrained regions, such as multi-way junctions and loops. This model stays, to a large extent, in agreement with the previously proposed secondary structure models for the 5′ and 3′ UTRs of BVDV strains, i.e. NADL, OSLOSS and SD-1 ,53. In pThe key finding relates to the most structurally well-defined domain III , which includes two single-stranded regions and manually represented H-type pseudoknot. The 3D modelling of the BVDV IRES RNA provides further insight into the structural arrangements of individual motifs, predicting quasi-coaxial stacking between SL IIIf and the pseudoknot helix. These coaxial stackings can be critical for stabilizing the IRES pseudoknot and can be formed only if the angle between SL IIIg and SL IIIf is acute. Similar stabilization processes have been shown for T2 bacteriophage mRNA of gene 32 , turnip Pestivirus IRES. Importantly, we could identify statistically significant covariation in 22 base pairs, including the pseudoknot, thus providing computational evidence for the crucial role of the pseudoknot in IRES functionality.Previously, the formation of a pseudoknot in BVDV IRES RNA has been proposed based on thermodynamic predictions and Monte Carlo simulations ,31. The Site-directed mutagenesis analysis indicated that BVDV IRES RNA constructs with impaired base-pairing within these residues lost their translational activity, while compensatory mutations reversed that effect . FurtherAlthough the relationship between the IRES pseudoknot and initiation codon in BVDV RNA has not been addressed, previous studies performed on HCV IRES RNA indicated that the formation of a pseudoknot leads to the correct positioning of mRNA open reading frame (ORF) in the 40S ribosomal binding cleft . In HCV Oligonucleotide primers used in the study were purchased (IDT) and are listed in (Table S2). All DNA products were purified from agarose gels and eluted using the elution kit . Quick Ligase  was used in all ligation reactions. PCR reactions were carried using Q5 Hot Start Polymerase .6 viral particles following Ribozol extraction protocol . Two hundred and fifty microlitres of virus suspension was mixed with three volumes of RiboZol and incubated for 5 min at room temperature. With an addition of 200 µl chloroform, the sample was shaken for 15 s and incubated for 15 min at room temperature. Phases were separated by centrifugation at 12,000 × g for 15 min in a cold microfuge. Six hundred microlitres aqueous phase was purified using Zymo Clean & Concentrator-25 . Purified RNA (~1.2 µg) was used as a template for the synthesis of 461 bp DNA fragment coding for the 5′ UTR sequence and 25 codons of the first viral gene Npro. The oligonucleotide primers were designed using BVDV-NADL. cDNA was synthesized using 40 ng of viral DNA and 1 µM reverse primer IRES-NADL-rev in 20 µl reaction with SuperScript IV reverse transcriptase . The 461 bp DNA fragment was amplified using IRES-NADL forward and reverse primers and Q5 Hot Start polymerase . The second round of PCR amplification extended the 461 bp fragment to include the KpnI restriction site at the 5′ end and the BamHI site at the 3′ end. Amplification with primer pair T7-IRES-for-3 and T7-NADL-rev yielded 509 bp DNA fragment, which was then digested with KpnI-HF  and BamHI-HF . The final 463 bp fragment was ligated with the pcDNA3.1(+)circ RNA Mini Vector  digested with KpnI-HF and BamHI-HF. Clones were selected and maintained in E. coli strain NEB5-alpha . All constructs were verified by Sanger sequencing (Eurofins). New plasmid was designated pIW-IRES.Inactivated BVDV-NADL virus was obtained from the laboratory of Paul Walz . Viral RNA was extracted from 10Plasmid pIRES-EGFP-puro  was digested with BstXI  and BsrGI , and 710 bp DNA fragment coding for EGFP protein was prepared for ligation. Plasmid pIW-IRES was digested with BamHI-HF  and ApaI . Insertion of the BstXI-BsrGI fragment with EGFP between BamHI and ApaI sites on the core plasmid was performed using the following adapters: A-5′EGFP-adapt-top and B-5′EGFP-adapt-bott for the 5′ end of the insert plus C-3′EGFP-top and D-3′EGFP-bott for the 3′ end of the insert. The 5′ end adapter pair added an EcoRI restriction site, while the 3′ end pair added an EcoRV site. Double digestion with EcoRI  and EcoRV  was used for the initial screening of clones. All putative correct clones were verified by sequencing. New plasmid was designated pIW-IRES-EGFP.The design of extended hairpin binding bacteriophage MS2 protein was described previously. Plasmid pIW-IRES-EGFP was linearized with BamHI-HF (the 3′ end of IRES) and BstXI (the 5′ end of EGFP). Gblock spanning the 3′ end of IRES, extended protein MS2-binding hairpin (eMS2hp), and the 5′ portion of EGFP were purchased (IDT) and amplified with primers R17L-for and R17L-rev gblock was digested with BamHI and BstXI and ligated to linearized pIW-IRES-EGFP. The sequence of plasmid pIW-IRES(eMS2hp)-EGFP was verified by sequencing.RNA was extracted with TRIzol reagent  followed by the RNA Clean and Concentrator kit  and DNase treatment provided with the kit according to manufacturer’s protocols. The RNA was eluted in 15 µl RNAse free water and stored at −80°C.2, 40 mM dithiothreitol (DTT), 1 mM spermidine, 7.5 mM NTP, SUPERNase-In (Ambion) – 20 U/100 µl reaction, inorganic pyrophosphatase  – 0.1 U/100 µl reaction and 100 µg/ml T7 RNA polymerase. The reactions were incubated for 2 h at 37°C with gentle rotation.Plasmid pIW-IRES/eMS2hp-EGFP was linearized either with EcoRI for synthesis of IRES/eMS2hp, or with EcoRV for synthesis of IRES/eMS2-EGFP-mRNA. 100 µl transcription reaction consisted of 2.5 µg linearized plasmid, 200 mM HEPES-KOH, 7.5, 30 mM MgClin vitro transcript  was heated at 95°C for 3 min and slowly cooled to 4°C (0.1°C/s). The in vitro transcripts were folded in a final volume of 36 µl in 1X folding buffer containing 50 mM Tris–HCl pH 8.0, 100 mM NaCl, 5 mM MgCl2, and incubated at 37°C for 15 min. The folded transcripts were divided into two reactions: positive and negative (18 µl each) with one sample treated with 2 µl of 100 mM 1-methyl-7-nitroisatoic anhydride (1M7)  for positive (+) reaction , and the second sample treated with 2 µl dimethyl sulphoxide (DMSO) for negative (-) reaction. Both tubes were incubated at 37°C for 5 min to facilitate RNA modification. Additionally, the denaturing control (DC) was prepared and included 5 pmol of transcript in 3 µl TE-like buffer , 5 µl formamide, and 1 µl denaturing buffer , that were mixed and incubated at 95°C. To 9 µl of the denatured transcript, 1 µl of 100 mM 1M7 was added and incubated at 95°C for 1 min. All samples were purified using ethanol precipitation with 5 mg/ml glycogen . After modification, RNA was reverse transcribed at 42°C for 3 h using zone-specific RT primers in the SHAPE-MaP buffer, including 50 mM Tris pH 8, 75 mM KCl, 10 mM dithiothreitol (DTT), 6 mM MnCl2 , 0.7 mM dNTPs with 10 U SuperScript II reverse transcriptase . The obtained cDNA products were purified using Mag-Bind Total Pure NGS beads . Each sample was amplified separately by two PCRs using Q5 high-fidelity polymerase  and zone-specific PCR primers. PCR 1 included 15 cycles: 98°C, 30 s; 98°C, 10 s; 55°C, 30 s; 72°C, 30 s; 72°C, 2 min. The obtained products were checked on 0.8% agarose gel, purified using Mag-Bind Total Pure NGS beads, and subjected to the second round of PCR amplification using 15 cycles: 98°C, 30 s; 98°C, 10 s; 55°C, 30 s; 72°C, 30 s; 72°C, 2 min. The final libraries were purified on 2% preparative agarose gel by Monarch DNA Gel Extraction kit . The concentration of each library was measured by performing quantitative PCR (qPCR) using PerfeCTa® NGS Quantification Kit (Quanta Biosciences 10029–558) and following the manufacturer’s protocol. Purified amplicon libraries were pooled and sequenced on Illumina MiSeq Instrument.The sequence corresponding to the BVDV IRES was divided into two overlapping zones (nts 1–283 and 217–504) that were reverse transcribed and amplified with zone-specific RT and PCR primers (Table S3). Five picomoles of BVDV IRES The dataset obtained from Illumina MiSeq was saved into separate FASTQ files for each of the respective zones and reactions. A custom Python script ‘trimmer.py’ was used to trim raw sequences and remove sequencing artefacts. The FASTQ files corresponding to the (+), (-) and DC reactions were concatenated and processed by ShapeMapper v1.2  to obtaiPestivirus species (A-K), as well as two unclassified Pestiviruses, i.e. Linda virus and Norway rat Pestivirus were downloaded from the NCBI GenBank database. The cmsearch tool from the Infernal suite v1.1.4 [Pestivirus internal ribosome entry site) against the library of 13 Pestivirus genomes, yielding structurally homologous elements within the 5′ UTR in each genome (Table S4). These were aligned with mafft v7.475 [Complete genomes of all ICTV-listed e v1.1.4  was usedt v7.475  and subjt v7.475  from thet v7.475 . As RNAat v7.475 .RNA tertiary structure predictions were performed on a trimmed 106 nt construct that encompasses the portion of the BVDV1-NADL IRES that is involved in pseudoknot formation. The construct was verified to form the expected H-type pseudoknot with pKiss v2.2.13 . To testClick here for additional data file.


As our understanding of the complex network of regulatory pathways for gene expression continues to grow, avenues of investigation for how these new findings can be utilised in therapeutics are emerging. The recent growth of interest in the RNA binding protein (RBP) interactome has revealed it to be rich in targets linked to, and causative of diseases. While this is, in and of itself, very interesting, evidence is also beginning to arise for how the RBP interactome can act to modulate the response of diseases to existing therapeutic treatments, especially in cancers. Here we highlight this topic, providing examples of work that exemplifies such modulation of chemotherapeutic sensitivity. Gene expression must be tightly controlled to maintain proper cellular functions. Post-transcriptionally, RNA-binding proteins (RBPs) interact with RNAs in a combinatorial fashion, forming so-called ribonucleoprotein complexes . ConsideGiven the crucial roles that mRNA-RBP interactions  play in maintaining proper levels, timing and localisation of gene products, there can be dire consequences when this process is perturbed . The disConsidering the importance of RNA–protein interactions in cells, there has been a widespread surge in the examination and analysis of the RBP ‘interactome' at a global level. Besides protein-centred approaches, aimed at identification of RNAs interacting with particular RBPs, a variety of RNA-centred techniques have recently emerged for capturing and examination of the interactome on entire RNA ‘populations' — such as RNA interactome capture (RIC) . As Whilst the above approaches can provide a picture of composition and dynamics of the entire RNA-binding proteome (RBPome), more specific approaches for the investigation of the cellular interactome of individual RNAs — by tagging the RNA of interest with an affinity aptamer and recovering with a high-affinity ligand coupled to beads or capture of native RNAs with modified ASOs) — have also come along recently (reviewed in ). With tThe combined use of RNA-centred alongside with protein-centric approaches has and will further allow for identification of RNA–RBP networks that could composite crucial regulatory nodes in gene expression. With a considerable knowledge of how, when, and where in cells these interactions occur, they may become promising targets for therapeutic intervention, especially for diseases arising from the consequences of aberrant gene expression.Many conventional small molecule drugs have been used for decades for effective cancer treatment, often leading to drug resistance after prolonged application. One such drug is cisplatin (CP) for cancer treatment. CP binds DNA forming intra- and inter-strand DNA cross-links and mono adducts, thereby interfering with transcription and replication, eventually leading to cell toxicity and apoptosis . Despitep27/CDKN1B mRNA, coding for an important tumour suppressor. The stability of this mRNAs was shown to be increased shortly after CP treatment of cells, which could be inferred through elements located in the 3′ untranslated regions (UTR) in the mRNA. Looking at the arrangement of RBPs in the presence of CP as compared with untreated control cells by applying an RNA-centred tandem RNA affinity isolation procedure (tobTRIP), 54 proteins were identified to interact with a GFP-tagged reporter bearing the 3′UTR of the p27 mRNA. Subsequent knockdown of several of the identified RBPs impeded CP induced expression of p27 mRNA in HEK293 cells, possibly modulating mRNA stability. Finally, knockdown of KH-type splicing regulatory protein (KHSRP) in MCF7 cells, which are rather resistant to CP treatment, rendered these cells sensitive to CP treatment. Besides highlighting the importance of a post-transcriptional regulatory networks in modulating drug sensitivity, the study demonstrated first use of an RNA-centric approach — applying a mRNA–protein interactome capture technique — to unravel critical factors involved.Thus, while the importance of RBP mediated post-transcriptional control of gene expression for modulation of drug sensitivity and resistance is becoming eminent , recent BCL2) and pro-apoptotic (BIK) protein-coding mRNAs [BCL2 transcripts, while stabilising BIK, which is in line with an overall increased apoptosis with LARP1 depletion. Knockdown of BCL2 mimicked the effects of LARP1 knockdown, including sensitivity to chemotherapeutics, whilst BCL2 overexpression rescued the increase in apoptosis caused by LARP1 depletion. Interestingly, parallel work by Heise et al. [Work by Hopkins et al.  examinedng mRNAs . LARP1 de et al.  likewisePRC1 mRNA, thereby down-regulating PRC1 expression and exerting an anti-tumour effect. From this, the authors postulate a new therapeutic strategy that could be explored by exploiting the ZFP36/Prc1 axis [FAM198B mRNAs, suggesting an CELF2/FAM198B axis as a therapeutic target for ovarian cancer treatment [Two further recent examples that showcase RNA-RBP interactions directly influencing survivability and drug sensitivity in cancer cells concern the zinc finger protein ZFP36, whose overexpression inhibited the growth of hepatocellular carcinoma (HCC) tumour cells and promoted 5-FU sensitivity in a xenograft tumour mice model ; and CELrc1 axis . CELF2 wreatment .https://www.cancerrxgene.org/), it was suggested that high risk RBP profiles are likely to have a weaker response to these drugs. Likewise, considering the GTEx dataset [Besides these exemplary studies, the information contained in various public databases provides a complementary and useful resource to predict and eventually establish links between RBPs, cancer outcome and sensitivity to a range of chemotherapeutics. For example, Gao et al.  consider dataset , Kosti e dataset  found thRecent data suggests that drugs designed to impede the functions of pro-survival RBPs, possibly in combination with conventional chemotherapeutics, could be a promising therapeutic intervention in the treatment of chemotherapy-resistant cancers . Thereby, the mechanisms by which RBP interactions may impact cell survival and growth do not have to have evolved to be specific to their role in chemoresistance. RBPs have been shown to play roles in the signalling pathways, regulation of RNAs, and protein interactions that impact crucial factors in cancer progression and survivability . As well as this, many RBPs are also involved in DNA damage response and DNA repair pathways ,36, whicWhile considered non-druggable for a long time , largely


In this review, we discuss the development pipeline for transcriptional biomarkers in molecular diagnostics and stress the importance of a reliable gene transcript quantification strategy. Hence, a further focus is put on the MIQE guidelines and how to adapt them for biomarker discovery, from signature validation up to routine diagnostic applications. First, the advantages and pitfalls of the holistic RNA sequencing for biomarker development will be described to establish a candidate biomarker signature. Sequentially, the RT-qPCR confirmation process will be discussed to validate the discovered biomarker signature. Examples for the successful application of RT-qPCR as a fast and reproducible quantification method in routinemolecular diagnostics are provided. Based on the MIQE guidelines, the importance of “key steps” in RT-qPCR is accurately described, e.g., reverse transcription, proper reference gene selection and, finally, the application of automated RT-qPCR data analysis software. In conclusion, RT-qPCR proves to be a valuable tool in the establishment of a disease-specific transcriptional biomarker signature and will have a great future in molecular diagnostics or personalized medicine. Traditionally, diagnosis and therefore treatment have been decided based on observation of specific patterns of symptoms in the patient. However, since many diseases are accompanied by similar and ergo unspecific implications, the consensus was reached that symptomatic diagnosis and treatment might not always be the best choice for a number of diseases. Thus, the focus has shifted toward the exploration of biological markers (biomarkers) in patient samples, which reveal distinct biological traits or changes in the organism and can therefore be connected to specific disorders . Those bvention” . The provention” ,4. HighlLiquid biopsies have recently become more and more popular in molecular diagnostic research, with some assays having already been approved by health authorities . Being mSo far, proteins have been used predominantly as biomarker molecules, since they are present in relatively high concentrations in blood and allow for relatively cheap and fast detection in liquid biopsies by reliable and widely available antibody-based methods, such as enzyme-linked immunosorbent assay (ELISA) or immunohistochemistry (IHC) . FurtherIn this review, we focus on molecular biomarkers on the transcriptional level. The transcriptome can be divided into RNA subtypes with distinct functions.Messenger RNA (mRNA) carries the genetic sequence in a form that can be recognized to synthesize proteins. Since its expression frequently correlates with the type and amount of proteins produced, it is often directly linked to pathological processes and, therefore, a popular target for transcriptional biomarker studies. Furthermore, it is one of the most easily accessible biomarkers in liquid biopsy, as its expression in blood is 80% identical to that of major tissues ,22. HoweLong non-coding RNAs (lncRNAs) include RNA transcripts longer than 200 nucleotides that do not encode for proteins. In the study performed by the consortium GENCODE, the human genome annotation consists of 15,512 lncRNA transcripts grouped in 9640 loci . AccordiShort non-coding RNAs (sncRNAs) include RNA transcripts of fewer than 200 nucleotides in length, such as ribosomal RNA (rRNA), transfer RNA (tRNA), small interfering RNA (siRNA), small nucleolar RNA (snoRNA) and, most importantly for biomarker research, micro-RNA (miRNA) . miRNAs Transcriptional biomarkers, in contrast to DNA and proteins, allow for immediate detection of cellular changes . The traAs will be elaborated in the following chapters, the establishment of RNA-based biomarkers starts with RNA-Seq, which gives a broad and holistic overview over potential candidate genes. These will be subsequently validated with RT-qPCR, which is ideally subjected to the MIQE standards. RT-qPCR is the method of choice for evaluation of candidate RNA biomarkers, due to its sensitivity in detection of less frequent biomarkers, as well as its specificity.The first step toward developing a transcriptional biomarker signature for a specific disease is holistic RNA screening of a training cohort. RNA-Seq is the method of choice for this purpose, since it allows the measurement of the RNA expression of all genes present in the sample. In many cases, there is not enough prior knowledge of potential RNA sequences linked to the disease of interest or whether they can be found in the chosen sample type to limit the expression analysis to only one RNA subset in the sample. Even if possible biomarker RNAs are already known, it is recommended to perform a holistic screening instead of a targeted expression analysis, since those candidate RNAs might not be the best suited biomarkers in the sample type of choice. Most diseases affect multiple cellular pathways and thereby the expression of a vast number of different RNAs. Some of them might be selectively enriched or depleted in the analyzed samples, either biologically or technically, e.g., by potentially selective sorting into EVs or by the choice of isolation method ,72,73.The training cohort contains isolated RNA from patients diagnosed with the disease of interest, along with RNA from a control group, i.e., healthy volunteers, and is used to generate a cDNA library for sequencing. The sample type from which the RNA is isolated always depends on the RNA type of interest and the samples typically available for the specific disease—it should be the same as the one that will be used later in routine diagnostics. In liquid biopsy research, serum might be used to develop an EV-associated miRNA signature for community-acquired pneumonia (CAP) and pneumonia-related sepsis . In cancAnother important step to consider is the method used to isolate RNA from the sample. After all, RNA isolation should be comparatively quick, easy and cost efficient for transcriptomic biomarkers to compete with already-established methods in routine diagnostics. On the other hand, a high RNA yield is crucial; otherwise, important transcripts with essential information might get lost. The studied disease, as well as the sample type chosen, directly influences which type of RNA is most promising as a biomarker. For instance, mRNAs overexpressed in certain cancers are a good target for biomarker studies in tissue samples. Since the RNA found in blood—especially cfRNA—might be partly degraded, small RNAs, such as miRNA, are usually investigated when working with blood samples or cfRNA. Consequently, the RNA type chosen as a possible biomarker source also affects the choice of isolation method, since different isolation strategies might enrich different RNAs ,75,76,77To reduce possible batch effects, all samples from the training cohort should be handled simultaneously during library preparation and sequenced in the same run. However, library preparation is a crucial step that is prone to introduce technical bias in RNA-Seq experiments ,79,80,81After next-generation sequencing, the results are processed in silico. The steps vary with the length and type of the analyzed RNAs and include filtering, trimming and mapping against a suitable database for the analyzed type of RNA. Obviously, the parameters used for filtering depend on type and size of the analyzed RNAs and have a big impact on the results. Using filtering parameters that are too broad, sequencing artifacts and low-quality sequences will remain in the data set, while overly strict parameters might eradicate some potential biomarkers. Applying multivariate statistics, e.g., hierarchical clustering (HCA), principal component analysis (PCA) or partial least-squares discriminant analysis (PLS-DA) to the processed data, a subset of RNA genes is identified, which are up- or down-regulated in the treatment compared to the control group samples ,83,84. PThe obtained preliminary signature of transcriptional biomarkers, derived from the first learning cohort of patients, is then validated with more samples in the second validation cohort using an orthologous method. Since the number of RNAs that are analyzed in the validation cohort has been reduced to a smaller subset of biomarker candidates, a more time- and cost-efficient method can be used. RT-qPCR is a well-established and—even more relevant—standardized method to confirm and validate the candidate biomarker signatures when in compliance with the MIQE guidelines, as was shown by several recently published studies ,86,87,88m) and amplicon size, among others. Repeats and runs of bases in the primer sequence, unspecific primers, as well as the formation of secondary structures, should be avoided [To validate the biomarkers found by RNA-Seq in the learning cohort, RT-PCR primers have to be designed first. Primers provide the binding site for the polymerase and act as a starting point for amplification of the RNA of interest via a hydroxyl group at their 3′ end. This primer design step is the basis for the following biomarker validation and should be given appropriate thought, since the specificity and efficiency of any PCR assay are critically dependent on the primers. Suitable primers are usually found with publicly available software, e.g., Primer3 . Importa avoided ,91. Sinc avoided .In order to amplify transcriptional biomarkers, the RNA of interest needs to be reverse transcribed from RNA to cDNA. Since reverse transcription is a major source of variability between RT-qPCR experiments, it should ideally be performed in two or three technical replicates, preferably at the RT level . To avoiFor every assay, all samples should be measured in the same PCR run to ensure inter-sample comparability. Additionally, it is recommended to include an inter-run calibrator (IRC) on each plate, so the different assays can be quantified relative to each other .q values for each biomarker assay are normalized against reference genes, resulting in a ΔCq value [q values of patient samples are subtracted from healthy volunteer ΔCq, yielding the ΔΔCq value. Relative fold gene expression is then calculated using the formula: 2−ΔΔCq [p values < 0.05 in a two-tailed Student’s t-test [Biomarker candidates are first validated in the same training cohort that was used in RNA-Seq. To ensure only specific PCR products are measured, a melting curve analysis must be performed when using intercalating dye-based qPCR. While a single peak in the melting curve indicates the presence of a single, specific PCR product, multiple peaks can be indicative either of a multi-stage melting transition  or of unspecific PCR products. In case of a preponderance of unspecific PCR products, primer design must be resumed and optimized. Additionally, potential outliers may be removed before further analysis, such as single wells containing products with incorrect melting temperatures . The proCq value . These r: 2−ΔΔCq . A fold s t-test . FinallyAlthough this approach might come along with additional laboratory and bioinformatical workload, as well as added costs regarding consumables and time , it is mSince its introduction to the scientific world by Kary Mullis in 1985 , the polRT-qPCR is unparalleled as the “gold standard” of gene expression profiling  and deteThe international standardization rules for RT-qPCR were first laid down in 2009 in form of the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines, which were established by active researchers under the auspices of Minimum Information for Biological and Biomedical Investigations (MIBBI) . Not onlAccording to the MIQE guidelines, the factors that will have an impact on the performance of RT-qPCR fall into three broad categories: (1) pre-PCR sample collection and handling, (2) PCR assay design and (3) post–PCR data analysis. Errors regarding the sample include inadequate sampling procedure, suboptimal buffer and storage conditions, inadequate sample preparation and, therefore, reduction in nucleic acid quality, purity and quantity. Presence of RT or PCR inhibitors in low-quality samples may lead to false negative results. Inhibitors can be detected using an internal amplification control or by diluting the sample and calculating the amplification efficiency, for example . AlternaThe need for a standardized study design using RT-qPCR as method is reflected in the number of scientific citations in peer-reviewed journals. As of today (December 2021), the first MIQE guidelines by Bustin et al.  have beeThe authors of the MIQE guidelines aim to accomplish three things: (1) to provide a guide to help scientists design and evaluate meaningful qPCR experiments; (2) to provide editors and reviewers with evaluation criteria to better assess the quality of submitted studies/papers; and (3) to facilitate the reproduction of papers that have followed the MIQE guidelines. Compliance with MIQE guidelines generated only slightly higher costs, as described by Dooms et al. . StandarNot only research, but also diagnostics recognized the accuracy and reliability of RT-qPCR. Due to the method’s relative simplicity , laboratERBB2, ESR1, PGR and MKI67 are used for subtyping the disease and to determine the treatment according to the St. Gallen guidelines (2017) [Liquid biopsy, as stated above, utilizes biofluids obtained with non- or minimal invasive techniques for diagnosis . The rels (2017) . Relativs (2017) ,119 and s (2017) .Another field where RT-qPCR proves to be a valuable tool is infectious disease diagnostics. In this case, gene expression levels of pathogens are determined. For instance, monitoring of the viral load is imperative in HIV patients. One commercially available kit for this purpose is the AltoStar HIV RT-PCR Kit 1.5. Without doubt, the most prominent example for the diagnostic use of RT-qPCR is the currently ongoing SARS-CoV-2 pandemic. Samples processed with RT-qPCR yield results within a few hours to determine whether the patient is infected. This allows for (1) suitable treatment, when other illnesses, such as cold or flu, are excluded and (2) quick reactions to quarantine the patient and thus minimize the potential of spreading the disease. Interestingly, the pandemic fuels further developments to reduce the cost of RT-qPCR tests for the benefit of the broader public ,123,124.When using RT-qPCR for molecular diagnostics, one should pay careful attention to experimental design, sample and assay quality, especially with focus on the efficiency of the reverse transcription reaction, the selection of proper reference genes and how the normalization will be calculated in the applied data software tool. All those factors significantly impact the chance of successful and reliable relative gene expression results . In the Reverse transcription (RT) is presumably the most crucial and variable step in the entire RT-qPCR workflow. For the quantitative measurement of any RNA family, the RNA is first reverse transcribed into complementary DNA (cDNA). Afterward, the cDNA is profoundly amplified by a DNA-dependent DNA polymerase during the PCR cycling process. However, even today, 50 years after the discovery of the reverse transcriptase enzyme , the reaq) and input copy number using a calibration (or standard) curve [PGK1 [GAPDH and ACTB turn out to be less reliable than assumed in cancer-related research. Underlying mechanisms explaining this observation might be the heterogeneity and genetic instability of cancerous tissue [Choosing the appropriate quantification strategy is a very important step of gene quantification and therefore required by the MIQE guidelines. Normally, two quantification strategies can be applied in the RT-qPCR: absolute and relative quantification. Absolute quantification creates a correlation between the quantification cycle (Cd) curve . This cad) curve . In contd) curve . A referd) curve . One welve [PGK1 . Howeverve [PGK1 ,78. As ds tissue . Therefos tissue . Cautions tissue ,135,136,For research purposes, software tools have been developed to identify stably expressed genes in a cohort. Convenient algorithms and software tools that analyze expression data obtained through any quantitative method to select valid reference genes include geNorm , BestKeeBesides the classical RT-PCR parameters, e.g., primer design, RNA quality, RT and polymerase performances and the selection of reference genes—as discussed above—the fidelity of the quantification process is highly dependent on valid data analysis. The choice of analysis method is an important topic and must also be MIQE compliant . Algoritq values to relative quantities, normalization, rescaling and a final statistical test of the derived qPCR results.Facilitating easy data management and providing tools for automated data analysis to obtain statistically proven results are the main goals in RT-qPCR data processing and application software development. All of the calculation and statistical software applications described were already summarized and discussed in other publications . HoweverAccurate and straightforward mathematical and statistical analysis of post-PCR data and management of growing data sets have become the major hurdles for effective implementation into gene expression analyses. Nowadays, high-throughput 384-well applications generate huge amounts of RT-qPCR data, which in turn need to be grouped, standardized, normalized and documented by the software applications . HoweverGenEx  is a sofq quantification model with efficiency correction, multiple reference gene normalization and accurate error propagation along all calculations. The qBase+ browser allows data storage and annotation while keeping track of all real-time PCR runs by hierarchically organizing data into projects, experiments and runs. As a big advantage, there is no limit on the number of samples, genes or replicates, and data from multiple runs can be combined and processed together. Furthermore, the ability to use up to five reference genes allows for reliable and robust normalization of gene expression levels on the basis of the geNorm procedure [The comprehensive software application qBase and the newer version qBase+ were developed as a generalized solution to accommodate virtually all relative quantification setups and for the management and automated analysis of real-time quantitative PCR data . It emplrocedure .q deviation of target genes between the treatment and control group, normalized by multiple reference genes and PCR efficiency-corrected [The relative expression software tool (REST), for comparison of several expressed genes, is based on Microsoft Excel and programed in the Visual Basic Application (VBA). The underlying mathematical model is based on the mean Corrected . Subsequorrected .The potential pitfalls mentioned above highlight how important it is to carefully consider every step in a transcriptional biomarker discovery and development study—from the initial RNA-Seq screening to data analysis in clinical practice. Through suboptimal sample choice or mistakes during the RNA-Seq workflow, one might overlook promising biomarkers. Careless primer design and neglect of the MIQE guidelines in the RT-qPCR experiment may lead to the wrongful validation of transcripts, insufficient as a measure for the disease of interest. Finally, faulty data evaluation might result in further misinterpretation of the obtained information and—in the worst case—publication of an incorrect biomarker signature. Therefore, having an overview of the advantages and pitfalls of each method used is critical to avoid drawing false conclusions from the acquired data. In the context of liquid biopsy, RNA is a suitable analyte to discover and monitor the course of disease, as well as the progress of the patient’s treatment. RNA is excessively more abundant than DNA; its detection level is significantly lower than that of proteins, and the potential of different RNA classes in research and diagnostics as biomarkers is constantly expanding. Discovering a set of disease-specific candidate genes is performed via hypothesis-free RNA-Seq, using a training cohort consisting of patient samples. The chosen RNA type, as well as extraction method, must be given appropriate thought to enrich high-quality nucleic acids for sequencing. After using suitable parameter filters, the resulting set of candidate genes should be validated using an orthologous method, such as RT-qPCR, both in the initial training cohort and an additional independent cohort. Abiding by international standardization rules ensures the accuracy and validity of RT-qPCR-based results. Subsequently, in a translational approach, the validated genes of interest can be incorporated into diagnostics. Again, RT-qPCR, as the method of choice for gene expression analysis, is guided by the MIQE guidelines to reinforce transparency of the experimental setup for the sake of reproducibility. Possible pitfalls are present in sample handling, primer design and post-PCR data analysis. One equally important factor to consider is the appropriate choice of reference genes, for which publicly available software tools provide extensive support. Post-PCR data processing and statistical analysis have a major impact on the expression profiling results of transcriptional biomarkers, and the development of different automated RT-qPCR data analysis software is aimed to answer distinct biological questions.In summary, RT-qPCR—when used in compliance with the MIQE guidelines—proves to be a valuable tool in the establishment of disease-specific biomarkers and in clinical setting, since it is an accurate and fast method with relatively simple handling. RT-qPCR for molecular diagnostics, particularly with regard to personalized medicine, is well positioned to pave its way into daily clinical routine in the near future. It is therefore all the more important to have and abide by standardized procedures for every step involved.


Saccharomyces cerevisiae. Among the 257 proteins that significantly changed RNA associations, we observed the coordinated remodeling of RNA-binding enzymes — particularly of the central carbon metabolism — that complemented known metabolic responses. Furthermore, we recognized the propensity for paralogous specific alterations of enzyme-RNA interactions. Our results suggest coordinated cross talk between RNA-enzyme interactions and intermediary metabolism to maintain the physiological and molecular balance upon oxidative stress, perhaps through specialization of paralogous during evolution.RNA-binding proteins (RBPs) are key post-transcriptional regulators that play a substantial role during stress adaptation. Recent proteome-wide surveys have uncovered a large number of new and “unconventional” RBPs such as metabolic enzymes, yet little is known about the reconfiguration of the RNA-binding proteome (RBPome) and RNA-enzyme interactions in response to cellular stress. Here, we applied RNA-interactome capture to monitor the dynamics of the mRBPome upon mild oxidative stress in the yeast   •Oxidative stress induces the rearrangement of 257 proteins on polyadenylated RNAs•Coordinated response of RNA-enzyme interactions and metabolism•Yeast RNA-binding enzymes are paralog specific•Integration of three different mass spectrometry analysis tools Molecular network; Microbial metabolism; Proteomics Caenorhabditis elegans induced changes in approximately 16% of the mRNA binding proteome (mRBPome), including translation initiation and elongation factors , acidic stress induced remodeling of 10% of RNA-associated proteins involved in ribosome biogenesis, translation and the formation of P-bodies and stress granules (RNA-binding proteins (RBPs) play essential roles in regulating the transcription, processing, transport, translation, and turnover of RNAs . Recent  factors , and ars factors . In the granules , while hgranules .Oxidative stress occurs as a consequence of aerobic respiration. Reactive oxygen species (ROS) are produced primarily through leakage of electrons from the mitochondrial electron transport chain and thus cells must uphold an antioxidant system to maintain their redox balance . OxidatiOxidative stress leads to activation of evolutionary conserved signaling pathways, that can either activate transcription factors that in turn promote stress-specific transcriptional responses, or modulate protein synthesis and RNA stability through modification of translation factors or the activity of specific RBPs . FurtherSaccharomyces cerevisiae. Surprisingly, we detected a strong rewiring of metabolic enzyme-RNA associations, a substantial fraction of them relating to enzymes acting in carbon and amino acids metabolism. Additionally, we observed that oxidative stress induced re-arrangements of enzyme-RNA interactions are highly paralogous-specific, suggesting evolutionary specification. These results suggest the coordinated response between enzyme-RNA interactions and metabolic activity for adaptation to cellular stress.Here we applied RNA-interactome capture (RIC) to profile oxidative stress induced changes of the mRBPome in the yeast S. cerevisiae cells were grown to mid-log phase in rich glucose-containing medium (YPD), and oxidative stress was induced by addition of 0.5 mM hydrogen peroxide (H2O2) for 15 min. We recorded increased expression of HSP30 and GRE2 mRNAs, both known to be induced by sub-lethal doses of H2O2 -containing RNAs with bound proteins were purified from cell lysates using oligo[dT]25 beads. We wish to note that the isolation of poly(A) RNA is thought to mainly recover mRNA-interacting proteins, although other types of RNA bearing a poly(A) tail could also be recovered. As a negative control, competition experiments with exogenously added polyadenylic acids were performed for each experiment  (r = 0.57–0.73)  and respective control samples to demarcate non-specific binders were subjected to liquid chromatography-tandem mass spectrometry (LC-MS/MS). To enable robust data interpretation and minimize method specific detection bias, we applied three label-free MS quantification approaches, namely TOP3  which we.7–0.85) A and fai57–0.73) B, the fo2O2 stress with the TOP3, iBAQ, and LFQ analysis approach, respectively , Tkl1, Tps1, and Pfk2. As negative controls, we included Act1, Sod2 and Tkl2, which have not been identified by MS and are therefore not expected to bind RNA , the translational rate (r = 0.11–0.12), or protein levels (r = −0.08)  B. Thus, 2O2 for 20 min  RNA associations of RBPs upon oxidative stress could relate to the different levels of corresponding mRNA targets. To address this, we searched for published targets of the 257 proteins that changed RNA-associations upon oxidative stress. RNA targets for 13 proteins, as previously determined by RBPs immunoprecipitation microarray analysis (RIP-Chip) were identified, namely Aco1, Gre3, Mdh1, Meu1, Nsr1, Scp160, Sti1, Pin4, Puf3, Puf5, She2, Slf1, and Ssd1 . As a cor 20 min . Interesr 20 min C. Thus, r 20 min , are sigr 20 min  and alonr 20 min . Conversr 20 min  are signr 20 min , which g−14), which includes the stress-dependent RBPs Puf3 and Slf1. More specifically, the 150 proteins increasingly associated with poly(A) RNA upon oxidative stress have assigned functions in RNA localization, ribosome biogenesis, RNA processing, and RNA degradation. Interestingly, the latter includes one of the two TRAMP complexes , an evolutionary conserved complex involved in RNA surveillance and quality control (−4), which aligns with the commonly observed ceasing of translation initiation upon stress  terms and pathways A and S2B control . Possibl control . Conversn stress  . Most striking, 12 enzymes acting in “principle pathways of carbon metabolism” were enriched among the 107 proteins with decreased RNA association upon oxidative stress (TOP3 = 5.06), Tkl1) commonly exhibited significantly lowered RNA associations in H2O2 treated cells  and therefore slightly overrepresented among the 257 proteins changing RNA associations significantly upon He stress . This ate stress . Glycolye stress , the glye stress ; and towe stress . Analogoed cells B. Likewied cells C. Convere stress  , fatty acids (e.g. Frm2) and amino acid metabolism. Regarding the latter, 20 enzymes associated with the “biosynthesis of amino acids” (KEGG) were found among the set of proteins with significantly decreased mRNA association upon oxidative stress. Likewise, GSEA across the entire mRBPome revealed significantly reduced RNA associations for proteins allocated to the “biosynthesis of amino acids” . This includes proteins required for the synthesis of aspartate (Aat2), asparagine , glutamate , serine/lysine , methionine (Met6), cysteine (Sah1), threonine (Thr4), isoleucine , and others. The relevance of the herein observed changes in RNA-enzyme interactions is not known but may suggest the existence of an RNA-imposed regulatory layer for amino acids synthesis during stress.We next asked whether functional specification toward RNA-binding enzymes could have been implemented through specialization of paralogous genes after early genome duplication in yeast. We therefore considered a list of 1,094 proteins, representing 547 paralogous pairs . One hun2O2 treatment stress, we found only two paralogous pairs  but 44 proteins that had a non-RNA binding paralog. In this regard, immunoblot analysis with tagged strains confirmed that Tkl1 but not Tkl2, and cytosolic superoxide dismutase 1 (Sod1) but not its mitochondrial paralog Sod2 interacts with poly(A) RNA, respectively (−5) and carbon metabolism  are overrepresented among those 44 unique paralogues as compared with all annotated paralogous proteins in yeast. Thus, oxidative stress may preferentially alter mRNA associations of selected paralogues acting in metabolic processes. If so, we speculate that environmental stress could have been a driver for specialization of RNA-binding paralogues during evolution.Interestingly although, among the 257 proteins that changed RNA associations significantly upon Hectively E. Furthe2O2. By performing RIC and integrating three complementary MS analysis methods, we defined a list of 257 proteins — corresponding to 25% of the yeast mRBPome — that changed association with poly(A) during oxidative stress (FDR <5%). This included stress-regulated RBPs with known roles in translation and RNA degradation, reminiscent of previous observations made with arsenite-treated human cells, which promotes a strong oxidative stress response . More surprising though, we observed profound changes in RNA-enzyme interactions, many of them acting in carbon and amino acids metabolisms that commensurate with altered metabolisms. Thereby, the imposed reconfiguration of enzymes-RNA interaction appeared to be paralogue-specific, proposing stress adaptation as a driver for the specialization of RNA-binding enzymes during evolution.We investigated the plasticity of the yeast mRBPome upon exposure of yeast cells to short mild oxidative stress caused by exposure to Hresponse . Likewisresponse . Importa2O2 stressed yeast cells  to characterize the mRBPome upon oxidative stress was based on capturing poly(A) RNAs from cells, a major component of it comprising mRNAs; likely disregarding changes for RBPs that preferentially interact with other RNA species, such as rRNAs and tRNAs. We have also analyzed changes of the mRBPome under one mild stress conditions. Therefore, we do not know whether the observed coherent changes in RNA-enzyme interactions are specific to the chosen stress conditions, and in how far they are dependent on the concentration and duration of stress application with Ha.gerber@surrey.ac.uk).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, André P. Gerber .All S. cerevisiae strains used in this study were derived from the BY4741 background strain  was added to the culture once cells reached mid-log phase (OD600 ∼ 0.6) and incubated for 15 min at 30°C. Cells were collected by centrifugation and washed in PBS. For UV-crosslinking, cells were resuspended in 25 mL of PBS and exposed to 1,200 mJ/cm2 of 254 nm UV light in a Stratalinker 1800 (Stratagene) with two 2-min breaks on ice and gentle mixing  at 30°C with constant shaking at 220 r.p.m. To induce the oxidative stress response 0.5 mM He mixing . Cells w−1 DNase I , 100 U ml−1 RNasin , complete EDTA-free protease-inhibitor cocktail ) and mechanically broken with glass beads in a Tissue Lyser  for 10 min at 30 Hz at 4°C. The cooled lysate was cleared by three sequential centrifugations at 4°C at 3,000 g for 3 min, and 5,000 g and 10,000 g for 5 min each. A negative control was introduced at this stage: the extract was supplemented with 20 mg of poly(A) for competition experiments. To capture polyadenylated RNAs for mRBPome analysis, one milligram of oligo[dT]25 Dynabeads  was equilibrated in lysis buffer, mixed with the extracts (∼5 mg) and incubated on a shaker for 10 min at room temperature (RT). The beads were collected with a magnet and the supernatant recovered for repeat incubations (see below). The beads were washed once with 500 μL of wash buffer A  and twice with 500 μL wash buffer B . The poly(A) RNA was eluted from beads in 60 μL of 10 mM Tris-HCl, pH 7.5 at 80°C for 2 min and collected. The entire procedure was repeated twice by reapplying the supernatant to the oligo[dT]25 beads, which were recovered after elution and washed three times with lysis buffer prior to reapplication. The three sequential eluates were combined and concentrated to 70 μL in a 0.5 mL Microcon-10 kD Centrifugal Filter Unit with Ultracel-10 membrane . In total, we subjected 12 samples to MS analysis: three independent experiments  of the mRBPome in control conditions as well as three poly(A) competition control experiments; and three independent experiments for profiling the mRBPome under oxidative stress conditions along with three poly(A) competition experiments.RIC was essentially performed as described previously . Pellet HSP30 and GRE2 upon oxidative stress, reverse transcription (RT) was performed with 500 ng of total RNA combined with a mixture of with oligo[dT]18 and random hexamer primers, and the Transcriptor High Fidelity cDNA Synthesis Kit according to the manufactures' instructions . PCR was conducted with 1 μL (5%) of cDNA with the following primer pairs: HSP30_Fwd, 5′-CTAGAGGGTTCAATGCACTTAT-3′; HSP30_Rev 5′-CTCACCGTCTGGTTGAATAC-3′; GRE2_Fw, 5′-CCGGAACTATTTGGTGGATAC-3′; GRE2_Rev, 5-CCTCCGATACGATTAGTCTTTG-3′; ACT1_Fw, 5′-GTCTGGATTGGTGGTTCTATC-3′; and ACT1_Rev, 5′-GGACCACTTTCGTCGTATTC-3′. PCR was performed for 5 min at 94°C, 30 cycles at 94°C for 30 s, 57°C for 30 s, 72°C for 40 s, and 8 min at 72°C. PCR products were electrophoresed on a 2% agarose gel and visualized with peqGreen DNA/RNA dye .Total RNA was isolated from 50 μL of extracts with the ZR RNA MiniPrep kit  with in column DNA digestion. To monitor induction of −1. Solvent A was 0.1% formic acid and Solvent B was aqueous 80% acetonitrile in 0.1% formic acid. Peptides were ionized by nano-electrospray ionization at 2.1 kV using a stainless steel emitter with an internal diameter of 30 μm (Thermo Scientific) and a capillary temperature of 250°C. Tandem mass spectra were acquired using an LTQ- Orbitrap Velos mass spectrometer controlled by Xcalibur 2.1 software (Thermo Scientific) and operated in data-dependent acquisition mode. The Orbitrap was set to analyze the survey scans at 60,000 resolution (at m/z 400) in the mass range m/z 300 to 2,000 and the top twenty multiply charged ions in each duty cycle selected for MS/MS in the LTQ linear ion trap. Charge state filtering, where unassigned precursor ions were not selected for fragmentation and dynamic exclusion , was used. Fragmentation conditions in the LTQ were as follows: normalized collision energy, 40%; activation q, 0.25; activation time 10 ms; and minimum ion selection intensity, 500 counts.MS analysis was performed at the Proteomics Facility, University of Bristol. 50 μL of the samples (∼8–10 μg of protein) were run on a 4–12% NuPAGE Novex acrylamide gel (Life Sciences). The gel lane was cut into one slice and subjected to in-gel tryptic digestion using a ProGest automated digestion unit (Digilab UK). The resulting peptides were fractionated using a Dionex Ultimate 3000 nanoHPLC system in line with an LTQ-Orbitrap Velos mass spectrometer (Thermo Scientific). In brief, peptides in 1% (vol/vol) formic acid were injected onto an Acclaim PepMap C18 nano-trap column (Dionex). After washing with 0.5% (vol/vol) acetonitrile 0.1% (vol/vol) formic acid peptides were resolved on a 250 mm × 75 μm Acclaim PepMap C18 reverse phase analytical column (Dionex) over a 150 min organic gradient, using 7 gradient segments  with a flow rate of 300 nL min0.05% of the yeast input extract (∼5 μg of protein) and 10%–50% of the eluates were resolved on 4–15% SDS polyacrylamide gels and transferred to polyvinylidene difluoride (PVDF) membranes (Thermo Scientific Pierce). Membranes were blocked in PBS-0.1% Tween 20 containing 5% low fat milk, probed with designated antibodies and horseradish peroxidase (HRP)-coupled secondary antibodies, and developed with the Immobilon Western Chemiluminescent HRP Substrate (Millipore). Blots were recorded with a FluorChem (Alpha Innotech) and quantified with ImageJ. The following antibodies were used: rabbit anti-Scp160  1:10,00 ; 1,070 proteins with TOP3, and 1,310 proteins for both MaxQuant modes were retained. The filtered raw peak area data was normalized such that the total peak area within each biological sample was equal to 106. A pseudocount of 1 was added to all normalized values and log2 transformed. Only proteins with an average (across replicates) raw biological peak area ≥ 3-fold greater than the corresponding average raw control peak area in both conditions were retained, leaving 935, 986 and 1,282 proteins in TOP3, LFQ and iBAQ analysis modes, respectively. Subsequently, proteins not identified in at least two of the three biological samples for at least one of the conditions were removed, leaving a final count of 812, 933 and 1,276 proteins in Top 3, LFQ and iBAQ analysis, respectively. These sets of proteins were further analyzed to estimate an FDR for declaring differentially abundant proteins between conditions. Since samples were not paired an average pairwise fold-change between replicate samples was calculated to identify proteins that change in abundance/presence between conditions. The probability of observing such a fold-change by chance (i.e. FDR) , was based on the distribution of average fold-changes generated from 10,000 random experiments. Random experiments were generated by shuffling the experimentally obtained fold-changes of replicates. From this randomized dataset, the average log2 fold-changes were calculated to assess, for each possible log2 H2O2/WT threshold of −10 to 10 in 0.5 increments, the percentage of the 10,000 randomly generated fold-changes that were above that threshold i.e. the FDR. From this simulated data a linear model of the form FDR ∼ log2 H2O2/WT fold-change was created and subsequently used to produce FDRs for all experimentally obtained (non-random) fold changes. Proteins having an FDR ≤5% were considered to be differentially abundant  were filtered to contain only the proteins identified with at least 2 peptides in at least one biological sample  were identified with the GO Term Finder at the Saccharomyces Genome Database (http://www.yeastgenome.org/) (complete analysis is given in Significantly enriched GO terms in Database  (SGD; htOverlap significance of Venn diagrams was calculated using hypergeometric distributions.


We summarize m6A main functions as “beacon” to recruit reader proteins and “structural switcher” to alter RNA–protein and RNA–RNA interactions to modulate phase separation and regulate the related biological processes.Phase separation is the driving force behind formation of various biomolecular condensates (BioMCs), which sub-compartmentalize certain cellular components in a membraneless manner to orchestrate numerous biological processes. Many BioMCs are composed of proteins and RNAs. While the features and functions of proteins are well studied, less attention was paid to the other essential component RNAs. Here, we describe how RNA contributes to the biogenesis, dissolution, and properties of BioMCs as a multivalence providing scaffold for proteins/RNA to undergo phase separation. Specifically, we focus on N Caenorhabditis elegans, were found to exhibit gel-like behaviors  lacking lipid bilayers, also constitute another form of cellular compartments. Although BioMCs and membrane-bound organelles are both efficient to accomplish biochemical reactions within the organelles, they differ significantly in their biogenesis, component, sensitivity to the environment, and so on . In 2009ehaviors . Since tehaviors , processehaviors , spindleehaviors , and cenehaviors  in the cehaviors  and paraehaviors  in the nehaviors , super eehaviors , and medehaviors  are all via phase separation, it was reported recently that phase separation might associate with oncogenic fusion protein degradation by heat stress  granules containing RNA and RNA-binding proteins (RBPs) , and the6-methyladenosine (m6A), contributes to phase separation and discuss its potential biological significance.Here, we mainly focus on the contribution of RNA in phase separation. Given post-transcriptional RNA modification is widely distributed and of critical importance to RNA processing and function , we summThe interaction between biological macromolecules is the core event in phase separation, and the valence as well as affinity of the interaction are the key parameters for regulating phase separation . PreviouOn one hand, RNA provides multivalency by introducing a non-specific negative charge to modulate phase separation . Most ofUnlike DNA, most cellular RNAs are single-stranded, leading to complete exposure of phosphate backbones and bases to the surrounding environment. This is beneficial for establishing an interaction between RNA and positively charged molecules. In a model composed of poly-U RNAs and cationic peptides, dephosphorylation at a serine is sufficient to cause a charge–charge interaction between the negatively charged RNA and the positively charged peptides, thereby mediating phase separation. Conversely, phosphorylation at this serine reverses this process, causing the formed droplets to dissolve . In addivia recognizing RNA structure  are important assembly factors of SGs, and knockout of G3BPs disrupts SGs’ formation  of proteins participating in phase separation . For ins5C) on the CpG island of DNA , “m6A readers” (m6A-binding proteins), and “m6A erasers” (m6A demethylases)  catalyze m6A in a site- and transcript-specific manner, and the m6A erasers (FTO and ALKBH5) specifically remove the methyl group. These enzymes make m6A modification reversible and adjustable. Notably, the function of m6A modification is extensively decided by its readers . Each of these readers exhibits distinct function in regulating the fate of m6A-modified RNA. For instance, YTHDC1 regulates alternative splicing , which play critical roles in modulating the properties of RNA and regulating RNA metabolism . Unlike d of DNA , m6A is hylases) . The m6Asplicing  and nuclsplicing , YTHDC2 splicing  and RNA splicing , YTHDF1 splicing , YTHDF2 splicing , and YTHsplicing . Therefoesponses , tumorigesponses , and difesponses .6A-modified RNAs act as scaffolds to gather YTHDF proteins and thus lead to phase separation both in vitro and in vivo  and further discuss how these changes potentially contribute to phase separation.In this part, based on the current literature, we describe how m6A readers is recognized as a major mechanism for regulating various fate for m6A-harboring RNA. By now, a large group of RBPs have been verified to directly bind to m6A-modified RNA  family are well-studied  and tend to induce linear, unstructured conformation, which would accelerate the formation of trans-RNA–RNA interaction  seem essential for nuclear phase separation events, which may be due to their length, flexible structure, and potentially multiple m6A sites. Multiple m6A-bearing mRNAs are predominantly located in the nucleoplasm and probably associated with chromatin remodeling in terms of molecular function in gene ontology  for example, it occurs at the Watson–Crick interface and endows a positive charge to the modified adenosine, thereby changing RNA structure and RNA–protein interactions  are often intervened  to modulate m6A modification levels on target RNA or change the interaction pattern between target RNA and certain m6A readers of interest. However, these measures would lead to uncontrollable off-target effects, because these regulators are shared by thousands of RNAs apart from the targeted one. An improved method is to reconstruct the target RNA to change its modifiable capacity and the spectrum of binding partners; in many cases, it contains the deletion or mutation applied to the target RNA. Though this improved method greatly eliminates the off-target possibility and makes the intervention more precise, some other risks occur. As the ideal research objects are long RNAs harboring multiple m6A sites, such intervention may result in deletion or sequence component changes of large RNA fragments, both of which are important parameters for interaction spectrum contributing to RNA-mediated phase separation. Therefore, most of the currently available methods are more or less defective.Apart from 6A levels and interaction spectrum of target RNA without changing the primary sequence. Recently, several biological tools have been developed to achieve site-specific m6A editing  fusions. This design provides a wider editing window for its flexible repeated GCN4 peptides, and it would help to intervene with m6A clusters and eliminate m6A modification when the m6A sites are only known vaguely. Apart from CRISPR–Cas13 system, the PUF RNA-binding protein and CRISPR–Cas-inspired RNA targeting system (CIRTS) were also applied to regulate site-specific m6A . More importantly, RNA m


E. coli strain deficient in 5-methylaminomethyl-2-thiouridine (mnm5s2U) (found in lysine and glutamate tRNA anticodon) is more vulnerable to oxidative RNA damage compared to its wildtype strain suggesting an auxiliary function for the mnm5s2U modification. These studies indicate that oxidative damage to RNA is altered by the presence of enzymatic modified nucleosides or protein association inside the cell.Oxidative stress triggered by the Fenton reaction  or UVR exposure (photo) can damage cellular biomolecules including RNA through oxidation of nucleotides. Besides such xenobiotic chemical modifications, RNA also contains several post-transcriptional nucleoside modifications that are installed by enzymes to modulate structure, RNA-protein interactions, and biochemical functions. We examined the extent of oxidative damage to naturally modified RNA which is required for cellular protein synthesis under two different contexts. The extent of oxidative damage is higher when RNA is not associated with proteins, but the degree of damage is lower when the RNA is presented in the form of a ribonucleoprotein complex, such as an intact ribosome. Our studies also indicate that absence of methylations in ribosomal RNA at specific positions could make it more susceptible to photooxidative stress. However, the extent of guanosine oxidation varied with the position at which the modification is deficient, indicating position-dependent structural effects. Further, an  ROS include superoxide -treated Escherichia coli cells exhibited ∼5-fold increase in 8-oxoG levels in RNA compared to DNA and is widely distributed in ribosomal and non-ribosomal RNAs as detected by HPLC analysis  compared to adenosine (1.42 V vs NHE) and pyrimidines (>1.6 V vs NHE) . Indeed,analysis . Oxidizeanalysis  and causanalysis . H2O2-inficiency . Apart fficiency  and theiE. coli upon UVA-induced oxidative stress (5s2U), found in ribosomal RNA and tRNA, respectively, of an E. coli model system.Liquid chromatography coupled with mass spectrometry (LC-MS) is an ideal platform for documenting the identity of oxidation products based on oxidation-induced alterations in mass values. This analytical platform is extensively used for comprehensive and unbiased detection and identification of xenobiotic or natural post-transcriptional modifications (PTMs) . The nate stress  and these stress . Here, wE. coli strains were obtained from the E. coli Genetic stock center . Iron chloride (FeCl2), riboflavin, H2O2 and all other chemicals were procured from Fisher Scientific  unless otherwise specified.E. coli strains listed in 600) and harvested by centrifugation. The pellet was resuspended in lysis buffer . Lysozyme was added (1.5 mg/ml) to the suspension and incubated on ice for 10 min and the cell lysate centrifuged . After incubation of the supernatant for five more min with 20 U of RQ1 DNase on ice, the suspension was re-centrifuged for 15 min at 30,000xg. The supernatant was layered on top of an equal volume of 32% sucrose solution and subject to centrifugation at 100,000xg for 18 h at 4°C to pellet the ribosomes  and stored at −80 °C until use.ibosomes . The ribTo obtain protein-free ribosomal RNA, ribosomes were extracted with Tri-Reagent® (Sigma-Aldrich) as per the manufacturer’s protocol. Ribosomes and ribosomal RNA were stored at −80°C.−1) were irradiated with UVA  at room temperature (25°C) for 1 h with addition of 100 µM riboflavin (RF). Unexposed samples were kept in the dark, under the same conditions. Another set of ribosomes and rRNA samples were also incubated with 10 µM iron (II) chloride and 20 mM H2O2 for 1 h at 37°C. Unexposed samples were treated similarly but no H2O2 was added. Soon after exposure, rRNA was extracted from ribosomes using Tri-reagent. RNA was reprecipitated with ½ volume of 7.5 M ammonium acetate and three times volume of ethanol, pelleted, and dissolved in sterile water.Ribosomes  and rRNA  in storage buffer  at Cincinnati Children core facility. The vendor-specific software (ProSize) was used to compute the signal intensity for 23S rRNA and 16S rRNA following gel electrophoresis.E. coli strains, K12 (wild type), ΔrlmKL and ΔrlmB were grown to mid-log phase (∼0.5 OD600 nm) and diluted to ∼0.01 OD for testing growth behavior when grown with LB media. Growth was monitored through changes in OD using a microplate reader (Biotek 80 TS) at 37°C with slowest shake speed setting for 24 h.2O2-exposed and unexposed RNA samples were digested to nucleosides following denaturation  and incubated with 0.3 U of nuclease P1 (Sigma-Aldrich) in 10 mM ammonium acetate at 45°C for 2 h. To this digest, ammonium bicarbonate (100 mM), 0.00125 U of snake venom phosphodiesterase and 0.1 U of bacterial alkaline phosphatase were added and incubated at 37°C for 2 h. Digests were dried and resuspended in mobile phase A (MPA)  before subjecting it to LC-MS. A Vanquish UHPLC coupled with an Orbitrap Fusion Lumos (Thermo Scientific) or TSQ Quantiva Triple Quadrupole Mass spectrometer were used for data acquisition. Reversed phase liquid chromatography was performed using an HSS T3 column  and the mass spectra were recorded in positive polarity. LC-MS/MS conditions were identical to those described previously  for subsequent interpretation.Four µg of UVA or Heviously . NucleosPurine nucleobases undergo oxidative damage to form oxidation products just like DNA . Howeverm/z 300.0944) lesions in unexposed rRNA at an average of ∼one to two for every 1,000 guanosines when cells were harvested at mid-log phase  levels were at baseline levels in the oxidant omitted samples. In these cases, the observed peak areas of oxidation products were normalized against defined number of guanosines (103 or 105).We noticed the presence of 8-oxo-G (og phase . When RNGuanosine oxidation products: Less than 60 min of UVA exposure of ribosomes did not cause significant increase in 8-oxo-G levels and more than 60 min exposure resulted in fragmentation of RNA (data not shown). Therefore, 60 min was chosen as the optimal exposure time to differentiate the oxidative effects due to protein presence in the ribosome. Upon UVA exposure we see higher levels of 8-oxo-G and guanidinohydantoin (Gh) (m/z 290.1097) lesions (5 G). This suggests that association with proteins could potentially decrease the chances of further oxidation of 8-oxo-G in the ribosomes. Lack of associated proteins presumably enabled advanced oxidation to Gh (4-electron oxidation product) in protein-free rRNA. Interestingly, the observed levels for FapyG (m/z 302.1101) (5 G) upon UVA exposure and do not show significant differences between ribosome and rRNA samples suggesting that this type of alternate oxidation process occurs at relatively low level irrespective of the protein association status ( lesions  compared02.1101)  were lown status .Fenton reaction-induced oxidative stress, on the other hand, generated higher levels of 8-oxo-G (11.5-fold) and FapyG (46-fold) for rRNA. On the other hand, ribosome samples did not show noticeable level of oxidation products with Fenton reaction. Gh levels were not significantly different between ribosome and rRNA. Observation of lower levels of oxidative lesions for ribosomes suggest that protein association could protect rRNA against ROS generated by the Fenton reaction. However, such protection was not observed for rRNA devoid of proteins . OverallAdenosine oxidation products: Either UVA or Fenton reaction induced oxidative stress generated 8-oxo-A and FapyA  and the rRNA analyzed for its integrity and oxidation products after LiCl-based fractionation. Gel electrophoresis of rRNA revealed elevated degradation of rRNA with increased exposure  at position 2069 and 2-methylguanine (m2G) at position 2,445) and ΔrlmB  that are deficient in respective methyl transferases. Although these strains exhibited significantly lower levels of corresponding methylations in the ribosomal RNA (lmB ribosomes compared to K12 (12-fold) or ΔrlmKL (19-fold) suggesting that the loss of ribose methylations could make the ribosomes more susceptible to ROS damage  or microsecond  to minutes  . RNA ass5 guanosines  did not cause significant levels of advanced oxidation products (other than 8-oxo-G) (data not shown). Higher concentrations of H2O2 (20 mM) in combination with iron chloride (induced Fenton reaction) caused higher levels of advanced oxidation products. However, Gh formation was predominantly noticed in UVA exposure and FapyG by the Fenton reaction. These observations indicate the prevalence of specific oxidation pathways depending on photooxidation or chemical oxidation. The predominant type of ROS generated, for example, singlet oxygen by UVA  products for UVA exposure . There wE. coli  did not show any significant differences in their growth behavior under normal cell culture conditions. The methylations catalyzed by these enzymes are located in Domain V of 23S rRNA. The ribose methylation, Gm is located at position 2,251 in the peptidyl transferase center (PTC) and is a universally conserved modification (7G (at 2069) and m2G  are installed by a unique dual fused methyltransferase RlmKL  for ΔrlmKL, those of ΔrlmB strain exhibited significantly higher levels than K12 cells (p = 0.00064). It is interesting to note that although RlmKL installs methyl groups at two different positions  of 23S rRNA compared to RlmB   , the latn 2,251) . Furthern 2,251)  could idn 2,251)  and the n 2,251) . Such vun 2,251) .5s2U also led to increased guanosine oxidation upon UVA exposure in the enzyme-deficient cells (ΔtrmU)  . This ob (ΔtrmU) . These oesponses  suggestiE. coli suggesting a position-specific dependence. Similarly, deficiency of ROS-susceptible nucleoside modification increased the overall oxidative damage in tRNA, and total RNA compared to wild type strain. This suggests that it is not the number, but the nature and status of modification at a defined position that determines the overall susceptibility of RNA to ROS.In summary, the pattern of oxidative damage to RNA (8-oxo-G and its advanced oxidation products) is altered by the protein association and presence of post-transcriptional nucleoside modifications. RNA oxidation and degradation levels under cellular condition were higher compared to the exposure of ribosomes. Although, loss of methyl groups at two different positions did not increase the susceptibility, absence of ribose methylation at another position increased the susceptibility of rRNA to oxidative damage in ribosomes compared to that of wild type